executor.cc 11.6 KB
Newer Older
1
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Q
qijun 已提交
2 3 4 5 6 7 8 9 10 11 12 13 14

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. */

Y
Yi Wang 已提交
15
#include "paddle/fluid/framework/executor.h"
Y
Yang Yang 已提交
16

Y
Yang Yang 已提交
17
#include <set>
Y
Yang Yang 已提交
18

Y
Yang Yu 已提交
19
#include "gflags/gflags.h"
Y
Yi Wang 已提交
20 21 22 23 24 25 26 27
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/framework/feed_fetch_type.h"
#include "paddle/fluid/framework/lod_rank_table.h"
#include "paddle/fluid/framework/lod_tensor_array.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/reader.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/platform/profiler.h"
Y
Yang Yu 已提交
28

D
dzhwinter 已提交
29
DECLARE_bool(benchmark);
Y
Yang Yu 已提交
30 31 32
DEFINE_bool(check_nan_inf, false,
            "Checking whether operator produce NAN/INF or not. It will be "
            "extremely slow so please use this flag wisely.");
Q
qijun 已提交
33 34 35 36

namespace paddle {
namespace framework {

D
dzhwinter 已提交
37
Executor::Executor(const platform::Place& place) : place_(place) {}
Q
qijun 已提交
38

Y
Yancey 已提交
39
static void CreateTensor(Variable* var, proto::VarDesc::VarType var_type) {
40
  if (var_type == proto::VarDesc::LOD_TENSOR) {
Q
QI JUN 已提交
41
    var->GetMutable<LoDTensor>();
42
  } else if (var_type == proto::VarDesc::SELECTED_ROWS) {
Q
QI JUN 已提交
43
    var->GetMutable<SelectedRows>();
44
  } else if (var_type == proto::VarDesc::FEED_MINIBATCH) {
Q
QI JUN 已提交
45
    var->GetMutable<FeedFetchList>();
46
  } else if (var_type == proto::VarDesc::FETCH_LIST) {
Q
QI JUN 已提交
47
    var->GetMutable<FeedFetchList>();
48
  } else if (var_type == proto::VarDesc::STEP_SCOPES) {
Y
Yu Yang 已提交
49
    var->GetMutable<std::vector<framework::Scope>>();
50
  } else if (var_type == proto::VarDesc::LOD_RANK_TABLE) {
Y
Yu Yang 已提交
51
    var->GetMutable<LoDRankTable>();
52
  } else if (var_type == proto::VarDesc::LOD_TENSOR_ARRAY) {
Y
Yu Yang 已提交
53
    var->GetMutable<LoDTensorArray>();
Y
Yang Yu 已提交
54 55
  } else if (var_type == proto::VarDesc::PLACE_LIST) {
    var->GetMutable<platform::PlaceList>();
F
fengjiayi 已提交
56 57
  } else if (var_type == proto::VarDesc::READER) {
    var->GetMutable<ReaderHolder>();
Q
QI JUN 已提交
58 59
  } else {
    PADDLE_THROW(
Y
Yu Yang 已提交
60
        "Variable type %d is not in "
F
fengjiayi 已提交
61 62
        "[LOD_TENSOR, SELECTED_ROWS, FEED_MINIBATCH, FETCH_LIST, "
        "LOD_RANK_TABLE, PLACE_LIST, READER]",
Y
Yu Yang 已提交
63
        var_type);
Q
QI JUN 已提交
64 65 66
  }
}

Y
Yang Yu 已提交
67 68
static void CheckTensorNANOrInf(const std::string& name,
                                const framework::Tensor& tensor) {
Y
Yang Yu 已提交
69
  if (tensor.memory_size() == 0) {
Y
Yang Yu 已提交
70 71
    return;
  }
Y
Yang Yu 已提交
72 73
  if (tensor.type().hash_code() != typeid(float).hash_code() &&
      tensor.type().hash_code() != typeid(double).hash_code()) {
Y
Yang Yu 已提交
74 75 76 77 78 79
    return;
  }
  PADDLE_ENFORCE(!framework::HasInf(tensor), "Tensor %s has Inf", name);
  PADDLE_ENFORCE(!framework::HasNAN(tensor), "Tensor %s has NAN", name);
}

Y
Yu Yang 已提交
80
void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
T
typhoonzero 已提交
81
                   bool create_local_scope, bool create_vars) {
Y
Yang Yang 已提交
82
  // TODO(tonyyang-svail):
Y
Yang Yang 已提交
83
  //    - only runs on the first device (i.e. no interdevice communication)
Y
Yang Yang 已提交
84
  //    - will change to use multiple blocks for RNN op and Cond Op
85
  PADDLE_ENFORCE_LT(static_cast<size_t>(block_id), pdesc.Size());
86
  auto& block = pdesc.Block(block_id);
Y
Yang Yang 已提交
87

Y
Yu Yang 已提交
88
  Scope* local_scope = scope;
T
typhoonzero 已提交
89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107
  if (create_vars) {
    if (create_local_scope) {
      local_scope = &scope->NewScope();
      for (auto& var : block.AllVars()) {
        if (var->Name() == framework::kEmptyVarName) {
          continue;
        }

        if (var->Persistable()) {
          auto* ptr = scope->Var(var->Name());
          CreateTensor(ptr, var->GetType());
          VLOG(3) << "Create Variable " << var->Name()
                  << " global, which pointer is " << ptr;
        } else {
          auto* ptr = local_scope->Var(var->Name());
          CreateTensor(ptr, var->GetType());
          VLOG(3) << "Create Variable " << var->Name()
                  << " locally, which pointer is " << ptr;
        }
108
      }
T
typhoonzero 已提交
109 110
    } else {
      for (auto& var : block.AllVars()) {
Y
Yu Yang 已提交
111 112
        auto* ptr = local_scope->Var(var->Name());
        CreateTensor(ptr, var->GetType());
T
typhoonzero 已提交
113 114
        VLOG(3) << "Create variable " << var->Name() << ", which pointer is "
                << ptr;
Y
Yu Yang 已提交
115
      }
T
typhoonzero 已提交
116 117
    }  // if (create_local_scope)
  }    // if (create_vars)
Y
Yang Yang 已提交
118

119 120
  for (auto& op_desc : block.AllOps()) {
    auto op = paddle::framework::OpRegistry::CreateOp(*op_desc);
Q
Qiao Longfei 已提交
121
    VLOG(4) << op->DebugStringEx(local_scope);
122 123

    platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
D
dangqingqing 已提交
124
    platform::RecordEvent record_event(op->Type(), pool.Get(place_));
125

D
dzhwinter 已提交
126
    op->Run(*local_scope, place_);
Q
Qiao Longfei 已提交
127
    VLOG(3) << op->DebugStringEx(local_scope);
D
dzhwinter 已提交
128
    if (FLAGS_benchmark) {
129 130 131
      VLOG(2) << "Memory used after operator " + op->Type() + " running: "
              << memory::memory_usage(place_);
    }
Y
Yang Yu 已提交
132 133 134 135 136 137 138 139 140
    if (FLAGS_check_nan_inf) {
      for (auto& vname : op->OutputVars(true)) {
        auto* var = local_scope->FindVar(vname);
        if (var == nullptr) continue;
        if (var->IsType<framework::LoDTensor>()) {
          CheckTensorNANOrInf(vname, var->Get<framework::LoDTensor>());
        }
      }
    }
Y
Yu Yang 已提交
141
  }
G
gongweibao 已提交
142
  if (create_vars && create_local_scope) {
Y
Yu Yang 已提交
143
    scope->DeleteScope(local_scope);
Q
qijun 已提交
144
  }
D
dzhwinter 已提交
145
  if (FLAGS_benchmark) {
146 147 148 149 150
    VLOG(2) << "-------------------------------------------------------";
    VLOG(2) << "Memory used after deleting local scope: "
            << memory::memory_usage(place_);
    VLOG(2) << "-------------------------------------------------------";
  }
Q
qijun 已提交
151 152
}

153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311
// Check whether the block already has feed operators and feed_holder.
// Return false if the block does not have any feed operators.
// If some feed operators have been prepended to the block, check that
// the info contained in these feed operators matches the feed_targets
// and feed_holder_name. Raise exception when any mismatch is found.
// Return true if the block has feed operators and holder of matching info.
static bool has_feed_operators(
    BlockDesc* block, std::map<std::string, const LoDTensor*>& feed_targets,
    const std::string& feed_holder_name) {
  size_t feed_count = 0;
  for (auto* op : block->AllOps()) {
    if (op->Type() == kFeedOpType) {
      feed_count++;
      PADDLE_ENFORCE_EQ(op->Input("X")[0], feed_holder_name,
                        "Input to feed op should be '%s'", feed_holder_name);
      std::string feed_target_name = op->Output("Out")[0];
      PADDLE_ENFORCE(
          feed_targets.find(feed_target_name) != feed_targets.end(),
          "Feed operator output name '%s' cannot be found in 'feed_targets'",
          feed_target_name);
    }
  }

  if (feed_count > 0) {
    PADDLE_ENFORCE_EQ(
        feed_count, feed_targets.size(),
        "The number of feed operators should match 'feed_targets'");

    // When feed operator are present, so should be feed_holder
    auto var = block->FindVar(feed_holder_name);
    PADDLE_ENFORCE_NOT_NULL(var, "Block should already have a '%s' variable",
                            feed_holder_name);
    PADDLE_ENFORCE_EQ(var->GetType(), proto::VarDesc::FEED_MINIBATCH,
                      "'%s' variable should be 'FEED_MINIBATCH' type",
                      feed_holder_name);
  }

  return feed_count > 0;
}

// Check whether the block already has fetch operators and fetch_holder.
// Return false if the block does not have any fetch operators.
// If some fetch operators have been appended to the block, check that
// the info contained in these fetch operators matches the fetch_targets
// and fetch_holder_name. Raise exception when any mismatch is found.
// Return true if the block has fetch operators and holder of matching info.
static bool has_fetch_operators(
    BlockDesc* block, std::map<std::string, LoDTensor*>& fetch_targets,
    const std::string& fetch_holder_name) {
  size_t fetch_count = 0;
  for (auto* op : block->AllOps()) {
    if (op->Type() == kFetchOpType) {
      fetch_count++;
      PADDLE_ENFORCE_EQ(op->Output("Out")[0], fetch_holder_name,
                        "Output of fetch op should be '%s'", fetch_holder_name);
      std::string fetch_target_name = op->Input("X")[0];
      PADDLE_ENFORCE(
          fetch_targets.find(fetch_target_name) != fetch_targets.end(),
          "Fetch operator input name '%s' cannot be found in 'fetch_targets'",
          fetch_target_name);
    }
  }

  if (fetch_count > 0) {
    PADDLE_ENFORCE_EQ(
        fetch_count, fetch_targets.size(),
        "The number of fetch operators should match 'fetch_targets'");

    // When fetch operator are present, so should be fetch_holder
    auto var = block->FindVar(fetch_holder_name);
    PADDLE_ENFORCE_NOT_NULL(var, "Block should already have a '%s' variable",
                            fetch_holder_name);
    PADDLE_ENFORCE_EQ(var->GetType(), proto::VarDesc::FETCH_LIST,
                      "'%s' variable should be 'FETCH_LIST' type",
                      fetch_holder_name);
  }

  return fetch_count > 0;
}

void Executor::Run(const ProgramDesc& program, Scope* scope,
                   std::map<std::string, const LoDTensor*>& feed_targets,
                   std::map<std::string, LoDTensor*>& fetch_targets,
                   const std::string& feed_holder_name,
                   const std::string& fetch_holder_name) {
  auto* copy_program = new ProgramDesc(program);
  auto* global_block = copy_program->MutableBlock(0);

  if (!has_feed_operators(global_block, feed_targets, feed_holder_name)) {
    // create feed_holder variable
    auto* feed_holder = global_block->Var(feed_holder_name);
    feed_holder->SetType(proto::VarDesc::FEED_MINIBATCH);
    feed_holder->SetPersistable(true);

    int i = 0;
    for (auto& feed_target : feed_targets) {
      std::string var_name = feed_target.first;
      VLOG(3) << "feed target's name: " << var_name;

      // prepend feed op
      auto* op = global_block->PrependOp();
      op->SetType(kFeedOpType);
      op->SetInput("X", {feed_holder_name});
      op->SetOutput("Out", {var_name});
      op->SetAttr("col", {static_cast<int>(i)});
      op->CheckAttrs();

      i++;
    }
  }

  // map the data of feed_targets to feed_holder
  for (auto* op : global_block->AllOps()) {
    if (op->Type() == kFeedOpType) {
      std::string feed_target_name = op->Output("Out")[0];
      int idx = boost::get<int>(op->GetAttr("col"));
      SetFeedVariable(scope, *feed_targets[feed_target_name], feed_holder_name,
                      idx);
    }
  }

  if (!has_fetch_operators(global_block, fetch_targets, fetch_holder_name)) {
    // create fetch_holder variable
    auto* fetch_holder = global_block->Var(fetch_holder_name);
    fetch_holder->SetType(proto::VarDesc::FETCH_LIST);
    fetch_holder->SetPersistable(true);

    int i = 0;
    for (auto& fetch_target : fetch_targets) {
      std::string var_name = fetch_target.first;
      VLOG(3) << "fetch target's name: " << var_name;

      // append fetch op
      auto* op = global_block->AppendOp();
      op->SetType(kFetchOpType);
      op->SetInput("X", {var_name});
      op->SetOutput("Out", {fetch_holder_name});
      op->SetAttr("col", {static_cast<int>(i)});
      op->CheckAttrs();

      i++;
    }
  }

  Run(*copy_program, scope, 0, true, true);

  // obtain the data of fetch_targets from fetch_holder
  for (auto* op : global_block->AllOps()) {
    if (op->Type() == kFetchOpType) {
      std::string fetch_target_name = op->Input("X")[0];
      int idx = boost::get<int>(op->GetAttr("col"));
      *fetch_targets[fetch_target_name] =
          GetFetchVariable(*scope, fetch_holder_name, idx);
    }
  }

  delete copy_program;
}

Q
qijun 已提交
312 313
}  // namespace framework
}  // namespace paddle