Selective Batching for Inference System for Transformer-Based Generation Tasks
AssigneeFriendliai Inc.
InventorsGyeongin Yu, Geon-Woo Kim, Joo Seong Jeong, Soojeong Kim, Byung-Gon Chun
Life of this patent
Estimating the term…
Loading patent…
AssigneeFriendliai Inc.
InventorsGyeongin Yu, Geon-Woo Kim, Joo Seong Jeong, Soojeong Kim, Byung-Gon Chun
Estimating the term…
An inference system applies a machine-learning transformer model to a batch of requests with variable input length or variable target length or variable internal sate length by selectively batching a subset of operations in the transformer model but processing requests in the batch individually for a subset of operations in the transformer model. In one embodiment, the operation to be processed individually is an attention operation of an encoder or a decoder of the transformer model. By selective batching, the inference system can allow batching operations to be performed for a batch of requests with variable input or target length or internal state length to utilize the parallel computation capabilities of hardware accelerators while preventing unnecessary computations that occur for workarounds that restrain the data of a batch of requests to a same length.
A method of executing a batch of requests by applying a machine-learning transformer model, comprising:
Dependencies are inferred from the claim text.