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MT-DNN Achieves Human Performance in General Language Understanding Benchmark
- SmooL 7y agoIt seems since submitting that they are no longer the leader on the GLUE leaderboard - https://gluebenchmark.com/leaderboard/ https://gluebenchmark.com/leaderboard/
- yorwba 7y agoIn fact the article was published on June 20, while the XLNet submission that dethroned them was already on June 19. I guess their publishing pipeline doesn't allow last-minute amendments.
- dweekly 7y agoThe relentlessness of the pace of ML remains breathtaking. Microsoft: We beat human performance and lapped everyone else! XLNet: Hold my beer. Code: https://github.com/zihangdai/xlnet https://github.com/zihangdai/xlnet Description: https://towardsdatascience.com/what-is-xlnet-and-why-it-outperforms-bert-8d8fce710335 https://towardsdatascience.com/what-is-xlnet-and-why-it-outp... Paper: https://arxiv.org/pdf/1906.08237.pdf https://arxiv.org/pdf/1906.08237.pdf
- m0zg 7y agoNotably, this result would be very hard for Microsoft to achieve, or indeed even reproduce fully in-house, because it requires more memory than GPUs have. GitHub mentions that TPUs are pretty much table stakes to train this.
- mda 7y agoI guess they can use TPUs with GCE.
- riku_iki 7y ago> because it requires more memory than GPUs have could you elaborate? TPU V3 unit has 16GB of memory, and old V100 also has 16GB of memory. Plus TPU has extra memory consumption for mandatory tensor padding, which GPU doesn't have.
- m0zg 7y agoThe XLNet GitHub elaborates pretty well, no need to duplicate it here: https://github.com/zihangdai/xlnet https://github.com/zihangdai/xlnet
- riku_iki 7y agoThere is no elaboration of your statement there. It says it is hard to reproduce results on single 16GB GPU, but nor they used single TPU in their result. They explicitly state that large number of GPU required: "Therefore, a large number (ranging from 32 to 128, equal to batch_size) of GPUs are required to reproduce many results in the paper.", and they used around 200 TPUs themself according to their publication.
- m0zg 7y agoYes, it boils down to using either a single machine with multiple TPUs (and therefore doing things "the easy way" and relatively quickly) or having to use 128 GPUs (up to 8 per machine) and working with a single sample per GPU, really, really slowly. Given that a single model often requires dozens, if not hundreds of training runs to figure out hyperparameters that achieve a SOTA result, this means that with TPU you can do this, and with GPU you aren't even going to bother training something this big because it will take forever, and someone with a TPU will figure out something better by the time you're done. Which is what happened in this case, it looks like.
- bitforger 7y agoThe memory constraint is certainly an issue, but I think they can be overcome with some software hacks (for a speed penalty, of course.) For example, gradient checkpointing and gradient accumulation might help: https://medium.com/tensorflow/fitting-larger-networks-into-memory-583e3c758ff9 https://medium.com/tensorflow/fitting-larger-networks-into-m... https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255 https://medium.com/huggingface/training-larger-batches-pract...
- p1esk 7y agoSeems like this is largely due to improved Winograd Schema results. I wonder if those questions made it into the training set in some form.
- daenz 7y agoOne of their test sentences was interesting: >For example, the task provides the sentence: “The city councilmen refused the demonstrators a permit because they [feared/advocated] violence.” If the word “feared” is selected, then “they” refers to the city council. If “advocated” is selected, then “they” presumably refers to the demonstrators.
- aisofteng 7y agoThis sort of sentence is exactly the point of the article. See: https://en.m.wikipedia.org/wiki/Winograd_Schema_Challenge https://en.m.wikipedia.org/wiki/Winograd_Schema_Challenge
- TheIronYuppie 7y agoDisclosure: I work at Azure on Machine Learning Hi all! Please let me know if you have any questions - happy to direct them to the right people! Thanks! (Email in my profile)