program.h 6.0 KB
Newer Older
T
tensor-tang 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194
// 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.

#pragma once
#include <list>
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include "paddle/fluid/lite/core/kernel.h"
#include "paddle/fluid/lite/core/mir/node.h"
#include "paddle/fluid/lite/core/op_lite.h"
#include "paddle/fluid/lite/core/op_registry.h"
#include "paddle/fluid/lite/model_parser/compatible_pb.h"
#ifdef LITE_WITH_PROFILE
#include "paddle/fluid/lite/core/profile/basic_profiler.h"
#endif  // LITE_WITH_PROFILE

namespace paddle {
namespace lite {

static const char kKernelTypeAttr[] = "__@kernel_type_attr@__";

// A program is used to represent a code program, in Paddle, a code program
// contains:
// - main block, which is a list of OpLite
// - scope: which contains all the weights
struct Program {
  std::list<std::string> tmp_vars;
  std::list<std::string> weights;
  std::list<std::shared_ptr<OpLite>> ops;
  // the scope to run the kernels, NOTE this is the execution scope.
  std::shared_ptr<lite::Scope> scope;
  std::vector<Place> valid_places;
  // Runtime scope.
  lite::Scope* exec_scope{};
  const framework::proto::ProgramDesc desc;

  explicit Program(const std::shared_ptr<Scope>& root) { scope = root; }
  Program(const framework::proto::ProgramDesc& desc,
          const std::shared_ptr<Scope>& root,
          const std::vector<Place>& valid_places)
      : scope(root), valid_places(valid_places), desc(desc) {
    CHECK(scope) << "scope should be init first";
    PrepareWorkspace(desc);
    Build(desc);
  }

  std::unique_ptr<Program> Clone() const {
    std::unique_ptr<Program> res(new Program(desc, scope, valid_places));
    return res;
  }

 private:
  // Build from a program and scope.
  void Build(const framework::proto::ProgramDesc& program) {
    CHECK(ops.empty()) << "Executor duplicate Build found";
    // Create operators.
    for (const auto& proto_op_desc : program.blocks(0).ops()) {
      pb::OpDesc op_desc(proto_op_desc);
      auto op_type = op_desc.Type();
      // if (op_type == "feed" || op_type == "fetch") continue;
      VLOG(4) << "create Op [" << op_type << "]";
      LOG(INFO) << "create Op [" << op_type << "]";
      auto op = LiteOpRegistry::Global().Create(op_type);
      CHECK(op) << "no Op found for " << op_type;
      ops.emplace_back(std::move(op));

      cpp::OpDesc cpp_op_desc;
      TransformOpDescPbToCpp(op_desc, &cpp_op_desc);
      ops.back()->Attach(cpp_op_desc, exec_scope);
    }
  }

  // Create temporary variables.
  void PrepareWorkspace(const framework::proto::ProgramDesc& program) {
    CHECK(!exec_scope) << "Duplicate PrepareWorkspace found";
    exec_scope = &scope->NewScope();
    // Create Feed and Fetch var.
    scope->Var("feed")->GetMutable<std::vector<lite::Tensor>>();
    scope->Var("fetch")->GetMutable<std::vector<lite::Tensor>>();

    tmp_vars.push_back("feed");
    tmp_vars.push_back("fetch");
    CHECK(!program.blocks().empty());
    for (auto proto_var_desc : program.blocks(0).vars()) {
      lite::VarDesc var_desc(proto_var_desc);
      if (!var_desc.Persistable()) {
        tmp_vars.push_back(var_desc.Name());
        exec_scope->Var(var_desc.Name());
      } else {
        if (var_desc.Name() == "feed" || var_desc.Name() == "fetch") continue;
        weights.push_back(var_desc.Name());
      }
    }
  }
};

struct Instruct {
  Instruct(const std::shared_ptr<OpLite>& op,
           std::unique_ptr<KernelBase>&& kernel)
      : op_(op), kernel_(std::move(kernel)) {
#ifdef LITE_WITH_PROFILE
    profile_id_ = profile::BasicProfiler<profile::BasicTimer>::Global()
                      .NewRcd(kernel_->SerializedKernelType())
                      .id();
#endif  // LITE_WITH_PROFILE
  }

  void Run() {
#ifdef LITE_WITH_PROFILE
    profile::ProfileBlock x(profile_id_);
#endif  // LITE_WITH_PROFILE
    CHECK(op_);
    CHECK(kernel_);
    if (first_epoch_) {
      first_epoch_ = false;
      CHECK(op_->CheckShape());
    }
    op_->InferShape();
    kernel_->Run();
  }

  friend std::ostream& operator<<(std::ostream& os, const Instruct& other) {
    os << other.kernel_->summary() << "\t(" << other.kernel_->doc() << ")";
    return os;
  }

  const OpLite* op() const { return op_.get(); }
  const KernelBase* kernel() const { return kernel_.get(); }

 private:
  std::shared_ptr<OpLite> op_;
  std::unique_ptr<KernelBase> kernel_;
  bool first_epoch_{true};

#ifdef LITE_WITH_PROFILE
  // for profiler
  int profile_id_{-1};
#endif  // LITE_WITH_PROFILE
};

/*
 * A program contains kernels for runtime.
 */
class RuntimeProgram {
 public:
  explicit RuntimeProgram(std::vector<Instruct>&& insts)
      : instructions_(std::move(insts)) {
    if (instructions_.empty()) {
      LOG(FATAL) << "no instructions";
    }
  }

  void Run() {
    for (auto& inst : instructions_) {
      LOG(INFO) << ">> Running kernel: " << inst;
      inst.Run();
    }
  }

  // Serialize the graph and save to the disk.
  void PersistModel(const std::string& dir,
                    const framework::proto::ProgramDesc& desc);

  void set_exec_scope(lite::Scope* x) { exec_scope_ = x; }
  lite::Scope* exec_scope() { return exec_scope_; }

  size_t num_instructions() const { return instructions_.size(); }

 protected:
  std::string SerializeProgram(const framework::proto::ProgramDesc& desc);
  void SaveParams(const std::string& dir,
                  const framework::proto::ProgramDesc& desc);

 private:
  RuntimeProgram(const RuntimeProgram&) = delete;
  std::vector<Instruct> instructions_;
  lite::Scope* exec_scope_{};
};

}  // namespace lite
}  // namespace paddle