DEED: Dynamic Early Exit on Decoder for Accelerating Encoder-Decoder
  Transformer Models
Peng Tang Pengkai Zhu Tian Li Srikar Appalaraju Vijay Mahadevan R. Manmatha
Encoder-decoder transformer models have achieved great success on various
vision-language (VL) tasks, but they suffer from high inference latency.
Typically, the decoder takes up most of the latency because of the
auto-regressive decoding. To accelerate the inference, we propose an approach
of performing Dynamic Early Exit on Decoder (DEED). We build a multi-exit
encoder-decoder transformer model which is trained with deep supervision so
that each of its decoder layers is capable of generating plausible predictions.
In addition, we leverage simple yet practical techniques, including shared
generation head and adaptation modules, to keep accuracy when exiting at
shallow decoder layers. Based on the multi-exit model, we perform step-level
dynamic early exit during inference, where the model may decide to use fewer
decoder layers based on its confidence of the current layer at each individual
decoding step. Considering different number of decoder layers may be used at
different decoding steps, we compute deeper-layer decoder features of previous
decoding steps just-in-time, which ensures the features from different decoding
steps are semantically aligned. We evaluate our approach with two
state-of-the-art encoder-decoder transformer models on various VL tasks. We
show our approach can reduce overall inference latency by 30%-60% with
comparable or even higher accuracy compared to baselines.