Perceiver AR: general-purpose, long-context autoregressive generation

Over the previous couple of years, autoregressive Transformers have introduced a gradual stream of breakthroughs in generative modeling. These fashions generate every aspect of a pattern – the pixels of a picture, the characters of a textual content (usually in “token” chunks), the samples of an audio waveform, and so forth – by predicting one aspect after the opposite. When predicting the subsequent aspect, the mannequin can look again at people who had been created earlier.

Nonetheless, every of a Transformer’s layers grows dearer as extra components are used as enter, and practitioners can solely afford to coach deep Transformers on sequences not more than about 2,048 components in size. And so, most Transformer-based fashions ignore all components past the newest previous (round 1,500 phrases or 1/6 of a small picture) when making a prediction.

In distinction, our just lately developed Perceiver fashions give glorious outcomes on a wide range of real-world duties with as much as round 100,000 components. Perceivers use cross-attention to encode inputs right into a latent house, decoupling the enter’s compute necessities from mannequin depth. Perceivers additionally spend a set value, no matter enter measurement, at practically each layer.

Whereas latent-space encoding handles all components in a single move, autoregressive era assumes processing occurs one aspect at a time. To handle this drawback, Perceiver AR proposes a easy answer: align the latents one after the other with the ultimate components of the enter, and thoroughly masks the enter so latents see solely earlier components.

Perceiver AR maps an enter sequence (P e r c e i v e r A R) to a small latent house by cross-attention to supply one latent for every goal token (3 latents proven, one for the targets A R , for End Of Sequence). These latents are then processed by a deep stack of self-attention layers. Perceiver AR will be educated for end-to-end autoregressive era, all whereas making use of very lengthy enter sequences.

The result’s an structure (proven above) that attends to as a lot as 50x longer inputs as customary Transformers, whereas deploying as extensively (and primarily as simply) as customary decoder-only Transformers.

As context size or mannequin measurement will increase, the quantity of compute wanted to coach a mannequin grows. We will quantify the compute price range for various fashions by measuring their pace on actual {hardware} (steps per second on TPUv3), because the enter context size and mannequin measurement enhance. Not like different generative fashions like Transformer or Transformer-XL, Perceiver AR decouples enter context size from mannequin depth, permitting us to simply deploy the deep fashions wanted to mannequin lengthy sequences on current-generation TPUs or GPUs.

Perceiver AR scales significantly higher with measurement than each customary Transformers and Transformer-XL fashions at a spread of sequence lengths in actual phrases. This property permits us to construct very efficient long-context fashions. For instance, we discover {that a} 60-layer Perceiver AR with context size 8192 outperforms a 42-layer Transformer-XL on a book-length era process, whereas operating sooner in actual wall-clock phrases.

On customary, long-context picture (ImageNet 64×64), language (PG-19), and music (MAESTRO) era benchmarks, Perceiver AR produces state-of-the-art outcomes. Rising enter context by decoupling enter measurement from compute price range results in a number of intriguing outcomes:

  • Compute price range will be tailored at eval time, permitting us to spend much less and easily degrade high quality or to spend extra for improved era.
  • A bigger context permits Perceiver AR to outperform Transformer-XL, even when spending the identical on compute. We discover that larger context results in improved mannequin efficiency even at reasonably priced scale (~1B parameters).
  • Perceiver AR’s pattern high quality reveals a lot much less sensitivity to the order during which it generates components. This makes Perceiver AR straightforward to use to settings that don’t have a pure left-to-right ordering, comparable to knowledge like photographs, with construction that spans a couple of dimension.

Utilizing a dataset of piano music, we educated Perceiver AR to generate new items of music from scratch. As a result of every new be aware is predicted primarily based on the total sequence of notes that got here earlier than, Perceiver AR is ready to produce items with a excessive degree of melodic, harmonic, and rhythmic coherence:

Be taught extra about utilizing Perceiver AR:

  • Obtain the JAX code for coaching Perceiver AR on Github
  • Learn our paper on arXiv
  • Take a look at our highlight presentation at ICML 2022

See the Google Magenta weblog submit with extra music!

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