operator.cc 40.5 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_call_stack.h"
27
#include "paddle/fluid/framework/op_proto_maker.h"
28
#include "paddle/fluid/framework/operator.h"
Y
Yi Wang 已提交
29
#include "paddle/fluid/framework/shape_inference.h"
30
#include "paddle/fluid/framework/transfer_scope_cache.h"
Y
Yi Wang 已提交
31
#include "paddle/fluid/framework/var_type.h"
32
#include "paddle/fluid/platform/profiler.h"
Q
Qiao Longfei 已提交
33

D
dzhwinter 已提交
34
DECLARE_bool(benchmark);
35
DECLARE_bool(check_nan_inf);
Q
Qiao Longfei 已提交
36
DEFINE_int32(inner_op_parallelism, 0, "number of threads for inner op");
P
pkpk 已提交
37 38 39
DEFINE_bool(fast_check_nan_inf, false,
            "Fast checking NAN/INF after each operation. It will be a little"
            "bit slow, much faster than check_nan_inf");
D
dzhwinter 已提交
40

Q
Qiao Longfei 已提交
41 42 43
namespace paddle {
namespace framework {

44 45 46 47 48 49
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
50

Q
qiaolongfei 已提交
51 52
proto::VarType::Type GetDataTypeOfVar(const Variable* var) {
  if (var->IsType<framework::LoDTensor>()) {
Y
Yu Yang 已提交
53
    return var->Get<framework::LoDTensor>().type();
Q
qiaolongfei 已提交
54
  } else if (var->IsType<framework::SelectedRows>()) {
Y
Yu Yang 已提交
55
    return var->Get<framework::SelectedRows>().value().type();
Q
qiaolongfei 已提交
56 57 58 59 60
  } else {
    PADDLE_THROW("Var should be LoDTensor or SelectedRows");
  }
}

61 62
static DDim GetDimsDebug(const Scope& scope, const std::string& name,
                         bool get_actual_dim = false) {
63
  Variable* var = scope.FindVar(name);
Q
qiaolongfei 已提交
64 65
  if (var == nullptr) {
    return DDim({-1});
Q
Qiao Longfei 已提交
66 67
  }

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

Q
Qiao Longfei 已提交
82 83 84 85 86 87
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
88 89 90 91 92
static std::string GetDtype(const Scope& scope, const std::string& name) {
  Variable* var = scope.FindVar(name);
  if (var == nullptr) {
    return "";
  }
93

M
minqiyang 已提交
94 95 96
  if (var->IsType<LoDTensor>()) {
    const LoDTensor& tensor = var->Get<LoDTensor>();
    if (UNLIKELY(!tensor.IsInitialized())) {
97 98
      return "";
    }
Y
Yu Yang 已提交
99
    return DataTypeToString(tensor.type());
M
minqiyang 已提交
100
  } else if (var->IsType<SelectedRows>()) {
Q
Qiao Longfei 已提交
101 102 103 104
    auto tensor = var->Get<SelectedRows>().value();
    if (UNLIKELY(!tensor.IsInitialized())) {
      return "uninited";
    } else {
Y
Yu Yang 已提交
105
      return DataTypeToString(tensor.type());
Q
Qiao Longfei 已提交
106
    }
D
dzhwinter 已提交
107 108 109 110 111
  } else {
    return "";
  }
}

112 113 114 115 116 117
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
118 119
  if (var->IsType<SelectedRows>()) {
    return var->Get<SelectedRows>().rows().size();
120 121 122 123 124
  }

  return -1;
}

125
static LoD GetLoDDebug(const Scope& scope, const std::string& name) {
Q
Qiao Longfei 已提交
126 127 128 129 130 131 132
  Variable* var = scope.FindVar(name);
  auto default_lod = LoD({{}});

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

M
minqiyang 已提交
133 134
  if (var->IsType<LoDTensor>()) {
    const LoDTensor& tensor = var->Get<LoDTensor>();
135 136 137
    if (UNLIKELY(!tensor.IsInitialized())) {
      return default_lod;
    }
M
minqiyang 已提交
138
    return tensor.lod();
Q
Qiao Longfei 已提交
139 140 141 142 143
  } else {
    return default_lod;
  }
}

X
Xin Pan 已提交
144 145 146 147 148
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
149
    input_vars.reserve(var_name_item.second.size());
X
Xin Pan 已提交
150 151 152 153 154 155
    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
156
    output_vars.reserve(var_name_item.second.size());
X
Xin Pan 已提交
157 158 159 160 161 162
    for (auto& var_name : var_name_item.second) {
      output_vars.push_back(scope.FindVar(var_name));
    }
  }
}

163
void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
P
peizhilin 已提交
164 165 166
  try {
    VLOG(4) << place << " " << DebugStringEx(&scope);
    if (platform::is_gpu_place(place)) {
167
#ifndef PADDLE_WITH_CUDA
P
peizhilin 已提交
168
      PADDLE_THROW("Cannot run operator on place %s", place);
169
#else
P
peizhilin 已提交
170 171
      auto dev_id = boost::get<platform::CUDAPlace>(place).device;
      platform::SetDeviceId(dev_id);
172
#endif
P
peizhilin 已提交
173
    }
P
peizhilin 已提交
174

P
peizhilin 已提交
175 176 177 178 179
    // 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()) {
180
      platform::RecordEvent record_event(Type());
P
peizhilin 已提交
181 182 183 184 185 186
      RunImpl(scope, place);
    } else {
      RunImpl(scope, place);
    }
    VLOG(3) << place << " " << DebugStringEx(&scope);
  } catch (platform::EnforceNotMet exception) {
187
    framework::InsertCallStackInfo(Type(), Attrs(), &exception);
188
    throw std::move(exception);
P
peizhilin 已提交
189 190
  } catch (...) {
    std::rethrow_exception(std::current_exception());
191
  }
192 193
}

194
bool OperatorBase::HasInputs(const std::string& name) const {
M
minqiyang 已提交
195
  return inputs_.find(name) != inputs_.end();
196 197
}

198
std::string OperatorBase::Input(const std::string& name) const {
Y
Yu Yang 已提交
199
  auto& ins = Inputs(name);
Y
Yu Yang 已提交
200
  PADDLE_ENFORCE_LE(ins.size(), 1UL,
201 202
                    "Operator %s's input %s should contain only one variable.",
                    type_, name);
Y
Yu Yang 已提交
203
  return ins.empty() ? kEmptyVarName : ins[0];
Y
Yan Chunwei 已提交
204 205
}

Y
Yu Yang 已提交
206 207
const std::vector<std::string>& OperatorBase::Inputs(
    const std::string& name) const {
Y
Yu Yang 已提交
208
  auto it = inputs_.find(name);
209 210
  PADDLE_ENFORCE(it != inputs_.end(), "Operator %s does not have the input %s.",
                 type_, name);
Y
Yu Yang 已提交
211
  return it->second;
Y
Yan Chunwei 已提交
212 213
}

214
bool OperatorBase::HasOutputs(const std::string& name) const {
215
  if (outputs_.find(name) != outputs_.end()) {
216 217 218 219 220 221
    return true;
  } else {
    return false;
  }
}

222
std::string OperatorBase::Output(const std::string& name) const {
Y
Yu Yang 已提交
223
  auto& outs = Outputs(name);
Y
Yu Yang 已提交
224
  PADDLE_ENFORCE_LE(outs.size(), 1UL,
225 226
                    "Operator %s's output %s should contain only one variable.",
                    type_, name);
Y
Yu Yang 已提交
227
  return outs.empty() ? kEmptyVarName : outs[0];
Y
Yan Chunwei 已提交
228 229
}

Y
Yu Yang 已提交
230 231
const std::vector<std::string>& OperatorBase::Outputs(
    const std::string& name) const {
Y
Yu Yang 已提交
232
  auto it = outputs_.find(name);
233 234
  PADDLE_ENFORCE(it != outputs_.end(),
                 "Operator %s does not have an output called %s.", type_, name);
Y
Yu Yang 已提交
235
  return it->second;
Y
Yan Chunwei 已提交
236 237
}

238
std::string OperatorBase::DebugStringEx(const Scope* scope) const {
Q
Qiao Longfei 已提交
239
  std::stringstream ss;
Y
Yu Yang 已提交
240
  ss << "Op(" << type_ << "), inputs:{";
Y
Yu Yang 已提交
241 242
  for (auto it = inputs_.begin(); it != inputs_.end();) {
    auto& input = *it;
Y
Yu Yang 已提交
243 244
    ss << input.first << "[";
    for (size_t i = 0; i < input.second.size(); ++i) {
Q
Qiao Longfei 已提交
245 246
      auto var_name = input.second[i];
      ss << var_name;
247
      if (scope) {
Q
Qiao Longfei 已提交
248 249 250 251 252 253 254 255 256
        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;
257 258
          ss << "[" << GetDimsDebug(*scope, var_name, true) << "]";
          ss << "(" << GetLoDDebug(*scope, var_name) << ")";
259
        }
260
      }
Y
Yu Yang 已提交
261 262 263
      if (i != input.second.size() - 1) {
        ss << ", ";
      }
264
    }
Y
Yu Yang 已提交
265
    ss << "]";
Y
Yu Yang 已提交
266 267
    ++it;
    if (it != inputs_.end()) {
268 269
      ss << ", ";
    }
Q
Qiao Longfei 已提交
270
  }
Y
Yu Yang 已提交
271
  ss << "}, outputs:{";
Y
Yu Yang 已提交
272 273
  for (auto it = outputs_.begin(); it != outputs_.end();) {
    auto& output = *it;
Y
Yu Yang 已提交
274 275
    ss << output.first << "[";
    for (size_t i = 0; i < output.second.size(); ++i) {
Q
Qiao Longfei 已提交
276 277
      auto var_name = output.second[i];
      ss << var_name;
278
      if (scope) {
Q
Qiao Longfei 已提交
279 280 281 282 283 284 285
        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
286 287
          std::string dtype = GetDtype(*scope, output.second[i]);
          ss << ":" << dtype;
288 289
          ss << "[" << GetDimsDebug(*scope, var_name, true) << "]";
          ss << "(" << GetLoDDebug(*scope, var_name) << ")";
290
        }
291
      }
Y
Yu Yang 已提交
292 293 294
      if (i != output.second.size() - 1) {
        ss << ", ";
      }
295
    }
Y
Yu Yang 已提交
296
    ss << "]";
Y
Yu Yang 已提交
297 298
    ++it;
    if (it != outputs_.end()) {
299 300
      ss << ", ";
    }
Q
Qiao Longfei 已提交
301
  }
Y
Yu Yang 已提交
302
  ss << "}.";
Q
Qiao Longfei 已提交
303 304 305
  return ss.str();
}

Y
Yu Yang 已提交
306
OperatorBase::OperatorBase(const std::string& type,
Y
Yu Yang 已提交
307 308
                           const VariableNameMap& inputs,
                           const VariableNameMap& outputs,
Y
Yu Yang 已提交
309
                           const AttributeMap& attrs)
S
sneaxiy 已提交
310 311 312 313 314 315
    : type_(type),
      inputs_(inputs),
      outputs_(outputs),
      attrs_(attrs),
      // NOTE(zjl): why op_info may be nullptr?
      info_(OpInfoMap::Instance().GetNullable(type)) {
316 317
  GenerateTemporaryNames();
  CheckAllInputOutputSet();
Y
Yu Yang 已提交
318
}
319

Q
qijun 已提交
320 321
std::vector<std::string> OperatorBase::InputVars() const {
  std::vector<std::string> ret_val;
Y
Yu Yang 已提交
322
  for (auto& o : inputs_) {
Q
qijun 已提交
323 324 325 326 327 328
    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
329 330 331 332 333 334 335 336 337 338
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
339
  auto& info = Info();
Y
Yu Yang 已提交
340 341

  // get all OpProto::Var for outputs
Y
Yu Yang 已提交
342
  for (auto& o : info.Proto().outputs()) {
Y
Yu Yang 已提交
343 344 345 346 347 348 349 350 351
    // 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
352 353
}

354
void OperatorBase::CheckAllInputOutputSet() const {
S
sneaxiy 已提交
355
  if (info_ == nullptr || info_->proto_ == nullptr) return;
356

S
sneaxiy 已提交
357
  for (auto& in : info_->Proto().inputs()) {
358 359 360 361
    if (!in.dispensable()) {
      PADDLE_ENFORCE(inputs_.find(in.name()) != inputs_.end(),
                     "Operator %s's input, %s, is not set", Type(), in.name());
    }
362 363
  }

S
sneaxiy 已提交
364
  for (auto& out : info_->Proto().outputs()) {
365 366 367 368 369
    if (!out.dispensable()) {
      PADDLE_ENFORCE(outputs_.find(out.name()) != outputs_.end(),
                     "Operator %s's output, %s, is not set", Type(),
                     out.name());
    }
370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385
  }
}

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
386
static bool VarIsTensor(const Variable& var) {
C
chengduo 已提交
387
  return var.IsType<LoDTensor>() || var.IsType<SelectedRows>();
388 389
}

C
chengduo 已提交
390
const Tensor* GetLoDTensorOrSelectedRowsValueFromVar(const Variable& var) {
C
chengduo 已提交
391 392 393 394
  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
395
  } else {
Y
Yang Yang 已提交
396
    PADDLE_THROW("Variable type_id %s, expect LoDTensor/SelectedRows.",
S
sneaxiy 已提交
397
                 ToTypeName(var.Type()));
Q
QI JUN 已提交
398 399 400
  }
}

C
chengduo 已提交
401
Tensor* GetMutableLoDTensorOrSelectedRowsValueFromVar(Variable* var) {
Q
QI JUN 已提交
402
  if (var->IsType<LoDTensor>()) {
403
    return var->GetMutable<LoDTensor>();
Q
QI JUN 已提交
404
  } else if (var->IsType<SelectedRows>()) {
405
    return var->GetMutable<SelectedRows>()->mutable_value();
Q
QI JUN 已提交
406
  } else {
Y
Yang Yang 已提交
407
    PADDLE_THROW("Variable type_id %s, expect LoDTensor/SelectedRows.",
S
sneaxiy 已提交
408
                 ToTypeName(var->Type()));
Q
QI JUN 已提交
409 410 411
  }
}

412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443
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
444 445 446 447 448 449 450 451 452 453
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
454
Variable* ExecutionContext::OutputVar(const std::string& name) const {
X
Xin Pan 已提交
455 456 457 458 459 460 461 462 463
  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];
}

464
template <>
465
const Tensor* ExecutionContext::Input<Tensor>(const std::string& name) const {
C
chengduo 已提交
466
  return Input<LoDTensor>(name);
467 468 469
}

template <>
470
const std::vector<const Tensor*> ExecutionContext::MultiInput<Tensor>(
471
    const std::string& name) const {
X
Xin Pan 已提交
472 473 474 475 476 477 478 479 480 481 482 483 484
  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
485
                       ToTypeName(var->Type()));
X
Xin Pan 已提交
486 487 488 489 490
                   return &(var->Get<LoDTensor>());
                 });
  return res;
}

491
template <>
492
Tensor* ExecutionContext::Output<Tensor>(const std::string& name) const {
C
chengduo 已提交
493
  return Output<LoDTensor>(name);
494 495 496
}

template <>
497
std::vector<Tensor*> ExecutionContext::MultiOutput<Tensor>(
498
    const std::string& name) const {
499 500 501 502 503
  auto it = ctx_.outputs.find(name);
  if (it == ctx_.outputs.end()) {
    return {};
  }
  const std::vector<Variable*>& vars = it->second;
504
  std::vector<Tensor*> res;
505 506 507 508 509
  res.reserve(vars.size());
  std::transform(vars.begin(), vars.end(), std::back_inserter(res),
                 [&](Variable* var) -> Tensor* {
                   return var == nullptr ? nullptr
                                         : var->GetMutable<LoDTensor>();
510
                 });
511 512 513
  return res;
}

Y
Yu Yang 已提交
514 515 516 517 518 519 520 521 522 523 524 525 526 527 528
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;
}

529 530
class RuntimeInferShapeContext : public InferShapeContext {
 public:
X
Xin Pan 已提交
531 532
  RuntimeInferShapeContext(const OperatorBase& op, const Scope& scope,
                           const RuntimeContext& ctx)
G
Gabor Buella 已提交
533
      : op_(op), ctx_(ctx) {}
534 535

  bool HasInput(const std::string& name) const override {
536
    // has only one input
X
Xin Pan 已提交
537
    const auto& ins = ctx_.inputs;
538 539
    auto it = ins.find(name);
    if (it == ins.end()) {
540 541
      return false;
    }
542
    const auto& in = it->second;
X
Xin Pan 已提交
543
    if (in.size() == 0) return false;
T
tensor-tang 已提交
544
    PADDLE_ENFORCE_EQ(in.size(), 1UL,
F
fengjiayi 已提交
545
                      "Input %s should not have more than one inputs", name);
X
Xin Pan 已提交
546
    return in[0] != nullptr;
547 548 549
  }

  bool HasOutput(const std::string& name) const override {
550
    // has only one output
X
Xin Pan 已提交
551
    const auto& outs = ctx_.outputs;
552 553
    auto it = outs.find(name);
    if (it == outs.end()) {
554 555
      return false;
    }
556
    const auto& out = it->second;
X
Xin Pan 已提交
557
    if (out.size() == 0) {
558 559
      return false;
    }
T
tensor-tang 已提交
560 561
    PADDLE_ENFORCE_EQ(out.size(), 1UL,
                      "Output %s should not have more than one outputs", name);
X
Xin Pan 已提交
562
    return out[0] != nullptr;
563 564 565
  }

  bool HasInputs(const std::string& name) const override {
X
Xin Pan 已提交
566 567
    const auto& ins = ctx_.inputs;
    auto it = ins.find(name);
X
fix  
Xin Pan 已提交
568
    if (it == ins.end() || it->second.empty()) {
569 570
      return false;
    }
X
Xin Pan 已提交
571 572
    for (auto& input : it->second) {
      if (input == nullptr) {
573 574 575 576 577 578 579
        return false;
      }
    }
    return true;
  }

  bool HasOutputs(const std::string& name) const override {
X
Xin Pan 已提交
580 581
    const auto& outs = ctx_.outputs;
    auto it = outs.find(name);
X
fix  
Xin Pan 已提交
582
    if (it == outs.end() || it->second.empty()) {
583 584
      return false;
    }
X
Xin Pan 已提交
585 586
    for (auto& output : it->second) {
      if (output == nullptr) {
587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604
        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);
  }

605 606
  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
607 608 609 610 611 612 613 614 615
    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];
616 617

    PADDLE_ENFORCE(in_var->Type() == out_var->Type(),
X
fix  
Xin Pan 已提交
618
                   "The type of %s and %s is not the same.", in, out);
619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636

    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
637 638
  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
639 640 641 642 643 644 645 646
    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
647
    if (!in_var->IsType<LoDTensor>()) return;
X
Xin Pan 已提交
648
    Variable* out_var = out_it->second.at(j);
Q
Qiao Longfei 已提交
649 650 651 652 653
    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
654

M
mozga-intel 已提交
655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673
// 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
674 675
  }

C
chengduo 已提交
676 677 678 679 680
  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.");
  }

681 682
  bool IsRuntime() const override { return true; }

683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701
  // 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
702 703 704 705 706 707 708 709 710 711 712 713 714
  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
715 716 717 718 719 720 721 722 723 724
  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
725 726 727 728 729 730 731 732 733 734 735 736 737 738
  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);
  }

739
 protected:
X
Xin Pan 已提交
740
  DDim GetDim(Variable* var) const {
F
fengjiayi 已提交
741
    PADDLE_ENFORCE_NOT_NULL(var);
742 743 744 745 746
    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
747
      PADDLE_THROW(
X
Xin Pan 已提交
748
          "Only LoDTensor/SelectedRows support 'GetDim', but Variables "
F
fengjiayi 已提交
749
          "type_id is %s.",
S
sneaxiy 已提交
750
          ToTypeName(var->Type()));
F
fengjiayi 已提交
751 752 753
    }
  }

X
Xin Pan 已提交
754 755 756 757 758 759 760 761
  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
762
  std::vector<DDim> GetRepeatedDims(const std::string& name) const override {
Y
Yu Yang 已提交
763
    PADDLE_THROW("Only compile time support this method");
764 765
  }

X
Xin Pan 已提交
766
  void SetDim(Variable* var, const DDim& dim) {
767 768 769 770 771
    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
772
      PADDLE_THROW("Variable type_id %s, expect LoDTensor/SelectedRows.",
S
sneaxiy 已提交
773
                   ToTypeName(var->Type()));
X
Xin Pan 已提交
774 775 776 777 778 779 780 781 782 783 784 785
    }
  }

  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]);
786 787 788
    }
  }

F
fengjiayi 已提交
789 790
  void SetRepeatedDims(const std::string& name,
                       const std::vector<DDim>& dims) override {
Y
Yu Yang 已提交
791
    PADDLE_THROW("Only compile time support this method");
F
fengjiayi 已提交
792 793
  }

X
Xin Pan 已提交
794 795 796 797 798 799 800 801 802 803 804
  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 {
805 806 807
    return ToVarType(var->Type());
  }

808 809 810 811 812 813 814 815 816 817 818 819 820 821
 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
822 823
  }

824
  const OperatorBase& op_;
X
Xin Pan 已提交
825
  const RuntimeContext& ctx_;
826 827
};

828 829
static void CheckTensorNANOrInf(const std::string& op_type,
                                const std::string& name,
C
chengduoZH 已提交
830 831 832 833
                                const framework::Tensor& tensor) {
  if (tensor.memory_size() == 0) {
    return;
  }
Y
Yu Yang 已提交
834 835
  if (tensor.type() != proto::VarType::FP32 &&
      tensor.type() != proto::VarType::FP64) {
C
chengduoZH 已提交
836 837 838
    return;
  }
  PADDLE_ENFORCE(!framework::TensorContainsInf(tensor),
839
                 "Operator %s output Tensor %s contains Inf", op_type, name);
C
chengduoZH 已提交
840
  PADDLE_ENFORCE(!framework::TensorContainsNAN(tensor),
841
                 "Operator %s output Tensor %s contains NAN", op_type, name);
C
chengduoZH 已提交
842 843
}

B
baojun-nervana 已提交
844
void OperatorWithKernel::RuntimeInferShape(const Scope& scope,
X
Xin Pan 已提交
845 846 847
                                           const platform::Place& place,
                                           const RuntimeContext& ctx) const {
  RuntimeInferShapeContext infer_shape_ctx(*this, scope, ctx);
B
baojun-nervana 已提交
848 849 850
  this->InferShape(&infer_shape_ctx);
}

X
polish  
Xin Pan 已提交
851 852 853 854 855 856 857 858 859 860
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
861 862
void OperatorWithKernel::RunImpl(const Scope& scope,
                                 const platform::Place& place) const {
L
luotao1 已提交
863 864
  // To reduce the elapsed time of HasAttr, we use bool variable to record the
  // result of HasAttr.
865 866 867
  if (!enable_cache_runtime_context_ && HasAttr(kEnableCacheRuntimeContext))
    enable_cache_runtime_context_ = true;
  if (!all_kernels_must_compute_runtime_shape_ &&
L
luotao1 已提交
868
      HasAttr(kAllKernelsMustComputeRuntimeShape))
869 870
    all_kernels_must_compute_runtime_shape_ = true;
  if (!enable_cache_runtime_context_) {
L
luotao1 已提交
871 872 873 874
    RuntimeContext ctx(Inputs(), Outputs(), scope);
    RunImpl(scope, place, &ctx);
  } else {
    const Scope* cur_scope = &scope;
875 876 877 878 879 880
    if (runtime_ctx_.get() == nullptr || pre_scope_ != cur_scope) {
      std::lock_guard<std::mutex> lock(cache_update_mutex_);
      if (runtime_ctx_.get() == nullptr || pre_scope_ != cur_scope) {
        runtime_ctx_.reset(new RuntimeContext(Inputs(), Outputs(), scope));
        pre_scope_ = cur_scope;
      }
L
luotao1 已提交
881 882 883 884 885 886 887 888
    }
    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
889
  platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
890
  auto* dev_ctx = pool.Get(place);
891

892
  if (kernel_type_.get() == nullptr || kernel_func_.get() == nullptr) {
893
    ChooseKernel(*runtime_ctx, scope, place);
894 895
  }

L
Liu Yiqun 已提交
896
  std::vector<KernelConfig>* kernel_configs = GetKernelConfig(*kernel_type_);
897

Y
yuyang18 已提交
898 899
  // do data transformScope &transfer_scope;
  std::vector<std::string> transfered_inplace_vars;
T
Tao Luo 已提交
900
  auto* transfer_scope =
901
      PrepareData(scope, *kernel_type_, &transfered_inplace_vars, runtime_ctx);
902

Y
yuyang18 已提交
903 904 905 906
  // exec scope is the scope that kernel actually executed on.
  const Scope& exec_scope =
      (transfer_scope == nullptr ? scope : *transfer_scope);

907 908
  if (!(kernel_type_->place_ == dev_ctx->GetPlace())) {
    dev_ctx = pool.Get(kernel_type_->place_);
909
  }
Q
QI JUN 已提交
910

911
  if (!all_kernels_must_compute_runtime_shape_) {
L
luotao1 已提交
912
    RuntimeInferShapeContext infer_shape_ctx(*this, exec_scope, *runtime_ctx);
913 914
    this->InferShape(&infer_shape_ctx);
  }
X
clean  
Xin Pan 已提交
915 916
  // TODO(panyx0718): ExecutionContext should only depend on RuntimeContext
  // not Scope. Imperative mode only pass inputs and get outputs.
917 918
  (*kernel_func_)(ExecutionContext(*this, exec_scope, *dev_ctx, *runtime_ctx,
                                   kernel_configs));
D
dzhwinter 已提交
919

Y
yuyang18 已提交
920 921 922
  if (!transfered_inplace_vars.empty()) {
    // there is inplace variable has been transfered.
    TransferInplaceVarsBack(scope, transfered_inplace_vars, *transfer_scope);
923 924
  }

D
dzhwinter 已提交
925
  /*For profiling/benchmark only*/
D
dzhwinter 已提交
926
  if (FLAGS_benchmark) {
Y
yuyang18 已提交
927
    dev_ctx->Wait();
D
dzhwinter 已提交
928
  }
C
chengduoZH 已提交
929

P
pkpk 已提交
930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948
  if (FLAGS_fast_check_nan_inf) {
    for (auto& vname : OutputVars(true)) {
      // only check inserted vars,
      // please see executor.py for details of fast_check_nan_inf
      if (vname.rfind("debug_var") == 0) {
        VLOG(3) << "debugging nan/inf in var " << vname;

        auto* var = exec_scope.FindVar(vname);
        if (var == nullptr) continue;
        if (var->IsType<framework::LoDTensor>()) {
          CheckTensorNANOrInf(type_, vname, var->Get<framework::LoDTensor>());
        } else if (var->IsType<framework::SelectedRows>()) {
          CheckTensorNANOrInf(type_, vname,
                              var->Get<framework::SelectedRows>().value());
        }
      }
    }
  }

C
chengduoZH 已提交
949 950
  if (FLAGS_check_nan_inf) {
    for (auto& vname : OutputVars(true)) {
Y
yuyang18 已提交
951
      auto* var = exec_scope.FindVar(vname);
C
chengduoZH 已提交
952 953
      if (var == nullptr) continue;
      if (var->IsType<framework::LoDTensor>()) {
954
        CheckTensorNANOrInf(type_, vname, var->Get<framework::LoDTensor>());
955
      } else if (var->IsType<framework::SelectedRows>()) {
956 957
        CheckTensorNANOrInf(type_, vname,
                            var->Get<framework::SelectedRows>().value());
C
chengduoZH 已提交
958 959 960
      }
    }
  }
961 962 963 964 965 966 967

  // To solve issue #15032, have a discussion with @Luotao for cpu inference,
  // do not cache transfer scope, hence in this case delete transfer scope
  // after run to avoid memory leak
  if (transfer_scope && !run_by_executor_ && !enable_cache_transfer_scope_) {
    scope.DeleteScope(transfer_scope);
  }
Q
Qiao Longfei 已提交
968
}
X
Xin Pan 已提交
969

L
Liu Yiqun 已提交
970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005
void OperatorWithKernel::ChooseKernel(const RuntimeContext& ctx,
                                      const Scope& scope,
                                      const platform::Place& place) const {
  platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
  auto* dev_ctx = pool.Get(place);

  // 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()) {
    PADDLE_THROW(
        "There are no kernels which are registered in the %s operator.", type_);
  }

  OpKernelMap& kernels = kernels_iter->second;

  auto expected_kernel_key = this->GetExpectedKernelType(
      ExecutionContext(*this, scope, *dev_ctx, ctx, nullptr));
  VLOG(3) << "expected_kernel_key:" << expected_kernel_key;

  auto kernel_iter = kernels.find(expected_kernel_key);
#ifdef PADDLE_WITH_MKLDNN
  // workaround for missing MKLDNN kernel when FLAGS_use_mkldnn env var is set
  if (kernel_iter == kernels.end() &&
      expected_kernel_key.library_type_ == LibraryType::kMKLDNN) {
    VLOG(3) << "missing MKLDNN kernel: fallbacking to PLAIN one";
    expected_kernel_key.library_type_ = LibraryType::kPlain;
    expected_kernel_key.data_layout_ = DataLayout::kAnyLayout;
    kernel_iter = kernels.find(expected_kernel_key);
  }
#endif
  if (kernel_iter == kernels.end()) {
    PADDLE_THROW("op %s does not have kernel for %s", type_,
                 KernelTypeToString(expected_kernel_key));
  }

1006 1007 1008 1009 1010
  std::lock_guard<std::mutex> lock(cache_update_mutex_);
  if (kernel_type_.get() == nullptr || kernel_func_.get() == nullptr) {
    kernel_type_.reset(new OpKernelType(expected_kernel_key));
    kernel_func_.reset(new OpKernelFunc(kernel_iter->second));
  }
L
Liu Yiqun 已提交
1011 1012
}

Y
yuyang18 已提交
1013 1014 1015 1016
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
1017
    VLOG(3) << "share inplace var " + var_name + " back to it's original scope";
C
chengduo 已提交
1018 1019 1020
    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
1021
    auto* original_tensor =
C
chengduo 已提交
1022
        GetMutableLoDTensorOrSelectedRowsValueFromVar(origin_var);
C
chengduo 已提交
1023
    auto* var = transfer_scope.FindVar(var_name);
C
chengduo 已提交
1024 1025
    PADDLE_ENFORCE_NOT_NULL(var, "The var[%s] should not be nullptr.",
                            var_name);
C
chengduo 已提交
1026
    auto* transformed_tensor = GetLoDTensorOrSelectedRowsValueFromVar(*var);
Y
yuyang18 已提交
1027 1028 1029 1030
    original_tensor->ShareDataWith(*transformed_tensor);
  }
}

X
Xin Pan 已提交
1031
Scope* OperatorWithKernel::PrepareData(
Y
yuyang18 已提交
1032
    const Scope& scope, const OpKernelType& expected_kernel_key,
X
Xin Pan 已提交
1033 1034
    std::vector<std::string>* transfered_inplace_vars,
    RuntimeContext* ctx) const {
Y
yuyang18 已提交
1035
  Scope* new_scope = nullptr;
S
sneaxiy 已提交
1036 1037 1038 1039 1040 1041 1042 1043 1044 1045

  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
1046
  for (auto& var_name_item : Inputs()) {
S
sneaxiy 已提交
1047 1048 1049 1050 1051
    // 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) {
G
gongweibao 已提交
1052
      VLOG(7) << "Skip scanning input " << var_name_item.first
S
sneaxiy 已提交
1053
              << " in Operator " << type_;
S
sneaxiy 已提交
1054 1055 1056
      continue;
    }

X
Xin Pan 已提交
1057 1058 1059 1060
    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
1061
      auto* var = input_vars[i];
X
Xin Pan 已提交
1062

Y
yuyang18 已提交
1063
      // Only tensor can be tranfer to another device.
C
chengduo 已提交
1064
      if (var == nullptr || !VarIsTensor(*var)) {
Y
yuyang18 已提交
1065 1066 1067
        continue;
      }

C
chengduo 已提交
1068
      auto* tensor_in = GetLoDTensorOrSelectedRowsValueFromVar(*var);
Y
yuyang18 已提交
1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085
      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
1086 1087
      VLOG(3) << "Transform Variable " << var_name << " from "
              << kernel_type_for_var << " to " << expected_kernel_key;
Y
yuyang18 已提交
1088

1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100
      // 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.
1101 1102 1103 1104 1105 1106 1107 1108 1109
      //
      // To solve issue #15032, have a discussion with @Luotao for cpu
      // inference, for all cpu kernels cases without GPU participation, here
      // not do transfer scope caching, and cpu inference performance is not
      // impacted by test.
      enable_cache_transfer_scope_ = false;
      if (!run_by_executor_ &&
          (platform::is_gpu_place(kernel_type_for_var.place_) ||
           platform::is_gpu_place(expected_kernel_key.place_))) {
1110 1111
        new_scope = TryCreateTransferScope(kernel_type_for_var,
                                           expected_kernel_key, &scope);
1112
        enable_cache_transfer_scope_ = true;
1113
      }
1114
      if (!new_scope) {
Y
yuyang18 已提交
1115 1116
        new_scope = &scope.NewScope();
      }
1117 1118 1119 1120
      // For inference, if a gpu model has an op which could only run on CPU,
      // each result of different input will be the same with the first one.
      // The reason is that if a gpu tensor is the input of a cpu kernel,
      // we will create a new cpu tensor in new scope.
1121
      // However, if enable_cache_runtime_context_, we get the cpu tensor each
1122 1123 1124
      // time, not the gpu tensor.
      // Thus, we set pre_scope_ = nullptr to trigger `new RuntimeContext()` in
      // RunImpl().
1125
      if (enable_cache_runtime_context_) {
1126 1127
        pre_scope_ = nullptr;
      }
Y
yuyang18 已提交
1128 1129

      auto* trans_var = new_scope->Var(var_name);
X
fix  
Xin Pan 已提交
1130
      input_vars[i] = trans_var;
1131

Y
yuyang18 已提交
1132
      Tensor out;
Y
yuyang18 已提交
1133
      TransformData(expected_kernel_key, kernel_type_for_var, *tensor_in, &out);
Y
yuyang18 已提交
1134 1135 1136 1137 1138 1139
      SetTensorToVariable(*var, out, trans_var);
    }
  }

  return new_scope;
}
Q
Qiao Longfei 已提交
1140

1141
proto::VarType::Type OperatorWithKernel::IndicateDataType(
Y
Yu Yang 已提交
1142
    const ExecutionContext& ctx) const {
1143 1144 1145
  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
1146
  for (auto& input : this->inputs_) {
X
Xin Pan 已提交
1147 1148 1149
    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
1150 1151 1152 1153 1154 1155 1156 1157 1158 1159
      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) {
1160
          PADDLE_ENFORCE(t->IsInitialized(), "Input %s(%lu) is not initialized",
X
Xin Pan 已提交
1161
                         input.first, i);
1162
          proto::VarType::Type tmp = t->type();
1163
          PADDLE_ENFORCE(
1164
              tmp == data_type || data_type == dafault_data_type,
1165
              "DataType of Paddle Op %s %s must be the same. Get (%s) != (%s)",
1166 1167
              Type(), input.first, DataTypeToString(data_type),
              DataTypeToString(tmp));
Y
Yu Yang 已提交
1168 1169 1170 1171 1172
          data_type = tmp;
        }
      }
    }
  }
1173 1174 1175
  PADDLE_ENFORCE(data_type != dafault_data_type,
                 "DataType should be indicated by input");
  return data_type;
Y
Yu Yang 已提交
1176
}
1177

1178 1179 1180 1181 1182 1183 1184 1185
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
1186 1187
  return OpKernelType(expected_kernel_type.data_type_, tensor.place(),
                      tensor.layout());
1188 1189
}

Q
Qiao Longfei 已提交
1190
}  // namespace framework
L
liaogang 已提交
1191
}  // namespace paddle