The newly launched PyTorch 1.12 has launched BetterTransformer which implements a backwards-compatible fastpath of torch.nn.TransformerEncoder for Transformer Encoder Inference. It helps in 2x in speedup and throughput for a lot of frequent execution eventualities.
Picture: Transformer Encoder structure
BetterTransformer launches with accelerated native implementations of MultiHeadAttention and TransformerEncoderLayer for CPUs and GPUs. These quick paths are built-in into the usual PyTorch Transformer APIs and assist in accelerating TransformerEncoder, TransformerEncoderLayer and MultiHeadAttention nn.modules.Â
Supply: pytorch.org
These new modules implement two forms of optimizations:
- Fused kernels mix a number of particular person operators usually used to implement Transformers to supply a extra environment friendly implementation.
- Benefit from sparsity within the inputs to keep away from performing pointless operations on padding tokens. Padding tokens incessantly account for a big fraction of enter batches in lots of Transformer fashions used for Pure Language Processing.
Backwards Compatibility
Advantageously, BetterTransformer doesn’t want any mannequin change. To learn from quick path execution, inputs and working circumstances should fulfill some entry circumstances. Whereas the inner implementation of Transformer APIs has modified, PyTorch 1.12 maintains strict compatibility with Transformer modules shipped in earlier variations, enabling PyTorch customers to make use of fashions created and educated with earlier PyTorch releases whereas benefiting from BetterTransformer enhancements.
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