def my_inputs(n_devices):
while True:
file = random.choice(os.listdir('files'))
with GFile('/files/' + file) as f:
text = f.read()
IDS = TOKENIZER.EncodeAsIds(text)
As long as the total batch size is a multiple of the number of devices (8), the code will work. Half a million tokens is close to the limit of what fits in 8GB, so if you increase the batch size you'll almost certainly need to shorten the length
@NGamma The parameters of
MultifactorSchedule control the learning rate schedule, which only affects how long training takes and not how much memory is used. You can try running with a little more warmup steps, and more steps_per_cycle in the cyclic cosine schedule.
TypeError: reshape total size must be unchanged, got new_sizes (1, 281525, 256) for shape (1, 512, 1024, 256).
Any ideas, TYIA!
Storing a position embedding vector for each of 0.5M positions is too wasteful, so the approach is to pretend that the text has 2D 512x1024 shape, and concatenate an "x-embedding" and a "y-embedding"
@dimeldo You can try modifying the text generation demo. Redefining
my_inputs will let you feed in your own data, and you can tune the model hyperparameters as well
The problem is that output logits are size (281K, 16K), which is more than 8GB. They need to be chunked to fit in memory.
This is on my agenda because I'm dealing with the exact same problem with MT (vocab size 32K)
@nkitaev curious whether there are plans to scale up the text Reformer model and release a pre-trained model a. la. BERT / RoBERTa, etc? Is the primary concern availability of appropriate corpora to train on where long-term context is useful in reducing an MLM loss?
I'm dealing with a domain where I have a strong indicator that context more than 512 tokens away (long-form scanned documents) is useful and am interested in building off of the work of you and your collaborators.
I'm dealing with a domain where I have a strong indicator that context more than 512 tokens away (long-form scanned documents) is useful and am interested in building off of the work of you and your collaborators.
@patil-suraj : to clarify the TF2 question: some lower-level things are quite hard to do in TF. One of them is really memory-efficient reversible layers, hashing is not trivial too. Trax in general can run with the TF2 backend (and, e.g., Trax Transformer runs using TF), but some things we needed in Reformer were just very hard to do without JAX. That's why we went with JAX and it's been working really well for us!
@madisonmay : We'd really like to train on a larger data-set and release a model then. I think currently we're thinking of using the C4 dataset, what would you think? It's not very long context though...
@patil-suraj @nkitaev the error I encountered first had indeed to do with shapes, and if I remember correctly adding multiples of 256 to 1024 before multiplying with 512 worked.... but ran out of memory. The Colab example fits a single TPU memory really well, and it seems way easier to work with this input length in this context.
@lukaszkaiser thanks for the reply, and glad to hear you do have plans to train on a larger dataset! Barring introducing a new dataset, it seems like C4 or one of its subsets is one of the few decent options out there. The T5 paper also benchmarked on Toronto Book Corpus + Wikipedia -- I imagine because of TBC you'd have much longer avg. sequence length at the expense of operating over a much narrower domain.
I'm sure there would be serious interest from industry in something trained on the text of long-form PDFs from Common Crawl and it does feel like there is long-term information to exploit there (section headers + subheaders, page headers, references to defined terms and other clauses in legal docs, etc.), but that comes with the added warts of having to deal with poor OCR, poor detected reading order, and all sorts of other warts to be sorted out in preprocessing that mean it's probably more trouble than it's worth.
@lucidrains I checked in a config for WMT en-de. The learning curves look the same as the regular Transformer. There's still work left in putting together beam search decoding and making sure the evaluation conditions match prior work (+ some hyperparameter tuning). We're still a few weeks out from having official results.
@lukaszkaiser Thank you for the clarification. and indeed Trax with JAX seems very powerful and easier than other DL tool. I'm hoping the community will get bigger and more diverse. Thank you for making it open source
Just wanted to chime in to say we are really excited to be making use of Trax in the Project Clarify (https://github.com/projectclarify/clarify) codebase and would like to invite anyone with experience with Trax or Jax and interest in mentoring to come give a tutorial session at one of our upcoming hackathon/training days (Jan 25th and Feb 29): https://forms.gle/oFWkN7UuAxS7NUGJ9 Especially you @afrozenator, didn't have your email to send you an invite. Looking forward to adding value to Trax as we get up to speed with using it.
Thanks @cwbeitel - that looks exciting, will follow up over email.
@patil-suraj - Nikita wrote the encoder/decoder version. That is what you should probably start with I guess.
@afrozenator great, I'll take a look at encoder/decoder version. Now I have already trained a summarization model using your approach. But I'm not being able to decode properly. I'm using the sampling code given in the colab and using the <text>[doc]<summary> as prompt. But its not generating summary instead I see the original text. Can you help me on this ?
@afrozenator I am not using the copy model also for sampling I used the same code in given in the Reformer colab where I changed the prompt to <text>[doc]<summary> adding the document text in [doc]. Still not able to get results. Anyone else here has any ideas about this ? The goal is I want to sample summary conditioned on <text>[doc]<summary>. TYIA!
@patil-suraj : did you make sure to make a loss mask, so the loss is only on the summary? The intro colab has an example how to do that for the copy task: https://colab.research.google.com/github/google/trax/blob/master/trax/intro.ipynb
I was doing some toy tests with trax, a Resnet50 trained on *custom Cifar10 dataset reported some weird results
Step 1563: train accuracy | 0.21875000
Step 1563: train loss | 13.42460442
-
Step 3126: train accuracy | 0.15625000
Step 3126: train loss | 2.90936065
-
Step 4689: train accuracy | 0.28125000
Step 4689: train loss | 1.86861885
-
Step 6252: train accuracy | 0.09375000
Step 6252: train loss | 20935.30468750
-
Step 7815: train accuracy | 0.46875000
Step 7815: train loss | 1.39475393
my first thought is the data (always), but not sure if data can make the model report this ~random~ results...
now testing using gin_config
I0123 00:01:49.315093 140688036902784 trainer_lib.py:752] Step 1: train accuracy | 0.07968750
Step 1: train accuracy | 0.07968750
I0123 00:01:49.315915 140688036902784 trainer_lib.py:752] Step 1: train loss | 4623.56787109
-
I0123 00:03:25.528132 140688036902784 trainer_lib.py:752] Step 2000: train accuracy | 0.16718750
Step 2000: train accuracy | 0.16718750
I0123 00:03:25.528812 140688036902784 trainer_lib.py:752] Step 2000: train loss | 5.00325918
-
I0123 00:04:39.171037 140688036902784 trainer_lib.py:752] Step 4000: train accuracy | 0.19687501
Step 4000: train accuracy | 0.19687501
I0123 00:04:39.171646 140688036902784 trainer_lib.py:752] Step 4000: train loss | 3.77166605
-
I0123 00:05:53.784074 140688036902784 trainer_lib.py:752] Step 6000: train accuracy | 0.13750000
Step 6000: train accuracy | 0.13750000
I0123 00:05:53.784580 140688036902784 trainer_lib.py:752] Step 6000: train loss | 4.01593637
-
I0123 00:07:08.749417 140688036902784 trainer_lib.py:752] Step 8000: train accuracy | 0.33906251
Step 8000: train accuracy | 0.33906251
I0123 00:07:08.749950 140688036902784 trainer_lib.py:752] Step 8000: train loss | 1.65593171
-
...
-
I0123 00:10:54.362399 140688036902784 trainer_lib.py:752] Step 14000: train accuracy | 0.13125001
Step 14000: train accuracy | 0.13125001
I0123 00:10:54.362976 140688036902784 trainer_lib.py:752] Step 14000: train loss | 78.67562866