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Remove unnessary logger from GRU (#951)

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Yufeng Li 2019-05-01 17:25:07 -07:00 committed by GitHub
parent 2c46fff69a
commit 628f4c3aa3
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GPG key ID: 4AEE18F83AFDEB23
1 changed files with 21 additions and 12 deletions
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33
onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.cc
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@ -165,7 +165,6 @@ template <typename T>
class UniDirectionalGru {
public:
UniDirectionalGru(AllocatorPtr allocator,
const logging::Logger& logger,
const int seq_length,
const int batch_size,
const int input_size,
@ -190,7 +189,6 @@ class UniDirectionalGru {
private:
AllocatorPtr allocator_;
const logging::Logger& logger_;
int seq_length_;
int batch_size_;
@ -277,8 +275,6 @@ Status DeepCpuGruOp::Compute(OpKernelContext* context) const {
template <typename T>
Status DeepCpuGruOp::ComputeImpl(OpKernelContext& context) const {
auto& logger = context.Logger();
const Tensor& X = *context.Input<Tensor>(0); // inputs. [seq_length, batch_size, input_size]
const Tensor& W = *context.Input<Tensor>(1); // weights. [num_directions, 3*hidden_size, input_size]
const Tensor& R = *context.Input<Tensor>(2); // recurrence weights. [num_directions, 3*hidden_size, hidden_size]
@ -380,8 +376,13 @@ Status DeepCpuGruOp::ComputeImpl(OpKernelContext& context) const {
hidden_output_size_per_direction);
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,
alloc,
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],
@ -389,8 +390,13 @@ Status DeepCpuGruOp::ComputeImpl(OpKernelContext& context) const {
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>> bw = std::make_unique<detail::UniDirectionalGru<T>>(
alloc, logger,
seq_length, batch_size, input_size, hidden_size_, linear_before_reset_, Direction::kReverse,
alloc,
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],
@ -398,8 +404,13 @@ Status DeepCpuGruOp::ComputeImpl(OpKernelContext& context) const {
bw->Compute(input, sequence_lens_span, num_directions_, input_weights_2, recurrent_weights_2, output_2, hidden_output_2);
} 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_,
alloc,
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],
@ -422,7 +433,6 @@ namespace detail {
template <typename T>
UniDirectionalGru<T>::UniDirectionalGru(AllocatorPtr allocator,
const logging::Logger& logger,
const int seq_length,
const int batch_size,
const int input_size,
@ -435,7 +445,6 @@ UniDirectionalGru<T>::UniDirectionalGru(AllocatorPtr allocator,
const ActivationFuncs::Entry& activation_func_g,
const float clip)
: allocator_(allocator),
logger_(logger),
seq_length_(seq_length),
batch_size_(batch_size),
input_size_(input_size),