test_prepare_op.cc

// Copyright (c) 2019 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.

//
// Created by Jiabin on 2019-08-19.
//

#include <paddle/fluid/framework/op_registry.h>
#include <memory>
#include <string>
#include <vector>
#include "gtest/gtest.h"
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/imperative/prepared_operator.h"
#include "paddle/fluid/imperative/type_defs.h"

namespace imperative = paddle::imperative;
namespace platform = paddle::platform;
namespace framework = paddle::framework;

namespace paddle {
namespace imperative {

static framework::RuntimeContext PrepareRuntimeContext(
    const NameVarBaseMap& ins, const NameVarBaseMap& outs) {
  framework::VariableValueMap inputs, outputs;
  for (auto& in_pair : ins) {
    auto& in_ctx = inputs[in_pair.first];
    in_ctx.reserve(in_pair.second.size());
    for (auto& in_var : in_pair.second) {
      in_ctx.emplace_back(in_var->MutableVar());
    }
  }

  for (auto& out_pair : outs) {
    auto& out_ctx = outputs[out_pair.first];
    out_ctx.reserve(out_pair.second.size());
    for (auto& out_var : out_pair.second) {
      out_ctx.emplace_back(out_var->MutableVar());
    }
  }
  return framework::RuntimeContext(std::move(inputs), std::move(outputs));
}

static framework::VariableNameMap CreateVarNameMap(
    const framework::OpInfo& op_info, const std::string& op_type,
    const NameVarBaseMap& varbase_map, bool is_input) {
  if (op_info.proto_ == nullptr) {
    return {};
  }

  framework::VariableNameMap result;

  for (auto& var :
       is_input ? op_info.Proto().inputs() : op_info.Proto().outputs()) {
    auto it = varbase_map.find(var.name());
    if (it == varbase_map.end()) {
      PADDLE_ENFORCE_EQ(
          var.dispensable(), true,
          "Var: %s not dispensable and there are no such var in inputs",
          var.name());
      result[var.name()] = {};
    } else {
      auto& var_vector = it->second;
      std::vector<std::string> args;
      args.reserve(var_vector.size());
      for (auto& var_base : var_vector) {
        args.emplace_back(var_base->Name());
      }
      result[var.name()] = std::move(args);
    }
  }
  return result;
}

using vb_vector = std::vector<std::shared_ptr<imperative::VarBase>>;

using var_pair = std::pair<std::string, vb_vector>;

TEST(test_prepare_op, test_prepare_op) {
  std::shared_ptr<imperative::VarBase> vin(
      new imperative::VarBase(false, "vin"));
  std::shared_ptr<imperative::VarBase> vout(
      new imperative::VarBase(false, "vout"));
  framework::OpDesc desc;
  platform::CPUPlace place;
  vin->MutableVar()->GetMutable<framework::LoDTensor>()->mutable_data<float>(
      place);
  var_pair x_pair = var_pair("X", vb_vector(1, vin));
  var_pair out_pair = var_pair("Out", vb_vector(1, vout));
  imperative::NameVarBaseMap ins = {x_pair};
  imperative::NameVarBaseMap outs = {out_pair};
  framework::AttributeMap split_attr_map;
  const auto& info = framework::OpInfoMap::Instance().Get("split");
  framework::VariableNameMap var_in_map =
      CreateVarNameMap(info, "split", ins, true);
  framework::VariableNameMap var_out_map =
      CreateVarNameMap(info, "split", outs, false);
  framework::OperatorWithKernel op("split", var_in_map, var_out_map,
                                   split_attr_map);
  framework::RuntimeContext ctx = PrepareRuntimeContext(ins, outs);
  ASSERT_NO_FATAL_FAILURE(PreparedOp preparedOp = PreparedOp::Prepare(
                              ins, outs, op, place, &split_attr_map));
}

const framework::Tensor* GetTensorFromVar(const framework::Variable& var);

TEST(test_prepare_op, test_get_tensor_from_var) {
  std::shared_ptr<imperative::VarBase> vout_error(
      new imperative::VarBase(false, "vout_error"));
  vout_error->MutableVar()->GetMutable<framework::SelectedRows>();
  auto* ts = GetTensorFromVar(*vout_error->MutableVar());
  ASSERT_TRUE(ts != nullptr);
}
#if defined(PADDLE_WITH_CUDA)
TEST(test_prepare_op, test_prepare_data) {
  std::shared_ptr<imperative::VarBase> vin(
      new imperative::VarBase(false, "vin"));
  std::shared_ptr<imperative::VarBase> vout(
      new imperative::VarBase(false, "vout"));

  framework::OpDesc desc;
  platform::CPUPlace cpu_place;
  platform::CUDAPlace gpu_place(0);
  std::vector<float> src_data(10, 2.0);
  std::vector<int64_t> dims = {2, 5};

  // prepare an cpu only input
  auto* vin_tensor = vin->MutableVar()->GetMutable<framework::LoDTensor>();
  vin_tensor->Resize(framework::make_ddim(dims));
  auto* vin_mutable_tensor = vin_tensor->mutable_data<float>(cpu_place);
  paddle::memory::Copy(cpu_place, vin_mutable_tensor, cpu_place,
                       src_data.data(), sizeof(float) * src_data.size());

  var_pair x_pair = var_pair("X", vb_vector(1, vin));
  var_pair out_pair = var_pair("Out", vb_vector(1, vout));
  imperative::NameVarBaseMap ins = {x_pair};
  imperative::NameVarBaseMap outs = {out_pair};
  framework::AttributeMap assign_attr_map;
  const auto& info = framework::OpInfoMap::Instance().Get("assign");
  framework::VariableNameMap var_in_map =
      CreateVarNameMap(info, "assign", ins, true);
  framework::VariableNameMap var_out_map =
      CreateVarNameMap(info, "assign", outs, false);
  framework::OperatorWithKernel assign_op("assign", var_in_map, var_out_map,
                                          assign_attr_map);
  framework::RuntimeContext ctx = PrepareRuntimeContext(ins, outs);

  // test if it can be transformed to GPU place
  PreparedOp prepared_op =
      PreparedOp::Prepare(ins, outs, assign_op, gpu_place, &assign_attr_map);
  for (const auto& name_pair : ins) {
    for (const auto& vb : name_pair.second) {
      ASSERT_TRUE(platform::is_same_place(
          vb->Var().Get<framework::LoDTensor>().place(), gpu_place));
    }
  }
}
#endif

TEST(test_prepare_op, test_prepare_data_same_place) {
  std::shared_ptr<imperative::VarBase> vin(
      new imperative::VarBase(false, "vin"));
  std::shared_ptr<imperative::VarBase> vout(
      new imperative::VarBase(false, "vout"));

  framework::OpDesc desc;
  platform::CPUPlace cpu_place;
  std::vector<float> src_data(10, 2.0);
  std::vector<int64_t> dims = {2, 5};

  // prepare an cpu only input
  auto* vin_tensor = vin->MutableVar()->GetMutable<framework::LoDTensor>();
  vin_tensor->Resize(framework::make_ddim(dims));
  auto* vin_mutable_tensor = vin_tensor->mutable_data<float>(cpu_place);
  paddle::memory::Copy(cpu_place, vin_mutable_tensor, cpu_place,
                       src_data.data(), sizeof(float) * src_data.size());

  var_pair x_pair = var_pair("X", vb_vector(1, vin));
  var_pair out_pair = var_pair("Out", vb_vector(1, vout));
  imperative::NameVarBaseMap ins = {x_pair};
  imperative::NameVarBaseMap outs = {out_pair};
  framework::AttributeMap assign_attr_map;
  const auto& info = framework::OpInfoMap::Instance().Get("assign");
  framework::VariableNameMap var_in_map =
      CreateVarNameMap(info, "assign", ins, true);
  framework::VariableNameMap var_out_map =
      CreateVarNameMap(info, "assign", outs, false);
  framework::OperatorWithKernel assign_op("assign", var_in_map, var_out_map,
                                          assign_attr_map);
  framework::RuntimeContext ctx = PrepareRuntimeContext(ins, outs);

  // test if it never transfered on GPU place
  PreparedOp prepared_op =
      PreparedOp::Prepare(ins, outs, assign_op, cpu_place, &assign_attr_map);
  for (const auto& name_pair : ins) {
    for (const auto& vb : name_pair.second) {
      ASSERT_TRUE(platform::is_same_place(
          vb->Var().Get<framework::LoDTensor>().place(), cpu_place));
    }
  }
}
}  // namespace imperative
}  // namespace paddle

USE_OP(split);
USE_OP(assign);