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CUDNN_RNN_DATA_LAYOUT_SEQ_MAJOR_UNPACKED works with CUDNN_RNN_PADDED_… (#1428)

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CUDNN_RNN_DATA_LAYOUT_SEQ_MAJOR_UNPACKED works with CUDNN_RNN_PADDED_IO_ENABLED, so that it will auto fill 0 for the shorter sequences.
This commit is contained in:
Hector Li 2019-07-18 09:17:44 -07:00 committed by GitHub
parent f720166887
commit 1ff957f96e
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3 changed files with 41 additions and 4 deletions
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3
onnxruntime/core/providers/cuda/cudnn_common.cc
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@ -83,7 +83,8 @@ Status CudnnDataTensor::Set(cudnnDataType_t dataType,
const int32_t* seq_lengths) {
ORT_RETURN_IF_ERROR(CreateTensorIfNeeded());
cudnnRNNDataLayout_t layout = CUDNN_RNN_DATA_LAYOUT_SEQ_MAJOR_PACKED;
// CUDNN_RNN_DATA_LAYOUT_SEQ_MAJOR_UNPACKED works with CUDNN_RNN_PADDED_IO_ENABLED, so that it will auto fill 0 for the shorter sequences
cudnnRNNDataLayout_t layout = CUDNN_RNN_DATA_LAYOUT_SEQ_MAJOR_UNPACKED;
float padding_fill = 0.0f;
CUDNN_RETURN_IF_ERROR(cudnnSetRNNDataDescriptor(tensor_, dataType, layout,
static_cast<int>(max_seq_length),

11
onnxruntime/core/providers/cuda/rnn/cudnn_rnn_base.cc
View file

@ -220,7 +220,6 @@ Status CudnnRnnBase<T>::ComputeInternal(OpKernelContext* ctx) const {
x_data = x_reversed_data.get();
}
auto byte_size = X->DataType()->Size();
const T* hx_data = (initial_h == nullptr) ? nullptr : initial_h->template Data<T>();
const T* cx_data = (initial_c == nullptr) ? nullptr : initial_c->template Data<T>();
T* y_h_data = (Y_h == nullptr) ? nullptr : Y_h->template MutableData<T>();
@ -234,10 +233,12 @@ Status CudnnRnnBase<T>::ComputeInternal(OpKernelContext* ctx) const {
y_alloc_data = GetScratchBuffer<T>(output_size);
y_data = y_alloc_data.get();
}
// Cudnn library doesn't guarantee the data beyond the shorter sequence will be initialized to 0, so we need to do it manually.
cudaMemset(y_data, 0, output_size * byte_size);
const int32_t* sequence_lens_data = (sequence_lens == nullptr) ? nullptr : sequence_lens->template Data<int32_t>();
// CUDNN_RNN_DATA_LAYOUT_SEQ_MAJOR_UNPACKED works with CUDNN_RNN_PADDED_IO_ENABLED, so that it will auto fill 0 for the shorter sequences
CUDNN_RETURN_IF_ERROR(cudnnSetRNNPaddingMode(rnn_desc_, CUDNN_RNN_PADDED_IO_ENABLED));
size_t workspace_bytes;
CUDNN_RETURN_IF_ERROR(cudnnGetRNNWorkspaceSize(CudnnHandle(), rnn_desc_, gsl::narrow_cast<int>(seq_length), x_desc.data(), &workspace_bytes));
auto workspace_cuda = GetScratchBuffer<void>(workspace_bytes);
@ -288,6 +289,10 @@ Status CudnnRnnBase<T>::ComputeInternal(OpKernelContext* ctx) const {
nullptr, nullptr, nullptr, nullptr,
workspace_cuda.get(),
workspace_bytes));
// Early terminate for this case since Y data is not required, and Y_h is obtained correctly, no need the following code to retrive Y_h from Y data.
if (nullptr == Y) {
return Status::OK();
}
}
IAllocatorUniquePtr<T> y_reorganized_data;

31
onnxruntime/test/providers/cpu/rnn/deep_cpu_lstm_op_test.cc
View file

@ -1090,6 +1090,37 @@ TEST(LSTMTest, ONNXRuntime_TestLSTMSequenceLengthShorterThanInputSequenceLength)
LstmOpContext2x1x2x2 context(direction);
context.RunTest(X_data, batch_size, seq_len, &initial_h, &initial_c, Y_data, Y_h_data, {}, &sequence_length);
}
TEST(LSTMTest, ONNXRuntime_TestLSTMSequenceLengthShorterThanInputSequenceLengthNoP) {
const int seq_len = 2;
const int batch_size = 1;
std::vector<float> X_data = {-0.455351f, -0.276391f,
-0.185934f, -0.269585f};
std::vector<int> sequence_length = {1};
std::vector<float> initial_h = {0.0f, 0.0f,
-0.0306872f, 0.028035f};
std::vector<float> initial_c = {0.0f, 0.0f,
-0.07243599f, 0.0467052f};
std::vector<float> Y_data = {0.0415416f, 0.0196912f,
0.0295027f, 0.0334400f,
0.0f, 0.0f,
0.0f, 0.0f};
std::vector<float> Y_h_data = {0.0415416f, 0.0196912f,
0.0295027f, 0.0334400f};
std::string direction = "bidirectional";
LstmOpContext2x1x2x2 context(direction);
// CUDA implementation doesn't support peephole
context.RunTest(X_data, batch_size, seq_len, &initial_h, &initial_c, Y_data, Y_h_data, {}, &sequence_length, false);
}
#endif // USE_NGRAPH
} // namespace test