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Fix mask in EmbedLayerNormalization (#2300)

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Tianlei Wu 2019-11-01 13:49:55 -07:00 committed by GitHub
parent 6e65dcf588
commit a6b2c9fc09
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2 changed files with 82 additions and 1 deletions
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2
onnxruntime/contrib_ops/cuda/bert/embed_layer_norm_impl.cu
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@ -182,7 +182,7 @@ bool LaunchEmbedLayerNormKernel(
const size_t element_size) {
const cudaStream_t stream = nullptr; // default stream
if (!ComputeMaskIndex(stream, sequence_length, hidden_size, input_mask, static_cast<int*>(mask_index))) {
if (!ComputeMaskIndex(stream, sequence_length, batch_size, input_mask, static_cast<int*>(mask_index))) {
return false;
}

81
onnxruntime/test/contrib_ops/embedlayernorm_op_test.cc
View file

@ -269,5 +269,86 @@ TEST(EmbedLayerNormTest, EmbedLayerNormBatch2) {
sequence_length,
hidden_size);
}
// BatchSize > HiddenSize to reproduce mask processing bug
TEST(EmbedLayerNormTest, EmbedLayerNormLargeBatchSmallHiddenSize) {
int batch_size = 5;
int sequence_length = 2;
int hidden_size = 4;
std::vector<int32_t> input_ids_data = {
1, 3,
1, 3,
2, 0,
1, 3,
2, 0};
std::vector<int32_t> segment_ids_data = {
0, 1,
0, 1,
0, 0,
0, 1,
0, 0};
std::vector<int32_t> mask_data = {
1, 1,
1, 1,
1, 0,
1, 1,
1, 0};
std::vector<float> word_embedding_data = {
0.2f, 0.1f, 0.4f, -0.6f,
0.3f, 0.2f, 0.5f, 0.6f,
0.6f, 0.7f, 0.0f, -0.1f,
0.8f, 0.6f, 0.9f, 1.2f,
0.1f, 0.3f, 0.5f, 0.9f,
1.0f, -2.0f, 1.1f, 0.8f};
std::vector<float> position_embedding_data = {
0.1f, 0.1f, 0.4f, 0.6f,
0.6f, 0.0f, 0.8f, 0.6f,
0.3f, 0.9f, -2.0f, 0.8f};
std::vector<float> segment_embedding_data = {
0.3f, 0.4f, 0.9f, 0.1f,
0.7f, 0.3f, 0.5f, 0.2f};
std::vector<float> gamma_data = {
0.25f, 0.15f, 0.45f, -0.66f};
std::vector<float> beta_data = {
0.6f, 0.2f, 0.5f, -0.6f};
std::vector<float> output_data = {
0.36917170882225037, 0.061503000557422638, 1.1598974466323853, -0.85092413425445557,
0.74301940202713013, -0.057434864342212677, 0.84324657917022705, -0.85171419382095337,
0.36917170882225037, 0.061503000557422638, 1.1598974466323853, -0.85092413425445557,
0.74301940202713013, -0.057434864342212677, 0.84324657917022705, -0.85171419382095337,
0.57668739557266235, 0.2979130744934082, 0.96158987283706665, 0.44627034664154053,
0.64977931976318359, 0.11039737612009048, 1.1869535446166992, 0.14469735324382782,
0.36917170882225037, 0.061503000557422638, 1.1598974466323853, -0.85092413425445557,
0.74301940202713013, -0.057434864342212677, 0.84324657917022705, -0.85171419382095337,
0.57668739557266235, 0.2979130744934082, 0.96158987283706665, 0.44627034664154053,
0.64977931976318359, 0.11039737612009048, 1.1869535446166992, 0.14469735324382782
};
std::vector<int32_t> mask_index_data = {
2, 2, 1, 2, 1};
RunTest(input_ids_data,
segment_ids_data,
mask_data,
word_embedding_data,
position_embedding_data,
segment_embedding_data,
gamma_data,
beta_data,
output_data,
mask_index_data,
batch_size,
sequence_length,
hidden_size);
}
} // namespace test
} // namespace onnxruntime