Sidharth Kashyap


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Efficient Machine Translation with Model Pruning and Quantization
Maximiliana Behnke | Nikolay Bogoychev | Alham Fikri Aji | Kenneth Heafield | Graeme Nail | Qianqian Zhu | Svetlana Tchistiakova | Jelmer van der Linde | Pinzhen Chen | Sidharth Kashyap | Roman Grundkiewicz
Proceedings of the Sixth Conference on Machine Translation

We participated in all tracks of the WMT 2021 efficient machine translation task: single-core CPU, multi-core CPU, and GPU hardware with throughput and latency conditions. Our submissions combine several efficiency strategies: knowledge distillation, a simpler simple recurrent unit (SSRU) decoder with one or two layers, lexical shortlists, smaller numerical formats, and pruning. For the CPU track, we used quantized 8-bit models. For the GPU track, we experimented with FP16 and 8-bit integers in tensorcores. Some of our submissions optimize for size via 4-bit log quantization and omitting a lexical shortlist. We have extended pruning to more parts of the network, emphasizing component- and block-level pruning that actually improves speed unlike coefficient-wise pruning.


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Edinburgh’s Submissions to the 2020 Machine Translation Efficiency Task
Nikolay Bogoychev | Roman Grundkiewicz | Alham Fikri Aji | Maximiliana Behnke | Kenneth Heafield | Sidharth Kashyap | Emmanouil-Ioannis Farsarakis | Mateusz Chudyk
Proceedings of the Fourth Workshop on Neural Generation and Translation

We participated in all tracks of the Workshop on Neural Generation and Translation 2020 Efficiency Shared Task: single-core CPU, multi-core CPU, and GPU. At the model level, we use teacher-student training with a variety of student sizes, tie embeddings and sometimes layers, use the Simpler Simple Recurrent Unit, and introduce head pruning. On GPUs, we used 16-bit floating-point tensor cores. On CPUs, we customized 8-bit quantization and multiple processes with affinity for the multi-core setting. To reduce model size, we experimented with 4-bit log quantization but use floats at runtime. In the shared task, most of our submissions were Pareto optimal with respect the trade-off between time and quality.