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Cerebras has proven off the capabilities of its second–era wafer–scale engine, asserting it has set the document for the most important AI mannequin ever educated on a single gadget.
For the primary time, a pure language processing community with 20 billion parameters, GPT–NeoX 20B, was educated on a single gadget. Right here’s why that issues.
Why do we have to prepare fashions this large?
A brand new kind of neural community, the transformer, is taking on. At present, transformers are primarily used for pure language processing (NLP) the place their consideration mechanism will help spot the connection between phrases in a sentence, however they’re spreading to different AI functions, together with imaginative and prescient. The larger a transformer is, the extra correct it’s. Language fashions now routinely have billions of parameters and they’re rising quickly, with none indicators of slowing down.
One key space the place large transformers are getting used is in medical analysis in functions equivalent to epigenomics, the place they’re used to mannequin the “language” of genes — DNA sequences.
Why does it matter that this was performed on a single gadget?
Enormous fashions right this moment are principally educated utilizing many–processor techniques, often GPUs. Cerebras says its clients have discovered partitioning large fashions throughout a whole bunch of processors to be a time–consuming course of, which is exclusive to every mannequin and every particular multi–processor system, primarily based on the mannequin’s properties and the traits of every processor (ie, what sort of processor it’s and the way a lot reminiscence it has) and traits of the I/O community. This work isn’t transportable to different fashions or techniques.
Usually for multi–processor techniques, there are three sorts of parallelism at play:
- If the mannequin matches on a single processor, it may be duplicated onto different processors and each educated with subsets of the information — that is known as knowledge parallelism, which is comparatively simple.
- If the mannequin doesn’t match on one processor, the mannequin could be break up between processors with a number of layers operating on every — that is known as pipelined mannequin parallelism. Nonetheless, the layers have to run sequentially, so the consumer has to manually consider how a lot reminiscence and I/O might be required for every layer to ensure there aren’t any bottlenecks. It’s extra difficult than knowledge parallelism.
- If a layer of the mannequin is so large that it doesn’t match on one processor, it’s much more difficult nonetheless. Tensor mannequin parallelism have to be used to separate layers throughout processors, including one other dimension of complexity which additionally strains reminiscence and I/O bandwidth.
Enormous fashions, such because the GPT–NeoX 20B in Cerebras’ announcement, require all three sorts of parallelism for coaching.
Cerebras’ CS–2 avoids the necessity to parallelize the mannequin, partly due to its processor’s sheer measurement — it’s successfully one large 850,000–core processor on a single wafer–sized chip sufficiently big for even the most important community layers — and partly as a result of Cerebras has disaggregated reminiscence from compute. Extra reminiscence could be added to help extra parameters with no need so as to add extra compute, protecting the structure of the compute a part of the system the identical.
With out the necessity to use parallelism, there isn’t a have to spend time and assets manually partitioning fashions to run on multi–processor techniques. Additional, with out the bespoke a part of the method, fashions develop into transportable. Altering between GPT fashions with a number of parameters includes altering merely 4 variables in a single file. Equally, altering between GPT–J and GPT–Neo took just a few keystrokes. In response to Cerebras, this may save months of engineering time.
What are the implications for the broader trade?
NLP fashions have grown so massive that, in apply, solely a handful of firms have ample assets — by way of each the price of compute and engineering time — to coach them.
Cerebras hopes that by making its CS–2 system accessible within the cloud, in addition to by serving to clients scale back the quantity of engineering time and assets wanted, it may possibly open up large mannequin coaching to many extra firms, even these with out large system engineering groups. This consists of accelerating scientific and medical analysis in addition to NLP.
A single CS–2 can prepare fashions with a whole bunch of billions and even trillions of parameters, so there’s loads of scope for tomorrow’s large networks in addition to right this moment’s.
Does Cerebras have actual–world examples?
Biopharmaceutical firm AbbVie is utilizing a CS–2 for its biomedical NLP transformer coaching, which powers the corporate’s translation service to make huge libraries of biomedical literature searchable throughout 180 languages.
“A typical problem we expertise with programming and coaching BERTLARGE fashions is offering adequate GPU cluster assets for adequate durations of time,” mentioned Brian Martin, head of AI at biopharmaceutical firm AbbVie, in a press release. “The CS–2 system will present wall–clock enhancements that alleviate a lot of this problem, whereas offering an easier programming mannequin that accelerates our supply by enabling our groups to iterate extra rapidly and take a look at extra concepts.”
GlaxoSmithKline used the primary–era Cerebras system, the CS–1, for its epigenomics analysis. The system enabled coaching a community with a dataset that in any other case would have been prohibitively massive.
“GSK generates extraordinarily massive datasets by means of its genomic and genetic analysis, and these datasets require new tools to conduct machine studying,” mentioned Kim Branson, SVP of Synthetic Intelligence and Machine Studying at GSK, in a press release. “The Cerebras CS–2 is a important element that enables GSK to coach language fashions utilizing organic datasets at a scale and measurement beforehand unattainable. These foundational fashions kind the premise of lots of our AI techniques and play an important function within the discovery of transformational medicines.”
Different Cerebras customers embrace TotalEnergies, who use a CS–2 to hurry up simulations of batteries, biofuels, wind movement, drilling, and CO2 storage; the Nationwide Vitality Expertise Laboratory accelerates physics–primarily based computational fluid dynamics with a CS–2; Argonne Nationwide Laboratory has been utilizing a CS–1 for Covid–19 analysis and most cancers medication; and there are numerous extra examples.
