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提交 d2c81529 编写于 作者: S sandyhouse
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update fb_scheduler

上级 6760cbd9
隐藏空白更改
内联 并排
Showing with 121 addition and 14 deletion
paddle/fluid/framework/section_worker.cc
浏览文件 @ d2c81529
......@@ -48,7 +48,18 @@ void SectionWorker::TrainFiles() {
#endif
}
for (int i = 0; i < num_microbatches_; ++i) {
auto startup_steps = num_pipeline_stages_ - pipeline_stage_ - 1;
VLOG(3) << "startup_steps:" << startup_steps
<< ", num_stages: " << num_pipeline_stages_
<< ", stage:" << pipeline_stage_;
if (startup_steps > num_microbatches_) {
startup_steps = num_microbatches_;
}
int fw_step = 0;
int bw_step = 0;
// startup phase
while (fw_step < startup_steps) {
VLOG(3) << "to run forward batch:" << fw_step;
for (auto& op : ops_) {
int op_role = op->Attr<int>(std::string("op_role"));
// We run op with op_role = kLRSched only for the first microbatch
......@@ -60,37 +71,129 @@ void SectionWorker::TrainFiles() {
bool run_others = op_role == static_cast<int>(OpRole::kForward) ||
op_role == (static_cast<int>(OpRole::kForward) |
static_cast<int>(OpRole::kLoss));
if ((i == 0 && run_first_mbatch) || (i != 0 && run_others)) {
if ((fw_step == 0 && run_first_mbatch) || (fw_step != 0 && run_others)) {
VLOG(3) << "Forward: running op " << op->Type() << " for micro-batch "
<< i;
op->Run(*microbatch_scopes_[i], place_);
<< fw_step;
op->Run(*microbatch_scopes_[fw_step], place_);
if (gc) {
DeleteUnusedTensors(*microbatch_scopes_[i], op.get(), unused_vars_,
gc.get());
DeleteUnusedTensors(*microbatch_scopes_[fw_step], op.get(),
unused_vars_, gc.get());
}
}
}
cudaDeviceSynchronize();
fw_step += 1;
}
// backward pass
for (int i = 0; i < num_microbatches_; ++i) {
// 1f1b phase
while (fw_step < num_microbatches_) {
VLOG(3) << "to run forward batch:" << fw_step;
for (auto& op : ops_) {
int op_role = op->Attr<int>(std::string("op_role"));
// We run op with op_role = kLRSched only for the first microbatch
// to avoid increasing the @LR_DECAY_STEP@ multiple times.
bool run_first_mbatch = op_role == static_cast<int>(OpRole::kForward) ||
op_role == (static_cast<int>(OpRole::kForward) |
static_cast<int>(OpRole::kLoss)) ||
op_role == static_cast<int>(OpRole::kLRSched);
bool run_others = op_role == static_cast<int>(OpRole::kForward) ||
op_role == (static_cast<int>(OpRole::kForward) |
static_cast<int>(OpRole::kLoss));
if ((fw_step == 0 && run_first_mbatch) || (fw_step != 0 && run_others)) {
VLOG(3) << "Forward: running op " << op->Type() << " for micro-batch "
<< fw_step;
op->Run(*microbatch_scopes_[fw_step], place_);
if (gc) {
DeleteUnusedTensors(*microbatch_scopes_[fw_step], op.get(),
unused_vars_, gc.get());
}
}
}
fw_step += 1;
VLOG(3) << "to run backward batch:" << bw_step;
for (auto& op : ops_) {
int op_role = op->Attr<int>(std::string("op_role"));
if (op_role == static_cast<int>(OpRole::kBackward) ||
op_role == (static_cast<int>(OpRole::kBackward) |
static_cast<int>(OpRole::kLoss))) {
VLOG(3) << "Backward: running op " << op->Type() << " for micro-batch "
<< i;
op->Run(*microbatch_scopes_[i], place_);
<< bw_step;
op->Run(*microbatch_scopes_[bw_step], place_);
if (gc) {
DeleteUnusedTensors(*microbatch_scopes_[i], op.get(), unused_vars_,
gc.get());
DeleteUnusedTensors(*microbatch_scopes_[bw_step], op.get(),
unused_vars_, gc.get());
}
}
}
cudaDeviceSynchronize();
bw_step += 1;
}
// backward phase
while (bw_step < num_microbatches_) {
VLOG(3) << "to run backward batch:" << bw_step;
for (auto& op : ops_) {
int op_role = op->Attr<int>(std::string("op_role"));
if (op_role == static_cast<int>(OpRole::kBackward) ||
op_role == (static_cast<int>(OpRole::kBackward) |
static_cast<int>(OpRole::kLoss))) {
VLOG(3) << "Backward: running op " << op->Type() << " for micro-batch "
<< bw_step;
op->Run(*microbatch_scopes_[bw_step], place_);
if (gc) {
DeleteUnusedTensors(*microbatch_scopes_[bw_step], op.get(),
unused_vars_, gc.get());
}
}
}
bw_step += 1;
}
// for (int i = 0; i < num_microbatches_; ++i) {
// for (auto& op : ops_) {
// int op_role = op->Attr<int>(std::string("op_role"));
// // We run op with op_role = kLRSched only for the first microbatch
// // to avoid increasing the @LR_DECAY_STEP@ multiple times.
// bool run_first_mbatch = op_role == static_cast<int>(OpRole::kForward)
// ||
// op_role == (static_cast<int>(OpRole::kForward)
// |
// static_cast<int>(OpRole::kLoss)) ||
// op_role == static_cast<int>(OpRole::kLRSched);
// bool run_others = op_role == static_cast<int>(OpRole::kForward) ||
// op_role == (static_cast<int>(OpRole::kForward) |
// static_cast<int>(OpRole::kLoss));
// if ((i == 0 && run_first_mbatch) || (i != 0 && run_others)) {
// VLOG(3) << "Forward: running op " << op->Type() << " for micro-batch
// "
// << i;
// op->Run(*microbatch_scopes_[i], place_);
// if (gc) {
// DeleteUnusedTensors(*microbatch_scopes_[i], op.get(), unused_vars_,
// gc.get());
// }
// }
// }
// cudaDeviceSynchronize();
// }
// // backward pass
// for (int i = 0; i < num_microbatches_; ++i) {
// for (auto& op : ops_) {
// int op_role = op->Attr<int>(std::string("op_role"));
// if (op_role == static_cast<int>(OpRole::kBackward) ||
// op_role == (static_cast<int>(OpRole::kBackward) |
// static_cast<int>(OpRole::kLoss))) {
// VLOG(3) << "Backward: running op " << op->Type() << " for micro-batch
// "
// << i;
// op->Run(*microbatch_scopes_[i], place_);
// if (gc) {
// DeleteUnusedTensors(*microbatch_scopes_[i], op.get(), unused_vars_,
// gc.get());
// }
// }
// }
// cudaDeviceSynchronize();
// }
// update pass
for (auto& op : ops_) {
......@@ -99,6 +202,10 @@ void SectionWorker::TrainFiles() {
VLOG(3) << "Update: running op " << op->Type();
op->Run(*microbatch_scopes_[num_microbatches_ - 1], place_);
if (gc) {
// for (int i = 0; i < num_microbatches_; ++i) {
// DeleteUnusedTensors(*microbatch_scopes_[i],
// op.get(), unused_vars_, gc.get());
//}
DeleteUnusedTensors(*microbatch_scopes_[num_microbatches_ - 1],
op.get(), unused_vars_, gc.get());
}
......
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