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Remove parallel logic for backward and forward GPU (#641)

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Yufeng Li 2019-03-16 02:37:51 -07:00 committed by GitHub
parent 4c2b1c3018
commit 8faccfcd92
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1 changed files with 31 additions and 69 deletions
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100
onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.cc
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@ -383,82 +383,44 @@ Status DeepCpuGruOp::ComputeImpl(OpKernelContext& context) const {
gsl::span<T> hidden_output_2 = hidden_output.subspan(hidden_output_size_per_direction,
hidden_output_size_per_direction);
#if defined(USE_MLAS) && !defined(USE_OPENMP)
#ifdef USE_EIGEN_THREADPOOL
auto fn =
#else
std::packaged_task<void()> task_fw{
#endif
[&]() {
#endif // USE_MLAS && ! USE_OPENMP
std::unique_ptr<detail::UniDirectionalGru<T>> fw = std::make_unique<detail::UniDirectionalGru<T>>(
alloc, logger,
seq_length, batch_size, input_size, hidden_size_, linear_before_reset_, Direction::kForward,
bias_1, initial_hidden_1,
activation_funcs_.Entries()[0],
activation_funcs_.Entries()[1],
clip_, ttp_);
fw->Compute(input, sequence_lens_span, num_directions_, input_weights_1, recurrent_weights_1, output_1, hidden_output_1);
std::unique_ptr<detail::UniDirectionalGru<T>> fw = std::make_unique<detail::UniDirectionalGru<T>>(
alloc, logger,
seq_length, batch_size, input_size, hidden_size_, linear_before_reset_, Direction::kForward,
bias_1, initial_hidden_1,
activation_funcs_.Entries()[0],
activation_funcs_.Entries()[1],
clip_, ttp_);
fw->Compute(input, sequence_lens_span, num_directions_, input_weights_1, recurrent_weights_1, output_1, hidden_output_1);
#if defined(USE_MLAS) && !defined(USE_OPENMP)
#ifndef USE_EIGEN_THREADPOOL
}
};
auto task_results_fw = task_fw.get_future();
ttp_.RunTask(std::move(task_fw));
#else
};
OrtCondVar cv;
OrtMutex lock;
bool done = false;
std::unique_ptr<detail::UniDirectionalGru<T>> bw = std::make_unique<detail::UniDirectionalGru<T>>(
alloc, logger,
seq_length, batch_size, input_size, hidden_size_, linear_before_reset_, Direction::kReverse,
bias_2, initial_hidden_2,
activation_funcs_.Entries()[2],
activation_funcs_.Entries()[3],
clip_, ttp_);
bw->Compute(input, sequence_lens_span, num_directions_, input_weights_2, recurrent_weights_2, output_2, hidden_output_2);
ttp_.Schedule([&]() {
fn();
auto ul = std::unique_lock<OrtMutex>(lock);
done = true;
cv.notify_one();
});
#endif // USE_EIGEN_THREADPOOL
#endif // USE_MLAS && ! USE_OPENMP
} else {
std::unique_ptr<detail::UniDirectionalGru<T>> gru_p = std::make_unique<detail::UniDirectionalGru<T>>(
alloc, logger,
seq_length, batch_size, input_size, hidden_size_, linear_before_reset_, direction_,
bias_1, initial_hidden_1,
activation_funcs_.Entries()[0],
activation_funcs_.Entries()[1],
clip_, ttp_);
std::unique_ptr<detail::UniDirectionalGru<T>> bw = std::make_unique<detail::UniDirectionalGru<T>>(
alloc, logger,
seq_length, batch_size, input_size, hidden_size_, linear_before_reset_, Direction::kReverse,
bias_2, initial_hidden_2,
activation_funcs_.Entries()[2],
activation_funcs_.Entries()[3],
clip_, ttp_);
bw->Compute(input, sequence_lens_span, num_directions_, input_weights_2, recurrent_weights_2, output_2, hidden_output_2);
gru_p->Compute(input, sequence_lens_span, num_directions_, input_weights_1, recurrent_weights_1, output_1, hidden_output_1);
}
#if defined(USE_MLAS) && !defined(USE_OPENMP)
#ifdef USE_EIGEN_THREADPOOL
auto ul = std::unique_lock<OrtMutex>(lock);
if (!done) cv.wait(ul);
#else
task_results_fw.get();
#endif
#endif // USE_MLAS && ! USE_OPENMP
} // namespace onnxruntime
else {
std::unique_ptr<detail::UniDirectionalGru<T>> gru_p = std::make_unique<detail::UniDirectionalGru<T>>(
alloc, logger,
seq_length, batch_size, input_size, hidden_size_, linear_before_reset_, direction_,
bias_1, initial_hidden_1,
activation_funcs_.Entries()[0],
activation_funcs_.Entries()[1],
clip_, ttp_);
if (!output.empty())
DumpMatrix("Y", output.data(), seq_length * num_directions_ * batch_size, hidden_size_);
gru_p->Compute(input, sequence_lens_span, num_directions_, input_weights_1, recurrent_weights_1, output_1, hidden_output_1);
DumpMatrix("Y_h", hidden_output.data(), num_directions_ * batch_size, hidden_size_);
return Status::OK();
}
if (!output.empty())
DumpMatrix("Y", output.data(), seq_length* num_directions_* batch_size, hidden_size_);
DumpMatrix("Y_h", hidden_output.data(), num_directions_* batch_size, hidden_size_);
return Status::OK();
} // namespace onnxruntime
//
// Implementation of internal helper code
namespace detail {