recurrent_op.cc

/* 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/recurrent_op.h"

#include <algorithm>
#include "paddle/fluid/string/string_helper.h"

namespace paddle {
namespace operators {

using StepScopeVar = std::vector<framework::Scope *>;

const char RecurrentBase::kInputs[] = "inputs";
const char RecurrentBase::kInitialStates[] = "initial_states";
const char RecurrentBase::kParameters[] = "parameters";
const char RecurrentBase::kOutputs[] = "outputs";
const char RecurrentBase::kStepScopes[] = "step_scopes";
const char RecurrentBase::kHasStates[] = "has_states";
const char RecurrentBase::kExStates[] = "ex_states";
const char RecurrentBase::kStates[] = "states";
const char RecurrentBase::kStepBlock[] = "sub_block";
const char RecurrentBase::kReverse[] = "reverse";
const char RecurrentBase::kIsTrain[] = "is_train";
const char RecurrentBase::kSkipEagerDeletionVars[] = "skip_eager_deletion_vars";
#define GRAD_SUFFIX "@GRAD"
const char RecurrentBase::kInputGrads[] = "inputs" GRAD_SUFFIX;
const char RecurrentBase::kOutputGrads[] = "outputs" GRAD_SUFFIX;
const char RecurrentBase::kParamGrads[] = "parameters" GRAD_SUFFIX;
const char RecurrentBase::kInitStateGrads[] = "initial_states" GRAD_SUFFIX;

static void ClearStepScopes(const platform::DeviceContext &dev_ctx,
                            framework::Scope *parent_scope,
                            StepScopeVar *step_scopes) {
  if (step_scopes->empty()) return;

  dev_ctx.Wait();

  for (auto *sub_scope : *step_scopes) {
    parent_scope->DeleteScope(sub_scope);
  }

  step_scopes->clear();
}

// StepScopes manages scopes inside RNN.
//    StepScopes::CurScope() get the current scope
//    StepScopes::ExScope() get the ex-scope, or scope in previous time step.
//    StepScopes::Next() move to next time step.
//
// if is_train = False, then
//   there are two scopes for the RNN and just support forward.
// else
//   the len(scopes) == seq_len
//
// if is_backward = True, then
//   reversely access scopes
// else
//   access scopes from begin to end.
StepScopes::StepScopes(const platform::DeviceContext &dev_ctx,
                       const framework::Scope &parent, StepScopeVar *scopes,
                       bool is_train, size_t seq_len, bool is_backward)
    : counter_(is_backward ? seq_len - 1 : 0UL),
      scopes_(scopes),
      is_train_(is_train),
      is_backward_(is_backward) {
  size_t num_step_scopes = is_train ? seq_len : 2;
  PADDLE_ENFORCE(is_train || !is_backward,
                 "Cannot backward when is not training");
  if (!is_backward_) {
    ClearStepScopes(dev_ctx, const_cast<framework::Scope *>(&parent), scopes);
    scopes->reserve(static_cast<size_t>(num_step_scopes));
    for (size_t i = 0; i < num_step_scopes; ++i) {
      scopes->emplace_back(&parent.NewScope());
    }
  }
}

framework::Scope &StepScopes::CurScope() { return GetScope(counter_); }

framework::Scope &StepScopes::ExScope() {
  auto &scope = GetScope(is_backward_ ? counter_ + 1 : counter_ - 1);
  return scope;
}

void StepScopes::Next() {
  if (is_backward_) {
    --counter_;
  } else {
    ++counter_;
  }
}

framework::Scope &StepScopes::GetScope(size_t scope_id) const {
  if (!is_train_) {
    scope_id %= 2;
  }
  PADDLE_ENFORCE_LT(scope_id, scopes_->size());
  return *(*scopes_)[scope_id];
}

RecurrentBase::RecurrentBase(const std::string &type,
                             const framework::VariableNameMap &inputs,
                             const framework::VariableNameMap &outputs,
                             const framework::AttributeMap &attrs)
    : OperatorBase(type, inputs, outputs, attrs) {}

// Get SequenceLength from Scope
//   The sequence length is got from input tensor. The input tensor's
//   dimension should be [SEQ_LEN, ..., ...]. The first of the tensor's shape
//   is SEQ_LEN. The second of the tensor's shape could be the batch size or
//   nested sequence length.
int64_t RecurrentBase::GetSequenceLength(const framework::Scope &scope) const {
  // Dim format SEQ_LEN, BATCH_SIZE, ...
  int64_t seq_len = -1;
  auto &all_inputs = Inputs(kInputs);
  PADDLE_ENFORCE(!all_inputs.empty());
  for (auto &iname : all_inputs) {
    auto *var = scope.FindVar(iname);
    PADDLE_ENFORCE(var != nullptr);
    PADDLE_ENFORCE(var->IsType<framework::LoDTensor>());
    auto &dim = var->Get<framework::LoDTensor>().dims();
    if (seq_len == -1) {
      seq_len = dim[0];
    } else {
      PADDLE_ENFORCE_EQ(seq_len, dim[0]);
    }
  }
  return seq_len;
}

// for src_tensor, dst_tensor in zip(map(src_scope.FindVar, src_vars),
//                                   map(dst_scope.Var, dst_vars)):
//   dst_tensor.ShareDataWith(src_tensor)
void RecurrentBase::LinkTensor(const framework::Scope &src_scope,
                               const std::vector<std::string> &src_vars,
                               framework::Scope *dst_scope,
                               const std::vector<std::string> &dst_vars) {
  LinkTensorWithCallback(
      src_scope, src_vars, dst_scope, dst_vars,
      [&](const framework::Tensor &src, framework::Tensor *dst) {
        dst->ShareDataWith(src);
      });
}

// (seq_len, shape) -> return [seq_len] + list(shape)
framework::DDim RecurrentBase::PrependDims(size_t seq_len,
                                           const framework::DDim &src) {
  auto dims = framework::vectorize(src);
  dims.insert(dims.begin(), static_cast<int64_t>(seq_len));
  return framework::make_ddim(dims);
}

RecurrentOp::RecurrentOp(const std::string &type,
                         const framework::VariableNameMap &inputs,
                         const framework::VariableNameMap &outputs,
                         const framework::AttributeMap &attrs)
    : RecurrentBase(type, inputs, outputs, attrs) {}

void RecurrentOp::RunImpl(const framework::Scope &scope,
                          const platform::Place &place) const {
  bool has_state = Attr<bool>(kHasStates);
  auto seq_len = static_cast<size_t>(this->GetSequenceLength(scope));

  // get device context from pool
  platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
  auto &dev_ctx = *pool.Get(place);

  VLOG(3) << "Static RNN input sequence length = " << seq_len;
  StepScopes scopes = CreateStepScopes(dev_ctx, scope, seq_len);
  auto reverse = Attr<bool>(kReverse);

  framework::Executor executor(place);
  auto *block = Attr<framework::BlockDesc *>(kStepBlock);

  auto *program = block->Program();
  auto ctx = executor.Prepare(
      *program, block->ID(), Attr<std::vector<std::string>>(
                                 kSkipEagerDeletionVars) /*skip_ref_cnt_vars*/);

  for (size_t i = 0; i < seq_len; ++i) {
    size_t seq_offset = reverse ? seq_len - i - 1 : i;
    VLOG(3) << "Recurrent operate at the time step " << seq_offset;

    auto &cur_scope = scopes.CurScope();

    // Link outside::input --> inside::input
    //   inside::input = outside::input[seq_offset: seq_offset+1]
    LinkTensorWithCallback(
        scope, Inputs(kInputs), &cur_scope, Inputs(kInputs),
        [&seq_offset](const framework::Tensor &outside,
                      framework::Tensor *inside) {
          inside->ShareDataWith(outside.Slice(seq_offset, seq_offset + 1));
          auto dims = framework::vectorize(inside->dims());
          dims.erase(dims.begin());
          inside->Resize(framework::make_ddim(dims));
        });

    if (has_state) {
      if (i == 0) {
        // Link initial states  --> ex_states
        LinkTensor(scope, Inputs(kInitialStates), &cur_scope,
                   Attr<std::vector<std::string>>(kExStates));
      } else {
        auto &ex_scope = scopes.ExScope();
        // Link ex_scope::state --> cur_scope::ex_state
        LinkTensor(ex_scope, Attr<std::vector<std::string>>(kStates),
                   &cur_scope, Attr<std::vector<std::string>>(kExStates));
      }
    }

    // Every inputs are linked now, execute!
    executor.RunPreparedContext(ctx.get(), &cur_scope,
                                false /*create_local_scope*/,
                                true /*create_vars*/, true /* keep_kids */);

    // Copy inside::output -> outside::output
    //    outside::output[seq_offset: seq_offset + 1] = inside::output
    this->LinkTensorWithCallback(
        cur_scope, Outputs(kOutputs), scope, Outputs(kOutputs),
        [&](const framework::LoDTensor &src_tensor,
            framework::LoDTensor *dst_tensor) {
          if (i == 0) {  // create output tensor at begin
            dst_tensor->Resize(PrependDims(seq_len, src_tensor.dims()));
            dst_tensor->mutable_data(place, src_tensor.type());
          }

          auto dst_out = dst_tensor->Slice(seq_offset, seq_offset + 1);
          // Explicit copy output since the local RNN scope can be destroyed
          // early.
          framework::TensorCopy(src_tensor, place, dev_ctx, &dst_out);
        });

    scopes.Next();
  }
}

StepScopes RecurrentOp::CreateStepScopes(const platform::DeviceContext &dev_ctx,
                                         const framework::Scope &scope,
                                         size_t seq_len) const {
  auto *var = scope.FindVar(Output(kStepScopes));
  PADDLE_ENFORCE(var != nullptr);
  return StepScopes(dev_ctx, scope, var->GetMutable<StepScopeVar>(),
                    Attr<bool>(kIsTrain), seq_len);
}

RecurrentGradOp::RecurrentGradOp(const std::string &type,
                                 const framework::VariableNameMap &inputs,
                                 const framework::VariableNameMap &outputs,
                                 const framework::AttributeMap &attrs)
    : RecurrentBase(type, inputs, outputs, attrs) {}

void RecurrentGradOp::RunImpl(const framework::Scope &scope,
                              const platform::Place &place) const {
  bool has_state = Attr<bool>(kHasStates);
  const size_t seq_len = static_cast<size_t>(GetSequenceLength(scope));

  // get device context from pool
  platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
  auto &dev_ctx = *pool.Get(place);

  StepScopes scopes = CreateStepScopes(dev_ctx, scope, seq_len);
  auto reverse = Attr<bool>(kReverse);

  framework::Executor executor(place);
  auto *block = Attr<framework::BlockDesc *>(kStepBlock);
  auto *program = block->Program();
  auto ctx = executor.Prepare(
      *program, block->ID(), Attr<std::vector<std::string>>(
                                 kSkipEagerDeletionVars) /*skip_ref_cnt_vars*/);

  for (size_t step_id = 0; step_id < seq_len; ++step_id) {
    size_t seq_offset = reverse ? step_id : seq_len - step_id - 1;
    VLOG(3) << "Recurrent backward operate at the time step " << seq_offset;
    auto &cur_scope = scopes.CurScope();

    // Link outside::output_grads --> inside::output_grads
    //   inside::output_grad = outside::output_grad[seq_offset:seq_offset+1]
    LinkTensorWithCallback(
        scope, Inputs(kOutputGrads), &cur_scope, Inputs(kOutputGrads),
        [&](const framework::Tensor &outside, framework::Tensor *inside) {
          inside->ShareDataWith(outside.Slice(seq_offset, seq_offset + 1));
          auto dims = framework::vectorize(inside->dims());
          dims.erase(dims.begin());
          inside->Resize(framework::make_ddim(dims));
        },
        true /*is_backward*/);
    auto og_set = List2Set(Inputs(kOutputGrads));

    if (VLOG_IS_ON(10)) {
      std::ostringstream sout;
      std::copy(og_set.begin(), og_set.end(),
                std::ostream_iterator<std::string>(sout, ","));
      VLOG(10) << " RNN output gradients = [" << sout.str() << "]";
    }

    if (has_state) {
      // Link states
      //   if cur_scope::cur_state_grad in out_grads:
      //     cur_scope::cur_state_grad += ex_scope::ex_state_grad
      //   else:
      //     ex_scope::ex_state_grad --> cur_scope::cur_state_grad
      if (step_id != 0) {  // not at beginning
        auto &ex_scope = scopes.ExScope();
        auto ex_state_grads =
            GradVarLists(Attr<std::vector<std::string>>(kExStates));
        auto cur_state_grads =
            GradVarLists(Attr<std::vector<std::string>>(kStates));

        PADDLE_ENFORCE_EQ(ex_state_grads.size(), cur_state_grads.size());
        for (size_t i = 0; i < ex_state_grads.size(); ++i) {
          auto &cur_grad = cur_state_grads[i];
          auto &ex_grad = ex_state_grads[i];
          auto &ex_tensor =
              ex_scope.FindVar(ex_grad)->Get<framework::LoDTensor>();

          VLOG(10) << " RNN link " << cur_grad << " from " << ex_grad;
          auto *cur_grad_var = cur_scope.Var(cur_grad);
          auto cur_grad_tensor =
              cur_grad_var->GetMutable<framework::LoDTensor>();
          framework::TensorCopy(ex_tensor, place, dev_ctx, cur_grad_tensor);
        }
      }
    }

    VLOG(5) << "Recurrent memory linking finished ";
    // Run step block with cur_scope
    executor.RunPreparedContext(ctx.get(), &cur_scope,
                                false /*create_local_scope*/,
                                true /*create_vars*/, true /* keep_kids */);

    VLOG(5) << "executor.Run finished ";

    auto local_var_names = LocalVarNames(cur_scope);

    // Accumulate params
    //   if (step == 0):
    //      outside::param_grad = 0.0
    //   outside::param_grad += inside::param_grad
    {
      auto &pg_names = Outputs(kParamGrads);
      auto &p_names = Inputs(kParameters);
      PADDLE_ENFORCE_EQ(pg_names.size(), p_names.size());

      for (size_t param_id = 0; param_id < pg_names.size(); ++param_id) {
        auto inside_grad_name = framework::GradVarName(p_names[param_id]);

        // If does not compute gradient of that variable inside rnn, just
        // continue
        if (local_var_names.find(inside_grad_name) == local_var_names.end()) {
          continue;
        }

        // zero gradient variable in step 0
        if (step_id == 0) {
          auto &inside_tensor =
              cur_scope.FindVar(inside_grad_name)->Get<framework::LoDTensor>();
          framework::AttributeMap attrs;
          attrs["dtype"] = inside_tensor.type();
          attrs["shape"] = framework::vectorize2int(inside_tensor.dims());
          attrs["value"] = 0.0f;

          auto zero_op = framework::OpRegistry::CreateOp(
              "fill_constant", framework::VariableNameMap{},
              {{"Out", {pg_names[param_id]}}}, attrs);
          zero_op->Run(scope, place);
        }

        auto new_inside_name = cur_scope.Rename(inside_grad_name);

        // sum gradient
        auto sum_op = framework::OpRegistry::CreateOp(
            "sum", {{"X", {pg_names[param_id], new_inside_name}}},
            {{"Out", {pg_names[param_id]}}},
            framework::AttributeMap{{"use_mkldnn", {false}}});
        sum_op->Run(cur_scope, place);

        cur_scope.Rename(new_inside_name, inside_grad_name);
      }
    }
    VLOG(5) << "Accumulate Parameter finished ";

    // Copy input gradient from inside to outside
    //   outside::input_grad[seq_offset: seq_offset + 1] = inside::input_grad
    LinkTensorWithCallback(
        cur_scope, GradVarLists(Inputs(kInputs)), scope, Outputs(kInputGrads),
        [&](const framework::LoDTensor &inside, framework::LoDTensor *outside) {
          if (inside.memory_size() == 0) {  // IG is not created.
            return;
          }
          if (step_id == 0) {  // alloc memory
            outside->Resize(PrependDims(seq_len, inside.dims()));
            outside->mutable_data(place, inside.type());
          }

          auto dst = outside->Slice(seq_offset, seq_offset + 1);
          framework::TensorCopy(inside, place, dev_ctx, &dst);
        },
        true /*is_backward*/);
    VLOG(5) << "Link outside gradient finished ";

    if (has_state) {
      if (step_id + 1 == seq_len) {  // at_end
        // copy initialize states gradient from inside to outside
        LinkTensorWithCallback(
            cur_scope, GradVarLists(Attr<std::vector<std::string>>(kExStates)),
            scope, Outputs(kInitStateGrads),
            [&](const framework::LoDTensor &inside,
                framework::LoDTensor *outside) {
              outside->Resize(inside.dims());
              outside->mutable_data(place, inside.type());
              framework::TensorCopy(inside, place, dev_ctx, outside);
            },
            true /*is_backward*/);
        VLOG(5) << "Link initialize state gradient finished ";
      }
    }
    scopes.Next();
  }
  // Delete the scope of StepScopes
  auto *var = scope.FindVar(Input(kStepScopes));
  PADDLE_ENFORCE(var != nullptr);
  auto *step_scopes = var->GetMutable<StepScopeVar>();
  ClearStepScopes(dev_ctx, const_cast<framework::Scope *>(&scope), step_scopes);
}

StepScopes RecurrentGradOp::CreateStepScopes(
    const platform::DeviceContext &dev_ctx, const framework::Scope &scope,
    size_t seq_len) const {
  auto *var = scope.FindVar(Input(kStepScopes));
  PADDLE_ENFORCE(var != nullptr);
  return StepScopes(dev_ctx, scope, var->GetMutable<StepScopeVar>(),
                    Attr<bool>(kIsTrain), seq_len, true /*is_backward*/);
}

std::unordered_set<std::string> RecurrentGradOp::List2Set(
    const std::vector<std::string> &list) const {
  std::unordered_set<std::string> local_var_name_set;
  local_var_name_set.reserve(list.size());
  for (auto &each : list) {
    local_var_name_set.insert(each);
  }
  return local_var_name_set;
}

std::unordered_set<std::string> RecurrentGradOp::LocalVarNames(
    const framework::Scope &scope) const {
  return this->List2Set(scope.LocalVarNames());
}
std::vector<std::string> RecurrentGradOp::GradVarLists(
    const std::vector<std::string> &var_names) {
  std::vector<std::string> retv;
  retv.reserve(var_names.size());
  std::transform(var_names.begin(), var_names.end(), std::back_inserter(retv),
                 framework::GradVarName);
  return retv;
}

class RecurrentOpProtoMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput(RecurrentBase::kInputs, "rnn inputs").AsDuplicable();
    AddInput(RecurrentBase::kInitialStates, "rnn initial states")
        .AsDuplicable();
    AddInput(RecurrentBase::kParameters,
             "Parameters are used by step block as its input. However, the "
             "input is not a sequence tensor. Every time step, each operator "
             "in step block just use the parameter directly.")
        .AsDuplicable();
    AddOutput(RecurrentBase::kOutputs,
              "The output sequence of RNN. The sequence length must be same.")
        .AsDuplicable();
    AddOutput(RecurrentBase::kStepScopes,
              "StepScopes contain all local variables in each time step.");
    AddAttr<bool>(RecurrentBase::kHasStates, "Whether has states.")
        .SetDefault(false);
    AddAttr<std::vector<std::string>>(
        RecurrentBase::kExStates,
        string::Sprintf(
            R"DOC(The ex-state variable names.
The ex-state means the state value in the ex-timestep or the previous time step
[%s, %s, %s] must be the same order)DOC",
            RecurrentBase::kExStates, RecurrentBase::kStates,
            RecurrentBase::kInitStateGrads));
    AddAttr<std::vector<std::string>>(
        RecurrentBase::kStates,
        string::Sprintf(
            "The state variable names. [%s, %s, %s] must be the same order",
            RecurrentBase::kExStates, RecurrentBase::kStates,
            RecurrentBase::kInitStateGrads));
    AddAttr<framework::BlockDesc *>(RecurrentBase::kStepBlock,
                                    "The step block inside RNN");
    AddAttr<bool>(RecurrentBase::kReverse, R"DOC(Calculate RNN reversely or not.
By default reverse=False

Assume the input data is [A, B, C, D]

if reverse is False:
  the computation of RNN is like
      A          B          C         D
      |          |          |         |
      v          v          v         v
     rnn -----> rnn -----> rnn ----> rnn
      |          |          |         |
      v          v          v         v
      o          o          o         o

if reverse is True
  the computation of RNN is like
      A          B          C         D
      |          |          |         |
      v          v          v         v
     rnn <----- rnn <----- rnn <---- rnn
      |          |          |         |
      v          v          v         v
      o          o          o         o
)DOC").SetDefault(false);
    AddAttr<bool>(RecurrentBase::kIsTrain, "").SetDefault(true);
    AddAttr<std::vector<std::string>>(RecurrentBase::kSkipEagerDeletionVars,
                                      "Vars that would skip eager deletion."
                                      "Users should not set this manually.")
        .SetDefault(std::vector<std::string>());

    AddComment(R"DOC(
Static Length Recurrent Operator.

The static length recurrent operator can only operate on fixed size sequence
data, i.e. in each mini-batch, the sequence length of all inputs are the same.

)DOC");
  }
};

class RecurrentGradOpDescMaker : public framework::SingleGradOpDescMaker {
 public:
  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;

 protected:
  virtual std::unique_ptr<framework::OpDesc> Apply() const {
    auto *grad = new framework::OpDesc();
    grad->SetType("recurrent_grad");
    for (auto &input_param : this->InputNames()) {
      grad->SetInput(input_param, this->Input(input_param));
      grad->SetOutput(framework::GradVarName(input_param),
                      this->InputGrad(input_param, false));
    }

    for (auto &output_param : this->OutputNames()) {
      if (output_param == RecurrentBase::kStepScopes) {
        grad->SetInput(output_param, this->Output(output_param));
        grad->SetInput(framework::GradVarName(output_param),
                       this->Output(output_param));
      } else {
        grad->SetInput(output_param, this->Output(output_param));
        grad->SetInput(framework::GradVarName(output_param),
                       this->OutputGrad(output_param));
      }
    }
    grad->SetAttrMap(this->Attrs());
    grad->SetBlockAttr(RecurrentBase::kStepBlock, grad_block_[0]);

    return std::unique_ptr<framework::OpDesc>(grad);
  }
};

class RecurrentGradOpShapeInference : public framework::InferShapeBase {
 public:
  void operator()(framework::InferShapeContext *ctx) const override {
    std::vector<std::string> output{RecurrentBase::kOutputs};

    // In some case the kInitialStates is empty.
    // If the kInitialStates is empty, all the states should be empty.
    if (!ctx->HasInputs(RecurrentBase::kInitialStates)) {
      PADDLE_ENFORCE_EQ(
          ctx->Attrs()
              .Get<std::vector<std::string>>(RecurrentBase::kExStates)
              .size(),
          0, "The Attr(%s) should be empty.", RecurrentBase::kExStates);
      PADDLE_ENFORCE_EQ(
          ctx->Attrs()
              .Get<std::vector<std::string>>(RecurrentBase::kStates)
              .size(),
          0, "The Attr(%s) should be empty.", RecurrentBase::kStates);
    }

    PADDLE_ENFORCE(ctx->HasInputs(RecurrentBase::kInputs),
                   "The input(%s) should not be empty.",
                   RecurrentBase::kInputs);
    PADDLE_ENFORCE(ctx->HasInputs(RecurrentBase::kOutputs),
                   "The input(%s) should not be empty.",
                   RecurrentBase::kOutputs);

    // In some case the kInitialStates is empty.
    if (ctx->HasInputs(RecurrentBase::kInitialStates)) {
      PADDLE_ENFORCE(ctx->HasOutputs(
                         framework::GradVarName(RecurrentBase::kInitialStates)),
                     "The output of(%s) should not be empty.",
                     framework::GradVarName(RecurrentBase::kInitialStates));
      ctx->SetOutputsDim(framework::GradVarName(RecurrentBase::kInitialStates),
                         ctx->GetInputsDim(RecurrentBase::kInitialStates));
    }

    PADDLE_ENFORCE(
        ctx->HasOutputs(framework::GradVarName(RecurrentBase::kInputs)),
        "The output of(%s) should not be empty.",
        framework::GradVarName(RecurrentBase::kInputs));
    ctx->SetOutputsDim(framework::GradVarName(RecurrentBase::kInputs),
                       ctx->GetInputsDim(RecurrentBase::kInputs));

    // In some case the kParameters is empty.
    if (ctx->HasInputs(RecurrentBase::kParameters)) {
      PADDLE_ENFORCE(
          ctx->HasOutputs(framework::GradVarName(RecurrentBase::kParameters)),
          "The output of(%s) should not be empty.",
          framework::GradVarName(RecurrentBase::kParameters));
      ctx->SetOutputsDim(framework::GradVarName(RecurrentBase::kParameters),
                         ctx->GetInputsDim(RecurrentBase::kParameters));
    }
  }
};

}  // namespace operators
}  // namespace paddle

REGISTER_OPERATOR(recurrent, paddle::operators::RecurrentOp,
                  paddle::operators::RecurrentOpProtoMaker,
                  paddle::operators::RecurrentGradOpDescMaker);
REGISTER_OPERATOR(recurrent_grad, paddle::operators::RecurrentGradOp,
                  paddle::operators::RecurrentGradOpShapeInference);