operator.cc 37.8 KB
Newer Older
1
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Q
Qiao Longfei 已提交
2 3 4 5 6 7 8 9 10 11 12 13

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. */
D
dzhwinter 已提交
14

15 16
#include <gflags/gflags.h>
#include <glog/logging.h>
17

18
#include <algorithm>
P
peizhilin 已提交
19 20
#include <sstream>
#include <string>
S
sneaxiy 已提交
21
#include <unordered_set>
P
peizhilin 已提交
22
#include <vector>
Y
Yi Wang 已提交
23 24
#include "paddle/fluid/framework/data_transform.h"
#include "paddle/fluid/framework/executor.h"
25
#include "paddle/fluid/framework/lod_tensor.h"
26
#include "paddle/fluid/framework/op_proto_maker.h"
27
#include "paddle/fluid/framework/operator.h"
Y
Yi Wang 已提交
28
#include "paddle/fluid/framework/shape_inference.h"
29
#include "paddle/fluid/framework/transfer_scope_cache.h"
Y
Yi Wang 已提交
30
#include "paddle/fluid/framework/var_type.h"
31
#include "paddle/fluid/platform/profiler.h"
Q
Qiao Longfei 已提交
32

D
dzhwinter 已提交
33
DECLARE_bool(benchmark);
C
chengduoZH 已提交
34 35 36
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
Qiao Longfei 已提交
37
DEFINE_int32(inner_op_parallelism, 0, "number of threads for inner op");
D
dzhwinter 已提交
38

Q
Qiao Longfei 已提交
39 40 41
namespace paddle {
namespace framework {

42 43 44 45 46 47
std::vector<std::tuple<platform::Place, LibraryType>> kKernelPriority = {
    std::make_tuple(platform::CUDAPlace(0), LibraryType::kCUDNN),
    std::make_tuple(platform::CUDAPlace(0), LibraryType::kPlain),
    std::make_tuple(platform::CPUPlace(), LibraryType::kMKLDNN),
    std::make_tuple(platform::CPUPlace(), LibraryType::kPlain),
};
D
dzhwinter 已提交
48

Q
qiaolongfei 已提交
49 50
proto::VarType::Type GetDataTypeOfVar(const Variable* var) {
  if (var->IsType<framework::LoDTensor>()) {
Y
Yu Yang 已提交
51
    return var->Get<framework::LoDTensor>().type();
Q
qiaolongfei 已提交
52
  } else if (var->IsType<framework::SelectedRows>()) {
Y
Yu Yang 已提交
53
    return var->Get<framework::SelectedRows>().value().type();
Q
qiaolongfei 已提交
54 55 56 57 58
  } else {
    PADDLE_THROW("Var should be LoDTensor or SelectedRows");
  }
}

59 60
static DDim GetDims(const Scope& scope, const std::string& name,
                    bool get_actual_dim = false) {
61
  Variable* var = scope.FindVar(name);
Q
qiaolongfei 已提交
62 63
  if (var == nullptr) {
    return DDim({-1});
Q
Qiao Longfei 已提交
64 65
  }

M
minqiyang 已提交
66 67
  if (var->IsType<LoDTensor>()) {
    const LoDTensor& tensor = var->Get<LoDTensor>();
L
liuwei1031 已提交
68 69 70
    // if (UNLIKELY(!tensor.IsInitialized())) {
    //   return DDim({-1});
    // }
M
minqiyang 已提交
71 72 73 74 75 76 77
    return tensor.dims();
  } else if (var->IsType<SelectedRows>()) {
    if (get_actual_dim) {
      return var->Get<SelectedRows>().value().dims();
    } else {
      return var->Get<SelectedRows>().GetCompleteDims();
    }
78 79 80 81 82
  } else {
    return DDim({-1});
  }
}

Q
Qiao Longfei 已提交
83 84 85 86 87 88
static bool VarInited(const Scope& scope, const std::string& name) {
  Variable* var = scope.FindVar(name);
  if (var == nullptr) return false;
  return var->IsInitialized();
}

D
dzhwinter 已提交
89 90 91 92 93
static std::string GetDtype(const Scope& scope, const std::string& name) {
  Variable* var = scope.FindVar(name);
  if (var == nullptr) {
    return "";
  }
94

M
minqiyang 已提交
95 96 97
  if (var->IsType<LoDTensor>()) {
    const LoDTensor& tensor = var->Get<LoDTensor>();
    if (UNLIKELY(!tensor.IsInitialized())) {
98 99
      return "";
    }
Y
Yu Yang 已提交
100
    return DataTypeToString(tensor.type());
M
minqiyang 已提交
101
  } else if (var->IsType<SelectedRows>()) {
Q
Qiao Longfei 已提交
102 103 104 105
    auto tensor = var->Get<SelectedRows>().value();
    if (UNLIKELY(!tensor.IsInitialized())) {
      return "uninited";
    } else {
Y
Yu Yang 已提交
106
      return DataTypeToString(tensor.type());
Q
Qiao Longfei 已提交
107
    }
D
dzhwinter 已提交
108 109 110 111 112
  } else {
    return "";
  }
}

113 114 115 116 117 118
static int GetRowSize(const Scope& scope, const std::string& name) {
  Variable* var = scope.FindVar(name);
  if (var == nullptr) {
    return -1;
  }

M
minqiyang 已提交
119 120
  if (var->IsType<SelectedRows>()) {
    return var->Get<SelectedRows>().rows().size();
121 122 123 124 125
  }

  return -1;
}

Q
Qiao Longfei 已提交
126 127 128 129 130 131 132 133
static LoD GetLoD(const Scope& scope, const std::string& name) {
  Variable* var = scope.FindVar(name);
  auto default_lod = LoD({{}});

  if (var == nullptr) {
    return default_lod;
  }

M
minqiyang 已提交
134 135
  if (var->IsType<LoDTensor>()) {
    const LoDTensor& tensor = var->Get<LoDTensor>();
L
liuwei1031 已提交
136 137 138
    // if (UNLIKELY(!tensor.IsInitialized())) {
    //   return default_lod;
    // }
M
minqiyang 已提交
139
    return tensor.lod();
Q
Qiao Longfei 已提交
140 141 142 143 144
  } else {
    return default_lod;
  }
}

X
Xin Pan 已提交
145 146 147 148 149
RuntimeContext::RuntimeContext(const VariableNameMap& innames,
                               const VariableNameMap& outnames,
                               const Scope& scope) {
  for (auto& var_name_item : innames) {
    std::vector<Variable*>& input_vars = inputs[var_name_item.first];
X
Xin Pan 已提交
150
    input_vars.reserve(var_name_item.second.size());
X
Xin Pan 已提交
151 152 153 154 155 156
    for (auto& var_name : var_name_item.second) {
      input_vars.push_back(scope.FindVar(var_name));
    }
  }
  for (auto& var_name_item : outnames) {
    std::vector<Variable*>& output_vars = outputs[var_name_item.first];
X
Xin Pan 已提交
157
    output_vars.reserve(var_name_item.second.size());
X
Xin Pan 已提交
158 159 160 161 162 163
    for (auto& var_name : var_name_item.second) {
      output_vars.push_back(scope.FindVar(var_name));
    }
  }
}

164
void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
P
peizhilin 已提交
165 166 167
  try {
    VLOG(4) << place << " " << DebugStringEx(&scope);
    if (platform::is_gpu_place(place)) {
168
#ifndef PADDLE_WITH_CUDA
P
peizhilin 已提交
169
      PADDLE_THROW("Cannot run operator on place %s", place);
170
#else
P
peizhilin 已提交
171 172
      auto dev_id = boost::get<platform::CUDAPlace>(place).device;
      platform::SetDeviceId(dev_id);
173
#endif
P
peizhilin 已提交
174
    }
P
peizhilin 已提交
175

P
peizhilin 已提交
176 177 178 179 180
    // The profile has a process-wide mutex, results in serious performance
    // issue
    // in concurrency scenerio. Here use an `if` to fix this issue.
    // Please not remove the `if`, ask @Superjomn if there are any concern.
    if (platform::IsProfileEnabled()) {
181
      platform::RecordEvent record_event(Type());
P
peizhilin 已提交
182 183 184 185
      RunImpl(scope, place);
    } else {
      RunImpl(scope, place);
    }
186

P
peizhilin 已提交
187 188 189
    VLOG(3) << place << " " << DebugStringEx(&scope);
  } catch (platform::EnforceNotMet exception) {
    if (Attrs().count("sub_block") != 0) {
190
      throw std::move(exception);
P
peizhilin 已提交
191
    }
192

P
peizhilin 已提交
193 194 195 196
    auto& callstack = Attr<std::vector<std::string>>(
        OpProtoAndCheckerMaker::OpCreationCallstackAttrName());

    if (callstack.empty()) {
197
      throw std::move(exception);
P
peizhilin 已提交
198 199 200 201 202 203 204 205 206 207
    }
    std::ostringstream sout;
    sout << "Invoke operator " << Type() << " error.\n";
    sout << "Python Callstacks: \n";
    for (auto& line : callstack) {
      sout << line;
    }
    sout << "C++ Callstacks: \n";
    sout << exception.err_str_;
    exception.err_str_ = sout.str();
208
    throw std::move(exception);
P
peizhilin 已提交
209 210
  } catch (...) {
    std::rethrow_exception(std::current_exception());
211
  }
212 213
}

214
bool OperatorBase::HasInputs(const std::string& name) const {
M
minqiyang 已提交
215
  return inputs_.find(name) != inputs_.end();
216 217
}

218
std::string OperatorBase::Input(const std::string& name) const {
Y
Yu Yang 已提交
219
  auto& ins = Inputs(name);
Y
Yu Yang 已提交
220
  PADDLE_ENFORCE_LE(ins.size(), 1UL,
221 222
                    "Operator %s's input %s should contain only one variable.",
                    type_, name);
Y
Yu Yang 已提交
223
  return ins.empty() ? kEmptyVarName : ins[0];
Y
Yan Chunwei 已提交
224 225
}

Y
Yu Yang 已提交
226 227
const std::vector<std::string>& OperatorBase::Inputs(
    const std::string& name) const {
Y
Yu Yang 已提交
228
  auto it = inputs_.find(name);
229 230
  PADDLE_ENFORCE(it != inputs_.end(), "Operator %s does not have the input %s.",
                 type_, name);
Y
Yu Yang 已提交
231
  return it->second;
Y
Yan Chunwei 已提交
232 233
}

234
bool OperatorBase::HasOutputs(const std::string& name) const {
235
  if (outputs_.find(name) != outputs_.end()) {
236 237 238 239 240 241
    return true;
  } else {
    return false;
  }
}

242
std::string OperatorBase::Output(const std::string& name) const {
Y
Yu Yang 已提交
243
  auto& outs = Outputs(name);
Y
Yu Yang 已提交
244
  PADDLE_ENFORCE_LE(outs.size(), 1UL,
245 246
                    "Operator %s's output %s should contain only one variable.",
                    type_, name);
Y
Yu Yang 已提交
247
  return outs.empty() ? kEmptyVarName : outs[0];
Y
Yan Chunwei 已提交
248 249
}

Y
Yu Yang 已提交
250 251
const std::vector<std::string>& OperatorBase::Outputs(
    const std::string& name) const {
Y
Yu Yang 已提交
252
  auto it = outputs_.find(name);
253 254
  PADDLE_ENFORCE(it != outputs_.end(),
                 "Operator %s does not have an output called %s.", type_, name);
Y
Yu Yang 已提交
255
  return it->second;
Y
Yan Chunwei 已提交
256 257
}

258
std::string OperatorBase::DebugStringEx(const Scope* scope) const {
Q
Qiao Longfei 已提交
259
  std::stringstream ss;
Y
Yu Yang 已提交
260
  ss << "Op(" << type_ << "), inputs:{";
Y
Yu Yang 已提交
261 262
  for (auto it = inputs_.begin(); it != inputs_.end();) {
    auto& input = *it;
Y
Yu Yang 已提交
263 264
    ss << input.first << "[";
    for (size_t i = 0; i < input.second.size(); ++i) {
Q
Qiao Longfei 已提交
265 266
      auto var_name = input.second[i];
      ss << var_name;
267
      if (scope) {
Q
Qiao Longfei 已提交
268 269 270 271 272 273 274 275 276 277 278
        if (!VarInited(*scope, var_name)) {
          ss << "[uninited]";
        } else {
          int row_size = GetRowSize(*scope, var_name);
          if (row_size >= 0) {
            ss << "[row_size=" << row_size << "]";
          }
          std::string dtype = GetDtype(*scope, var_name);
          ss << ":" << dtype;
          ss << "[" << GetDims(*scope, var_name, true) << "]";
          ss << "(" << GetLoD(*scope, var_name) << ")";
279
        }
280
      }
Y
Yu Yang 已提交
281 282 283
      if (i != input.second.size() - 1) {
        ss << ", ";
      }
284
    }
Y
Yu Yang 已提交
285
    ss << "]";
Y
Yu Yang 已提交
286 287
    ++it;
    if (it != inputs_.end()) {
288 289
      ss << ", ";
    }
Q
Qiao Longfei 已提交
290
  }
Y
Yu Yang 已提交
291
  ss << "}, outputs:{";
Y
Yu Yang 已提交
292 293
  for (auto it = outputs_.begin(); it != outputs_.end();) {
    auto& output = *it;
Y
Yu Yang 已提交
294 295
    ss << output.first << "[";
    for (size_t i = 0; i < output.second.size(); ++i) {
Q
Qiao Longfei 已提交
296 297
      auto var_name = output.second[i];
      ss << var_name;
298
      if (scope) {
Q
Qiao Longfei 已提交
299 300 301 302 303 304 305
        if (!VarInited(*scope, var_name)) {
          ss << "[uninited]";
        } else {
          int row_size = GetRowSize(*scope, output.second[i]);
          if (row_size >= 0) {
            ss << "[row_size=" << row_size << "]";
          }
C
chengduo 已提交
306 307
          std::string dtype = GetDtype(*scope, output.second[i]);
          ss << ":" << dtype;
Q
Qiao Longfei 已提交
308 309
          ss << "[" << GetDims(*scope, var_name, true) << "]";
          ss << "(" << GetLoD(*scope, var_name) << ")";
310
        }
311
      }
Y
Yu Yang 已提交
312 313 314
      if (i != output.second.size() - 1) {
        ss << ", ";
      }
315
    }
Y
Yu Yang 已提交
316
    ss << "]";
Y
Yu Yang 已提交
317 318
    ++it;
    if (it != outputs_.end()) {
319 320
      ss << ", ";
    }
Q
Qiao Longfei 已提交
321
  }
Y
Yu Yang 已提交
322
  ss << "}.";
Q
Qiao Longfei 已提交
323 324 325
  return ss.str();
}

Y
Yu Yang 已提交
326
OperatorBase::OperatorBase(const std::string& type,
Y
Yu Yang 已提交
327 328
                           const VariableNameMap& inputs,
                           const VariableNameMap& outputs,
Y
Yu Yang 已提交
329
                           const AttributeMap& attrs)
S
sneaxiy 已提交
330 331 332 333 334 335
    : type_(type),
      inputs_(inputs),
      outputs_(outputs),
      attrs_(attrs),
      // NOTE(zjl): why op_info may be nullptr?
      info_(OpInfoMap::Instance().GetNullable(type)) {
336 337
  GenerateTemporaryNames();
  CheckAllInputOutputSet();
Y
Yu Yang 已提交
338
}
339

Q
qijun 已提交
340 341
std::vector<std::string> OperatorBase::InputVars() const {
  std::vector<std::string> ret_val;
Y
Yu Yang 已提交
342
  for (auto& o : inputs_) {
Q
qijun 已提交
343 344 345 346 347 348
    ret_val.reserve(ret_val.size() + o.second.size());
    ret_val.insert(ret_val.end(), o.second.begin(), o.second.end());
  }
  return ret_val;
}

Y
Yu Yang 已提交
349 350 351 352 353 354 355 356 357 358
std::vector<std::string> OperatorBase::OutputVars(bool has_intermediate) const {
  std::vector<std::string> ret_val;
  if (has_intermediate) {
    // push all outputs into ret_val
    for (auto& o : outputs_) {
      ret_val.reserve(ret_val.size() + o.second.size());
      ret_val.insert(ret_val.end(), o.second.begin(), o.second.end());
    }
    return ret_val;
  }
S
sneaxiy 已提交
359
  auto& info = Info();
Y
Yu Yang 已提交
360 361

  // get all OpProto::Var for outputs
Y
Yu Yang 已提交
362
  for (auto& o : info.Proto().outputs()) {
Y
Yu Yang 已提交
363 364 365 366 367 368 369 370 371
    // ignore all intermediate output
    if (o.intermediate()) continue;
    auto out = outputs_.find(o.name());
    if (out != outputs_.end()) {
      ret_val.reserve(ret_val.size() + out->second.size());
      ret_val.insert(ret_val.end(), out->second.begin(), out->second.end());
    }
  }
  return ret_val;
D
dongzhihong 已提交
372 373
}

374
void OperatorBase::CheckAllInputOutputSet() const {
S
sneaxiy 已提交
375
  if (info_ == nullptr || info_->proto_ == nullptr) return;
376

S
sneaxiy 已提交
377
  for (auto& in : info_->Proto().inputs()) {
378 379 380 381
    if (!in.dispensable()) {
      PADDLE_ENFORCE(inputs_.find(in.name()) != inputs_.end(),
                     "Operator %s's input, %s, is not set", Type(), in.name());
    }
382 383
  }

S
sneaxiy 已提交
384
  for (auto& out : info_->Proto().outputs()) {
385 386 387 388 389
    if (!out.dispensable()) {
      PADDLE_ENFORCE(outputs_.find(out.name()) != outputs_.end(),
                     "Operator %s's output, %s, is not set", Type(),
                     out.name());
    }
390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405
  }
}

void OperatorBase::GenerateTemporaryNames() {
  static std::atomic<size_t> gUniqId(0UL);
  for (auto& output : outputs_) {
    for (auto& output_name : output.second) {
      if (output_name == kTempVarName) {
        output_name += type_;
        output_name += "@";
        output_name += std::to_string(gUniqId.fetch_add(1));
      }
    }
  }
}

B
baojun-nervana 已提交
406
static bool VarIsTensor(const Variable& var) {
C
chengduo 已提交
407
  return var.IsType<LoDTensor>() || var.IsType<SelectedRows>();
408 409
}

C
chengduo 已提交
410
const Tensor* GetLoDTensorOrSelectedRowsValueFromVar(const Variable& var) {
C
chengduo 已提交
411 412 413 414
  if (var.IsType<LoDTensor>()) {
    return static_cast<const Tensor*>(&(var.Get<LoDTensor>()));
  } else if (var.IsType<SelectedRows>()) {
    return &(var.Get<SelectedRows>().value());
Q
QI JUN 已提交
415
  } else {
Y
Yang Yang 已提交
416
    PADDLE_THROW("Variable type_id %s, expect LoDTensor/SelectedRows.",
S
sneaxiy 已提交
417
                 ToTypeName(var.Type()));
Q
QI JUN 已提交
418 419 420
  }
}

C
chengduo 已提交
421
Tensor* GetMutableLoDTensorOrSelectedRowsValueFromVar(Variable* var) {
Q
QI JUN 已提交
422
  if (var->IsType<LoDTensor>()) {
423
    return var->GetMutable<LoDTensor>();
Q
QI JUN 已提交
424
  } else if (var->IsType<SelectedRows>()) {
425
    return var->GetMutable<SelectedRows>()->mutable_value();
Q
QI JUN 已提交
426
  } else {
Y
Yang Yang 已提交
427
    PADDLE_THROW("Variable type_id %s, expect LoDTensor/SelectedRows.",
S
sneaxiy 已提交
428
                 ToTypeName(var->Type()));
Q
QI JUN 已提交
429 430 431
  }
}

432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463
bool ExecutionContext::HasInput(const std::string& name) const {
  if (!op_.HasInputs(name)) {
    return false;
  }
  auto& ins = Inputs(name);
  size_t length = ins.size();
  if (length == 0) {
    return false;
  }
  PADDLE_ENFORCE_EQ(length, 1UL,
                    "Input %s should not have more than one inputs", name);
  auto arg = ins[0];
  auto* var = arg == kEmptyVarName ? nullptr : scope_.FindVar(arg);
  return var != nullptr;
}

bool ExecutionContext::HasOutput(const std::string& name) const {
  if (!op_.HasOutputs(name)) {
    return false;
  }
  auto& outs = Outputs(name);
  size_t length = outs.size();
  if (length == 0) {
    return false;
  }
  PADDLE_ENFORCE_EQ(length, 1UL,
                    "Output %s should not have more than one inputs", name);
  auto arg = outs[0];
  auto* var = arg == kEmptyVarName ? nullptr : scope_.FindVar(arg);
  return var != nullptr;
}

X
Xin Pan 已提交
464 465 466 467 468 469 470 471 472 473
const Variable* ExecutionContext::InputVar(const std::string& name) const {
  auto it = ctx_.inputs.find(name);
  if (it == ctx_.inputs.end()) return nullptr;

  PADDLE_ENFORCE_LE(it->second.size(), 1UL,
                    "Operator %s's input %s should contain only one variable.",
                    op_.Type(), name);
  return it->second.empty() ? nullptr : it->second[0];
}

X
clean  
Xin Pan 已提交
474
Variable* ExecutionContext::OutputVar(const std::string& name) const {
X
Xin Pan 已提交
475 476 477 478 479 480 481 482 483
  auto it = ctx_.outputs.find(name);
  if (it == ctx_.outputs.end()) return nullptr;

  PADDLE_ENFORCE_LE(it->second.size(), 1UL,
                    "Operator %s's output %s should contain only one variable.",
                    op_.Type(), name);
  return it->second.empty() ? nullptr : it->second[0];
}

484
template <>
485
const Tensor* ExecutionContext::Input<Tensor>(const std::string& name) const {
C
chengduo 已提交
486
  return Input<LoDTensor>(name);
487 488 489
}

template <>
490
const std::vector<const Tensor*> ExecutionContext::MultiInput<Tensor>(
491
    const std::string& name) const {
X
Xin Pan 已提交
492 493 494 495 496 497 498 499 500 501 502 503 504
  auto it = ctx_.inputs.find(name);
  if (it == ctx_.inputs.end()) {
    return {};
  }
  const std::vector<Variable*>& vars = it->second;
  std::vector<const Tensor*> res;
  res.reserve(vars.size());
  std::transform(vars.begin(), vars.end(), std::back_inserter(res),
                 [&](Variable* var) -> const Tensor* {
                   if (var == nullptr) return nullptr;
                   PADDLE_ENFORCE(
                       var->IsType<LoDTensor>(),
                       "should be LoDTensor, but the received type is %s",
S
sneaxiy 已提交
505
                       ToTypeName(var->Type()));
X
Xin Pan 已提交
506 507 508 509 510
                   return &(var->Get<LoDTensor>());
                 });
  return res;
}

511
template <>
512
Tensor* ExecutionContext::Output<Tensor>(const std::string& name) const {
C
chengduo 已提交
513
  return Output<LoDTensor>(name);
514 515 516
}

template <>
517
std::vector<Tensor*> ExecutionContext::MultiOutput<Tensor>(
518
    const std::string& name) const {
519 520 521 522 523
  auto it = ctx_.outputs.find(name);
  if (it == ctx_.outputs.end()) {
    return {};
  }
  const std::vector<Variable*>& vars = it->second;
524
  std::vector<Tensor*> res;
525 526 527 528 529
  res.reserve(vars.size());
  std::transform(vars.begin(), vars.end(), std::back_inserter(res),
                 [&](Variable* var) -> Tensor* {
                   return var == nullptr ? nullptr
                                         : var->GetMutable<LoDTensor>();
530
                 });
531 532 533
  return res;
}

Y
Yu Yang 已提交
534 535 536 537 538 539 540 541 542 543 544 545 546 547 548
bool OpSupportGPU(const std::string& op_type) {
  auto& all_kernels = OperatorWithKernel::AllOpKernels();
  auto it = all_kernels.find(op_type);
  if (it == all_kernels.end()) {
    // All control operator must support GPU
    return true;
  }
  for (auto& kern_pair : it->second) {
    if (platform::is_gpu_place(kern_pair.first.place_)) {
      return true;
    }
  }
  return false;
}

549 550
class RuntimeInferShapeContext : public InferShapeContext {
 public:
X
Xin Pan 已提交
551 552
  RuntimeInferShapeContext(const OperatorBase& op, const Scope& scope,
                           const RuntimeContext& ctx)
G
Gabor Buella 已提交
553
      : op_(op), ctx_(ctx) {}
554 555

  bool HasInput(const std::string& name) const override {
556
    // has only one input
X
Xin Pan 已提交
557
    const auto& ins = ctx_.inputs;
558 559
    auto it = ins.find(name);
    if (it == ins.end()) {
560 561
      return false;
    }
562
    const auto& in = it->second;
X
Xin Pan 已提交
563
    if (in.size() == 0) return false;
T
tensor-tang 已提交
564
    PADDLE_ENFORCE_EQ(in.size(), 1UL,
F
fengjiayi 已提交
565
                      "Input %s should not have more than one inputs", name);
X
Xin Pan 已提交
566
    return in[0] != nullptr;
567 568 569
  }

  bool HasOutput(const std::string& name) const override {
570
    // has only one output
X
Xin Pan 已提交
571
    const auto& outs = ctx_.outputs;
572 573
    auto it = outs.find(name);
    if (it == outs.end()) {
574 575
      return false;
    }
576
    const auto& out = it->second;
X
Xin Pan 已提交
577
    if (out.size() == 0) {
578 579
      return false;
    }
T
tensor-tang 已提交
580 581
    PADDLE_ENFORCE_EQ(out.size(), 1UL,
                      "Output %s should not have more than one outputs", name);
X
Xin Pan 已提交
582
    return out[0] != nullptr;
583 584 585
  }

  bool HasInputs(const std::string& name) const override {
X
Xin Pan 已提交
586 587
    const auto& ins = ctx_.inputs;
    auto it = ins.find(name);
X
fix  
Xin Pan 已提交
588
    if (it == ins.end() || it->second.empty()) {
589 590
      return false;
    }
X
Xin Pan 已提交
591 592
    for (auto& input : it->second) {
      if (input == nullptr) {
593 594 595 596 597 598 599
        return false;
      }
    }
    return true;
  }

  bool HasOutputs(const std::string& name) const override {
X
Xin Pan 已提交
600 601
    const auto& outs = ctx_.outputs;
    auto it = outs.find(name);
X
fix  
Xin Pan 已提交
602
    if (it == outs.end() || it->second.empty()) {
603 604
      return false;
    }
X
Xin Pan 已提交
605 606
    for (auto& output : it->second) {
      if (output == nullptr) {
607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624
        return false;
      }
    }
    return true;
  }

  AttrReader Attrs() const override { return AttrReader(op_.Attrs()); }

  const std::vector<std::string>& Inputs(
      const std::string& name) const override {
    return op_.Inputs(name);
  }

  const std::vector<std::string>& Outputs(
      const std::string& name) const override {
    return op_.Outputs(name);
  }

625 626
  void ShareDim(const std::string& in, const std::string& out, size_t i = 0,
                size_t j = 0) override {
X
Xin Pan 已提交
627 628 629 630 631 632 633 634 635
    auto in_it = ctx_.inputs.find(in);
    auto out_it = ctx_.outputs.find(out);
    PADDLE_ENFORCE(in_it != ctx_.inputs.end() && in_it->second.size() > i,
                   "Inputs %s should have %llu argument", in, i);
    PADDLE_ENFORCE(out_it != ctx_.outputs.end() && out_it->second.size() > j,
                   "Outputs %s should have %llu argument", out, j);

    Variable* in_var = in_it->second[i];
    Variable* out_var = out_it->second[j];
636 637

    PADDLE_ENFORCE(in_var->Type() == out_var->Type(),
X
fix  
Xin Pan 已提交
638
                   "The type of %s and %s is not the same.", in, out);
639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656

    if (in_var->IsType<framework::SelectedRows>()) {
      auto& in_sele_rows = in_var->Get<framework::SelectedRows>();
      auto out_sele_rows = out_var->GetMutable<framework::SelectedRows>();
      out_sele_rows->mutable_value()->Resize(in_sele_rows.value().dims());
      out_sele_rows->set_rows(in_sele_rows.rows());
      out_sele_rows->set_height(in_sele_rows.height());
    } else if (in_var->IsType<framework::LoDTensor>()) {
      auto& in_lod_tensor = in_var->Get<framework::LoDTensor>();
      auto* out_lod_tensor = out_var->GetMutable<framework::LoDTensor>();
      out_lod_tensor->Resize(in_lod_tensor.dims());
    } else {
      PADDLE_THROW(
          "Currently, the input type of ShareDim only can be LoDTensor "
          "or SelectedRows.");
    }
  }

Q
Qiao Longfei 已提交
657 658
  void ShareLoD(const std::string& in, const std::string& out, size_t i = 0,
                size_t j = 0) const override {
X
Xin Pan 已提交
659 660 661 662 663 664 665 666
    auto in_it = ctx_.inputs.find(in);
    auto out_it = ctx_.outputs.find(out);
    PADDLE_ENFORCE(in_it != ctx_.inputs.end() && in_it->second.size() > i,
                   "Inputs %s should have %llu argument", in, i);
    PADDLE_ENFORCE(out_it != ctx_.outputs.end() && out_it->second.size() > j,
                   "Outputs %s should have %llu argument", out, j);

    Variable* in_var = in_it->second.at(i);
Q
Qiao Longfei 已提交
667
    if (!in_var->IsType<LoDTensor>()) return;
X
Xin Pan 已提交
668
    Variable* out_var = out_it->second.at(j);
Q
Qiao Longfei 已提交
669 670 671 672 673
    PADDLE_ENFORCE(out_var->IsType<LoDTensor>(),
                   "The %d-th output of Output(%s) must be LoDTensor.", j, out);
    auto in_tensor = in_var->Get<LoDTensor>();
    auto* out_tensor = out_var->GetMutable<LoDTensor>();
    out_tensor->set_lod(in_tensor.lod());
D
dzhwinter 已提交
674

M
mozga-intel 已提交
675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693
// TODO(dzhwinter) : reuse ShareLoD in most operators.
// Need to call ShareLayout explicitly in sequence related ops.
// Shall we have a better method to shared info between in/out Tensor?
#ifdef PADDLE_WITH_MKLDNN
    // Fix me: ugly workaround below
    // Correct solution:
    //    set_layout() should NOT be called here (i.e. ShareLoD). Instead,
    //    layout of output tensor should be set "manually" in Compute()
    //    of each OPKernel. The reason layout should NOT be shared between
    //    input and output "automatically" (now by InferShape()->ShareLoD())
    //    is that layout transform may occur after InferShape().
    // Workaround:
    //    Skip set_layout() when input layout is kMKLDNN
    //    This is to avoid kMKLDNN is populated wrongly into a non-MKLDNN
    //    OPKernel. In all MKLDNN OPkernel, set_layout(kMKLDNN) should be called
    //    in Compute()
    if (in_tensor.layout() != DataLayout::kMKLDNN)
#endif
      out_tensor->set_layout(in_tensor.layout());
D
dzhwinter 已提交
694 695
  }

C
chengduo 已提交
696 697 698 699 700
  void DecreaseLoDLevel(const std::string& in, const std::string& out,
                        size_t i = 0, size_t j = 0) const override {
    PADDLE_THROW("DecreaseLoDLevel is only used in compile time.");
  }

701 702
  bool IsRuntime() const override { return true; }

703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721
  // TODO(paddle-dev): Can this be template?
  std::vector<InferShapeVarPtr> GetInputVarPtrs(
      const std::string& name) override {
    const std::vector<Variable*>& vars = InputVars(name);
    std::vector<InferShapeVarPtr> res;
    res.reserve(vars.size());
    res.insert(res.begin(), vars.begin(), vars.end());
    return res;
  }

  std::vector<InferShapeVarPtr> GetOutputVarPtrs(
      const std::string& name) override {
    const std::vector<Variable*>& vars = OutputVars(name);
    std::vector<InferShapeVarPtr> res;
    res.reserve(vars.size());
    res.insert(res.begin(), vars.begin(), vars.end());
    return res;
  }

X
Xin Pan 已提交
722 723 724 725 726 727 728 729 730 731 732 733 734
  DDim GetInputDim(const std::string& name) const override {
    const std::vector<Variable*>& vars = InputVars(name);
    PADDLE_ENFORCE_EQ(vars.size(), 1UL,
                      "Input(%s) should hold one element, but now it holds %d",
                      name, vars.size());
    return this->GetDim(vars[0]);
  }

  std::vector<DDim> GetInputsDim(const std::string& name) const override {
    const std::vector<Variable*>& vars = InputVars(name);
    return GetDims(vars);
  }

X
Xin Pan 已提交
735 736 737 738 739 740 741 742 743 744
  std::vector<proto::VarType::Type> GetInputsVarType(
      const std::string& name) const override {
    return GetVarTypes(InputVars(name));
  }

  std::vector<proto::VarType::Type> GetOutputsVarType(
      const std::string& name) const override {
    return GetVarTypes(OutputVars(name));
  }

X
Xin Pan 已提交
745 746 747 748 749 750 751 752 753 754 755 756 757 758
  void SetOutputDim(const std::string& name, const DDim& dim) override {
    auto& vars = OutputVars(name);
    PADDLE_ENFORCE_EQ(vars.size(), 1UL,
                      "Output(%s) should hold one element, but now it holds %d",
                      name, vars.size());
    SetDim(vars[0], dim);
  }

  void SetOutputsDim(const std::string& name,
                     const std::vector<DDim>& dims) override {
    auto& vars = OutputVars(name);
    SetDims(vars, dims);
  }

759
 protected:
X
Xin Pan 已提交
760
  DDim GetDim(Variable* var) const {
F
fengjiayi 已提交
761
    PADDLE_ENFORCE_NOT_NULL(var);
762 763 764 765 766
    if (var->IsType<LoDTensor>()) {
      return var->Get<LoDTensor>().dims();
    } else if (var->IsType<SelectedRows>()) {
      return var->Get<SelectedRows>().GetCompleteDims();
    } else {
F
fengjiayi 已提交
767
      PADDLE_THROW(
X
Xin Pan 已提交
768
          "Only LoDTensor/SelectedRows support 'GetDim', but Variables "
F
fengjiayi 已提交
769
          "type_id is %s.",
S
sneaxiy 已提交
770
          ToTypeName(var->Type()));
F
fengjiayi 已提交
771 772 773
    }
  }

X
Xin Pan 已提交
774 775 776 777 778 779 780 781
  std::vector<DDim> GetDims(const std::vector<Variable*>& vars) const {
    std::vector<DDim> ret;
    ret.reserve(vars.size());
    std::transform(vars.begin(), vars.end(), std::back_inserter(ret),
                   [this](Variable* var) { return this->GetDim(var); });
    return ret;
  }

F
fengjiayi 已提交
782
  std::vector<DDim> GetRepeatedDims(const std::string& name) const override {
Y
Yu Yang 已提交
783
    PADDLE_THROW("Only compile time support this method");
784 785
  }

X
Xin Pan 已提交
786
  void SetDim(Variable* var, const DDim& dim) {
787 788 789 790 791
    if (var->IsType<LoDTensor>()) {
      var->GetMutable<LoDTensor>()->Resize(dim);
    } else if (var->IsType<SelectedRows>()) {
      var->GetMutable<SelectedRows>()->set_height(dim[0]);
    } else {
X
Xin Pan 已提交
792
      PADDLE_THROW("Variable type_id %s, expect LoDTensor/SelectedRows.",
S
sneaxiy 已提交
793
                   ToTypeName(var->Type()));
X
Xin Pan 已提交
794 795 796 797 798 799 800 801 802 803 804 805
    }
  }

  void SetDims(const std::vector<Variable*>& vars,
               const std::vector<DDim>& dims) {
    size_t length = vars.size();
    PADDLE_ENFORCE_EQ(length, dims.size());
    for (size_t i = 0; i < length; ++i) {
      if (vars[i] == nullptr) {
        continue;
      }
      SetDim(vars[i], dims[i]);
806 807 808
    }
  }

F
fengjiayi 已提交
809 810
  void SetRepeatedDims(const std::string& name,
                       const std::vector<DDim>& dims) override {
Y
Yu Yang 已提交
811
    PADDLE_THROW("Only compile time support this method");
F
fengjiayi 已提交
812 813
  }

X
Xin Pan 已提交
814 815 816 817 818 819 820 821 822 823 824
  std::vector<proto::VarType::Type> GetVarTypes(
      const std::vector<Variable*>& vars) const {
    std::vector<proto::VarType::Type> retv;
    retv.resize(vars.size());
    std::transform(vars.begin(), vars.end(), retv.begin(),
                   std::bind(std::mem_fn(&RuntimeInferShapeContext::GetVarType),
                             this, std::placeholders::_1));
    return retv;
  }

  proto::VarType::Type GetVarType(Variable* var) const {
825 826 827
    return ToVarType(var->Type());
  }

828 829 830 831 832 833 834 835 836 837 838 839 840 841
 private:
  const std::vector<Variable*>& InputVars(const std::string& name) const {
    auto it = ctx_.inputs.find(name);
    PADDLE_ENFORCE(it != ctx_.inputs.end(),
                   "Operator %s does not have the input %s.", op_.Type(), name);
    return it->second;
  }

  const std::vector<Variable*>& OutputVars(const std::string& name) const {
    auto it = ctx_.outputs.find(name);
    PADDLE_ENFORCE(it != ctx_.outputs.end(),
                   "Operator %s does not have the outputs %s.", op_.Type(),
                   name);
    return it->second;
F
fengjiayi 已提交
842 843
  }

844
  const OperatorBase& op_;
X
Xin Pan 已提交
845
  const RuntimeContext& ctx_;
846 847
};

848 849
static void CheckTensorNANOrInf(const std::string& op_type,
                                const std::string& name,
C
chengduoZH 已提交
850 851 852 853
                                const framework::Tensor& tensor) {
  if (tensor.memory_size() == 0) {
    return;
  }
Y
Yu Yang 已提交
854 855
  if (tensor.type() != proto::VarType::FP32 &&
      tensor.type() != proto::VarType::FP64) {
C
chengduoZH 已提交
856 857 858
    return;
  }
  PADDLE_ENFORCE(!framework::TensorContainsInf(tensor),
859
                 "Operator %s output Tensor %s contains Inf", op_type, name);
C
chengduoZH 已提交
860
  PADDLE_ENFORCE(!framework::TensorContainsNAN(tensor),
861
                 "Operator %s output Tensor %s contains NAN", op_type, name);
C
chengduoZH 已提交
862 863
}

B
baojun-nervana 已提交
864
void OperatorWithKernel::RuntimeInferShape(const Scope& scope,
X
Xin Pan 已提交
865 866 867
                                           const platform::Place& place,
                                           const RuntimeContext& ctx) const {
  RuntimeInferShapeContext infer_shape_ctx(*this, scope, ctx);
B
baojun-nervana 已提交
868 869 870
  this->InferShape(&infer_shape_ctx);
}

X
polish  
Xin Pan 已提交
871 872 873 874 875 876 877 878 879 880
std::vector<KernelConfig>* OperatorWithKernel::GetKernelConfig(
    const OpKernelType& key) const {
  auto config_iter = kernel_configs_map_.find(key);
  std::vector<KernelConfig>* kernel_configs = nullptr;
  if (config_iter != kernel_configs_map_.end()) {
    kernel_configs = &(config_iter->second);
  }
  return kernel_configs;
}

L
luotao1 已提交
881 882
void OperatorWithKernel::RunImpl(const Scope& scope,
                                 const platform::Place& place) const {
L
luotao1 已提交
883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898
  if (!HasAttr(kEnableCacheRuntimeContext)) {
    RuntimeContext ctx(Inputs(), Outputs(), scope);
    RunImpl(scope, place, &ctx);
  } else {
    const Scope* cur_scope = &scope;
    if (!runtime_ctx_ || pre_scope_ != cur_scope) {
      runtime_ctx_.reset(new RuntimeContext(Inputs(), Outputs(), scope));
      pre_scope_ = cur_scope;
    }
    RunImpl(scope, place, runtime_ctx_.get());
  }
}

void OperatorWithKernel::RunImpl(const Scope& scope,
                                 const platform::Place& place,
                                 RuntimeContext* runtime_ctx) const {
Y
Yu Yang 已提交
899
  platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
900
  auto* dev_ctx = pool.Get(place);
901

902 903 904 905
  // check if op[type] has kernel registered.
  auto& all_op_kernels = AllOpKernels();
  auto kernels_iter = all_op_kernels.find(type_);
  if (kernels_iter == all_op_kernels.end()) {
Y
Yu Yang 已提交
906 907
    PADDLE_THROW(
        "There are no kernels which are registered in the %s operator.", type_);
908 909
  }

Q
qiaolongfei 已提交
910 911
  OpKernelMap& kernels = kernels_iter->second;

X
Xin Pan 已提交
912
  auto expected_kernel_key = this->GetExpectedKernelType(
L
luotao1 已提交
913
      ExecutionContext(*this, scope, *dev_ctx, *runtime_ctx, nullptr));
M
minqiyang 已提交
914
  VLOG(3) << "expected_kernel_key:" << expected_kernel_key;
Q
qiaolongfei 已提交
915

916
  auto kernel_iter = kernels.find(expected_kernel_key);
917
#ifdef PADDLE_WITH_MKLDNN
P
Paweł Żelazko 已提交
918
  // workaround for missing MKLDNN kernel when FLAGS_use_mkldnn env var is set
919 920
  if (kernel_iter == kernels.end() &&
      expected_kernel_key.library_type_ == LibraryType::kMKLDNN) {
M
minqiyang 已提交
921
    VLOG(3) << "missing MKLDNN kernel: fallbacking to PLAIN one";
922 923 924 925 926
    expected_kernel_key.library_type_ = LibraryType::kPlain;
    expected_kernel_key.data_layout_ = DataLayout::kAnyLayout;
    kernel_iter = kernels.find(expected_kernel_key);
  }
#endif
927 928 929 930 931
  if (kernel_iter == kernels.end()) {
    PADDLE_THROW("op %s does not have kernel for %s", type_,
                 KernelTypeToString(expected_kernel_key));
  }

X
polish  
Xin Pan 已提交
932 933
  std::vector<KernelConfig>* kernel_configs =
      GetKernelConfig(expected_kernel_key);
934

Y
yuyang18 已提交
935 936
  // do data transformScope &transfer_scope;
  std::vector<std::string> transfered_inplace_vars;
L
luotao1 已提交
937 938
  auto* transfer_scope = PrepareData(scope, expected_kernel_key,
                                     &transfered_inplace_vars, runtime_ctx);
939

Y
yuyang18 已提交
940 941 942 943 944 945
  // exec scope is the scope that kernel actually executed on.
  const Scope& exec_scope =
      (transfer_scope == nullptr ? scope : *transfer_scope);

  if (!(expected_kernel_key.place_ == dev_ctx->GetPlace())) {
    dev_ctx = pool.Get(expected_kernel_key.place_);
946
  }
Q
QI JUN 已提交
947

L
luotao1 已提交
948
  if (!HasAttr(kAllKernelsMustComputeRuntimeShape)) {
L
luotao1 已提交
949
    RuntimeInferShapeContext infer_shape_ctx(*this, exec_scope, *runtime_ctx);
950 951
    this->InferShape(&infer_shape_ctx);
  }
X
clean  
Xin Pan 已提交
952 953
  // TODO(panyx0718): ExecutionContext should only depend on RuntimeContext
  // not Scope. Imperative mode only pass inputs and get outputs.
L
luotao1 已提交
954 955
  kernel_iter->second(ExecutionContext(*this, exec_scope, *dev_ctx,
                                       *runtime_ctx, kernel_configs));
D
dzhwinter 已提交
956

Y
yuyang18 已提交
957 958 959
  if (!transfered_inplace_vars.empty()) {
    // there is inplace variable has been transfered.
    TransferInplaceVarsBack(scope, transfered_inplace_vars, *transfer_scope);
960 961
  }

D
dzhwinter 已提交
962
  /*For profiling/benchmark only*/
D
dzhwinter 已提交
963
  if (FLAGS_benchmark) {
Y
yuyang18 已提交
964
    dev_ctx->Wait();
D
dzhwinter 已提交
965
  }
C
chengduoZH 已提交
966 967 968

  if (FLAGS_check_nan_inf) {
    for (auto& vname : OutputVars(true)) {
Y
yuyang18 已提交
969
      auto* var = exec_scope.FindVar(vname);
C
chengduoZH 已提交
970 971
      if (var == nullptr) continue;
      if (var->IsType<framework::LoDTensor>()) {
972
        CheckTensorNANOrInf(type_, vname, var->Get<framework::LoDTensor>());
973
      } else if (var->IsType<framework::SelectedRows>()) {
974 975
        CheckTensorNANOrInf(type_, vname,
                            var->Get<framework::SelectedRows>().value());
C
chengduoZH 已提交
976 977 978
      }
    }
  }
Q
Qiao Longfei 已提交
979
}
X
Xin Pan 已提交
980

Y
yuyang18 已提交
981 982 983 984
void OperatorWithKernel::TransferInplaceVarsBack(
    const Scope& scope, const std::vector<std::string>& inplace_vars,
    const Scope& transfer_scope) const {
  for (auto& var_name : inplace_vars) {
M
minqiyang 已提交
985
    VLOG(3) << "share inplace var " + var_name + " back to it's original scope";
C
chengduo 已提交
986 987 988
    auto* origin_var = scope.FindVar(var_name);
    PADDLE_ENFORCE_NOT_NULL(origin_var, "The var[%s] should not be nullptr.",
                            var_name);
C
chengduo 已提交
989
    auto* original_tensor =
C
chengduo 已提交
990
        GetMutableLoDTensorOrSelectedRowsValueFromVar(origin_var);
C
chengduo 已提交
991
    auto* var = transfer_scope.FindVar(var_name);
C
chengduo 已提交
992 993
    PADDLE_ENFORCE_NOT_NULL(var, "The var[%s] should not be nullptr.",
                            var_name);
C
chengduo 已提交
994
    auto* transformed_tensor = GetLoDTensorOrSelectedRowsValueFromVar(*var);
Y
yuyang18 已提交
995 996 997 998
    original_tensor->ShareDataWith(*transformed_tensor);
  }
}

X
Xin Pan 已提交
999
Scope* OperatorWithKernel::PrepareData(
Y
yuyang18 已提交
1000
    const Scope& scope, const OpKernelType& expected_kernel_key,
X
Xin Pan 已提交
1001 1002
    std::vector<std::string>* transfered_inplace_vars,
    RuntimeContext* ctx) const {
Y
yuyang18 已提交
1003
  Scope* new_scope = nullptr;
S
sneaxiy 已提交
1004 1005 1006 1007 1008 1009 1010 1011 1012 1013

  std::unordered_set<std::string> no_buffer_ins;
  if (info_) {
    auto& no_buffer_inferer = info_->NoNeedBufferVarsInferer();
    // Some op may not register NoNeedBufferVarsInferer
    if (no_buffer_inferer) {
      no_buffer_ins = no_buffer_inferer(Inputs(), Outputs(), Attrs());
    }
  }

Y
yuyang18 已提交
1014
  for (auto& var_name_item : Inputs()) {
S
sneaxiy 已提交
1015 1016 1017 1018 1019
    // NOTE(zjl): STL does not guarantee fast std::unordered_set::count when set
    // is empty. At least STL implemented on my mac does calculate hash code
    // of search key even though the set is empty.
    if (!no_buffer_ins.empty() &&
        no_buffer_ins.count(var_name_item.first) > 0) {
S
sneaxiy 已提交
1020 1021
      VLOG(1) << "Skip scanning input " << var_name_item.first
              << " in Operator " << type_;
S
sneaxiy 已提交
1022 1023 1024
      continue;
    }

X
Xin Pan 已提交
1025 1026 1027 1028
    std::vector<Variable*>& input_vars = ctx->inputs[var_name_item.first];

    for (size_t i = 0; i < var_name_item.second.size(); ++i) {
      auto& var_name = var_name_item.second[i];
X
Xin Pan 已提交
1029
      auto* var = input_vars[i];
X
Xin Pan 已提交
1030

Y
yuyang18 已提交
1031
      // Only tensor can be tranfer to another device.
C
chengduo 已提交
1032
      if (var == nullptr || !VarIsTensor(*var)) {
Y
yuyang18 已提交
1033 1034 1035
        continue;
      }

C
chengduo 已提交
1036
      auto* tensor_in = GetLoDTensorOrSelectedRowsValueFromVar(*var);
Y
yuyang18 已提交
1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053
      if (!tensor_in->IsInitialized()) {
        continue;
      }

      auto kernel_type_for_var = GetKernelTypeForVar(
          var_name_item.first, *tensor_in, expected_kernel_key);

      if (!NeedTransform(kernel_type_for_var, expected_kernel_key)) {
        continue;
      }

      auto out_var_names = OutputVars(true);
      if (std::find(out_var_names.begin(), out_var_names.end(), var_name) !=
          out_var_names.end()) {
        transfered_inplace_vars->emplace_back(var_name);
      }

M
minqiyang 已提交
1054 1055
      VLOG(3) << "Transform Variable " << var_name << " from "
              << kernel_type_for_var << " to " << expected_kernel_key;
Y
yuyang18 已提交
1056

1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071
      // In the inference scenerio, the scopes will be reused across the
      // batches, so the `new_scope` here will result in GPU memroy explosion
      // over the  running of operators.
      // We use a thread_local cache to fix that issue, the key in the cache is
      // the combination of the `scope` argument, from_kernel_type,
      // target_kernel_type.
      // Have a discussion with @Superjomn or the inference developers if some
      // changes on this logic for this macro might not tested on the other
      // scenerios.
      // If this op is not called by an Executor or ParallelExecutor, it should
      // called by a NaiveExecutor, the NaiveExecutor will cache the scopes and
      // variables, that behavior a lot different.
      if (!run_by_executor_) {
        new_scope = TryCreateTransferScope(kernel_type_for_var,
                                           expected_kernel_key, &scope);
1072
      }
1073
      if (!new_scope) {
Y
yuyang18 已提交
1074 1075 1076 1077
        new_scope = &scope.NewScope();
      }

      auto* trans_var = new_scope->Var(var_name);
X
fix  
Xin Pan 已提交
1078
      input_vars[i] = trans_var;
1079

Y
yuyang18 已提交
1080
      Tensor out;
Y
yuyang18 已提交
1081
      TransformData(expected_kernel_key, kernel_type_for_var, *tensor_in, &out);
Y
yuyang18 已提交
1082 1083 1084 1085 1086 1087
      SetTensorToVariable(*var, out, trans_var);
    }
  }

  return new_scope;
}
Q
Qiao Longfei 已提交
1088

1089
proto::VarType::Type OperatorWithKernel::IndicateDataType(
Y
Yu Yang 已提交
1090
    const ExecutionContext& ctx) const {
1091 1092 1093
  proto::VarType::Type dafault_data_type =
      static_cast<proto::VarType::Type>(-1);
  proto::VarType::Type data_type = dafault_data_type;
Y
Yu Yang 已提交
1094
  for (auto& input : this->inputs_) {
X
Xin Pan 已提交
1095 1096 1097
    const std::vector<const Variable*> vars = ctx.MultiInputVar(input.first);
    for (size_t i = 0; i < vars.size(); ++i) {
      const Variable* var = vars[i];
Y
Yu Yang 已提交
1098 1099 1100 1101 1102 1103 1104 1105 1106 1107
      if (var != nullptr) {
        const Tensor* t = nullptr;
        if (var->IsType<Tensor>()) {
          t = &var->Get<Tensor>();
        } else if (var->IsType<LoDTensor>()) {
          t = &var->Get<LoDTensor>();
        } else if (var->IsType<SelectedRows>()) {
          t = &(var->Get<SelectedRows>().value());
        }
        if (t != nullptr) {
X
Xin Pan 已提交
1108 1109
          PADDLE_ENFORCE(t->IsInitialized(), "Input %s(%lu)is not initialized",
                         input.first, i);
1110
          proto::VarType::Type tmp = t->type();
1111
          PADDLE_ENFORCE(
1112
              tmp == data_type || data_type == dafault_data_type,
X
Xin Pan 已提交
1113
              "DataType of Paddle Op %s must be the same. Get (%d) != (%d)",
1114
              Type(), DataTypeToString(data_type), DataTypeToString(tmp));
Y
Yu Yang 已提交
1115 1116 1117 1118 1119
          data_type = tmp;
        }
      }
    }
  }
1120 1121 1122
  PADDLE_ENFORCE(data_type != dafault_data_type,
                 "DataType should be indicated by input");
  return data_type;
Y
Yu Yang 已提交
1123
}
1124

1125 1126 1127 1128 1129 1130 1131 1132
OpKernelType OperatorWithKernel::GetExpectedKernelType(
    const ExecutionContext& ctx) const {
  return OpKernelType(IndicateDataType(ctx), ctx.GetPlace());
}

OpKernelType OperatorWithKernel::GetKernelTypeForVar(
    const std::string& var_name, const Tensor& tensor,
    const OpKernelType& expected_kernel_type) const {
M
mozga-intel 已提交
1133 1134
  return OpKernelType(expected_kernel_type.data_type_, tensor.place(),
                      tensor.layout());
1135 1136
}

Q
Qiao Longfei 已提交
1137
}  // namespace framework
L
liaogang 已提交
1138
}  // namespace paddle