  We present the first unified study of the efficiency of self-attention-based
Transformer variants spanning text, speech and vision. We identify input length
thresholds (tipping points) at which efficient Transformer variants become more
efficient than vanilla models, using a variety of efficiency metrics (latency,
throughput, and memory). To conduct this analysis for speech, we introduce
L-HuBERT, a novel local-attention variant of a self-supervised speech model. We
observe that these thresholds are (a) much higher than typical dataset sequence
lengths and (b) dependent on the metric and modality, showing that choosing the
right model depends on modality, task type (long-form vs. typical context) and
resource constraints (time vs. memory). By visualising the breakdown of the
computational costs for transformer components, we also show that
non-self-attention components exhibit significant computational costs. We
release our profiling toolkit at
https://github.com/ajd12342/profiling-transformers .
