/* 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. */

#include "paddle/fluid/operators/controlflow/conditional_block_op.h"

namespace paddle {
namespace operators {

/* We will implement the op with block separately in the future.
 * The main reason is that some of the training requirements
 * in these OPS can lead to problems(such as memory leaks) during inference.
 */
class ConditionalBlockInferOp : public ConditionalOp {
 public:
  ConditionalBlockInferOp(const std::string &type,
                          const framework::VariableNameMap &inputs,
                          const framework::VariableNameMap &outputs,
                          const framework::AttributeMap &attrs)
      : ConditionalOp(type, inputs, outputs, attrs) {}

 private:
  void RunImpl(const framework::Scope &scope,
               const platform::Place &dev_place) const override {
    bool need_run;
    if (Attr<bool>("is_scalar_condition")) {
      // When is_scalar_condition is True, the conditional variable is a scalar,
      // whether need to execute the operators in sub-block depends on the
      // conditional variable (Cond).
      auto xs = InputTensors(scope, "Cond");
      need_run = ScalarCondition(xs);
    } else {
      // When is_scalar_condition is False, the conditional variable maybe a
      // vector or tensor, whether need to execute the operators in sub-block
      // depends on the input variables (Input).
      auto xs = InputTensors(scope, "Input");
      need_run = std::all_of(
          xs.begin(), xs.end(),
          [](const framework::LoDTensor *t) { return t->numel() != 0; });
    }

    if (need_run) {
      auto *scope_var = scope.FindVar(Output("Scope"));
      PADDLE_ENFORCE(scope_var != nullptr, "Must set scope");
      auto *scopes = scope_var->GetMutable<std::vector<framework::Scope *>>();
      scopes->resize(1);
      scopes->front() = &scope.NewScope();
      auto &cur_scope = *scopes->front();

      framework::Executor exec(dev_place);
      auto *block = Attr<framework::BlockDesc *>("sub_block");
      exec.Run(*block->Program(), &cur_scope, block->ID(), false);
      scope.DeleteScope(scopes->front());
    }
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OPERATOR(conditional_block_infer, ops::ConditionalBlockInferOp,
                  ops::ConditionalBlockOpProtoMaker,
                  paddle::framework::EmptyGradOpMaker);