multi_devices_graph_pass.cc 35.3 KB
Newer Older
Y
Yu Yang 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13
//   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
C
chengduoZH 已提交
14
#include <algorithm>
Y
Yancey1989 已提交
15
#include <fstream>
C
chengduoZH 已提交
16
#include <string>
C
chengduoZH 已提交
17
#include <utility>
C
chengduoZH 已提交
18 19
#include <vector>

20
#include "paddle/fluid/framework/details/all_reduce_op_handle.h"
C
chengduoZH 已提交
21
#include "paddle/fluid/framework/details/broadcast_op_handle.h"
Y
Yu Yang 已提交
22
#include "paddle/fluid/framework/details/computation_op_handle.h"
23
#include "paddle/fluid/framework/details/data_balance_op_handle.h"
24
#include "paddle/fluid/framework/details/fused_broadcast_op_handle.h"
X
Xin Pan 已提交
25
#include "paddle/fluid/framework/details/multi_devices_graph_pass.h"
C
chengduoZH 已提交
26
#include "paddle/fluid/framework/details/reduce_op_handle.h"
Y
Yancey1989 已提交
27
#include "paddle/fluid/framework/details/rpc_op_handle.h"
Y
Yu Yang 已提交
28
#include "paddle/fluid/framework/details/scale_loss_grad_op_handle.h"
X
better  
Xin Pan 已提交
29
#include "paddle/fluid/framework/ir/graph_helper.h"
X
Xin Pan 已提交
30
#include "paddle/fluid/framework/ir/node.h"
Y
Fix bug  
yuyang18 已提交
31
#include "paddle/fluid/framework/op_info.h"
Y
Yu Yang 已提交
32
#include "paddle/fluid/framework/scope.h"
Y
Yu Yang 已提交
33

Y
Yu Yang 已提交
34 35 36
namespace paddle {
namespace framework {
namespace details {
X
Xin Pan 已提交
37

X
Xin Pan 已提交
38
namespace {
X
Xin Pan 已提交
39
// TODO(panyx0718): Clean this up as well.
X
Xin Pan 已提交
40 41 42 43 44
// all operators. NOTE that even we use a vector here, the operators is
// unordered.
typedef std::vector<OpHandleBase *> GraphOps;
const char kGraphOps[] = "ops";

C
chengduo 已提交
45 46 47 48 49 50
bool OpHaveRole(const ir::Node &node, const framework::OpRole &role) {
  return boost::get<int>(
             node.Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleAttrName())) ==
         static_cast<int>(role);
}

X
Xin Pan 已提交
51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107
void PolishGraphToSupportDataHazards(ir::Graph *graph) {
  for (auto &var_map : graph->Get<GraphVars>(kGraphVars)) {
    for (auto &name_pair : var_map) {
      if (name_pair.second.size() <= 1) {
        continue;
      }
      auto it_new = name_pair.second.rbegin();
      auto it_old = name_pair.second.rbegin();
      ++it_old;
      for (; it_old != name_pair.second.rend(); it_new = it_old, ++it_old) {
        OpHandleBase *write_op = (*it_new)->GeneratedOp();
        const auto &read_ops = (*it_old)->PendingOps();

        for (auto *read_op : read_ops) {
          // Manually add a dependency var from read_op to write_op;
          if (read_op == write_op) {
            // Read Write is the same op.
            continue;
          }
          bool has_dep = false;
          for (auto *r_out : read_op->Outputs()) {
            for (auto *w_in : write_op->Inputs()) {
              if (r_out->Node() == w_in->Node()) {
                has_dep = true;
                break;
              }
            }
          }
          if (has_dep) continue;

          auto *dep_var = new DummyVarHandle(graph->CreateControlDepVar());
          read_op->AddOutput(dep_var);
          write_op->AddInput(dep_var);
          graph->Get<GraphDepVars>(kGraphDepVars).emplace(dep_var);
        }
      }
    }
  }
}

VarHandle *CreateOrGetLatestVarHandle(ir::Graph *graph, ir::Node *node,
                                      const platform::Place &place,
                                      size_t place_offset) {
  auto &var_holders = graph->Get<GraphVars>(kGraphVars)[place_offset];
  auto &var_holder = var_holders[node->Name()];
  VarHandle *var = nullptr;
  if (var_holder.empty()) {
    if (node->Var()) {
      var = new VarHandle(graph->CreateVarNode(node->Var()), 0, place_offset,
                          node->Name(), place);
    } else {
      var = new VarHandle(
          graph->CreateEmptyNode(node->Name(), ir::Node::Type::kVariable), 0,
          place_offset, node->Name(), place);
    }
    var_holder.emplace_back(var);
  } else {
X
clean1  
Xin Pan 已提交
108
    var = *var_holder.rbegin();
X
Xin Pan 已提交
109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135
  }
  return var;
}

void CreateOpOutput(ir::Graph *graph, OpHandleBase *op_handle,
                    ir::Node *new_node, const platform::Place &place,
                    size_t place_offset) {
  auto &vars =
      graph->Get<GraphVars>(kGraphVars)[place_offset][new_node->Name()];
  size_t version = vars.size();
  auto var =
      new VarHandle(new_node, version, place_offset, new_node->Name(), place);
  vars.emplace_back(var);
  op_handle->AddOutput(var);
}

void AddOutputToLeafOps(ir::Graph *graph) {
  for (auto &op : graph->Get<GraphOps>(kGraphOps)) {
    if (!op->Outputs().empty()) {
      continue;
    }
    auto *dummy_leaf = new DummyVarHandle(graph->CreateControlDepVar());
    graph->Get<GraphDepVars>(kGraphDepVars).emplace(dummy_leaf);
    op->AddOutput(dummy_leaf);
  }
}
}  // namespace
Y
Yu Yang 已提交
136

137
void MultiDevSSAGraphBuilderBase::Init() const {
X
clean  
Xin Pan 已提交
138 139
  all_vars_.clear();

X
Xin Pan 已提交
140 141 142 143
  loss_var_name_ = Get<const std::string>(kLossVarName);
  places_ = Get<const std::vector<platform::Place>>(kPlaces);
  local_scopes_ = Get<const std::vector<Scope *>>(kLocalScopes);
  strategy_ = Get<const BuildStrategy>(kStrategy);
P
peizhilin 已提交
144
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
X
Xin Pan 已提交
145
  nccl_ctxs_ = &Get<platform::NCCLContextMap>("nccl_ctxs");
Y
Yu Yang 已提交
146
#endif
Y
Yu Yang 已提交
147 148
}

149
std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilderBase::ApplyImpl(
X
Xin Pan 已提交
150
    std::unique_ptr<ir::Graph> graph) const {
X
Xin Pan 已提交
151
  Init();
152
  std::vector<ir::Node *> sorted_ops = SortOperations(*graph);
C
chengduo 已提交
153

X
Xin Pan 已提交
154 155
  auto nodes = graph->ReleaseNodes();
  ir::Graph &result = *graph;
156 157

  for (auto &node : nodes) {
X
Xin Pan 已提交
158
    if (node->IsVar() && node->Var()) {
X
Xin Pan 已提交
159
      all_vars_.emplace(node->Name(), node->Var());
160
    }
C
fix ci  
chengduoZH 已提交
161
  }
Y
Yu Yang 已提交
162 163

  // We cannot invoke resize. It is a bug of GCC 4.8
X
Xin Pan 已提交
164 165 166
  result.Set(kGraphVars, new GraphVars(places_.size()));
  result.Set(kGraphDepVars, new GraphDepVars);
  result.Set(kGraphOps, new GraphOps);
167

Y
Yu Yang 已提交
168
  bool is_forwarding = true;
169
  bool insert_collection_ops = NeedCollectiveOps();
X
Xin Pan 已提交
170

X
better  
Xin Pan 已提交
171
  for (ir::Node *node : sorted_ops) {
172 173
    if (DealWithSpecialOp(&result, node)) {
      continue;
Y
Yu Yang 已提交
174
    } else {
175 176 177 178 179 180 181 182 183
      // This op runs on all devices
      if (IsScaleLossOp(node)) {
        // user can customize loss@grad if not use_default_grad_scale_
        InsertScaleLossGradOp(&result, node);
        // This assumes the backward generating code will ensure IsScaleLossOp
        // is true only for the op that scale the final scalar loss.
        // It also assumes backward op will always follow the forward op in
        // the block.
        is_forwarding = false;
C
chengduo 已提交
184
      } else {
185 186
        CreateComputationalOps(&result, node, places_.size());
      }
187

188 189 190
      // Insert collection ops
      if (!is_forwarding && insert_collection_ops) {
        try {
C
chengduo 已提交
191 192 193 194 195
          bool is_bk_op =
              static_cast<bool>(boost::get<int>(node->Op()->GetAttr(
                                    OpProtoAndCheckerMaker::OpRoleAttrName())) &
                                static_cast<int>(OpRole::kBackward));
          if (!is_bk_op) continue;
196

C
chengduo 已提交
197 198
          // Currently, we assume that once gradient is generated, it can be
          // broadcast, and each gradient is only broadcast once.
199 200 201 202 203 204 205 206 207 208 209
          auto backward_vars =
              boost::get<std::vector<std::string>>(node->Op()->GetNullableAttr(
                  OpProtoAndCheckerMaker::OpRoleVarAttrName()));
          PADDLE_ENFORCE_EQ(backward_vars.size() % 2, 0);

          for (size_t i = 0; i < backward_vars.size(); i += 2) {
            auto &p_name = backward_vars[i];
            auto &g_name = backward_vars[i + 1];
            VLOG(10) << "Bcast " << g_name << " for parameter " << p_name;

            InsertCollectiveOp(&result, p_name, g_name);
Y
Yu Yang 已提交
210
          }
211
        } catch (boost::bad_get e) {
Y
Yu Yang 已提交
212 213 214 215
        }
      }
    }
  }
216

217 218
  InsertPostprocessOps(&result);

Y
Yu Yang 已提交
219
  /*
X
Xin Pan 已提交
220 221
  Dependency graph has been constructed. However, there are still data
  hazards need to be handled.
222
  */
Y
Yu Yang 已提交
223
  PolishGraphToSupportDataHazards(&result);
Y
Yu Yang 已提交
224

Y
Yu Yang 已提交
225 226 227 228
  /*
   * Only variables should be the leaves of graph.
   */
  AddOutputToLeafOps(&result);
F
flame 已提交
229
  result.Erase(kGraphOps);
Q
qiaolongfei 已提交
230
  return graph;
Y
Yu Yang 已提交
231 232
}

233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259
void MultiDevSSAGraphBuilderBase::InsertScaleLossGradOp(
    ir::Graph *result, const ir::Node *node) const {
  // user can customize loss@grad if not use_default_grad_scale_
  size_t loss_scale = 0;
  switch (this->strategy_.gradient_scale_) {
    case BuildStrategy::GradientScaleStrategy::kOne:
      loss_scale = 1;
      break;
    case BuildStrategy::GradientScaleStrategy::kCoeffNumDevice:
      loss_scale = Get<size_t>(kNRanks);
      break;
    case BuildStrategy::GradientScaleStrategy::kCustomized:
      loss_scale = 0;
      break;
    default:
      LOG(FATAL) << "Unknown gradient scale strategy.";
      break;
  }

  if (loss_scale) {
    // TODO(paddle-dev): Why is there no input for this op_handle?
    auto loss_grad_name = node->Op()->OutputArgumentNames()[0];
    auto out_dtype = this->all_vars_.at(loss_grad_name)->GetDataType();
    this->CreateScaleLossGradOp(result, loss_grad_name, node->outputs[0],
                                loss_scale, out_dtype);
  }
}
C
chengduo 已提交
260

261 262 263 264
std::vector<ir::Node *> MultiDevSSAGraphBuilderBase::SortOperations(
    const ir::Graph &graph) const {
  return ir::TopologySortOperations(graph);
}
C
chengduo 已提交
265

266 267 268 269 270 271 272
bool MultiDevSSAGraphBuilderBase::UseGPU() const {
  bool use_gpu = false;
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
  use_gpu = nccl_ctxs_ != nullptr;
#endif
  return use_gpu;
}
C
chengduo 已提交
273

274 275
bool MultiDevSSAGraphBuilderBase::NeedCollectiveOps() const {
  return Get<size_t>(kNRanks) > 1;
C
chengduo 已提交
276 277
}

278 279 280
void MultiDevSSAGraphBuilderBase::CreateOpHandleIOs(ir::Graph *result,
                                                    ir::Node *node,
                                                    size_t place_id) const {
C
chengduo 已提交
281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302
  auto p = places_[place_id];
  auto *op_handle = result->Get<GraphOps>(kGraphOps).back();
  op_handle->SetDeviceContext(p,
                              platform::DeviceContextPool::Instance().Get(p));

  for (ir::Node *input : node->inputs) {
    VarHandle *var = CreateOrGetLatestVarHandle(result, input, p, place_id);
    op_handle->AddInput(var);
  }

  for (ir::Node *output : node->outputs) {
    ir::Node *new_node = nullptr;
    if (output->Var()) {
      new_node = result->CreateVarNode(output->Var());
    } else {
      new_node =
          result->CreateEmptyNode(output->Name(), ir::Node::Type::kVariable);
    }
    CreateOpOutput(result, op_handle, new_node, p, place_id);
  }
}

303
void MultiDevSSAGraphBuilderBase::SetCommunicationContext(
304
    OpHandleBase *op_handle, const platform::Place &p) const {
P
peizhilin 已提交
305
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
306 307 308 309 310 311 312 313 314 315
  if (nccl_ctxs_ == nullptr) {
    op_handle->SetDeviceContext(p,
                                platform::DeviceContextPool::Instance().Get(p));
  }
#else
  op_handle->SetDeviceContext(p,
                              platform::DeviceContextPool::Instance().Get(p));
#endif
}

316 317 318
void MultiDevSSAGraphBuilderBase::CreateBroadcastOp(ir::Graph *result,
                                                    const std::string &p_name,
                                                    size_t src_dev_id) const {
P
peizhilin 已提交
319
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
X
polish  
Xin Pan 已提交
320 321 322
  auto *op_handle = new BroadcastOpHandle(
      result->CreateEmptyNode("broadcast", ir::Node::Type::kOperation),
      local_scopes_, places_, nccl_ctxs_);
C
chengduoZH 已提交
323
#else
X
polish  
Xin Pan 已提交
324 325 326
  auto *op_handle = new BroadcastOpHandle(
      result->CreateEmptyNode("broadcast", ir::Node::Type::kOperation),
      local_scopes_, places_);
C
chengduoZH 已提交
327
#endif
X
Xin Pan 已提交
328
  result->Get<GraphOps>(kGraphOps).emplace_back(op_handle);
X
Xin Pan 已提交
329

X
Xin Pan 已提交
330
  auto *in =
X
clean1  
Xin Pan 已提交
331
      result->Get<GraphVars>(kGraphVars).at(src_dev_id).at(p_name).back();
C
chengduoZH 已提交
332 333 334 335
  op_handle->AddInput(in);

  for (size_t i = 0; i < places_.size(); ++i) {
    auto &p = places_[i];
C
chengduoZH 已提交
336
    SetCommunicationContext(op_handle, p);
X
Xin Pan 已提交
337
    auto &vars = result->Get<GraphVars>(kGraphVars).at(i).at(p_name);
X
polish  
Xin Pan 已提交
338 339 340
    auto *out_var = new VarHandle(
        result->CreateEmptyNode(p_name, ir::Node::Type::kVariable), vars.size(),
        i, p_name, p);
C
chengduoZH 已提交
341 342 343 344 345
    vars.emplace_back(out_var);
    op_handle->AddOutput(out_var);
  }
}

346
void MultiDevSSAGraphBuilderBase::CreateFusedBroadcastOp(
347 348
    ir::Graph *result,
    const std::vector<std::unordered_set<std::string>> &bcast_varnames) const {
P
peizhilin 已提交
349
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367
  auto *op_handle = new FusedBroadcastOpHandle(
      result->CreateEmptyNode("fused_broadcast", ir::Node::Type::kOperation),
      local_scopes_, places_, nccl_ctxs_);
#else
  auto *op_handle = new FusedBroadcastOpHandle(
      result->CreateEmptyNode("fused_broadcast", ir::Node::Type::kOperation),
      local_scopes_, places_);
#endif
  result->Get<GraphOps>(kGraphOps).emplace_back(op_handle);

  for (size_t i = 0; i < places_.size(); ++i) {
    auto &p = places_[i];
    SetCommunicationContext(op_handle, p);
  }

  for (size_t dev_id = 0; dev_id < bcast_varnames.size(); ++dev_id) {
    for (auto &p_name : bcast_varnames[dev_id]) {
      auto *in =
X
clean1  
Xin Pan 已提交
368
          result->Get<GraphVars>(kGraphVars).at(dev_id).at(p_name).back();
369 370 371 372 373 374 375 376 377 378 379 380 381 382 383
      op_handle->AddInput(in);
      for (size_t out_dev_id = 0; out_dev_id < places_.size(); ++out_dev_id) {
        auto &p = places_[out_dev_id];
        auto &vars =
            result->Get<GraphVars>(kGraphVars).at(out_dev_id).at(p_name);
        auto *out_var = new VarHandle(
            result->CreateEmptyNode(p_name, ir::Node::Type::kVariable),
            vars.size(), out_dev_id, p_name, p);
        vars.emplace_back(out_var);
        op_handle->AddOutput(out_var);
      }
    }
  }
}

384 385 386
void MultiDevSSAGraphBuilderBase::CreateComputationalOp(ir::Graph *result,
                                                        ir::Node *node,
                                                        int dev_id) const {
X
Xin Pan 已提交
387
  result->Get<GraphOps>(kGraphOps).emplace_back(
X
Xin Pan 已提交
388
      new ComputationOpHandle(result->CreateOpNode(node->Op()),
S
sneaxiy 已提交
389
                              local_scopes_[dev_id], places_[dev_id], dev_id));
390
  CreateOpHandleIOs(result, node, dev_id);
C
chengduoZH 已提交
391 392
}

393 394
void MultiDevSSAGraphBuilderBase::CreateAllReduceOp(
    ir::Graph *result, const std::string &og) const {
P
peizhilin 已提交
395
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
X
Xin Pan 已提交
396
  result->Get<GraphOps>(kGraphOps).emplace_back(new AllReduceOpHandle(
X
polish  
Xin Pan 已提交
397 398
      result->CreateEmptyNode("allreduce", ir::Node::Type::kOperation),
      local_scopes_, places_, nccl_ctxs_));
C
chengduoZH 已提交
399
#else
X
Xin Pan 已提交
400
  result->Get<GraphOps>(kGraphOps).emplace_back(new AllReduceOpHandle(
X
polish  
Xin Pan 已提交
401 402
      result->CreateEmptyNode("allreduce", ir::Node::Type::kOperation),
      local_scopes_, places_));
C
chengduoZH 已提交
403
#endif
X
clean1  
Xin Pan 已提交
404
  auto *op_handle = result->Get<GraphOps>(kGraphOps).back();
Y
Yu Yang 已提交
405 406 407

  for (size_t i = 0; i < places_.size(); ++i) {
    auto &p = places_[i];
C
chengduoZH 已提交
408
    SetCommunicationContext(op_handle, p);
X
Xin Pan 已提交
409
    auto &vars = result->Get<GraphVars>(kGraphVars)[i][og];
Y
Yu Yang 已提交
410 411
    PADDLE_ENFORCE(!vars.empty());
    auto &prev_grad = vars.back();
X
clean1  
Xin Pan 已提交
412
    op_handle->AddInput(prev_grad);
Y
Yu Yang 已提交
413

X
Xin Pan 已提交
414
    auto var =
X
polish  
Xin Pan 已提交
415 416
        new VarHandle(result->CreateEmptyNode(og, ir::Node::Type::kVariable),
                      vars.size(), i, og, p);
Y
Yu Yang 已提交
417 418 419 420 421
    vars.emplace_back(var);
    op_handle->AddOutput(var);
  }
}

422
void MultiDevSSAGraphBuilderBase::CreateScaleLossGradOp(
423
    ir::Graph *result, const std::string &loss_grad_name,
424 425
    ir::Node *out_var_node, size_t loss_scale,
    proto::VarType::Type dtype) const {
Y
Yu Yang 已提交
426
  for (size_t i = 0; i < places_.size(); ++i) {
Y
yuyang18 已提交
427
    auto *dev_ctx = platform::DeviceContextPool::Instance().Get(places_[i]);
X
Xin Pan 已提交
428
    auto *op_handle = new ScaleLossGradOpHandle(
X
polish  
Xin Pan 已提交
429
        result->CreateEmptyNode("scale_loss_grad", ir::Node::Type::kOperation),
430
        loss_scale, local_scopes_[i], places_[i], dev_ctx, dtype);
X
Xin Pan 已提交
431
    result->Get<GraphOps>(kGraphOps).emplace_back(op_handle);
Y
Yu Yang 已提交
432 433 434 435 436 437 438

    // FIXME: Currently ScaleLossGradOp only use device_count as scale
    // factor. So it does not depend on any other operators.
    // VarHandle *loss = GetVarHandle(loss_var_name, place);
    // loss->pending_ops_.emplace_back(op_handle);
    // op_handle->inputs_.emplace_back(loss);

439 440
    CreateOpOutput(result, op_handle,
                   result->CreateVarNode(out_var_node->Var()), places_[i], i);
Y
Yu Yang 已提交
441 442 443
  }
}

444 445
void MultiDevSSAGraphBuilderBase::CreateComputationalOps(
    ir::Graph *result, ir::Node *node, size_t num_places) const {
T
typhoonzero 已提交
446
  for (size_t scope_idx = 0; scope_idx < num_places; ++scope_idx) {
Y
Yu Yang 已提交
447 448
    auto p = places_[scope_idx];
    auto s = local_scopes_[scope_idx];
S
sneaxiy 已提交
449 450
    result->Get<GraphOps>(kGraphOps).emplace_back(new ComputationOpHandle(
        result->CreateOpNode(node->Op()), s, p, scope_idx));
451
    CreateOpHandleIOs(result, node, scope_idx);
Y
Yu Yang 已提交
452 453 454
  }
}

455 456 457
VarHandle *MultiDevSSAGraphBuilderBase::CreateReduceOp(ir::Graph *result,
                                                       const std::string &og,
                                                       int dst_dev_id) const {
P
peizhilin 已提交
458
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
X
Xin Pan 已提交
459
  result->Get<GraphOps>(kGraphOps).emplace_back(new ReduceOpHandle(
X
polish  
Xin Pan 已提交
460 461
      result->CreateEmptyNode("reduce", ir::Node::Type::kOperation),
      local_scopes_, places_, nccl_ctxs_));
C
chengduoZH 已提交
462
#else
X
Xin Pan 已提交
463
  result->Get<GraphOps>(kGraphOps).emplace_back(new ReduceOpHandle(
X
polish  
Xin Pan 已提交
464 465
      result->CreateEmptyNode("reduce", ir::Node::Type::kOperation),
      local_scopes_, places_));
C
chengduoZH 已提交
466
#endif
X
clean1  
Xin Pan 已提交
467
  auto *op_handle = result->Get<GraphOps>(kGraphOps).back();
C
chengduoZH 已提交
468 469 470

  for (size_t i = 0; i < places_.size(); ++i) {
    auto &p = places_[i];
C
chengduoZH 已提交
471
    SetCommunicationContext(op_handle, p);
X
Xin Pan 已提交
472
    auto &vars = result->Get<GraphVars>(kGraphVars)[i][og];
C
chengduoZH 已提交
473 474
    PADDLE_ENFORCE(!vars.empty());
    auto &prev_grad = vars.back();
X
clean1  
Xin Pan 已提交
475
    op_handle->AddInput(prev_grad);
C
chengduoZH 已提交
476
  }
X
Xin Pan 已提交
477
  auto &vars = result->Get<GraphVars>(kGraphVars)[dst_dev_id][og];
X
polish  
Xin Pan 已提交
478 479 480
  auto var =
      new VarHandle(result->CreateEmptyNode(og, ir::Node::Type::kVariable),
                    vars.size(), dst_dev_id, og, places_[dst_dev_id]);
C
chengduoZH 已提交
481 482 483 484 485
  vars.emplace_back(var);
  op_handle->AddOutput(var);
  return var;
}

486 487 488 489 490 491 492 493 494 495 496 497 498
bool MultiDevSSAGraphBuilderBase::IsScaleLossOp(ir::Node *node) const {
  return boost::get<int>(
             node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleAttrName())) ==
             (static_cast<int>(OpRole::kBackward) |
              static_cast<int>(OpRole::kLoss)) &&
         !loss_var_name_.empty();  // If loss_var is empty. This is test mode
}

bool MultiDevSSAGraphBuilderBase::IsSparseGradient(
    const std::string &og) const {
  PADDLE_ENFORCE(all_vars_.count(og) != 0);
  if (all_vars_.at(og)->GetType() == proto::VarType::SELECTED_ROWS) {
    return true;
499
  }
500 501 502 503 504 505 506 507 508 509 510
  return false;
}

void AllReduceSSAGraphBuilder::InsertCollectiveOp(
    ir::Graph *result, const std::string &p_name,
    const std::string &g_name) const {
  if (IsSparseGradient(g_name)) {
    CreateReduceOp(result, g_name, 0);
    CreateBroadcastOp(result, g_name, 0);
  } else {
    CreateAllReduceOp(result, g_name);
511
  }
512
}
513

514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614
int BalanceVarSSAGraphBuilder::GetVarDeviceID(
    const std::string &varname) const {
  auto got = sharded_var_device_.find(varname);
  if (got == sharded_var_device_.end()) {
    auto pos = varname.find(framework::kNewGradSuffix);
    if (pos != std::string::npos) {
      got = sharded_var_device_.find(varname.substr(0, pos));
    }
  }
  return got == sharded_var_device_.end() ? -1 : got->second;
}

int BalanceVarSSAGraphBuilder::GetOpDeviceID(ir::Node *node) const {
  if (strategy_.reduce_ != BuildStrategy::ReduceStrategy::kReduce) {
    return -1;
  }
  if (!OpHaveRole(*node, framework::OpRole::kOptimize)) {
    return -1;
  }
  auto param_grad = boost::get<std::vector<std::string>>(
      node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleVarAttrName()));

  PADDLE_ENFORCE_EQ(param_grad.size(), 2U);
  int dev_id = GetVarDeviceID(param_grad[1]);
  PADDLE_ENFORCE_NE(dev_id, -1, "dev_id should not be -1.[%s, %s, %s]",
                    node->Op()->Type(), param_grad[0], param_grad[1]);
  return dev_id;
}

size_t BalanceVarSSAGraphBuilder::GetAppropriateDeviceID(
    const std::vector<std::string> &var_names) const {
  int64_t numel_sum = 0;
  for (auto var_name : var_names) {
    if (all_vars_.find(var_name) == all_vars_.end()) continue;
    auto var_desc = all_vars_.at(var_name);
    PADDLE_ENFORCE_NOT_NULL(var_desc);
    auto dim = framework::make_ddim(var_desc->GetShape());
    int64_t numel = framework::product(dim);
    PADDLE_ENFORCE_GT(numel, 0);
    numel_sum += numel;
  }

  auto smallest =
      std::min_element(std::begin(balance_vars_), std::end(balance_vars_));
  size_t dev_id =
      static_cast<size_t>(std::distance(std::begin(balance_vars_), smallest));
  balance_vars_[dev_id] += numel_sum;
  return dev_id;
}

void BalanceVarSSAGraphBuilder::ResetState() const {
  balance_vars_.clear();
  sharded_var_device_.clear();

  balance_vars_.resize(places_.size(), 0);
}

void ReduceSSAGraphBuilder::Init() const {
  MultiDevSSAGraphBuilderBase::Init();
  ResetState();
}

void ReduceSSAGraphBuilder::ResetState() const {
  BalanceVarSSAGraphBuilder::ResetState();
  bcast_var_name_set_.clear();
  bcast_var_name_set_.resize(places_.size());
}

void ReduceSSAGraphBuilder::InsertCollectiveOp(
    ir::Graph *result, const std::string &p_name,
    const std::string &g_name) const {
  size_t cur_device_id = GetAppropriateDeviceID({g_name});
  CreateReduceOp(result, g_name, cur_device_id);
  sharded_var_device_.emplace(g_name, cur_device_id);
  bcast_var_name_set_[cur_device_id].emplace(p_name);
}

bool ReduceSSAGraphBuilder::DealWithSpecialOp(ir::Graph *result,
                                              ir::Node *node) const {
  int op_dev_id = BalanceVarSSAGraphBuilder::GetOpDeviceID(node);
  if (op_dev_id != -1) {
    // This op only runs on one specific device.
    CreateComputationalOp(result, node, op_dev_id);
    for (ir::Node *n : node->outputs) {
      sharded_var_device_.emplace(n->Name(), op_dev_id);
    }
    return true;
  }
  return false;
}

void ReduceSSAGraphBuilder::InsertPostprocessOps(ir::Graph *result) const {
  if (UseGPU()) {
    if (strategy_.fuse_broadcast_op_) {
      CreateFusedBroadcastOp(result, bcast_var_name_set_);
    } else {
      for (size_t dev_id = 0; dev_id < bcast_var_name_set_.size(); ++dev_id) {
        auto &to_bcast_set = bcast_var_name_set_[dev_id];
        for (auto &bcast_name : to_bcast_set) {
          CreateBroadcastOp(result, bcast_name, dev_id);
        }
Y
Yancey1989 已提交
615 616
      }
    }
617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658
  }
}

int ReduceSSAGraphBuilder::GetOpDeviceID(
    ir::Node *node,
    std::unordered_map<std::string, std::vector<ir::Node *>> *delay_ops) const {
  if (!OpHaveRole(*node, framework::OpRole::kOptimize)) {
    return -1;
  }

  auto param_grad = boost::get<std::vector<std::string>>(
      node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleVarAttrName()));

  PADDLE_ENFORCE_EQ(param_grad.size(), 2U);
  int dev_id = GetVarDeviceID(param_grad[1]);

  if (dev_id == -1) {
    (*delay_ops)[param_grad[1]].push_back(node);
    return -2;
  }
  return dev_id;
}

std::vector<ir::Node *> ReduceSSAGraphBuilder::SortOperations(
    const ir::Graph &graph) const {
  std::vector<ir::Node *> sorted_ops = ir::TopologySortOperations(graph);
  return SortForReduceMode(sorted_ops);
}

std::vector<ir::Node *> ReduceSSAGraphBuilder::SortForReduceMode(
    const std::vector<ir::Node *> &topo_ops) const {
  std::vector<ir::Node *> sorted_ops;
  std::unordered_map<std::string, std::vector<ir::Node *>> delayed_op;
  sorted_ops.reserve(topo_ops.size());
  ResetState();

  auto insert_delayed_op = [&](const std::string &var_name, int dev_id) {
    sharded_var_device_.emplace(var_name, dev_id);
    if (delayed_op.count(var_name)) {
      auto &ops = delayed_op.at(var_name);
      sorted_ops.insert(sorted_ops.end(), ops.begin(), ops.end());
      delayed_op.at(var_name).clear();
Y
Yancey1989 已提交
659
    }
660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696
  };

  for (ir::Node *node : topo_ops) {
    int op_dev_id = GetOpDeviceID(node, &delayed_op);
    if (op_dev_id > -1) {
      // This op only runs on one specific device.
      sorted_ops.emplace_back(node);
      for (ir::Node *n : node->outputs) {
        insert_delayed_op(n->Name(), op_dev_id);
      }
    } else if (op_dev_id == -1) {
      // This op runs on all devices, and its output may have parameter's
      // gradients.
      sorted_ops.emplace_back(node);
      bool is_bk_op =
          static_cast<bool>(boost::get<int>(node->Op()->GetAttr(
                                OpProtoAndCheckerMaker::OpRoleAttrName())) &
                            static_cast<int>(OpRole::kBackward));
      if (!is_bk_op) continue;
      // Currently, we assume that once gradient is generated, it can be
      // broadcast, and each gradient is only broadcast once.
      std::vector<std::string> backward_vars;
      try {
        backward_vars =
            boost::get<std::vector<std::string>>(node->Op()->GetNullableAttr(
                OpProtoAndCheckerMaker::OpRoleVarAttrName()));
      } catch (boost::bad_get e) {
      }
      PADDLE_ENFORCE_EQ(backward_vars.size() % 2, 0);

      for (size_t i = 0; i < backward_vars.size(); i += 2) {
        auto &g_name = backward_vars[i + 1];
        size_t cur_device_id = GetAppropriateDeviceID({g_name});
        insert_delayed_op(g_name, static_cast<int>(cur_device_id));
      }
    } else if (op_dev_id == -2) {
      // The Op on which the Op depends has not yet been generated.
Y
yi.wu 已提交
697
    }
Y
Yancey1989 已提交
698 699
  }

700
  PADDLE_ENFORCE_EQ(sorted_ops.size(), topo_ops.size());
Y
Yancey1989 已提交
701

702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752
  ResetState();
  return sorted_ops;
}

void DistSSAGraphBuilder::Init() const {
  MultiDevSSAGraphBuilderBase::Init();
  ResetState();
}

void DistSSAGraphBuilder::ResetState() const {
  BalanceVarSSAGraphBuilder::ResetState();
  bcast_var_name_set_.clear();
  bcast_var_name_set_.resize(places_.size());
}

bool DistSSAGraphBuilder::DealWithSpecialOp(ir::Graph *result,
                                            ir::Node *node) const {
  bool insert_op = false;
  if (OpHaveRole(*node, OpRole::kRPC)) {
    int op_dev_id = CreateRPCOp(result, node);
    PADDLE_ENFORCE(op_dev_id != -1,
                   "Can not schedule the RPC operator to the right place.");
    if (node->Op()->Type() == "recv") {
      auto recv_vars_attr =
          boost::get<std::vector<std::string>>(node->Op()->GetNullableAttr(
              OpProtoAndCheckerMaker::OpRoleVarAttrName()));
      PADDLE_ENFORCE(recv_vars_attr.size() == 2UL);  // [parameter, gradient]
      if (recv_vars_attr[0].find(".block") == std::string::npos) {
        bcast_var_name_set_[op_dev_id].emplace(recv_vars_attr[0]);
      }
    }
    insert_op = true;
    need_broadcast_var_ = true;
  } else if (OpHaveRole(*node, OpRole::kDist)) {
    int op_dev_id = CreateDistTrainOp(result, node);
    if (node->Op()->Type() == "concat") {
      auto origin_param_name = node->Op()->OutputArgumentNames()[0];
      bcast_var_name_set_[op_dev_id].emplace(origin_param_name);
    }
    insert_op = true;
  } else {
    int op_dev_id = GetOpDeviceID(node);
    if (op_dev_id != -1) {  // This op only runs on one specific device.
      CreateComputationalOp(result, node, op_dev_id);
      for (ir::Node *n : node->outputs) {
        sharded_var_device_.emplace(n->Name(), op_dev_id);
      }
      insert_op = true;
    }
  }
  return insert_op;
W
Wu Yi 已提交
753 754 755
}

void SetOpInputsAllPlaces(ir::Graph *result, ir::Node *node, int num_places) {
X
clean1  
Xin Pan 已提交
756
  auto *op_handle = result->Get<GraphOps>(kGraphOps).back();
W
Wu Yi 已提交
757 758 759 760 761 762
  for (ir::Node *input : node->inputs) {
    VarHandle *var = nullptr;
    for (int place_offset = 0; place_offset < num_places; ++place_offset) {
      auto &var_holders = result->Get<GraphVars>(kGraphVars)[place_offset];
      auto &var_holder = var_holders[input->Name()];
      if (!var_holder.empty()) {
X
clean1  
Xin Pan 已提交
763
        var = *var_holder.rbegin();
W
Wu Yi 已提交
764 765 766
        op_handle->AddInput(var);
      }
    }
Y
Yancey1989 已提交
767 768 769
  }
}

770
// Create RPC related op handles that connects its in ops and out ops.
771
int DistSSAGraphBuilder::CreateRPCOp(ir::Graph *result, ir::Node *node) const {
Y
Yancey1989 已提交
772
  int op_dev_id = -1;
773
  if (node->Op()->Type() == "send") {
X
Xin Pan 已提交
774
    // TODO(paddle-dev): getting the first var is not safe.
775
    op_dev_id = GetVarDeviceID(node->inputs[0]->Name());
X
Xin Pan 已提交
776 777
    PADDLE_ENFORCE(!ir::IsControlDepVar(*node->inputs[0]),
                   "This hack no longer holds, please fix.");
Y
Yancey1989 已提交
778 779 780
    // the variable name which contains .block means it was splited by
    // split_byref op
    if (strategy_.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce &&
X
Xin Pan 已提交
781
        node->inputs[0]->Name().find(".block") == std::string::npos) {
782 783
      std::vector<std::string> input_var_names;
      for (ir::Node *n : node->inputs) {
X
Xin Pan 已提交
784
        input_var_names.push_back(n->Name());
785
      }
W
Wu Yi 已提交
786 787 788 789
      auto send_param_grad = boost::get<std::vector<std::string>>(
          node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleVarAttrName()));
      PADDLE_ENFORCE_EQ(send_param_grad.size(), 2U);
      op_dev_id = GetAppropriateDeviceID({send_param_grad[1]});
M
minqiyang 已提交
790 791
      VLOG(10) << "send grad " << input_var_names[0] << " origin "
               << send_param_grad[1] << " place: " << op_dev_id;
792
      for (auto &varname : input_var_names) {
793
        sharded_var_device_.emplace(varname, op_dev_id);
Y
Yancey1989 已提交
794
      }
795
      sharded_var_device_.emplace(send_param_grad[1], op_dev_id);
Y
Yancey1989 已提交
796
    }
797 798 799
  } else if (node->Op()->Type() == "recv") {
    std::vector<std::string> output_var_names;
    for (ir::Node *n : node->outputs) {
X
Xin Pan 已提交
800
      output_var_names.push_back(n->Name());
801
    }
W
Wu Yi 已提交
802 803 804
    auto recv_param_grad = boost::get<std::vector<std::string>>(
        node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleVarAttrName()));
    if (recv_param_grad.size() == 2U) {
805
      op_dev_id = GetVarDeviceID(recv_param_grad[1]);
M
minqiyang 已提交
806 807 808
      VLOG(10) << "recv param " << recv_param_grad[0]
               << " get grad place: " << recv_param_grad[1]
               << " place: " << op_dev_id;
W
Wu Yi 已提交
809 810 811
    } else {
      op_dev_id = GetAppropriateDeviceID(output_var_names);
    }
812
    for (auto &varname : output_var_names) {
813
      sharded_var_device_.emplace(varname, op_dev_id);
Y
Yancey1989 已提交
814 815
    }
  } else {
W
Wu Yi 已提交
816
    // send_barrier, fetch_barrier will run on place 0;
Y
Yancey1989 已提交
817 818 819 820
    op_dev_id = 0;
  }

  PADDLE_ENFORCE(op_dev_id != -1, "can not find the right place for rpc op: %s",
821
                 node->Op()->Type());
X
Xin Pan 已提交
822
  result->Get<GraphOps>(kGraphOps).emplace_back(new RPCOpHandle(
823 824
      result->CreateOpNode(node->Op()), *node->Op(), local_scopes_[op_dev_id],
      node->Op()->Type(), places_[op_dev_id]));
Y
fix pe  
Yancey1989 已提交
825

W
Wu Yi 已提交
826 827
  if (node->Op()->Type() == "send") {
    CreateOpHandleIOs(result, node, op_dev_id);
Y
Yancey1989 已提交
828
  } else {
W
Wu Yi 已提交
829 830 831
    // send_barrier, recv, fetch_barrier's inputs are deps var, get them from
    // all places
    auto p = places_[op_dev_id];
X
clean1  
Xin Pan 已提交
832
    auto *op_handle = result->Get<GraphOps>(kGraphOps).back();
W
Wu Yi 已提交
833 834
    op_handle->SetDeviceContext(p,
                                platform::DeviceContextPool::Instance().Get(p));
Y
Yancey1989 已提交
835

W
Wu Yi 已提交
836 837 838 839
    SetOpInputsAllPlaces(result, node, places_.size());
    for (ir::Node *output : node->outputs) {
      int outvar_dev_id = op_dev_id;
      if (node->Op()->Type() == "fetch_barrier") {
840
        outvar_dev_id = GetVarDeviceID(output->Name());
Q
Qiao Longfei 已提交
841
        PADDLE_ENFORCE_NE(outvar_dev_id, -1, "output name %s", output->Name());
W
Wu Yi 已提交
842 843 844 845 846 847 848 849 850 851 852 853
      }
      p = places_[outvar_dev_id];
      ir::Node *new_node = nullptr;
      if (output->Var()) {
        new_node = result->CreateVarNode(output->Var());
      } else {
        new_node =
            result->CreateEmptyNode(output->Name(), ir::Node::Type::kVariable);
      }
      CreateOpOutput(result, op_handle, new_node, p, outvar_dev_id);
    }
  }
Y
Yancey1989 已提交
854
  return op_dev_id;
Y
Yu Yang 已提交
855 856
}

857 858 859 860 861 862 863
int DistSSAGraphBuilder::CreateDistTrainOp(ir::Graph *result,
                                           ir::Node *node) const {
  int op_dev_id = -1;
  std::vector<std::string> input_var_names;
  std::vector<std::string> output_var_names;
  for (ir::Node *input : node->inputs) {
    input_var_names.push_back(input->Name());
C
chengduo 已提交
864
  }
865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900
  for (ir::Node *output : node->outputs) {
    output_var_names.push_back(output->Name());
  }

  if (node->Op()->Type() == "split_byref" ||
      node->Op()->Type() == "split_selected_rows" ||
      node->Op()->Type() == "split_ids") {
    // TODO(paddle-dev): getting the first var is not safe.
    op_dev_id = GetVarDeviceID(input_var_names[0]);
    if (strategy_.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce) {
      op_dev_id = GetAppropriateDeviceID(input_var_names);
      for (auto &varname : input_var_names) {
        sharded_var_device_.emplace(varname, op_dev_id);
      }
    }
    for (auto &varname : output_var_names) {
      sharded_var_device_.emplace(varname, op_dev_id);
    }
  } else if (node->Op()->Type() == "concat") {
    op_dev_id = GetVarDeviceID(input_var_names[0]);
    for (auto &varname : output_var_names) {
      sharded_var_device_.emplace(varname, op_dev_id);
    }
  } else {
    LOG(ERROR) << "got unexpected dist op: " << node->Op()->Type();
    PADDLE_THROW(
        "the distribute training related op should be in [split_byref, "
        "concat].");
  }

  PADDLE_ENFORCE(op_dev_id != -1,
                 "can not find right place for distributed op: %s",
                 node->Op()->Type());

  CreateComputationalOp(result, node, op_dev_id);
  return op_dev_id;
C
chengduo 已提交
901 902
}

903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946
void DistSSAGraphBuilder::InsertCollectiveOp(ir::Graph *result,
                                             const std::string &p_name,
                                             const std::string &g_name) const {
  size_t cur_device_id = 0;
  switch (strategy_.reduce_) {
    case BuildStrategy::ReduceStrategy::kReduce:
      cur_device_id = GetAppropriateDeviceID({g_name});
      CreateReduceOp(result, g_name, cur_device_id);
      sharded_var_device_.emplace(g_name, cur_device_id);
      break;
    case BuildStrategy::ReduceStrategy::kAllReduce:
      if (IsSparseGradient(g_name)) {
        CreateReduceOp(result, g_name, 0);
        CreateBroadcastOp(result, g_name, 0);
      } else {
        CreateAllReduceOp(result, g_name);
      }
      break;
    default:
      LOG(FATAL) << "Unknown reduce strategy.";
      break;
  }
}

void DistSSAGraphBuilder::InsertPostprocessOps(ir::Graph *result) const {
  if (need_broadcast_var_ ||
      (UseGPU() &&
       strategy_.reduce_ == BuildStrategy::ReduceStrategy::kReduce)) {
    if (strategy_.fuse_broadcast_op_) {
      CreateFusedBroadcastOp(result, bcast_var_name_set_);
    } else {
      for (size_t dev_id = 0; dev_id < bcast_var_name_set_.size(); ++dev_id) {
        auto &to_bcast_set = bcast_var_name_set_[dev_id];
        for (auto &bcast_name : to_bcast_set) {
          CreateBroadcastOp(result, bcast_name, dev_id);
        }
      }
    }
  }
}

std::unordered_set<std::string> &MultiDevSSAGraphBuilder() {
  static std::unordered_set<std::string> regs;
  return regs;
Y
Yu Yang 已提交
947
}
948 949 950 951 952 953

static int MultiDevSSAGraphBuilderRegister(const std::string &builder_mode) {
  MultiDevSSAGraphBuilder().insert(builder_mode);
  return 0;
}

Y
Yu Yang 已提交
954 955 956
}  // namespace details
}  // namespace framework
}  // namespace paddle
X
Xin Pan 已提交
957

958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977
#define REGISTER_MULTI_DEVICES_PASS(pass_name, pass_class)                     \
  STATIC_ASSERT_GLOBAL_NAMESPACE(                                              \
      _reg_ssa_graph_builder_##pass_name,                                      \
      "REGISTER_MULTI_DEVICES_PASS must be called in global namespace.");      \
  int _reg_ssa_graph_builder_entry_##pass_name =                               \
      paddle::framework::details::MultiDevSSAGraphBuilderRegister(#pass_name); \
  REGISTER_PASS(pass_name, pass_class)                                         \
      .RequirePassAttr(paddle::framework::details::kLossVarName)               \
      .RequirePassAttr(paddle::framework::details::kPlaces)                    \
      .RequirePassAttr(paddle::framework::details::kLocalScopes)               \
      .RequirePassAttr(paddle::framework::details::kStrategy)                  \
      .RequirePassAttr(paddle::framework::details::kNRanks)

REGISTER_MULTI_DEVICES_PASS(reduce_mode_multi_devices_pass,
                            paddle::framework::details::ReduceSSAGraphBuilder);
REGISTER_MULTI_DEVICES_PASS(
    allreduce_mode_multi_devices_pass,
    paddle::framework::details::AllReduceSSAGraphBuilder);
REGISTER_MULTI_DEVICES_PASS(dist_multi_devices_pass,
                            paddle::framework::details::DistSSAGraphBuilder);
Q
can run  
Qiao Longfei 已提交
978 979
REGISTER_MULTI_DEVICES_PASS(async_multi_devices_pass,
                            paddle::framework::details::AsyncSSAGraphBuilder);