Merge pull request #14782 from PaddlePaddle/revert-14398-imperative

Revert "Imperative"

paddle/fluid/CMakeLists.txt

 add_subdirectory(memory)
 add_subdirectory(platform)
 add_subdirectory(framework)
-add_subdirectory(imperative)
 add_subdirectory(operators)
 add_subdirectory(string)
 add_subdirectory(recordio)

paddle/fluid/framework/feed_fetch_method.cc

@@ -16,9 +16,7 @@ limitations under the License. */
 #include <string>
 #include <vector>
 #include "glog/logging.h"
-#include "paddle/fluid/framework/var_type.h"
 #include "paddle/fluid/framework/variable.h"
-#include "paddle/fluid/platform/place.h"
 
 namespace paddle {
 namespace framework {
@@ -55,12 +53,5 @@ LoDTensor& GetFetchVariable(const Scope& scope, const std::string& var_name,
   return tensor;
 }
 
-LoDTensor& GetVariableTensor(const Scope& scope, const std::string& var_name) {
-  Variable* var = scope.FindVar(var_name);
-  PADDLE_ENFORCE(var, "%s no in scope", var_name);
-  PADDLE_ENFORCE(var->IsType<LoDTensor>(), "Only support lod tensor now.");
-  return *var->GetMutable<LoDTensor>();
-}
-
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/feed_fetch_method.h

@@ -27,7 +27,5 @@ void SetFeedVariable(Scope* scope, const LoDTensor& input,
 LoDTensor& GetFetchVariable(const Scope& scope, const std::string& var_name,
                             size_t index);
 
-LoDTensor& GetVariableTensor(const Scope& scope, const std::string& var_name);
-
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/ir/graph.cc

@@ -38,7 +38,8 @@ void CheckProgram(const ProgramDesc &program) {
     switch (role_id) {
       case _INT(OpRole::kForward):
         if (visit.find(_INT(OpRole::kBackward)) != visit.end()) {
-          LOG(ERROR) << "Cannot add backward operator before forward operator "
+          LOG(ERROR)
+              << "Cannot add backward operator before forward operator %s."
               << op->Type();
         }
         break;

paddle/fluid/imperative/CMakeLists.txt

-cc_library(layer SRCS layer.cc DEPS proto_desc operator)
-cc_library(tracer SRCS tracer.cc DEPS proto_desc)
-cc_library(engine SRCS engine.cc)

paddle/fluid/imperative/engine.cc

-// Copyright (c) 2018 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.
-
-#include "paddle/fluid/imperative/engine.h"
-
-#include <mutex>  // NOLINT
-#include <vector>
-
-#include "glog/logging.h"
-
-namespace paddle {
-namespace imperative {
-
-static std::once_flag init_engine;
-static Engine* engine;
-
-class DummyEngine : public Engine {
- public:
-  void Enqueue(Runnable* runnable) override {
-    queued_runnables_.push_back(runnable);
-  }
-
-  size_t Size() const override { return queued_runnables_.size(); }
-
-  void Sync() override {
-    for (Runnable* l : queued_runnables_) {
-      LOG(INFO) << "running " << reinterpret_cast<void*>(l);
-    }
-    queued_runnables_.clear();
-  }
-
- private:
-  std::vector<Runnable*> queued_runnables_;
-};
-
-Engine* GetEngine() {
-  std::call_once(init_engine, []() { engine = new DummyEngine(); });
-  return engine;
-}
-
-}  // namespace imperative
-}  // namespace paddle

paddle/fluid/imperative/engine.h

-// Copyright (c) 2018 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 <cstddef>
-#include <cstdint>
-
-namespace paddle {
-namespace imperative {
-
-struct Runnable {};
-
-class Engine {
- public:
-  virtual ~Engine() {}
-
-  virtual void Enqueue(Runnable* runnable) = 0;
-
-  virtual size_t Size() const = 0;
-
-  virtual void Sync() = 0;
-};
-
-Engine* GetEngine();
-
-}  // namespace imperative
-}  // namespace paddle

paddle/fluid/imperative/layer.cc

-// Copyright (c) 2018 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.
-
-#include "paddle/fluid/imperative/layer.h"
-#include <deque>
-#include <limits>
-#include <map>
-#include <random>
-#include <utility>
-
-#include "paddle/fluid/framework/lod_tensor.h"
-#include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/string/printf.h"
-
-namespace paddle {
-namespace imperative {
-
-using framework::Variable;
-
-void AddTo(Variable* src, Variable* dst) {
-  framework::LoDTensor* dst_tensor = dst->GetMutable<framework::LoDTensor>();
-  framework::LoDTensor* src_tensor = src->GetMutable<framework::LoDTensor>();
-  PADDLE_ENFORCE(dst_tensor->numel() == src_tensor->numel(), "%lld vs %lld",
-                 dst_tensor->numel(), 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) {
-    dst_data[i] += src_data[i];
-  }
-}
-
-class Autograd {
- public:
-  explicit Autograd(framework::Scope* scope) : scope_(scope) {}
-
-  void RunBackward(VarBase* var) {
-    PADDLE_ENFORCE(var->pre_op_->op_desc_);
-    // TODO(panyx0718): Only create for vars that "require_grad"
-    (*var->pre_op_->output_vars_)[var->pre_op_out_idx_]->grads_ = var->grads_;
-
-    std::deque<OpBase*> ready;
-    ready.push_back(var->pre_op_);
-
-    std::map<OpBase*, int> dep_counts = ComputeDepCounts(var->pre_op_);
-
-    while (!ready.empty()) {
-      OpBase* ready_op = ready.front();
-      ready.pop_front();
-      std::vector<Variable*> input_grads = ready_op->ApplyGrad(scope_);
-
-      for (size_t i = 0; i < input_grads.size(); ++i) {
-        if (!input_grads[i]) continue;
-        OpBase* pre_op = ready_op->pre_ops_->at(i);
-        if (!pre_op) continue;
-
-        dep_counts[pre_op] -= 1;
-        PADDLE_ENFORCE(dep_counts[pre_op] >= 0);
-        bool pre_op_ready = dep_counts[pre_op] == 0;
-        if (pre_op_ready) {
-          ready.push_back(pre_op);
-        }
-      }
-    }
-  }
-
- private:
-  std::map<OpBase*, int> ComputeDepCounts(OpBase* op) {
-    std::map<OpBase*, int> ret;
-
-    std::deque<OpBase*> queue;
-    queue.push_back(op);
-    std::unordered_set<OpBase*> visited;
-    visited.insert(op);
-    while (!queue.empty()) {
-      OpBase* candidate = queue.front();
-      queue.pop_front();
-      for (OpBase* pre_op : *(candidate->pre_ops_)) {
-        if (!pre_op) continue;
-        if (visited.find(pre_op) == visited.end()) {
-          visited.insert(pre_op);
-          queue.push_back(pre_op);
-        }
-        ret[pre_op] += 1;
-      }
-    }
-
-    return ret;
-  }
-
-  framework::Scope* scope_;
-};
-
-framework::Variable* CreateVariable(const std::string& name,
-                                    const framework::DDim& dim, float val,
-                                    framework::Scope* scope,
-                                    bool random_name = true) {
-  std::string varname = name;
-  if (random_name) {
-    std::mt19937 rng;
-    rng.seed(std::random_device()());
-    std::uniform_int_distribution<std::mt19937::result_type> dist6(
-        1, std::numeric_limits<int>::max());
-    int id = dist6(rng);
-    varname = string::Sprintf("%s@%d", varname, id);
-  }
-
-  VLOG(3) << "creating var " << varname;
-  framework::Variable* var = scope->Var(varname);
-  framework::LoDTensor* tensor = var->GetMutable<framework::LoDTensor>();
-
-  float* data = tensor->mutable_data<float>(dim, platform::CPUPlace());
-  std::fill(data, data + tensor->numel(), val);
-  return var;
-}
-
-framework::LoDTensor& VarBase::Grad() {
-  VLOG(3) << "get var grad " << var_desc_->Name();
-  return *grads_->GetMutable<framework::LoDTensor>();
-}
-
-void VarBase::ApplyGrad(framework::Scope* scope, Variable* grad) {
-  VLOG(3) << "apply var grad " << var_desc_->Name() << " "
-          << grad->Get<framework::LoDTensor>().data<float>()[0];
-  if (!grads_) {
-    grads_ =
-        CreateVariable(string::Sprintf("%s@IGrad", var_desc_->Name()),
-                       var_->Get<framework::LoDTensor>().dims(), 0.0, scope);
-  }
-  AddTo(grad, grads_);
-  VLOG(3) << "grad_ after apply var grad " << var_desc_->Name() << " "
-          << grads_->Get<framework::LoDTensor>().data<float>()[0];
-}
-
-std::vector<Variable*> OpBase::ApplyGrad(framework::Scope* scope) {
-  VLOG(3) << "op grad " << grad_op_desc_->Type();
-
-  for (const std::string& grad_invar : grad_op_desc_->InputArgumentNames()) {
-    if (grad_to_var_->find(grad_invar) == grad_to_var_->end()) {
-      // grad op inputs can be forward inputs, so not in grad_to_var.
-      continue;
-    }
-    VLOG(3) << "op grad in var " << grad_invar;
-    block_->FindRecursiveOrCreateVar(grad_invar);
-    framework::Variable* var = scope->Var(grad_invar);
-    const std::string& invar = grad_to_var_->at(grad_invar);
-    for (VarBase* varbase : *output_vars_) {
-      // Use the accumulated grads_ by sharing the input with grads_.
-      if (varbase->var_desc_->Name() == invar) {
-        var->GetMutable<framework::LoDTensor>()->ShareDataWith(
-            varbase->grads_->Get<framework::LoDTensor>());
-        break;
-      }
-    }
-  }
-
-  for (const std::string& outvar : grad_op_desc_->OutputArgumentNames()) {
-    VLOG(3) << "grad outvar " << outvar;
-    block_->FindRecursiveOrCreateVar(outvar);
-    framework::Variable* var = scope->Var(outvar);
-    if (!var->IsInitialized()) {
-      framework::VarDesc* var_desc = block_->FindVar(outvar);
-      if (var_desc->GetType() == framework::proto::VarType::LOD_TENSOR) {
-        var->GetMutable<framework::LoDTensor>();
-      } else {
-        LOG(ERROR) << "tracer doesn't support yet";
-      }
-    }
-  }
-  grad_op_desc_->InferShape(*block_);
-  grad_op_desc_->InferVarType(block_);
-  std::unique_ptr<framework::OperatorBase> opbase =
-      framework::OpRegistry::CreateOp(*grad_op_desc_);
-
-  opbase->Run(*scope, platform::CPUPlace());
-
-  // `ret` matches exactly with `input_vars_` of forward op.
-  std::vector<Variable*> ret;
-  for (size_t i = 0; i < input_vars_->size(); ++i) {
-    bool found = false;
-    for (const std::string& outvar : grad_op_desc_->OutputArgumentNames()) {
-      Variable* var = scope->FindVar(outvar);
-      VarBase* origin_var = (*input_vars_)[i];
-      std::string orig_var = grad_to_var_->at(outvar);
-      PADDLE_ENFORCE(origin_var->var_desc_->Name() == orig_var);
-      VLOG(3) << "apply grad " << outvar << " with origin " << orig_var;
-      origin_var->ApplyGrad(scope, var);
-      found = true;
-      ret.push_back(var);
-      // TODO(panyx0718): There might be another outvar with the same name.
-      // In that case, it doesn't matter the first one or the second one is
-      // used.
-      break;
-    }
-    if (!found) {
-      ret.push_back(nullptr);
-    }
-  }
-  return ret;
-}
-
-void VarBase::RunBackward(framework::Scope* scope) {
-  grads_ = CreateVariable(framework::GradVarName(var_desc_->Name()),
-                          var_->Get<framework::LoDTensor>().dims(), 1.0, scope,
-                          false);
-  if (!pre_op_) return;
-  Autograd(scope).RunBackward(this);
-}
-
-}  // namespace imperative
-}  // namespace paddle

paddle/fluid/imperative/layer.h

-// Copyright (c) 2018 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 <string>
-#include <vector>
-#include "paddle/fluid/framework/op_desc.h"
-#include "paddle/fluid/framework/operator.h"
-#include "paddle/fluid/framework/scope.h"
-#include "paddle/fluid/framework/var_desc.h"
-#include "paddle/fluid/platform/enforce.h"
-
-namespace paddle {
-namespace imperative {
-
-class OpBase;
-
-class VarBase {
- public:
-  VarBase()
-      : pre_op_(nullptr),
-        pre_op_out_idx_(-1),
-        var_desc_(nullptr),
-        var_(nullptr),
-        grads_(nullptr) {}
-
-  virtual ~VarBase() {}
-
-  void ApplyGrad(framework::Scope* scope, framework::Variable* grad);
-
-  void RunBackward(framework::Scope* scope);
-
-  framework::LoDTensor& Grad();
-
-  OpBase* pre_op_;
-  int pre_op_out_idx_;
-
-  framework::VarDesc* var_desc_;
-  framework::Variable* var_;
-  framework::Variable* grads_;
-};
-
-class OpBase {
- public:
-  OpBase()
-      : input_vars_(new std::vector<VarBase*>()),
-        output_vars_(new std::vector<VarBase*>()),
-        pre_ops_(new std::vector<OpBase*>()),
-        pre_ops_out_idx_(new std::vector<int>()),
-        op_desc_(nullptr),
-        grad_op_desc_(nullptr) {}
-
-  virtual ~OpBase() {
-    delete input_vars_;
-    delete output_vars_;
-
-    delete pre_ops_;
-    delete pre_ops_out_idx_;
-
-    if (grad_op_desc_) delete grad_op_desc_;
-    if (grad_to_var_) delete grad_to_var_;
-  }
-
-  std::vector<framework::Variable*> ApplyGrad(framework::Scope* scope);
-
-  std::vector<VarBase*>* input_vars_;
-  std::vector<VarBase*>* output_vars_;
-  std::vector<OpBase*>* pre_ops_;
-  std::vector<int>* pre_ops_out_idx_;
-  framework::OpDesc* op_desc_;
-
-  framework::OpDesc* grad_op_desc_;
-  std::unordered_map<std::string, std::string>* grad_to_var_;
-  framework::BlockDesc* block_;
-};
-
-class Layer {
- public:
-  virtual ~Layer() {}
-
-  virtual std::vector<VarBase> Forward(const std::vector<VarBase>& inputs) {
-    std::vector<VarBase> vars;
-    return vars;
-  }
-
-  virtual void Backward() { LOG(ERROR) << "To support customize"; }
-};
-
-}  // namespace imperative
-}  // namespace paddle

paddle/fluid/imperative/tracer.cc

-// Copyright (c) 2018 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.
-
-#include "paddle/fluid/imperative/tracer.h"
-
-namespace paddle {
-namespace imperative {}  // namespace imperative
-}  // namespace paddle

paddle/fluid/imperative/tracer.h

-// Copyright (c) 2018 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>
-
-#include "paddle/fluid/framework/op_desc.h"
-#include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/framework/scope.h"
-#include "paddle/fluid/imperative/engine.h"
-#include "paddle/fluid/imperative/layer.h"
-
-namespace paddle {
-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();
-}
-
-class Tracer {
- public:
-  explicit Tracer(framework::BlockDesc* root_block) : root_block_(root_block) {
-    root_scope_ = new framework::Scope();
-    scopes_[root_block_] = root_scope_;
-  }
-
-  virtual ~Tracer() { delete root_scope_; }
-
-  void Trace(OpBase* op, const std::vector<VarBase*>& inputs,
-             const std::vector<VarBase*>& outputs,
-             framework::BlockDesc* block) {
-    framework::Scope* scope = GetScope(block);
-    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);
-
-    *op->input_vars_ = inputs;
-    for (VarBase* input : inputs) {
-      const std::string vname = input->var_desc_->Name();
-      framework::Variable* var = scope->Var(vname);
-      input->var_ = var;
-      if (!var->IsInitialized()) {
-        framework::VarDesc* var_desc = block->FindVar(vname);
-        if (var_desc->GetType() == framework::proto::VarType::LOD_TENSOR) {
-          var->GetMutable<framework::LoDTensor>();
-        } else {
-          LOG(ERROR) << "tracer doesn't support yet";
-        }
-      }
-      if (input->pre_op_) {
-        op->pre_ops_->push_back(input->pre_op_);
-        op->pre_ops_out_idx_->push_back(input->pre_op_out_idx_);
-      } else {
-        op->pre_ops_->push_back(nullptr);
-      }
-    }
-
-    *op->output_vars_ = outputs;
-    for (size_t i = 0; i < outputs.size(); ++i) {
-      const std::string vname = outputs[i]->var_desc_->Name();
-      framework::Variable* var = scope->Var(vname);
-      if (!var->IsInitialized()) {
-        framework::VarDesc* var_desc = block->FindVar(vname);
-        if (var_desc->GetType() == framework::proto::VarType::LOD_TENSOR) {
-          var->GetMutable<framework::LoDTensor>();
-        } else {
-          LOG(ERROR) << "tracer doesn't support yet";
-        }
-      }
-      outputs[i]->var_ = var;
-      outputs[i]->pre_op_ = op;
-      outputs[i]->pre_op_out_idx_ = i;
-    }
-    op_base->Run(*scope, platform::CPUPlace());
-    framework::OpDesc* grad_op_desc;
-    auto grad_to_var = new std::unordered_map<std::string, std::string>();
-    CreateGradOp(*op_desc, {}, {block}, &grad_op_desc, grad_to_var);
-    op->grad_op_desc_ = grad_op_desc;
-    op->grad_to_var_ = grad_to_var;
-    op->block_ = block;
-  }
-
-  framework::Scope* GetScope(framework::BlockDesc* block) {
-    if (scopes_.find(block) != scopes_.end()) {
-      return scopes_.at(block);
-    }
-    framework::BlockDesc* parent_block = block->ParentBlock();
-    PADDLE_ENFORCE(scopes_.find(parent_block) != scopes_.end());
-    framework::Scope* scope = &scopes_[parent_block]->NewScope();
-    scopes_[block] = scope;
-    return scope;
-  }
-
- private:
-  std::map<framework::BlockDesc*, framework::Scope*> scopes_;
-  framework::BlockDesc* root_block_;
-  framework::Scope* root_scope_;
-};
-
-}  // 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)
-set(PYBIND_SRCS pybind.cc exception.cc protobuf.cc const_value.cc recordio.cc async_executor_py.cc imperative.cc)
-
+set(PYBIND_DEPS pybind python proto_desc memory executor async_executor prune feed_fetch_method pass_builder parallel_executor profiler)
+set(PYBIND_SRCS pybind.cc exception.cc protobuf.cc const_value.cc recordio.cc async_executor_py.cc)
 if(WITH_PYTHON)
   if(WITH_AMD_GPU)
     hip_library(paddle_pybind SHARED

paddle/fluid/pybind/imperative.cc

-/* Copyright (c) 2018 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. */
-
-#include "paddle/fluid/pybind/imperative.h"
-#include "paddle/fluid/framework/block_desc.h"
-#include "paddle/fluid/framework/scope.h"
-#include "paddle/fluid/imperative/tracer.h"
-
-namespace paddle {
-namespace pybind {
-
-// Bind Methods
-void BindTracer(pybind11::module *m) {
-  pybind11::class_<imperative::Tracer>(*m, "Tracer", "")
-      .def("__init__",
-           [](imperative::Tracer &self, framework::BlockDesc *root_block) {
-             new (&self) imperative::Tracer(root_block);
-           })
-      .def("trace", &imperative::Tracer::Trace)
-      .def("get_scope", &imperative::Tracer::GetScope,
-           pybind11::return_value_policy::reference);
-}
-
-}  // namespace pybind
-}  // namespace paddle

paddle/fluid/pybind/imperative.h

-/* Copyright (c) 2018 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 <Python.h>
-#include <vector>
-#include "paddle/fluid/imperative/layer.h"
-#include "pybind11/pybind11.h"
-#include "pybind11/stl.h"
-
-namespace paddle {
-namespace pybind {
-
-class PyLayer : public imperative::Layer {
- public:
-  using imperative::Layer::Layer;  // Inherit constructors
-
-  std::vector<imperative::VarBase> Forward(
-      const std::vector<imperative::VarBase>& inputs) override {
-    PYBIND11_OVERLOAD(std::vector<imperative::VarBase>, Layer, Forward,
-                      inputs);  // NOLINT
-  }
-
-  void Backward() override {
-    PYBIND11_OVERLOAD(void, Layer, Backward, );  // NOLINT
-  }
-};
-
-class PyOpBase : public imperative::OpBase {
- public:
-  using imperative::OpBase::OpBase;  // Inherit constructors
-};
-
-class PyVarBase : public imperative::VarBase {
- public:
-  using imperative::VarBase::VarBase;  // Inherit constructors
-};
-
-void BindTracer(pybind11::module* m);
-
-}  // namespace pybind
-}  // namespace paddle

paddle/fluid/pybind/pybind.cc

@@ -34,7 +34,6 @@ limitations under the License. */
 #include "paddle/fluid/framework/reader.h"
 #include "paddle/fluid/framework/selected_rows.h"
 #include "paddle/fluid/framework/version.h"
-#include "paddle/fluid/imperative/layer.h"
 #include "paddle/fluid/memory/allocation/allocator_strategy.h"
 #include "paddle/fluid/operators/activation_op.h"
 #include "paddle/fluid/operators/reader/lod_tensor_blocking_queue.h"
@@ -46,7 +45,6 @@ limitations under the License. */
 #include "paddle/fluid/pybind/async_executor_py.h"
 #include "paddle/fluid/pybind/const_value.h"
 #include "paddle/fluid/pybind/exception.h"
-#include "paddle/fluid/pybind/imperative.h"
 #include "paddle/fluid/pybind/protobuf.h"
 #include "paddle/fluid/pybind/pybind.h"  // NOLINT
 #include "paddle/fluid/pybind/recordio.h"
@@ -102,42 +100,6 @@ PYBIND11_MODULE(core, m) {
 
   BindException(&m);
 
-  py::class_<imperative::VarBase, PyVarBase>(m, "VarBase", R"DOC()DOC")
-      .def(py::init<>())
-      .def("_run_backward",
-           [](imperative::VarBase &self, framework::Scope *scope) {
-             self.RunBackward(scope);
-           })
-      .def("_grad", &imperative::VarBase::Grad)
-      .def_property(
-          "desc",
-          [](const imperative::VarBase &self) { return self.var_desc_; },
-          [](imperative::VarBase &self, framework::VarDesc *var_desc) {
-            self.var_desc_ = var_desc;
-          },
-          py::return_value_policy::reference);
-
-  py::class_<imperative::OpBase, PyOpBase>(m, "OpBase", R"DOC()DOC")
-      .def(py::init<>())
-      .def_property(
-          "desc", [](const imperative::OpBase &self) { return self.op_desc_; },
-          [](imperative::OpBase &self, framework::OpDesc *op_desc) {
-            if (op_desc) {
-              self.op_desc_ = op_desc;
-            }
-          },
-          py::return_value_policy::reference);
-
-  py::class_<imperative::Layer, PyLayer /* <--- trampoline*/> layer(m, "Layer");
-  layer.def(py::init<>())
-      .def("forward",
-           [](imperative::Layer &self,
-              const std::vector<imperative::VarBase> &inputs) {
-             return self.Forward(inputs);
-           })
-      .def("backward", &imperative::Layer::Backward);
-  BindTracer(&m);
-
   py::class_<Tensor>(m, "Tensor", py::buffer_protocol())
       .def_buffer(
           [](Tensor &self) -> py::buffer_info { return CastToPyBuffer(self); })
@@ -639,7 +601,6 @@ All parameter, weight, gradient are variables in Paddle.
 
   m.def("set_feed_variable", framework::SetFeedVariable);
   m.def("get_fetch_variable", framework::GetFetchVariable);
-  m.def("get_variable_tensor", framework::GetVariableTensor);
 
   m.def("_is_program_version_supported", IsProgramVersionSupported);
 

python/paddle/fluid/__init__.py

@@ -34,7 +34,6 @@ from . import io
 from . import evaluator
 from . import initializer
 from . import layers
-from . import imperative
 from . import contrib
 from . import nets
 from . import optimizer
@@ -68,7 +67,6 @@ __all__ = framework.__all__ + executor.__all__ + \
         'initializer',
         'layers',
         'contrib',
-        'imperative',
         'transpiler',
         'nets',
         'optimizer',

python/paddle/fluid/framework.py

@@ -18,7 +18,6 @@ import collections
 import contextlib
 import re
 import six
-import sys
 
 import numpy as np
 
@@ -50,16 +49,6 @@ GRAD_VAR_SUFFIX = core.kGradVarSuffix()
 ZERO_VAR_SUFFIX = core.kZeroVarSuffix()
 CONTROL_DEP_VAR_PREFIX = core.kControlDepVarName()
 
-_imperative_tracer_ = None
-
-
-def _in_imperative_mode():
-    return _imperative_tracer_ is not None
-
-
-def _imperative_tracer():
-    return _imperative_tracer_
-
 
 class NameScope(object):
     def __init__(self, name="", parent=None):
@@ -213,7 +202,7 @@ def _debug_string_(proto, throw_on_error=True):
     return proto.__str__()
 
 
-class Variable(core.VarBase):
+class Variable(object):
     """
     In Fluid, every input and output of an operator is a variable. In most
     cases, variables are used for holding different kinds of data or training
@@ -277,7 +266,6 @@ class Variable(core.VarBase):
                  stop_gradient=False,
                  is_data=False,
                  **kwargs):
-        core.VarBase.__init__(self)
         self.block = block
         self.error_clip = error_clip
 
@@ -358,18 +346,6 @@ class Variable(core.VarBase):
         self.stop_gradient = stop_gradient
         self.is_data = is_data
 
-    def _numpy(self):
-        scope = _imperative_tracer().get_scope(self.block.desc)
-        tensor = core.get_variable_tensor(scope, self.desc.name())
-        return np.array(tensor)
-
-    def _backward(self):
-        scope = _imperative_tracer().get_scope(self.block.desc)
-        self._run_backward(scope)
-
-    def _gradient(self):
-        return np.array(self._grad())
-
     def __str__(self):
         return self.to_string(True)
 
@@ -516,7 +492,7 @@ class OpProtoHolder(object):
         }
 
 
-class Operator(core.OpBase):
+class Operator(object):
     """
     In Fluid, all the operation are represented by Operator, and Operator
     is regarded as a build in an instruction of a Block. Users can use the
@@ -572,7 +548,6 @@ class Operator(core.OpBase):
                  inputs=None,
                  outputs=None,
                  attrs=None):
-        core.OpBase.__init__(self)
         self.block = block
         self.desc = desc
         # note: not add self.attrs here:
@@ -612,7 +587,6 @@ class Operator(core.OpBase):
                     return True
             return False
 
-        self.inputs = []
         if inputs is not None:
             for in_proto in proto.inputs:
                 found = find_name(inputs, in_proto.name)
@@ -639,13 +613,6 @@ class Operator(core.OpBase):
                 else:
                     self.desc.set_input(in_proto.name, [])
 
-            for inp in inputs.values():
-                if isinstance(inp, Variable):
-                    self.inputs.append(inp)
-                elif isinstance(inp, list) or isinstance(inp, tuple):
-                    self.inputs.extend(inp[:])
-
-        self.outputs = []
         if outputs is not None:
             given = set()
             need = set()
@@ -674,12 +641,6 @@ class Operator(core.OpBase):
                     arg.op = self
                 self.desc.set_output(out_proto.name, out_arg_names)
 
-            for out in outputs.values():
-                if isinstance(out, Variable):
-                    self.outputs.append(out)
-                elif isinstance(out, list) or isinstance(out, tuple):
-                    self.outputs.extend(out[:])
-
         if op_attrs is not None:
             if not isinstance(op_attrs, dict):
                 raise TypeError("'attrs' should be a dict.")
@@ -1245,8 +1206,6 @@ class Block(object):
         """
         op_desc = self.desc.append_op()
         op = Operator(block=self, desc=op_desc, *args, **kwargs)
-        if _in_imperative_mode():
-            _imperative_tracer().trace(op, op.inputs, op.outputs, self.desc)
         self.ops.append(op)
         return op
 
@@ -2250,12 +2209,3 @@ def _get_var(name, program=None):
     assert isinstance(program, Program)
 
     return program.global_block().var(name)
-
-
-@contextlib.contextmanager
-def _imperative_guard(tracer):
-    global _imperative_tracer_
-    tmp_trace = _imperative_tracer_
-    _imperative_tracer_ = tracer
-    yield
-    _imperative_tracer_ = tmp_trace

python/paddle/fluid/imperative/__init__.py

-#   Copyright (c) 2018 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.
-
-from __future__ import print_function
-
-from . import base
-from .base import *
-
-from . import layers
-from .layers import *
-
-__all__ = []
-__all__ += layers.__all__
-__all__ += base.__all__

python/paddle/fluid/imperative/base.py

-# Copyright (c) 2018 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.
-import contextlib
-import numpy as np
-
-from paddle.fluid import core
-from paddle.fluid import framework
-
-__all__ = ['enabled', 'guard', 'to_variable']
-
-
-def enabled():
-    return framework._in_imperative_mode()
-
-
-@contextlib.contextmanager
-def guard():
-    train = framework.Program()
-    startup = framework.Program()
-    tracer = core.Tracer(train.current_block().desc)
-    with framework.program_guard(train, startup):
-        with framework.unique_name.guard():
-            with framework._imperative_guard(tracer):
-                yield
-
-
-def to_variable(value, block=None):
-    if isinstance(value, np.ndarray):
-        if not block:
-            block = framework.default_main_program().current_block()
-        py_var = framework.Variable(
-            block,
-            type=core.VarDesc.VarType.LOD_TENSOR,
-            name=None,
-            shape=value.shape,
-            dtype=value.dtype)
-        scope = framework._imperative_tracer().get_scope(block.desc)
-        var = scope.var(py_var.name)
-        tensor = var.get_tensor()
-        tensor.set(value, core.CPUPlace())
-        return py_var
-    elif isinstance(value, framework.Variable):
-        return value
-    else:
-        raise ValueError("Unsupported type %s" % type(value))

python/paddle/fluid/imperative/layers.py

-# Copyright (c) 2018 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.
-
-import contextlib
-import sys
-import numpy as np
-
-from paddle.fluid import core
-from paddle.fluid import framework
-from paddle.fluid.imperative import base
-
-__all__ = ['PyLayer']
-
-
-class PyLayer(core.Layer):
-    def __init__(self):
-        pass
-
-    def __call__(self, inputs):
-        # TODO(panyx0718): Support declarative mode as well.
-        assert base.enabled()
-        if not isinstance(inputs, list) and not isinstance(inputs, tuple):
-            inputs = [inputs]
-
-        var_inputs = []
-        for x in inputs:
-            py_var = base.to_variable(x)
-            var_inputs.append(py_var)
-        outputs = self.forward(var_inputs)
-        return outputs
-
-    def forward(self, inputs):
-        return []

python/paddle/fluid/layer_helper.py

@@ -17,13 +17,10 @@ from __future__ import print_function
 import copy
 import itertools
 import six
-import sys
-import numpy as np
 
 from .framework import Variable, Parameter, default_main_program, default_startup_program, dtype_is_floating
 from . import unique_name
 from paddle.fluid.initializer import Constant, Xavier
-from paddle.fluid.imperative import base
 from .param_attr import ParamAttr, WeightNormParamAttr
 from . import core
 from six.moves import zip
@@ -49,21 +46,23 @@ class LayerHelper(object):
     def startup_program(self):
         return default_startup_program()
 
-    def to_variable(self, x):
-        return base.to_variable(x, self.main_program.current_block())
-
     def append_op(self, *args, **kwargs):
         return self.main_program.current_block().append_op(*args, **kwargs)
 
     def multiple_input(self, input_param_name='input'):
         inputs = self.kwargs.get(input_param_name, [])
-        ret = []
-        if isinstance(inputs, list) or isinstance(inputs, tuple):
-            for inp in inputs:
-                ret.append(self.to_variable(inp))
+        type_error = TypeError(
+            "Input of {0} layer should be Variable or sequence of Variable".
+            format(self.layer_type))
+        if isinstance(inputs, Variable):
+            inputs = [inputs]
+        elif not isinstance(inputs, list) and not isinstance(inputs, tuple):
+            raise type_error
         else:
-            ret.append(self.to_variable(inputs))
-        return ret
+            for each in inputs:
+                if not isinstance(each, Variable):
+                    raise type_error
+        return inputs
 
     def input(self, input_param_name='input'):
         inputs = self.multiple_input(input_param_name)

python/paddle/fluid/layers/nn.py

@@ -6623,8 +6623,7 @@ def relu(x, name=None):
     helper = LayerHelper('relu', **locals())
     dtype = helper.input_dtype(input_param_name='x')
     out = helper.create_variable_for_type_inference(dtype)
-    helper.append_op(
-        type="relu", inputs={"X": helper.input('x')}, outputs={"Out": out})
+    helper.append_op(type="relu", inputs={"X": x}, outputs={"Out": out})
     return out
 
 

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

-# Copyright (c) 2018 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.
-
-import unittest
-import sys
-import numpy as np
-
-import paddle.fluid as fluid
-from paddle.fluid import core
-
-
-class MyLayer(fluid.imperative.PyLayer):
-    def __init__(self):
-        super(MyLayer, self).__init__()
-
-    def forward(self, inputs):
-        x = fluid.layers.relu(inputs[0])
-        self._x_for_debug = x
-        return [fluid.layers.elementwise_mul(x, x)]
-
-
-class TestImperative(unittest.TestCase):
-    def test_layer(self):
-        with fluid.imperative.guard():
-            cl = core.Layer()
-            cl.forward([])
-            l = fluid.imperative.PyLayer()
-            l.forward([])
-
-    def test_layer_in_out(self):
-        with fluid.imperative.guard():
-            l = MyLayer()
-            x = l(np.array([1.0, 2.0, -1.0], dtype=np.float32))[0]
-            self.assertIsNotNone(x)
-            sys.stderr.write("%s output: %s\n" % (x, x._numpy()))
-            x._backward()
-            sys.stderr.write("grad %s\n" % l._x_for_debug._gradient())
-
-
-if __name__ == '__main__':
-    unittest.main()

python/setup.py.in

@@ -101,7 +101,6 @@ packages=['paddle',
           'paddle.dataset',
           'paddle.reader',
           'paddle.fluid',
-          'paddle.fluid.imperative',
           'paddle.fluid.proto',
           'paddle.fluid.proto.profiler',
           'paddle.fluid.layers',

tools/print_signatures.py

@@ -27,8 +27,6 @@ import pydoc
 
 member_dict = collections.OrderedDict()
 
-experimental_namespace = {"paddle.fluid.imperative"}
-
 
 def visit_member(parent_name, member):
     cur_name = ".".join([parent_name, member.__name__])
@@ -53,8 +51,6 @@ def visit_member(parent_name, member):
 
 
 def visit_all_module(mod):
-    if (mod.__name__ in experimental_namespace):
-        return
     for member_name in (
             name
             for name in (mod.__all__ if hasattr(mod, "__all__") else dir(mod))