<commit_before>/*
  Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
  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/operators/recurrent_op.h"

#include <glog/logging.h>
#include <gtest/gtest.h>

#include "paddle/framework/ddim.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/tensor.h"
#include "paddle/operators/net_op.h"

namespace paddle {
namespace operators {

using framework::make_ddim;
using framework::DDim;

class RecurrentOpTest : public ::testing::Test {
 protected:
  virtual void SetUp() override {
    CreateGlobalVariables();
    CreateStepNet();
    CreateRNNOp();
  }

  virtual void TearDown() override {}

  void CreateGlobalVariables() {
    // create input, and init content
    LOG(INFO) << "create global variable x";
    for (auto inlink : std::vector<std::string>{"x", "x0", "x1", "h"}) {
      Variable* x = scope_.NewVar(inlink);
      DDim dims = make_ddim(std::vector<int>{
          10 /*sent size*/, 20 /*batch size*/, 30 /*input dim*/});
      x->GetMutable<Tensor>()->mutable_data<float>(dims, platform::CPUPlace());
    }
    // create output alias just for test
    for (auto inlink : std::vector<std::string>{"h@alias"}) {
      Variable* x = scope_.NewVar(inlink);
      DDim dims =
          make_ddim(std::vector<int>{20 /*batch size*/, 30 /*input dim*/});
      x->GetMutable<Tensor>()->mutable_data<float>(dims, platform::CPUPlace());
    }

    LOG(INFO) << "create global variable w";
    Variable* w = scope_.NewVar("rnn/w");
    w->GetMutable<Tensor>()->mutable_data<float>(
        make_ddim(std::vector<int>{30, 30}), platform::CPUPlace());

    for (auto boot : std::vector<std::string>{"h_boot"}) {
      LOG(INFO) << "create global variable " << boot;
      Variable* h_boot = scope_.NewVar(boot);
      h_boot->GetMutable<Tensor>()->mutable_data<float>(
          make_ddim(std::vector<int>{20 /*batch size*/, 30 /*input dim*/}),
          platform::CPUPlace());
    }

    LOG(INFO) << "create variable step_scopes";
    scope_.NewVar("step_scopes");

    LOG(INFO) << "create variable h";
    scope_.NewVar("h");
  }

  void CreateRNNOp() {
    framework::OpDesc op_desc;

    op_desc.set_type("recurrent_op");
    // inlinks 0
    op_desc.add_inputs("x");
    op_desc.add_inputs("x0");
    op_desc.add_inputs("x1");
    // boot_memories 3
    op_desc.add_inputs("h_boot");
    // step net 5
    op_desc.add_inputs("step_net");
    // outlinks 6
    op_desc.add_outputs("h");
    // step scopes 7
    op_desc.add_outputs("step_scopes");

    auto _input_format = std::vector<int>{
        0,  // in_link
        3,  // memories
        4   // step_net
    };
    auto input_format = op_desc.add_attrs();
    input_format->set_name("input_format");
    input_format->set_type(paddle::framework::AttrType::INTS);
    for (auto i : _input_format) {
      input_format->add_ints(i);
    }

    auto output_format = op_desc.add_attrs();
    output_format->set_name("output_format");
    output_format->set_type(paddle::framework::AttrType::INTS);
    for (auto i : std::vector<int>{0, 1, 2}) {
      output_format->add_ints(i);
    }

    auto inlink_alias = op_desc.add_attrs();
    inlink_alias->set_name("inlink_alias");
    inlink_alias->set_type(paddle::framework::AttrType::STRINGS);

    auto outlink_alias = op_desc.add_attrs();
    outlink_alias->set_name("outlink_alias");
    outlink_alias->set_type(paddle::framework::AttrType::STRINGS);

    auto pre_memories = op_desc.add_attrs();
    pre_memories->set_name("pre_memories");
    pre_memories->set_type(paddle::framework::AttrType::STRINGS);

    auto memories = op_desc.add_attrs();
    memories->set_name("memories");
    memories->set_type(paddle::framework::AttrType::STRINGS);

    // create inlink_alias
    for (const auto& item :
         std::vector<std::string>{"x@alias", "x0@alias", "x1@alias"}) {
      inlink_alias->add_strings(item);
    }
    // pre memories
    for (const auto& item : std::vector<std::string>{"rnn/h@pre"}) {
      pre_memories->add_strings(item);
    }
    // memories
    for (const auto& item : std::vector<std::string>{"rnn/h"}) {
      memories->add_strings(item);
    }
    // output alias
    for (const auto& item : std::vector<std::string>{"h@alias"}) {
      outlink_alias->add_strings(item);
    }

    rnn_op_ = OpRegistry::CreateOp(op_desc);

    LOG(INFO) << "rnn_op finish init";
  }

  void CreateStepNet() {
    LOG(INFO) << "create variable step_net";
    Variable* var = scope_.NewVar("step_net");
    auto net = var->GetMutable<NetOp>();
    net->AddOp(
        OpRegistry::CreateOp("mul", {"rnn/h@pre", "rnn/w"}, {"rnn/s"}, {}));

    net->AddOp(
        OpRegistry::CreateOp("add_two", {"x@alias", "rnn/s"}, {"rnn/h"}, {}));
    net->CompleteAddOp();
  }

  // father scope
  Scope scope_;
  std::shared_ptr<OperatorBase> rnn_op_;
};

TEST_F(RecurrentOpTest, Run) {
  platform::CPUDeviceContext ctx;
  rnn_op_->InferShape(scope_);
  rnn_op_->Run(scope_, ctx);
}

class RecurrentGradientAlgorithmTest : public ::testing::Test {
 protected:
  virtual void SetUp() override {<commit_msg>"fix recurrent op test"<commit_after>/*
  Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
  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/operators/recurrent_op.h"

#include <glog/logging.h>
#include <gtest/gtest.h>

#include "paddle/framework/ddim.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/tensor.h"
#include "paddle/operators/net_op.h"

namespace paddle {
namespace operators {

using framework::make_ddim;
using framework::DDim;
using framework::Tensor;
using framework::Variable;
using framework::Scope;
using framework::OpRegistry;

class RecurrentOpTest : public ::testing::Test {
 protected:
  virtual void SetUp() override {
    CreateGlobalVariables();
    CreateStepNet();
    CreateRNNOp();
  }

  virtual void TearDown() override {}

  void CreateGlobalVariables() {
    // create input, and init content
    LOG(INFO) << "create global variable x";
    for (auto inlink : std::vector<std::string>{"x", "x0", "x1", "h"}) {
      Variable* x = scope_.NewVar(inlink);
      DDim dims = make_ddim(std::vector<int>{
          10 /*sent size*/, 20 /*batch size*/, 30 /*input dim*/});
      x->GetMutable<Tensor>()->mutable_data<float>(dims, platform::CPUPlace());
    }
    // create output alias just for test
    for (auto inlink : std::vector<std::string>{"h@alias"}) {
      Variable* x = scope_.NewVar(inlink);
      DDim dims =
          make_ddim(std::vector<int>{20 /*batch size*/, 30 /*input dim*/});
      x->GetMutable<Tensor>()->mutable_data<float>(dims, platform::CPUPlace());
    }

    LOG(INFO) << "create global variable w";
    Variable* w = scope_.NewVar("rnn/w");
    w->GetMutable<Tensor>()->mutable_data<float>(
        make_ddim(std::vector<int>{30, 30}), platform::CPUPlace());

    for (auto boot : std::vector<std::string>{"h_boot"}) {
      LOG(INFO) << "create global variable " << boot;
      Variable* h_boot = scope_.NewVar(boot);
      h_boot->GetMutable<Tensor>()->mutable_data<float>(
          make_ddim(std::vector<int>{20 /*batch size*/, 30 /*input dim*/}),
          platform::CPUPlace());
    }

    LOG(INFO) << "create variable step_scopes";
    scope_.NewVar("step_scopes");

    LOG(INFO) << "create variable h";
    scope_.NewVar("h");
  }

  void CreateRNNOp() {
    framework::OpDesc op_desc;

    op_desc.set_type("recurrent_op");
    // inlinks 0
    op_desc.add_inputs("x");
    op_desc.add_inputs("x0");
    op_desc.add_inputs("x1");
    // boot_memories 3
    op_desc.add_inputs("h_boot");
    // step net 5
    op_desc.add_inputs("step_net");
    // outlinks 6
    op_desc.add_outputs("h");
    // step scopes 7
    op_desc.add_outputs("step_scopes");

    auto _input_format = std::vector<int>{
        0,  // in_link
        3,  // memories
        4   // step_net
    };
    auto input_format = op_desc.add_attrs();
    input_format->set_name("input_format");
    input_format->set_type(paddle::framework::AttrType::INTS);
    for (auto i : _input_format) {
      input_format->add_ints(i);
    }

    auto output_format = op_desc.add_attrs();
    output_format->set_name("output_format");
    output_format->set_type(paddle::framework::AttrType::INTS);
    for (auto i : std::vector<int>{0, 1, 2}) {
      output_format->add_ints(i);
    }

    auto inlink_alias = op_desc.add_attrs();
    inlink_alias->set_name("inlink_alias");
    inlink_alias->set_type(paddle::framework::AttrType::STRINGS);

    auto outlink_alias = op_desc.add_attrs();
    outlink_alias->set_name("outlink_alias");
    outlink_alias->set_type(paddle::framework::AttrType::STRINGS);

    auto pre_memories = op_desc.add_attrs();
    pre_memories->set_name("pre_memories");
    pre_memories->set_type(paddle::framework::AttrType::STRINGS);

    auto memories = op_desc.add_attrs();
    memories->set_name("memories");
    memories->set_type(paddle::framework::AttrType::STRINGS);

    // create inlink_alias
    for (const auto& item :
         std::vector<std::string>{"x@alias", "x0@alias", "x1@alias"}) {
      inlink_alias->add_strings(item);
    }
    // pre memories
    for (const auto& item : std::vector<std::string>{"rnn/h@pre"}) {
      pre_memories->add_strings(item);
    }
    // memories
    for (const auto& item : std::vector<std::string>{"rnn/h"}) {
      memories->add_strings(item);
    }
    // output alias
    for (const auto& item : std::vector<std::string>{"h@alias"}) {
      outlink_alias->add_strings(item);
    }

    rnn_op_ = OpRegistry::CreateOp(op_desc);

    LOG(INFO) << "rnn_op finish init";
  }

  void CreateStepNet() {
    LOG(INFO) << "create variable step_net";
    Variable* var = scope_.NewVar("step_net");
    auto net = var->GetMutable<NetOp>();
    net->AddOp(
        OpRegistry::CreateOp("mul", {"rnn/h@pre", "rnn/w"}, {"rnn/s"}, {}));

    net->AddOp(
        OpRegistry::CreateOp("add_two", {"x@alias", "rnn/s"}, {"rnn/h"}, {}));
    net->CompleteAddOp();
  }

  // father scope
  Scope scope_;
  std::shared_ptr<framework::OperatorBase> rnn_op_;
};

TEST_F(RecurrentOpTest, Run) {
  platform::CPUDeviceContext ctx;
  rnn_op_->InferShape(scope_);
  rnn_op_->Run(scope_, ctx);
}

class RecurrentGradientAlgorithmTest : public ::testing::Test {
 protected:
  virtual void SetUp() override {
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{'dir': 'git-commits-cleaned', 'id': '3000052', 'n_tokens_mistral': 3891, 'n_tokens_neox': 3775, 'n_words': 785}