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This paper describes the submissions of the "Marian" team to the WNMT 2018 shared task. We investigate combinations of teacher-student training, low-precision matrix products, auto-tuning and other methods to optimize the Transformer model on GPU and CPU. By further integrating these methods with the new averaging attention networks, a recently introduced faster Transformer variant, we create a number of high-quality, high-performance models on the GPU and CPU, dominating the Pareto frontier for this shared task.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1805.12096")
get_code_for_paper("1805.12096")
have("1805.12096")
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