Merge pull request #15220 from velconia/imperative_shared_ptr

Refine imperative VarBase

paddle/fluid/imperative/layer.cc

@@ -44,7 +44,7 @@ void AddTo(Variable* src, Variable* dst) {
                  src_tensor->numel());
   float* dst_data = dst_tensor->mutable_data<float>(platform::CPUPlace());
   const float* src_data = src_tensor->data<float>();
-  for (size_t i = 0; i < src_tensor->numel(); ++i) {
+  for (int64_t i = 0; i < src_tensor->numel(); ++i) {
     dst_data[i] += src_data[i];
   }
 }
@@ -117,9 +117,9 @@ class Autograd {
   }
 };
 
-framework::LoDTensor& VarBase::Grad() {
+framework::LoDTensor& VarBase::GradValue() {
   VLOG(3) << "get var grad " << var_desc_->Name();
-  return *grads_->GetMutable<framework::LoDTensor>();
+  return *(grads_->var_->GetMutable<framework::LoDTensor>());
 }
 
 std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
@@ -183,7 +183,7 @@ void VarBase::RunBackward() {
   if (!pre_op_) return;
 
   VLOG(3) << "start backward";
-  auto grads_t = grads_->GetMutable<framework::LoDTensor>();
+  auto grads_t = grads_->var_->GetMutable<framework::LoDTensor>();
   float* data = grads_t->mutable_data<float>(platform::CPUPlace());
   std::fill(data, data + grads_t->numel(), 1.0);
 
@@ -209,7 +209,7 @@ std::vector<VarBase*> PyLayer::Apply(int func_id,
   std::vector<Variable*> outvars = CallPythonFunc(py_funcs_[func_id], invars);
   std::vector<VarBase*> ret;
   for (Variable* v : outvars) {
-    ret.push_back(new VarBase(v, new Variable()));
+    ret.push_back(new VarBase(v, new VarBase(true)));
   }
   return ret;
 }

paddle/fluid/imperative/layer.h

@@ -17,13 +17,14 @@
 #include <map>
 #include <string>
 #include <vector>
-#include "pybind11/pybind11.h"
 
-#include "Python.h"
 #include "paddle/fluid/framework/op_desc.h"
 #include "paddle/fluid/framework/operator.h"
 #include "paddle/fluid/framework/var_desc.h"
 #include "paddle/fluid/platform/enforce.h"
+#include "pybind11/pybind11.h"
+
+#include "paddle/fluid/imperative/type_defs.h"
 
 namespace paddle {
 namespace imperative {
@@ -85,13 +86,19 @@ class PreparedOp {
 
 class OpBase;
 
+/* The wrapper for Variable which holds a Variable and a VarBase of its
+ * gradient. This object should be managed totally by Python intepreter.
+ *
+ * Nearly all interface should be implemented in C++.
+ */
 class VarBase {
  public:
-  VarBase() : VarBase(new framework::Variable(), new framework::Variable()) {}
+  VarBase() : VarBase(new framework::Variable(), new VarBase(true)) {}
 
   // Owns `var` and `grad`
-  VarBase(framework::Variable* var, framework::Variable* grad)
+  VarBase(framework::Variable* var, VarBase* grad)
       : pre_op_(nullptr),
+        pre_op_out_name_(),
         pre_op_out_idx_(-1),
         var_desc_(nullptr),
         var_(var),
@@ -100,17 +107,26 @@ class VarBase {
 
   explicit VarBase(bool stop_gradient)
       : pre_op_(nullptr),
+        pre_op_out_name_(),
         pre_op_out_idx_(-1),
         var_desc_(nullptr),
         var_(new framework::Variable()),
-        grads_(new framework::Variable()),
+        grads_(stop_gradient ? nullptr : new VarBase(true)),
         stop_gradient_(stop_gradient) {}
 
-  virtual ~VarBase() {}
+  virtual ~VarBase() {
+    if (var_) {
+      delete var_;
+    }
+
+    if (grads_) {
+      delete grads_;
+    }
+  }
 
   void RunBackward();
 
-  framework::LoDTensor& Grad();
+  framework::LoDTensor& GradValue();
 
   inline std::string GradName() const {
     PADDLE_ENFORCE(
@@ -124,12 +140,16 @@ class VarBase {
   int pre_op_out_idx_;
 
   framework::VarDesc* var_desc_;
+
   framework::Variable* var_;
-  framework::Variable* grads_;
+  VarBase* grads_;
 
   bool stop_gradient_;
 };
 
+/* The wrapper for OpDesc which holds a OpDesc and a OpDesc of its
+ * gradient. This object should be managed totally by Python intepreter.
+ */
 class OpBase {
  public:
   OpBase()
@@ -153,13 +173,13 @@ class OpBase {
   framework::OpDesc* grad_op_desc_;
   int backward_id_;
 
-  std::map<std::string, std::vector<VarBase*>> input_vars_;
-  std::map<std::string, std::vector<VarBase*>> output_vars_;
-  std::map<std::string, std::vector<OpBase*>> pre_ops_;
+  VarBasePtrMap input_vars_;
+  VarBasePtrMap output_vars_;
+  OpBasePtrMap pre_ops_;
   std::map<std::string, std::vector<int>> pre_ops_out_idx_;
 
-  std::map<std::string, std::vector<framework::Variable*>> grad_input_vars_;
-  std::map<std::string, std::vector<framework::Variable*>> grad_output_vars_;
+  framework::VariableValueMap grad_input_vars_;
+  framework::VariableValueMap grad_output_vars_;
   framework::BlockDesc* block_;
 };
 

paddle/fluid/imperative/tracer.cc

@@ -15,5 +15,199 @@
 #include "paddle/fluid/imperative/tracer.h"
 
 namespace paddle {
-namespace imperative {}  // namespace imperative
+namespace imperative {
+
+void CreateGradOp(const framework::OpDesc& op_desc,
+                  const std::unordered_set<std::string>& no_grad_set,
+                  const std::vector<framework::BlockDesc*>& grad_sub_block,
+                  framework::OpDesc** grad_op_desc,
+                  std::unordered_map<std::string, std::string>* grad_to_var) {
+  std::vector<std::unique_ptr<framework::OpDesc>> grad_op_descs =
+      framework::OpInfoMap::Instance()
+          .Get(op_desc.Type())
+          .GradOpMaker()(op_desc, no_grad_set, grad_to_var, grad_sub_block);
+  PADDLE_ENFORCE(grad_op_descs.size() == 1, "Only support 1 grad op now.");
+  // TODO(panyx0718): Leak?
+  *grad_op_desc = grad_op_descs[0].release();
+}
+
+void InitVar(framework::Variable* var, framework::Variable* grad_var) {
+  auto& var_t = var->Get<framework::LoDTensor>();
+  float* data =
+      grad_var->GetMutable<framework::LoDTensor>()->mutable_data<float>(
+          var_t.dims(), platform::CPUPlace());
+  std::fill(data, data + var_t.numel(), 0.0);
+}
+
+void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
+                   const VarBasePtrMap& outputs, framework::BlockDesc* block,
+                   const bool stop_gradient) {
+  std::map<std::string, VarBase*> vars;
+
+  framework::OpDesc* op_desc = op->op_desc_;
+  VLOG(3) << "tracer tracing " << op_desc->Type();
+  op_desc->InferShape(*block);
+  op_desc->InferVarType(block);
+  std::unique_ptr<framework::OperatorBase> op_base =
+      framework::OpRegistry::CreateOp(*op_desc);
+
+  framework::VariableValueMap invars_map;
+  framework::VariableValueMap outvars_map;
+
+  op->input_vars_ = inputs;
+  for (auto it : op->input_vars_) {
+    auto& invars = invars_map[it.first];
+    for (VarBase* inp : it.second) {
+      PADDLE_ENFORCE_NOT_NULL(inp->var_, "op %s input %s nullptr",
+                              op->op_desc_->Type(), inp->var_desc_->Name());
+
+      invars.push_back(inp->var_);
+      vars[inp->var_desc_->Name()] = inp;
+      if (inp->pre_op_) {
+        op->pre_ops_[it.first].push_back(inp->pre_op_);
+        op->pre_ops_out_idx_[it.first].push_back(inp->pre_op_out_idx_);
+      } else {
+        op->pre_ops_[it.first].push_back(nullptr);
+      }
+      VLOG(3) << "input vname " << inp->var_desc_->Name() << " "
+              << inp->var_->IsInitialized();
+    }
+  }
+
+  op->output_vars_ = outputs;
+  for (auto it : op->output_vars_) {
+    auto& outvars = outvars_map[it.first];
+    const std::vector<VarBase*>& outputs = it.second;
+    for (size_t i = 0; i < outputs.size(); ++i) {
+      VarBase* out = outputs[i];
+      outvars.push_back(out->var_);
+      vars[out->var_desc_->Name()] = out;
+
+      framework::VarDesc* var_desc = block->FindVar(out->var_desc_->Name());
+      if (var_desc->GetType() == framework::proto::VarType::LOD_TENSOR) {
+        out->var_->GetMutable<framework::LoDTensor>();
+      } else {
+        LOG(ERROR) << "tracer doesn't support yet";
+      }
+      out->stop_gradient_ = stop_gradient;
+      out->pre_op_ = op;
+      out->pre_op_out_name_ = it.first;
+      out->pre_op_out_idx_ = i;
+
+      VLOG(3) << "output vname " << out->var_desc_->Name() << " "
+              << out->var_->IsInitialized();
+    }
+  }
+
+  VLOG(3) << "tracer running " << op_desc->Type();
+  framework::RuntimeContext ctx(invars_map, outvars_map);
+
+  // TODO(panyx0718): Cache p.
+  framework::OperatorWithKernel* op_kernel =
+      dynamic_cast<framework::OperatorWithKernel*>(op_base.get());
+  PADDLE_ENFORCE_NOT_NULL(op_kernel, "only support op with kernel");
+
+  framework::Scope scope;
+  platform::CPUPlace place;
+  PreparedOp p = PreparedOp::Prepare(ctx, *op_kernel, place);
+  p.op.RuntimeInferShape(scope, place, ctx);
+  p.func(framework::ExecutionContext(p.op, scope, *p.dev_ctx, p.ctx));
+
+  if (!stop_gradient) {
+    framework::OpDesc* grad_op_desc;
+    // TODO(panyx): Is this leaked?
+    std::unique_ptr<std::unordered_map<std::string, std::string>> grad_to_var(
+        new std::unordered_map<std::string, std::string>());
+    CreateGradOp(*op_desc, {}, {block}, &grad_op_desc, grad_to_var.get());
+    op->grad_op_desc_ = grad_op_desc;
+
+    for (auto it : grad_op_desc->Inputs()) {
+      auto& grad_in_vars = op->grad_input_vars_[it.first];
+      for (const std::string& grad_invar : it.second) {
+        block->FindRecursiveOrCreateVar(grad_invar);
+        auto var_it = grad_to_var->find(grad_invar);
+        if (var_it == grad_to_var->end()) {
+          auto fwd_var_it = vars.find(grad_invar);
+          PADDLE_ENFORCE(fwd_var_it != vars.end());
+          // Forward inputs or outputs.
+          grad_in_vars.push_back(fwd_var_it->second->var_);
+        } else {
+          VarBase* var = vars[var_it->second];
+          if (!var->grads_->var_->IsInitialized()) {
+            InitVar(var->var_, var->grads_->var_);
+          }
+          // Douts.
+          grad_in_vars.push_back(var->grads_->var_);
+        }
+      }
+    }
+
+    for (auto it : grad_op_desc->Outputs()) {
+      auto& grad_out_vars = op->grad_output_vars_[it.first];
+      for (const std::string& grad_outvar : it.second) {
+        block->FindRecursiveOrCreateVar(grad_outvar);
+        auto var_it = grad_to_var->find(grad_outvar);
+        PADDLE_ENFORCE(var_it != grad_to_var->end());
+        VarBase* var = vars[var_it->second];
+        if (!var->grads_->var_->IsInitialized()) {
+          InitVar(var->var_, var->grads_->var_);
+        }
+        grad_out_vars.push_back(var->grads_->var_);
+      }
+    }
+  }
+
+  op->block_ = block;
+}
+
+std::vector<VarBase*> Tracer::PyTrace(OpBase* op,
+                                      const std::vector<VarBase*>& inputs,
+                                      bool stop_gradient) {
+  VLOG(3) << "py_trace";
+  op->input_vars_["X"] = inputs;
+  op->output_vars_["Out"] = PyLayer::Apply(op->forward_id_, inputs);
+  for (VarBase* inp : inputs) {
+    if (inp->pre_op_) {
+      op->pre_ops_["X"].push_back(inp->pre_op_);
+      op->pre_ops_out_idx_["X"].push_back(inp->pre_op_out_idx_);
+    } else {
+      op->pre_ops_["X"].push_back(nullptr);
+    }
+  }
+
+  auto& outputs = op->output_vars_["Out"];
+  for (size_t i = 0; i < outputs.size(); ++i) {
+    VarBase* out = outputs[i];
+    out->stop_gradient_ = stop_gradient;
+    out->pre_op_ = op;
+    out->pre_op_out_name_ = "Out";
+    out->pre_op_out_idx_ = i;
+  }
+  if (!stop_gradient) {
+    auto& grad_input_vars = op->grad_input_vars_["X@GRAD"];
+    auto& grad_output_vars = op->grad_output_vars_["Out@GRAD"];
+
+    for (const VarBase* inp : inputs) {
+      grad_input_vars.push_back(inp->var_);
+    }
+    for (VarBase* out : outputs) {
+      grad_input_vars.push_back(out->var_);
+    }
+    for (VarBase* out : outputs) {
+      grad_input_vars.push_back(out->grads_->var_);
+      if (!grad_input_vars.back()->IsInitialized()) {
+        InitVar(out->var_, grad_input_vars.back());
+      }
+    }
+    for (const VarBase* inp : inputs) {
+      grad_output_vars.push_back(inp->grads_->var_);
+      if (!grad_output_vars.back()->IsInitialized()) {
+        InitVar(inp->var_, grad_output_vars.back());
+      }
+    }
+  }
+  return outputs;
+}
+
+}  // namespace imperative
 }  // namespace paddle

paddle/fluid/imperative/tracer.h

@@ -30,23 +30,9 @@ void CreateGradOp(const framework::OpDesc& op_desc,
                   const std::unordered_set<std::string>& no_grad_set,
                   const std::vector<framework::BlockDesc*>& grad_sub_block,
                   framework::OpDesc** grad_op_desc,
-                  std::unordered_map<std::string, std::string>* grad_to_var) {
-  std::vector<std::unique_ptr<framework::OpDesc>> grad_op_descs =
-      framework::OpInfoMap::Instance()
-          .Get(op_desc.Type())
-          .GradOpMaker()(op_desc, no_grad_set, grad_to_var, grad_sub_block);
-  PADDLE_ENFORCE(grad_op_descs.size() == 1, "Only support 1 grad op now.");
-  // TODO(panyx0718): Leak?
-  *grad_op_desc = grad_op_descs[0].release();
-}
+                  std::unordered_map<std::string, std::string>* grad_to_var);
 
-void InitVar(framework::Variable* var, framework::Variable* grad_var) {
-  auto& var_t = var->Get<framework::LoDTensor>();
-  float* data =
-      grad_var->GetMutable<framework::LoDTensor>()->mutable_data<float>(
-          var_t.dims(), platform::CPUPlace());
-  std::fill(data, data + var_t.numel(), 0.0);
-}
+void InitVar(framework::Variable* var, framework::Variable* grad_var);
 
 class Tracer {
  public:
@@ -57,172 +43,10 @@ class Tracer {
   void Trace(OpBase* op,
              const std::map<std::string, std::vector<VarBase*>>& inputs,
              const std::map<std::string, std::vector<VarBase*>>& outputs,
-             framework::BlockDesc* block, const bool stop_gradient = false) {
-    std::map<std::string, VarBase*> vars;
-
-    framework::OpDesc* op_desc = op->op_desc_;
-    VLOG(3) << "tracer tracing " << op_desc->Type();
-    op_desc->InferShape(*block);
-    op_desc->InferVarType(block);
-    std::unique_ptr<framework::OperatorBase> op_base =
-        framework::OpRegistry::CreateOp(*op_desc);
-
-    framework::VariableValueMap invars_map;
-    framework::VariableValueMap outvars_map;
-
-    op->input_vars_ = inputs;
-    for (auto it : op->input_vars_) {
-      auto& invars = invars_map[it.first];
-      for (VarBase* inp : it.second) {
-        PADDLE_ENFORCE_NOT_NULL(inp->var_, "op %s input %s nullptr",
-                                op->op_desc_->Type(), inp->var_desc_->Name());
-
-        invars.push_back(inp->var_);
-        vars[inp->var_desc_->Name()] = inp;
-        if (inp->pre_op_) {
-          op->pre_ops_[it.first].push_back(inp->pre_op_);
-          op->pre_ops_out_idx_[it.first].push_back(inp->pre_op_out_idx_);
-        } else {
-          op->pre_ops_[it.first].push_back(nullptr);
-        }
-        VLOG(3) << "input vname " << inp->var_desc_->Name() << " "
-                << inp->var_->IsInitialized();
-      }
-    }
-
-    op->output_vars_ = outputs;
-    for (auto it : op->output_vars_) {
-      auto& outvars = outvars_map[it.first];
-      const std::vector<VarBase*>& outputs = it.second;
-      for (size_t i = 0; i < outputs.size(); ++i) {
-        VarBase* out = outputs[i];
-        outvars.push_back(out->var_);
-        vars[out->var_desc_->Name()] = out;
-
-        framework::VarDesc* var_desc = block->FindVar(out->var_desc_->Name());
-        if (var_desc->GetType() == framework::proto::VarType::LOD_TENSOR) {
-          out->var_->GetMutable<framework::LoDTensor>();
-        } else {
-          LOG(ERROR) << "tracer doesn't support yet";
-        }
-        out->stop_gradient_ = stop_gradient;
-        out->pre_op_ = op;
-        out->pre_op_out_name_ = it.first;
-        out->pre_op_out_idx_ = i;
-
-        VLOG(3) << "output vname " << out->var_desc_->Name() << " "
-                << out->var_->IsInitialized();
-      }
-    }
-
-    VLOG(3) << "tracer running " << op_desc->Type();
-    framework::RuntimeContext ctx(invars_map, outvars_map);
-
-    // TODO(panyx0718): Cache p.
-    framework::OperatorWithKernel* op_kernel =
-        dynamic_cast<framework::OperatorWithKernel*>(op_base.get());
-    PADDLE_ENFORCE_NOT_NULL(op_kernel, "only support op with kernel");
-
-    framework::Scope scope;
-    platform::CPUPlace place;
-    PreparedOp p = PreparedOp::Prepare(ctx, *op_kernel, place);
-    p.op.RuntimeInferShape(scope, place, ctx);
-    p.func(framework::ExecutionContext(p.op, scope, *p.dev_ctx, p.ctx));
-
-    if (!stop_gradient) {
-      framework::OpDesc* grad_op_desc;
-      // TODO(panyx): Is this leaked?
-      std::unique_ptr<std::unordered_map<std::string, std::string>> grad_to_var(
-          new std::unordered_map<std::string, std::string>());
-      CreateGradOp(*op_desc, {}, {block}, &grad_op_desc, grad_to_var.get());
-      op->grad_op_desc_ = grad_op_desc;
-
-      for (auto it : grad_op_desc->Inputs()) {
-        auto& grad_in_vars = op->grad_input_vars_[it.first];
-        for (const std::string& grad_invar : it.second) {
-          block->FindRecursiveOrCreateVar(grad_invar);
-          auto var_it = grad_to_var->find(grad_invar);
-          if (var_it == grad_to_var->end()) {
-            auto fwd_var_it = vars.find(grad_invar);
-            PADDLE_ENFORCE(fwd_var_it != vars.end());
-            // Forward inputs or outputs.
-            grad_in_vars.push_back(fwd_var_it->second->var_);
-          } else {
-            VarBase* var = vars[var_it->second];
-            if (!var->grads_->IsInitialized()) {
-              InitVar(var->var_, var->grads_);
-            }
-            // Douts.
-            grad_in_vars.push_back(var->grads_);
-          }
-        }
-      }
-
-      for (auto it : grad_op_desc->Outputs()) {
-        auto& grad_out_vars = op->grad_output_vars_[it.first];
-        for (const std::string& grad_outvar : it.second) {
-          block->FindRecursiveOrCreateVar(grad_outvar);
-          auto var_it = grad_to_var->find(grad_outvar);
-          PADDLE_ENFORCE(var_it != grad_to_var->end());
-          VarBase* var = vars[var_it->second];
-          if (!var->grads_->IsInitialized()) {
-            InitVar(var->var_, var->grads_);
-          }
-          grad_out_vars.push_back(var->grads_);
-        }
-      }
-    }
-
-    op->block_ = block;
-  }
+             framework::BlockDesc* block, const bool stop_gradient = false);
 
   std::vector<VarBase*> PyTrace(OpBase* op, const std::vector<VarBase*>& inputs,
-                                bool stop_gradient = false) {
-    VLOG(3) << "py_trace";
-    op->input_vars_["X"] = inputs;
-    op->output_vars_["Out"] = PyLayer::Apply(op->forward_id_, inputs);
-    for (VarBase* inp : inputs) {
-      if (inp->pre_op_) {
-        op->pre_ops_["X"].push_back(inp->pre_op_);
-        op->pre_ops_out_idx_["X"].push_back(inp->pre_op_out_idx_);
-      } else {
-        op->pre_ops_["X"].push_back(nullptr);
-      }
-    }
-
-    auto& outputs = op->output_vars_["Out"];
-    for (size_t i = 0; i < outputs.size(); ++i) {
-      VarBase* out = outputs[i];
-      out->stop_gradient_ = stop_gradient;
-      out->pre_op_ = op;
-      out->pre_op_out_name_ = "Out";
-      out->pre_op_out_idx_ = i;
-    }
-    if (!stop_gradient) {
-      auto& grad_input_vars = op->grad_input_vars_["X@GRAD"];
-      auto& grad_output_vars = op->grad_output_vars_["Out@GRAD"];
-
-      for (const VarBase* inp : inputs) {
-        grad_input_vars.push_back(inp->var_);
-      }
-      for (VarBase* out : outputs) {
-        grad_input_vars.push_back(out->var_);
-      }
-      for (VarBase* out : outputs) {
-        grad_input_vars.push_back(out->grads_);
-        if (!grad_input_vars.back()->IsInitialized()) {
-          InitVar(out->var_, grad_input_vars.back());
-        }
-      }
-      for (const VarBase* inp : inputs) {
-        grad_output_vars.push_back(inp->grads_);
-        if (!grad_output_vars.back()->IsInitialized()) {
-          InitVar(inp->var_, grad_output_vars.back());
-        }
-      }
-    }
-    return outputs;
-  }
+                                bool stop_gradient = false);
 
  private:
   framework::BlockDesc* root_block_;

paddle/fluid/imperative/type_defs.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+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. */
+
+#pragma once
+
+#include <map>
+#include <string>
+#include <vector>
+
+namespace paddle {
+namespace imperative {
+
+class VarBase;
+class OpBase;
+
+typedef std::map<std::string, std::vector<VarBase*>> VarBasePtrMap;
+typedef std::map<std::string, std::vector<OpBase*>> OpBasePtrMap;
+
+}  // namespace imperative
+}  // namespace paddle

paddle/fluid/pybind/CMakeLists.txt

-
-set(PYBIND_DEPS pybind python proto_desc memory executor async_executor prune feed_fetch_method pass_builder parallel_executor profiler layer scope_pool)
+set(PYBIND_DEPS pybind python proto_desc memory executor async_executor prune
+  feed_fetch_method pass_builder parallel_executor profiler layer scope_pool
+  tracer)
 if(WITH_PYTHON)
   list(APPEND PYBIND_DEPS py_func_op)
 endif()

paddle/fluid/pybind/pybind.cc

@@ -126,25 +126,17 @@ PYBIND11_MODULE(core, m) {
   m.add_object("_cleanup",
                py::capsule([]() { ScopePool::Instance().Clear(); }));
 
-  py::class_<imperative::VarBase, std::shared_ptr<imperative::VarBase>>(
-      m, "VarBase", R"DOC()DOC")
+  py::class_<imperative::VarBase>(m, "VarBase", R"DOC()DOC")
       // .def(py::init<>())
       .def(py::init<bool>(), py::arg("stop_gradient") = false)
       .def("_run_backward",
            [](imperative::VarBase &self) { self.RunBackward(); })
       .def("_grad_name", &imperative::VarBase::GradName)
-      .def("_grad", &imperative::VarBase::Grad)
-      .def_property("grad_value",
+      .def("_grad_value", &imperative::VarBase::GradValue)
+      .def("_grad_ivar",
            [](const imperative::VarBase &self) { return self.grads_; },
-                    [](imperative::VarBase &self, framework::Variable *grad) {
-                      self.grads_ = grad;
-                    },
            py::return_value_policy::reference)
-      .def_property("value",
-                    [](const imperative::VarBase &self) { return self.var_; },
-                    [](imperative::VarBase &self, framework::Variable *var) {
-                      self.var_ = var;
-                    },
+      .def("value", [](const imperative::VarBase &self) { return self.var_; },
            py::return_value_policy::reference)
       .def_property(
           "desc",

python/paddle/fluid/framework.py

@@ -372,30 +372,21 @@ class Variable(object):
         self.stop_gradient = stop_gradient
         self.is_data = is_data
         if _in_imperative_mode():
-            if 'ivar' in kwargs:
-                self._ivar = kwargs['ivar']
-            else:
+            self._ivar = kwargs.get("ivar", None)
+            if not self._ivar:
                 self._ivar = core.VarBase()
             self._ivar.desc = self.desc
             self._ivar.stop_gradient = stop_gradient
 
     def _numpy(self):
-        tensor = self._ivar.value.get_tensor()
+        tensor = self._ivar.value().get_tensor()
         return np.array(tensor)
 
     def _backward(self):
         self._ivar._run_backward()
 
     def _gradient(self):
-        return np.array(self._ivar._grad())
-
-    @property
-    def _value(self):
-        return self._ivar.value
-
-    @_value.setter
-    def _value(self, v):
-        self._ivar.value = v
+        return np.array(self._ivar._grad_value())
 
     def __str__(self):
         return self.to_string(True)

python/paddle/fluid/imperative/base.py

@@ -45,7 +45,7 @@ def to_variable(value, block=None):
             name=None,
             shape=value.shape,
             dtype=value.dtype)
-        var = py_var._ivar.value
+        var = py_var._ivar.value()
         tensor = var.get_tensor()
         tensor.set(value, core.CPUPlace())
         return py_var

python/paddle/fluid/imperative/layers.py

@@ -55,18 +55,18 @@ class PyLayer(core.PyLayer):
         super(PyLayer, self).__init__()
 
     @staticmethod
-    def forward(inputs):
+    def forward(*inputs):
         raise NotImplementedError
 
     @staticmethod
-    def backward(douts):
+    def backward(*douts):
         raise NotImplementedError
 
     @classmethod
-    def __call__(cls, inputs):
+    def __call__(cls, *inputs):
         tracer = framework._imperative_tracer()
         block = framework.default_main_program().current_block()
-        inputs = [x._ivar for x in inputs]
+        ivar_inputs = [x._ivar for x in inputs]
 
         if not hasattr(cls, 'forward_id'):
             cls.forward_id = core.PyLayer.num_funcs() + 1
@@ -78,11 +78,11 @@ class PyLayer(core.PyLayer):
         iop.forward_id = cls.forward_id
         iop.backward_id = cls.backward_id
         block.ops.append(iop)
-        ivars = tracer.py_trace(iop, inputs, False)
+        ivars = tracer.py_trace(iop, ivar_inputs, False)
         # ivars = core.PyLayer.apply(cls.forward, inputs)
         ret = []
         for ivar in ivars:
-            tensor = ivar.value.get_tensor()
+            tensor = ivar.value().get_tensor()
             py_var = framework.Variable(
                 block,
                 type=core.VarDesc.VarType.LOD_TENSOR,

python/paddle/fluid/optimizer.py

@@ -390,8 +390,8 @@ class Optimizer(object):
                     grad_var = Variable(
                         block=loss.block,
                         name=param._ivar._grad_name(),
-                        stop_gradient=True)
-                    grad_var._value = param._ivar.grad_value
+                        stop_gradient=True,
+                        ivar=param._ivar._grad_ivar())
                     params_grads.append((param, grad_var))
             with program_guard(program, startup_program):
                 optimize_ops = self._create_optimization_pass(params_grads)

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

@@ -97,35 +97,35 @@ class TestImperative(unittest.TestCase):
                     super(PyLayer1, self).__init__()
 
                 @staticmethod
-                def forward(inputs):
-                    return inputs
+                def forward(input):
+                    return input
 
                 @staticmethod
-                def backward(inputs):
-                    return inputs
+                def backward(input):
+                    return input
 
             class PyLayer2(fluid.imperative.PyLayer):
                 def __init__(self):
                     super(PyLayer2, self).__init__()
 
                 @staticmethod
-                def forward(inputs):
-                    return inputs
+                def forward(input):
+                    return input
 
                 @staticmethod
-                def backward(inputs):
-                    return inputs
+                def backward(input):
+                    return input
 
             py_layer_1 = PyLayer1()
             py_layer_2 = PyLayer2()
-            py_layer_1([fluid.imperative.base.to_variable(np.ones([2, 2]))])
-            py_layer_2([fluid.imperative.base.to_variable(np.ones([2, 2]))])
+            py_layer_1(fluid.imperative.base.to_variable(np.ones([2, 2])))
+            py_layer_2(fluid.imperative.base.to_variable(np.ones([2, 2])))
             id = py_layer_1.forward_id
             self.assertGreater(id, 0)
             self.assertEqual(py_layer_1.backward_id, id + 1)
             self.assertEqual(py_layer_2.forward_id, id + 2)
             self.assertEqual(py_layer_2.backward_id, id + 3)
-            py_layer_1([fluid.imperative.base.to_variable(np.ones([2, 2]))])
+            py_layer_1(fluid.imperative.base.to_variable(np.ones([2, 2])))
             self.assertEqual(py_layer_1.forward_id, id)
 
     def test_pylayer(self):
@@ -133,7 +133,7 @@ class TestImperative(unittest.TestCase):
         with fluid.imperative.guard():
             my_py_layer = MyPyLayer()
             var_inp = fluid.imperative.base.to_variable(np_inp)
-            outs = my_py_layer([var_inp])
+            outs = my_py_layer(var_inp)
             dy_out = np.sum(outs[0]._numpy())
             outs[0]._backward()
             dy_grad = var_inp._gradient()

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

@@ -105,7 +105,6 @@ class TestImperativeMnist(unittest.TestCase):
             fluid.default_startup_program().random_seed = seed
             fluid.default_main_program().random_seed = seed
 
-            #  mnist = Conv2D(1, 20, 5)
             mnist = MNIST()
             sgd = SGDOptimizer(learning_rate=1e-3)
             train_reader = paddle.batch(
@@ -126,16 +125,17 @@ class TestImperativeMnist(unittest.TestCase):
                 label._stop_gradient = True
 
                 cost = mnist(img)
-                loss = fluid.layers.reduce_mean(cost)
-                dy_out = loss._numpy()
+                loss = fluid.layers.cross_entropy(cost, label)
+                avg_loss = fluid.layers.mean(loss)
+                dy_out = avg_loss._numpy()
 
                 if batch_id == 0:
                     for param in fluid.default_main_program().global_block(
                     ).all_parameters():
                         dy_param_init_value[param.name] = param._numpy()
 
-                loss._backward()
-                sgd.minimize(loss)
+                avg_loss._backward()
+                sgd.minimize(avg_loss)
                 dy_param_value = {}
                 for param in fluid.default_main_program().global_block(
                 ).all_parameters():
@@ -147,7 +147,6 @@ class TestImperativeMnist(unittest.TestCase):
 
             exe = fluid.Executor(fluid.CPUPlace())
 
-            #  mnist = Conv2D(1, 20, 5)
             mnist = MNIST()
             sgd = SGDOptimizer(learning_rate=1e-3)
             train_reader = paddle.batch(
@@ -157,8 +156,9 @@ class TestImperativeMnist(unittest.TestCase):
                 name='pixel', shape=[1, 28, 28], dtype='float32')
             label = fluid.layers.data(name='label', shape=[1], dtype='int64')
             cost = mnist(img)
-            loss = fluid.layers.reduce_mean(cost)
-            sgd.minimize(loss)
+            loss = fluid.layers.cross_entropy(cost, label)
+            avg_loss = fluid.layers.mean(loss)
+            sgd.minimize(avg_loss)
 
             # initialize params and fetch them
             static_param_init_value = {}
@@ -182,7 +182,7 @@ class TestImperativeMnist(unittest.TestCase):
                 y_data = np.array([x[1] for x in data]).astype('int64').reshape(
                     [128, 1])
 
-                fetch_list = [loss.name]
+                fetch_list = [avg_loss.name]
                 fetch_list.extend(static_param_name_list)
                 out = exe.run(fluid.default_main_program(),
                               feed={"pixel": x_data,