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DanielPFlorian

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Transformers-Github-Issues

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[ "Hello! Indeed, this is a mistake! Do you want to update the docs to show the generator instead?", "This issue has been automatically marked as stale and been closed because it has not had recent activity. Thank you for your contributions.\n\nIf you think this still needs to be addressed please comment on this thread." ]

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[ "Hey @ouwenjie03,\r\n\r\nyou should not do this steps:\r\n\r\n```python\r\ninput_ids = torch.cat([input_ids, added_input_ids], dim=1)\r\n```\r\n\r\nIt should just be \r\n\r\n```pyhton \r\ninput_ids = added_input_ids\r\n```\r\n\r\nWhen passing `past_key_values` the input_ids should correspond **only** to the last tokens. I think if you take a look at this test: https://github.com/huggingface/transformers/blob/3f290e6c8403c6a2cf80dce068869793bde49540/tests/test_modeling_gpt2.py#L446 you'll understand a bit better.\r\n", "Oh, get it! Thank you so much!" ]

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[ "That's a better solution actually! Thanks @patil-suraj ", "Also, there is a whole bunch of issues including this one I believe fixed in this PR: https://github.com/huggingface/transformers/pull/9323 where we no longer do it one by one." ]

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[ "This issue has been automatically marked as stale and been closed because it has not had recent activity. Thank you for your contributions.\n\nIf you think this still needs to be addressed please comment on this thread.", "I ran into this recently and posted a question about it [on the forum](https://discuss.huggingface.co/t/dpr-pretrained-context-encoder-unused-weight-warning/11265), awaiting response.\r\n\r\nI don't have a solution but, as a workaround, if you use `DPRContextEncoder` instead of `AutoModel`,\r\n```\r\nfrom transformers import DPRContextEncoder\r\ncontext_encoder = DPRContextEncoder.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\n```\r\nyou don't get the runtime warning." ]

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[ "You can check https://github.com/uf-hobi-informatics-lab/ClinicalTransformerNER\r\nWe essentially wrap the models in transformers with our NER implementation that can handle sentences longer than the max_len.", "Hi @Stimmot ,\r\n\r\ndid you run the `scripts/preprocess.py` to make sure that there are no sentences > 300 subtokens in your final data splits :thinking: \r\n\r\nThis should heavily prevent these kind of \"maximum sequence length execeeded\" errors :)", "Thank you for the response @stefan-it, but I don't think it actually has to do with the sequence lengths. The longest sentences in the documents are around 200 tokens, not even near the maximal length.\r\nBesides, the script crashes at some point with the following error:\r\n```\r\nTraceback (most recent call last):\r\n File \"/tmp/pycharm_project_44/examples/token-classification/run_ner_crossval.py\", line 226, in <module>\r\n main(sys.argv[1])\r\n File \"/tmp/pycharm_project_44/examples/token-classification/run_ner_crossval.py\", line 153, in main\r\n result_string, pred_dict = run_ner.main(json_config)\r\n File \"/tmp/pycharm_project_44/examples/token-classification/run_ner.py\", line 359, in main\r\n token_classification_task.write_predictions_to_file(writer, f, preds_list)\r\n File \"/tmp/pycharm_project_44/examples/token-classification/tasks.py\", line 53, in write_predictions_to_file\r\n elif preds_list[example_id]:\r\nIndexError: list index out of range\r\n100%|█████████████████████████████████████████████| 1/1 [00:00<00:00, 1.70it/s]\r\n```\r\nThe script doesn't work through the predictions list as it normally would, since the indices are shifted. Any other ideas what this could be?\r\n\r\n(Thank you @bugface as well but as said above I don't think it's because of the sequence length)", "@stefan-it I now also used the preprocess.py script just to be sure, but unfortunately it didn't change anything.", "Hi @Stimmot ,\r\n\r\nI think I found an interesting information in your provided log: `cached_test_BertTokenizer_340.lock`\r\n\r\nThat means, that the dataset was initially pre-processed with a sequence length of 340! Then I think you changed the max. sequence length to 300, but your ner script is still using the cached pre-processed test dataset that has a max. sequence length of 340.\r\n\r\nCould you try to remove all `cached*` files, so that the dataset features are newly written :thinking: Hope this helps :)", "Thanks @stefan-it, it had indeed to do with the cached models.\r\nI built a script that one by one takes test documents and runs the run_ner.py script on them, however, it seems that it saves a cached version of the model for the first document (which it predicts correctly) and then uses this cached version for all subsequent ones. The cached dimensions of the document don't work on the new ones so, naturally, it runs into errors.\r\n\r\nThe solution is to let the model be rebuilt after each run, without using cached verisons.\r\n\r\nAnother quick question on that: is there an option to tell the run_ner.py script not to build cached models, so that it will use new ones each time it predicts?", "Yeah, this can be done via `--overwrite_cache` argument :hugs: ", "Thank you very much, worked perfectly!", "This issue has been automatically marked as stale and been closed because it has not had recent activity. Thank you for your contributions.\n\nIf you think this still needs to be addressed please comment on this thread." ]

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[ "Thank you for this excellent report, @oborchers - I'll investigate and report back.", "Fixed in https://github.com/huggingface/transformers/pull/9736 \r\n\r\nBut found another problem: https://github.com/huggingface/transformers/issues/9737. Fixed in https://github.com/huggingface/transformers/pull/9738\r\n\r\nSo you will need both PRs for your task to work in case you want to try before they are merged.\r\n\r\n\r\n", "Awesome! Thank you, @stas00! Looking forward to try it out after PRs have been merged. Much appreciated ", "The problem you reported has been fixed in https://github.com/huggingface/transformers/pull/9736 (merged already)\r\n\r\nBut then another one poped up in https://github.com/huggingface/transformers/issues/9737\r\n\r\nYou can just use the https://github.com/huggingface/transformers/pull/9738 branch - since it contains both fixes.\r\n\r\nNot sure how quickly it will get merged, since we might want to solve this for other models too. I made only a local for fsmt fix in that PR.", "Great, thank you for the fast response and issue handling. I will provide a followup on #9738. While export works as intended, there is an issue I encounter while running the following code (built on 1st example):\r\n\r\n```\r\nsess = rt.InferenceSession(str(Path(\"encoder/en_de_trans.onnx\")), opt)\r\nspans = [\r\n \"My name is Bert\", # Succeeds\r\n \"My name is Bert and\" # Fails\r\n]\r\nfor span in spans:\r\n model_input = nlp.tokenizer.encode_plus(span)\r\n model_input = {name : np.atleast_2d(value) for name, value in model_input.items()}\r\n out = nlp.model(**nlp.tokenizer(span, return_tensors=\"pt\"))\r\n trans_1 = out[0].detach().cpu().numpy()\r\n trans_2 = out[1].detach().cpu().numpy()\r\n onnx_1, onnx_2 = sess.run(None, model_input)\r\n assert np.allclose(trans_1, onnx_1, atol=1e-5)\r\n assert np.allclose(trans_2, onnx_2, atol=1e-5)\r\n```\r\n\r\n\"My name is Bert and\" will raise:\r\n```\r\n---------------------------------------------------------------------------\r\nRuntimeException Traceback (most recent call last)\r\n<ipython-input-3-3ef2da9bdd5e> in <module>\r\n 10 trans_1 = out[0].detach().cpu().numpy()\r\n 11 trans_2 = out[1].detach().cpu().numpy()\r\n---> 12 onnx_1, onnx_2 = sess.run(None, model_input)\r\n 13 assert np.allclose(trans_1, onnx_1, atol=1e-5)\r\n 14 assert np.allclose(trans_2, onnx_2, atol=1e-5)\r\n\r\n~/anaconda3/envs/dev/lib/python3.8/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py in run(self, output_names, input_feed, run_options)\r\n 122 output_names = [output.name for output in self._outputs_meta]\r\n 123 try:\r\n--> 124 return self._sess.run(output_names, input_feed, run_options)\r\n 125 except C.EPFail as err:\r\n 126 if self._enable_fallback:\r\n\r\nRuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Reshape node. Name:'Reshape_74' Status Message: /data/shared/packages/onnxruntime/onnxruntime/core/providers/cpu/tensor/reshape_helper.h:43 onnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape&, std::vector<long int>&) gsl::narrow_cast<int64_t>(input_shape.Size()) == size was false. The input tensor cannot be reshaped to the requested shape. Input shape:{1,6}, requested shape:{5}\r\n```\r\n\r\nSolely based on intuition I'd assume that some dynamic shape of was not inferred properly/not passed to the dynamic_shapes of torch.onnx.export. But thats just a quick guess. Or did I miss something?\r\n\r\n\r\nI see that I would have to look-into/re-implement the generate function, as only the tensors are passed back. I'm going to create a feature suggestion to support the [ORT Custom Ops](https://github.com/microsoft/ort-customops). Perhaps It would be possible to retrieve the actual translated string in the far future, instead of the tensors (or specify the output). \r\n\r\nAs promised follow up feature request + suggestion under #9784 ", "Honestly, I don't know much about the ONNX-side of things. I asked @mfuntowicz to hopefully have a look and address this.\r\n\r\nAlso tagging @LysandreJik and @patrickvonplaten who perhaps may have some answers as well.\r\n\r\nI wonder if this is an issue project-wise, e.g. do you have the same issue if you do this on a Bart model? I'm asking since fsmt is Bart with some tweaks.\r\n\r\nAlso I think it's best to open a new issue, since now we are dealing with a different issue, so it'd be easier to track and monitor.", "Thank you for your help, @stas00! I followed your advice and created a new issue.", "@oborchers It seems that it is a problem of the pythorch export of the dynamic_axes.\r\nUsing the nightly version (torch-1.9.0.dev20210212 + cpu) it works.\r\n\r\nOn the other hand, I am interested in using the onnx models to generate, (translate and summarize). \r\nCould you give me some indication of how to do a custom forward using the onnx model, to use in the generation_utils.generate function.\r\n\r\nPS: for what you comment here [9784](https://github.com/huggingface/transformers/issues/9784) you plan to work on a User-specific re-implementation.\r\nThanks" ]

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[ "Actually the init takes care of this so no need for this PR" ]

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[ "If you run into other issues please try this PR: https://github.com/huggingface/transformers/pull/9323\r\nwhich has lots of improvements. It just hasn't been merged since we are waiting for me I think to sort the whole MP/PP out before moving forward." ]

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[ "> Also, I think you forgot to add the model to the README.md (I'll forget it all the time as well :D)\r\n\r\nOh and while you are at it, a short entry in the `model_summary` would be great too!" ]

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[ "There's not enough information here to help you. Please provide env info, the model/tasks you are using, and a short text code snippet to reproduce the error. ", "@patil-suraj I have edited the comment. Please help me out. Thanks.", "@patil-suraj Same issue here\r\n", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "Update: this error was present when using Python 3.6.9, it disappeared when using Python 3.7.5\r\nI found the hint at: https://github.com/huggingface/transformers/issues/8212 ", "This issue has been automatically marked as stale and been closed because it has not had recent activity. Thank you for your contributions.\n\nIf you think this still needs to be addressed please comment on this thread." ]

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[ "I wonder why the docstring says that, I believe both the slow and fast tokenizers replace with the unknown token. @thomwolf do you remember if that wasn't the case when you wrote the docstring?", "Hi @thomwolf, any thoughts on this?\r\n\r\nAlso @LysandreJik, do you know if there's a way to prevent the replacement of the unknown token, or at least to identify what string it replaced?", "If it did return strings that it could not understand, then the model would crash (which is why the docstring is surprising, and should be changed imo), and why we don't have a flag that would allow that. We can work around it, however.\r\n\r\nThe BERT tokenizer uses a whitespace tokenizer, which means that the first step is to split the input sequence on whitespace, before trying to convert each piece (or each \"word\") to tokens. When it fails to do that, it replaces that piece with the unknown token, so we can be confident that the unknown tokens are always space delimited strings.\r\n\r\nTherefore, we can do the following:\r\n\r\n```py\r\ntext_with_unknown_words = \"Let's try it with some 🤗 emojis 🤗 every 🤗 where 🤗.\"\r\n\r\n# Strip it and split it on whitespace\r\nlist_of_space_separated_pieces = text_with_unknown_words.strip().split()\r\n\r\n# Let's try it with the BERT-base-cased tokenizer\r\nfrom transformers import BertTokenizer\r\ntokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")\r\n\r\n# Now let's encode the list word by word, removing the special tokens for simplicity\r\nids = [tokenizer(piece, add_special_tokens=False)[\"input_ids\"] for piece in list_of_space_separated_pieces]\r\n# [[2421, 112, 188], [2222], [1122], [1114], [1199], [100], [9712, 1186, 3454, 1116], [100], [1451], [100], [1187], [100, 119]]\r\n# The tokenizer's unknown token is 100 and we can identify it easily here\r\n\r\n# Identify the tokens which are unknown according to the tokenizer's `unk_token_id`\r\nunk_indices = [i for i, encoded in enumerate(ids) if tokenizer.unk_token_id in encoded]\r\n\r\n# Retrieve the strings that were converted into unknown tokens\r\nunknown_strings = [piece for i, piece in enumerate(list_of_space_separated_pieces) if i in unk_indices]\r\n# ['🤗', '🤗', '🤗', '🤗.'] Victory!\r\n```\r\n\r\nLet me know if that helps.", "Thanks @LysandreJik -- this is really helpful! I ended up adding a couple more steps from BERT's basic tokenizer, since it also splits on punctuation, etc.\r\n\r\nNot sure if I should close this issue now, or if it should stay open until the docstring issue is figured out?", "Yes, the docstring should be updated. Do you want to take a stab at contributing? :)", "Sure -- would it just involve fixing the docstrings that say this in the python code and then building the docs as specified [here](https://github.com/huggingface/transformers/blob/master/docs/README.md)? Or is there more to it?", "Actually just fixing the docstrings and comitting! As soon as we merge the PR the documentation of the `master` branch will be updated." ]

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[ "I agree with Patrick here, the collator was added to encode the text and to prepare the `decoder_input_ids` and `labels`, replace pad with 100 etc. Now we could encode and prepare `labels` in datasets.map(...) so collator won't be needed anymore. \r\n\r\nThe only thing we need IMO is to be able to prepare `decoder_input_ids` outside of the model for label smoothing as Sylvain said. Could we maybe make the add `shift_right` method to every s2s model to able to prepare the `decoder_input_ids` outside of the model ?", "Note that this is fixing the old script with the old data collator. The new one will be fixed with the proper fix (once we agree on it and there seems to be a consensus on having a model with a `shift_right` method) but is still necessary to do dynamic padding. The `Dataset.map` method is very nice for static things but when you want to pad to the length of the biggest sample in the batch, you need a special data collator, especially if it has to pad special keys like `\"labels\"`, `\"decoder_input_ids\"`...\r\n\r\nThe old `Seq2SeqDataCollator` in the utils file will be removed in a couple of weeks when the new seq2seq example is perfectly running, so I think it's fine to merge the quick hack in the meantime :-)" ]

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[ "Thanks for fixing!" ]

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[ "Decided it would be easier for us to take care of this since we plan to directly release the model checkpoint in huggingface.\r\n\r\nStarted working on it over the week-end, will share PR once it is more polished. ", "This is great news @Iwontbecreative! Let us know if you need help." ]

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[ "Pinging @stas00", "Heh, actually I wrote this section: https://www.deepspeed.ai/tutorials/advanced-install/#building-for-the-correct-architectures and the autodetector, since I originally had the same issue.\r\n\r\nThis problem is also partially in pytorch - which is now fixed too in pytorch-nightly.\r\n\r\n `TORCH_CUDA_ARCH_LIST` is there if you say want to use the binary build on another machine or want to optimize it for whatever reason. e.g. I build it with:\r\n```\r\nTORCH_CUDA_ARCH_LIST=\"6.1;8.6\" DS_BUILD_OPS=1 pip install --no-cache -v --disable-pip-version-check -e .\r\n```\r\nbecause I have 1070 and 3090 cards.\r\n\r\nI'm glad you found a way to solve it.\r\n\r\nNow, this is a purely DeepSpeed issue and has nothing to do with transformers, other than perhaps a documentation issue.\r\n\r\nI'm all ears at how perhaps `transformers` can improve the doc on our side to help the users find a solution quickly.\r\n\r\n1. Probably should recommend to install from source\r\n2. but then when we bail on missing `deepspeed` we say do `pip install deepspeed` - do you think we should change that to:\r\n> `pip install deepspeed` or if it doesn't work install from source?\r\n\r\nThe thing is `pip install deepspeed` is installing from source, but I think it perhaps isn't using the same build script? So should we say:\r\n> `pip install deepspeed` or if it doesn't work install from https://github.com/microsoft/deepspeed? \r\n\r\nor may be easier to just say: \r\n> install from https://github.com/microsoft/deepspeed?\r\n\r\nWhat happens if you install with:\r\n```\r\nDS_BUILD_OPS=1 pip install deepspeed\r\n```\r\n\r\nPerhaps your issue is JIT/PTX which happens if you don't do the above - i.e. the binary build gets postpone till run time. `DS_BUILD_OPS=1` forces the binary build.\r\n\r\nIn any case let's discuss this over at DeepSpeed Issues - @PeterAJansen, would you please open an issue there because only you can report/reproduce the specific error - should they fix the pip build. and tag me?\r\n\r\nBTW, fairscale has its own issues with `pip install fairscale` - I also have to build from the the repo, because I am forced to use pytorch-nightly due to rtx-30* and it won't build at all via `pip` directly.\r\n\r\nso whatever we decide we should do the same for `fairscale`.\r\n\r\nThank you!", "This issue has been automatically marked as stale and been closed because it has not had recent activity. Thank you for your contributions.\n\nIf you think this still needs to be addressed please comment on this thread." ]

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[ "Thanks a lot for fixing it @guillaume-be " ]

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[ "Once the PR template is complete and everybody is happy I will sync with the Issue template. So please only review the former if you're just joining in.", "should we add bullets? As in:\r\n\r\n```\r\n\r\nModels:\r\n\r\n- albert, bert, xlm: @LysandreJik\r\n- blenderbot, bart, marian, pegasus, encoderdecoder, longformer, reformer, t5, transfoxl, xlnet: @patrickvonplaten\r\n- fsmt: @stas00\r\n- funnel: @sgugger\r\n- gpt2: @patrickvonplaten, @LysandreJik\r\n- rag: @patrickvonplaten, @lhoestq\r\n- tensorflow: @jplu\r\n\r\nLibrary:\r\n\r\n- benchmarks: @patrickvonplaten\r\n- deepspeed: @stas00\r\n- ray/raytune: @richardliaw, @amogkam\r\n- text generation: @patrickvonplaten\r\n- tokenizers: @n1t0\r\n- trainer: @sgugger\r\n\r\nDocumentation: @sgugger\r\n\r\nHF projects:\r\n\r\n- nlp datasets: [different repo](https://github.com/huggingface/nlp)\r\n- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)\r\n\r\nExamples:\r\n\r\n- maintained examples (not research project or legacy): @sgugger, @patil-suraj\r\n- research_projects/bert-loses-patience: @JetRunner\r\n- research_projects/distillation: @VictorSanh\r\n```", "I like bullets.", "someone to tag for ONNX issues? @mfuntowicz?" ]

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[ "Thanks for fixing!" ]

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[ "Maybe @jplu has an idea!", "Hello!\r\n\r\nThis error is always raised by the TFTrainer when your dataset has not a cardinality attached.\r\n\r\nCan you give me the version of the `run_tf_ner.py` you are using please?", "The run_tf_ner.py I used was downloaded from this https://github.com/huggingface/transformers/tree/master/examples/token-classification， transformer version is 4.2.0， tensorflow == 2.4.0\r\n @jplu ", "Are you sure this is the exact version or not from another commit? Because I see a cardinality assigned in the current script. Even thought the script is not working since 4.2.0 but for a diffeerent reason.", "This issue has been automatically marked as stale and been closed because it has not had recent activity. Thank you for your contributions.\n\nIf you think this still needs to be addressed please comment on this thread." ]

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[ "You're totally right @gui11aume! Thanks for posting this issue - it would be great if you could open a PR to fix it. The checkpoint I uploaded won't work well because I just took the decoder part of the encoder-decoder model and removed all cross-attention layer. The model would have to be fine-tuned to work correctly. The main motivation to add `ProphetNetForCausalLM` however was to enable things like `Longformer2ProphetNet` as described here: https://github.com/huggingface/transformers/pull/9033" ]

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[ "Please use the [forums](https://discuss.huggingface.co/) to ask questions like this. Also note that there is no `finetune` script in the example folder anymore, so you should probably be using `finetune_trainer` or `run_seq2seq`." ]

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[ "It may cause similar issues in other models and other versions of the same model as well.", "HI @KaiQiangSong \r\n\r\nWe haven't yet observed `NaN`s with BART specifically, could you post a code snippet where the model returns `NaN` so we could take a look ?", "> HI @KaiQiangSong\r\n> \r\n> We haven't yet observed `NaN`s with BART specifically, could you post a code snippet where the model returns `NaN` so we could take a look ?\r\n\r\nSorry that, I couldn't publish my code now due to it is unpublished research.\r\nI've fixed the issue myself with changing the mask_fill of float(\"-inf\") to -1e5 (for supporting AMP as well).\r\nJust post this issue here to let you know there might be a potential issue.", "I have the same exact problem.\r\n\r\nIll try with the -1e5 trick and see if it helps me too.\r\n\r\nThanks a lot!", "> I have the same exact problem.\r\n> \r\n> Ill try with the -1e5 trick and see if it helps me too.\r\n> \r\n> Thanks a lot!\r\n\r\nglad that my solution helps.", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "You're right, thanks for reporting! The PR mentioned above should fix that." ]

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[ "Hello! It's currently not implemented for DeBERTa, unfortunately. Following the document you linked, it should be pretty easy to do it in a script!", "Hi @LysandreJik ,\r\n\r\nWill DeBERTa (or any of RoBERTa, ALBERT) work if I separate these layers as two or three parts and connect them sequentially?\r\nBecause this is what is happening in [previous link](https://pytorch.org/tutorials/intermediate/model_parallel_tutorial.html#apply-model-parallel-to-existing-modules) that I shared", "You would need to cast the intermediate hidden states to the correct devices as well. You can see that in the example you shared, see how the intermediate hidden states were cast to cuda 1:\r\n```py\r\n def forward(self, x):\r\n x = self.seq2(self.seq1(x).to('cuda:1'))\r\n return self.fc(x.view(x.size(0), -1))\r\n```", "Hi @LysandreJik ,\r\n\r\nFor DeBERTa, I'm able to split entire model into 'embedding', 'encoder', 'pooler', 'classifier' and 'dropout' layers as shown in below pic.\r\n\r\n\r\n\r\nWith this approach, I trained on IMDB classification task by assigning 'encoder' to second GPU and others to first 'GPU'. At the end of the training, second GPU consumed lot of memory when compared to first GPU and this resulted in 20-80 split of the entire model.\r\n\r\nSo, I tried splitting encoder layers also as shown below but getting this error - **\"TypeError: forward() takes 1 positional argument but 2 were given\"**\r\n\r\n```\r\nembed = dberta.deberta.embeddings.to('cuda:0')\r\n\r\nf6e = dberta.deberta.encoder.layer[:6].to('cuda:0')\r\n\r\nl6e = dberta.deberta.encoder.layer[6:].to('cuda:1')\r\n\r\npooler = dberta.pooler.to('cuda:0')\r\n\r\nclassifier = dberta.classifier.to('cuda:0')\r\n\r\ndropout = dberta.dropout.to('cuda:0')\r\n\r\ntest = \"this is to test deberta\"\r\n\r\ninp_ids = tok_dberta(test, return_tensors='pt').input_ids\r\natt_mask = tok_dberta(test, return_tensors='pt').attention_mask\r\n\r\nemb_out = embed(inp_ids.to('cuda:0'))\r\n\r\nfirst_6_enc_lay_out = f6e(emb_out)\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\n<ipython-input-15-379d948e5ba5> in <module>\r\n----> 1 first_6_enc_lay_out = f6e(emb_out)\r\n\r\n/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)\r\n 725 result = self._slow_forward(*input, **kwargs)\r\n 726 else:\r\n--> 727 result = self.forward(*input, **kwargs)\r\n 728 for hook in itertools.chain(\r\n 729 _global_forward_hooks.values(),\r\n\r\nTypeError: forward() takes 1 positional argument but 2 were given\r\n\r\n```\r\n\r\nPlz suggest how to proceed further..", "Hi @LysandreJik ,\r\n\r\nPlz update on the above issue that I'm facing", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "Thanks for fixing!" ]

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[ "Hello, thanks for opening an issue! We try to keep the github issues for bugs/feature requests.\r\nCould you ask your question on the [forum](https://discusss.huggingface.co) instead? You'll get more answers over there.\r\n\r\nThanks!" ]

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[ "> It would appear that line 1286 in trainer.py actually calls the backward method on the model, not the loss object. I will try rebuilding after fixing that line and seeing if it helps.\r\n\r\nThis is incorrect. It appears that the ``model_wrapped.module`` in the aforementioned trainer.py actually resolves to GPT2LMHeadModel. Another big shot in the dark, but maybe ``model_wrapped`` is never actually wrapping because I'm only using one GPU? It's very late where I live, I'll take another shot at this in the morning.", "You then need to launch your script with the `deepspeed` launcher. Could you tell us which command you ran?\r\nAlso cc @stas00 since he added deepspeed to Trainer.", "Yes, please tag me on any deepspeed issues.\r\n\r\nThank you for this report.\r\n\r\nI think it's a bug, it should be:\r\n\r\n```\r\nself.deepspeed.backward(loss)\r\n```\r\n\r\nI will test and send a fix.\r\n", "The merged PR closed this report, but should you still have an issue please don't hesitate to re-open it. \r\n", "Hello,\r\nThanks for this great deepspeed feature. I am also running into the same error both for \r\nDistilBertForSequenceClassification' object has no attribute 'backward'\r\nand for\r\nBertForSequenceClassification object has no attribute 'backward'\r\n\r\nhere is the full error:\r\n\r\n> ---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\n<ipython-input-6-beaae64139c1> in <module>\r\n 23 )\r\n 24 \r\n---> 25 trainer.train()\r\n\r\n~/anaconda3/lib/python3.7/site-packages/transformers/trainer.py in train(self, model_path, trial)\r\n 886 tr_loss += self.training_step(model, inputs)\r\n 887 else:\r\n--> 888 tr_loss += self.training_step(model, inputs)\r\n 889 self._total_flos += self.floating_point_ops(inputs)\r\n 890 \r\n\r\n~/anaconda3/lib/python3.7/site-packages/transformers/trainer.py in training_step(self, model, inputs)\r\n 1263 elif self.deepspeed:\r\n 1264 # calling on DS engine (model_wrapped == DDP(Deepspeed(PretrainedModule)))\r\n-> 1265 self.model_wrapped.module.backward(loss)\r\n 1266 else:\r\n 1267 loss.backward()\r\n\r\n~/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in __getattr__(self, name)\r\n 574 return modules[name]\r\n 575 raise AttributeError(\"'{}' object has no attribute '{}'\".format(\r\n--> 576 type(self).__name__, name))\r\n 577 \r\n 578 def __setattr__(self, name, value):\r\n\r\nAttributeError: 'DistilBertForSequenceClassification' object has no attribute 'backward'\r\n\r\nAny idea?\r\nThanks", "@victorstorchan, can you please ensure you use an up-to-date master?", "Thanks for your answer. I just pip installed transformers 1h ago. It should be up-to-date right?", "no, it won't. pip installs the released version. you need the unreleased master build, which there are several ways to go about, one of them is just:\r\n\r\n```\r\npip install git+https://github.com/huggingface/transformers\r\n```\r\n\r\n\r\n\r\n", "My bad! Thanks @stas00 ", "You did nothing wrong, @victorstorchan. \r\n\r\nI will propose an update to the installation page so that the distinction is loud and clear." ]

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[ "https://github.com/huggingface/transformers/issues/9694" ]

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[ "Hello, thanks for opening an issue! We try to keep the github issues for bugs/feature requests.\r\nCould you ask your question on the [forum](https://discusss.huggingface.co) instead? You'll get more answers there!\r\n\r\nThanks!" ]

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[ "Thanks for the review @LysandreJik, the test did fail because of the pooler. Is fixed now!" ]

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[ "No, only Python.", "> No, only Python.\r\n\r\nthx.\r\nIt means that using torch cannot call bert with c++, right?" ]

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[ "Looking at the [docs](https://huggingface.co/transformers/model_doc/deberta.html), it seems like there's currently no `DeBERTaForMaskedLM` defined. I will make a PR that adds this." ]

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[ "Hi @wilcoln \r\n\r\nYes, the links point to Github, feel free to open a PR to replace the GitHub links with colab :). Thanks!" ]

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[ "Hello! To make sure I understand your issue, you're doing the following:\r\n\r\n```py\r\nAutoTokenizer.from_pretrained('facebook/blenderbot-400M-distill')\r\n```\r\n\r\non a node which has internet access, and then you're doing the same once you have no internet access. You want the library to rely on the cache that it had previously downloaded, is that right?\r\n\r\nCould you make sure you are up to date with the `master` branch, and try the following once you have no internet access:\r\n\r\n```py\r\nAutoTokenizer.from_pretrained('facebook/blenderbot-400M-distill', local_files_only=True)\r\n```\r\n\r\nThank you.", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "Hi @anicolson ,\r\n\r\nWe would love to help, but sadly when you post such a long script it will be very hard and time-consuming for us to take a look at. We're happy to assist if you could provide a short, precise, and complete code snippet that is based on Transformers Seq2SeqTrainer only. Here's our guide on [how to request support](https://discuss.huggingface.co/t/how-to-request-support/3128).\r\n\r\nAlso from what I can see, seems like you are initializing bert encoder and bert decoder separately, you could directly instantiate it using the `EncoderDecoder` model class to get a seq2seq model. Here are two colab notebooks that show how to train `EncoderDecoder` models using `Seq2SeqTrainer`. The notebooks show how to fine-tune for summarization task, but could be easily adapted for translation as well.\r\n\r\n[Leverage BERT for Encoder-Decoder Summarization on CNN/Dailymail](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb)\r\n\r\n[Leverage RoBERTa for Encoder-Decoder Summarization on BBC XSum](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/RoBERTaShared_for_BBC_XSum.ipynb)", "Thanks for your reply, \r\n\r\nI am attempting to create a shorter version that is not so time-consuming.\r\n\r\nCertainly, the `EncoderDecoder` is an attractive option if one is using natural language, but I would like to highlight that using `BertGenerateDecoder` allows the user to provide any sequence for cross-attention, even those derived from encoders that operate on modalities other than natural language, which I think is powerful.", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.", "> Thanks for your reply,\r\n> \r\n> I am attempting to create a shorter version that is not so time-consuming.\r\n> \r\n> Certainly, the `EncoderDecoder` is an attractive option if one is using natural language, but I would like to highlight that using `BertGenerateDecoder` allows the user to provide any sequence for cross-attention, even those derived from encoders that operate on modalities other than natural language, which I think is powerful.\r\n\r\nHi, have you tackled the problem? I encounter the exactly same problem. Any cues?" ]

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[ "Thanks for opening the PR. As it stands this would break every existing script leveraging the parameters defined, so renaming the parameters is probably not the way to go.\r\n\r\n@sgugger, your insight on this would be very welcome.\r\n\r\n", "There is no reason to change all the names of the parameters indeed, and it would be a too-heavy breaking change. `AdamW` is not a different optimizer from `Adam`, it's just `Adam` with a different way (some might say the right way) of doing weight decay. I don't think we need to do more than a mention at the beginning of the docstring saying that all mentions of `Adam` are actually about `AdamW`, with a link to the paper.", "Hi @LysandreJik @sgugger. Thanks for your comments, I'll be changing the variables back.\r\n\r\nI apologize if this is too silly a question, but how can I run and see how the docs look on a browser after the changes?", "You should check [this page](https://github.com/huggingface/transformers/tree/master/docs#generating-the-documentation) for all the information on generating/writing the documentation :-)", "I have updated it and also added that by default, the weight decay is applied to all layers except bias and LayerNorm weights while training.", "@sgugger My code passed only 3 out of 12 checks, I was unable to run CirlceCI properly. Can you point out the reasons why this happened?", "We are trying to get support from them to understand why, but the checks on your PR were all cancelled. I couldn't retrigger them from our interface either.", "Hi @sgugger \r\n\r\nI believe a possible reason could be that I followed `transformers` on CircleCI. Maybe it performs checks on my fork of transformers and expects to find some \"resources\" which aren't there.\r\n\r\nI'm not sure how CircleCI works, so this is just a wild guess." ]

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[ "Do not miss ```transformers/cammand/convert.py``` for ```transformer-cli``` user.\r\nNeed ```base_model``` arg for ```convert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output)```." ]

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[ "Hello, thanks for opening an issue! We try to keep the github issues for bugs/feature requests.\r\nCould you ask your question on the [forum](https://discusss.huggingface.co) instead?\r\n\r\nThanks!" ]

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[ "Hello, thanks for opening an issue! We try to keep the github issues for bugs/feature requests.\r\nCould you ask your question on the [forum](https://discusss.huggingface.co) instead?\r\n\r\nThanks!" ]

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[ "The pipeline for masked filling can only be used to fill one token, so you should be using different code for your evaluation if you want to be able to predict more than one masked token.", "> The pipeline for masked filling can only be used to fill one token, so you should be using different code for your evaluation if you want to be able to predict more than one masked token.\r\n\r\nThank you for your reply. I am not sure, whether I understand you right. So do you mean, that it is not possible to predict words like \"Los Angeles\" with two words or that it is also not possible to predict words like \"pseudogene\", which are one word but are not in the vocabulary and so the tokenizer splits it into ['pseudo', '##gene']? I would only like to predict words like \"pseudogene\".", "The pipeline in itself is only coded to return one token to replace the [MASK]. So it won't be able to predict two tokens to replace one [MASK]. The model is also only trained to replace each [MASK] in its sentence by one token, so it won't be able to predict two tokens for one [MASK].\r\n\r\nFor this task, you need to either use a different model (coded yourself as it's not present in the library) or have your training set contain one [MASK] per token you want to mask. For instance if you want to mask all the tokens corresponding to one word (a technique called whole-word masking) what is typically done in training scripts is to replace all parts of one word by [MASK]. For pseudogener tokenized as pseudo, ##gene, that would mean having [MASK] [MASK].\r\n\r\nAlso, this is not a bug of the library, so the discussion should continue on the [forum](https://discuss.huggingface.co/)\r\n\r\n" ]

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[ "Hi @caralen \r\n\r\nthe `Seq2SeqTrainer` is now integrated with the main lib, now it's under `src/trainer_seq2seq.py`, and the seq2seq_trainer in examples is about to be deprecated, this bug is fixed in the new version, so I would recommend you to use the new`Seq2SeqTrainer` from the lib rather than examples folder,\r\n\r\nyou could directly import it from transformers using\r\n```python\r\nfrom transformers import Seq2SeqTrainer\r\n```", "Hi @patil-suraj, thanks for the quick reply. I will close this issue now." ]

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[ "Hi! I think you're looking for `padding=\"longest\"`?", "Your result if of length 512 because you asked `padding=\"max_length\"`, and the tokenizer max length is 512. If you ask for `\"longest\"`, it will pad up to the longest value in your batch:\r\n\r\n```py\r\n>>> text = \"After stealing money from the bank vault, the bank robber was seen fishing on the Mississippi river bank.\"\r\n... features = nlp([text, text * 2], padding=\"longest\", truncation=True, max_length=40)\r\n```\r\n\r\nreturns features which are of size [42, 768].", "> Your result if of length 512 because you asked `padding=\"max_length\"`, and the tokenizer max length is 512. If you ask for `\"longest\"`, it will pad up to the longest value in your batch:\r\n> \r\n> ```python\r\n> >>> text = \"After stealing money from the bank vault, the bank robber was seen fishing on the Mississippi river bank.\"\r\n> ... features = nlp([text, text * 2], padding=\"longest\", truncation=True, max_length=40)\r\n> ```\r\n> \r\n> returns features which are of size [42, 768].\r\n\r\nThank you very much! This method works! And I think the 'longest' padding strategy is enough for me to use in my dataset.\r\nBut I just wonder that can I specify a fixed padding size? Like all sentence could be padded to length 40?\r\nBecause in my former 'inconvenient general method', I just use\r\n```\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\r\ntext = 'After stealing money from the bank vault, the bank robber was seen fishing on the Mississippi river bank.'\r\n\r\nencoded_input = tokenizer(text, padding='max_length', truncation=True, max_length=40)\r\n```\r\nand get the fixed size padding sentence though... \r\n(I found this method from the official documentation [https://huggingface.co/transformers/preprocessing.html#everything-you-always-wanted-to-know-about-padding-and-truncation](url)", "Well it seems impossible for now... I just tried\r\n```\r\ntext = \"After stealing money from the bank vault, the bank robber was seen \" \\\r\n \"fishing on the Mississippi river bank.\"\r\nfeatures = nlp(text, padding='length', truncation=True, length=40)\r\n```\r\nAnd the error message showed that:\r\n**ValueError: 'length' is not a valid PaddingStrategy, please select one of ['longest', 'max_length', 'do_not_pad']**\r\nAnyway, thank you very much!", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "Hi! This is a normal behavior of the BERT tokenizer. You can add the tokens you do not wish to see split to the vocabulary:\r\n```py\r\n>>> tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\r\n... tokenizer.add_tokens([\"no_passages_used\"]) # <--------------------------------- Here\r\n... paragraph = \"no_passages_used knowledge no_passages_used\"\r\n... print(tokenizer.encode(paragraph))\r\n... print(tokenizer.decode(tokenizer.encode(paragraph)))\r\n[101, 30522, 3716, 30522, 102]\r\n[CLS] no_passages_used knowledge no_passages_used [SEP]\r\n```", "Don't forget to resize the embedding matrix of your model if you add new tokens to the vocabulary: [docs for add_tokens method](https://huggingface.co/transformers/internal/tokenization_utils.html?highlight=resize_token_embeddings#transformers.tokenization_utils_base.SpecialTokensMixin.add_tokens)" ]

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[ "Hello! Could you open an issue on the [tokenizers](https://github.com/huggingface/tokenizers) repository instead? @n1t0 will probably know what's up!", "Done! https://github.com/huggingface/tokenizers/issues/604", "This is actually a transformers problem I think. It's the old versions of tokenizers imported using a path that has since become private. It's fixed in the newer versions, but transformers is still pinned to the old version: https://github.com/huggingface/transformers/issues/9649", "Ah, installing something newer, e.g. `transformers==4.2.2`, has fixed it. Thanks so much!" ]

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[ "Hi @ahmedkotb98 \r\n\r\nWe would love to help, but it would be better if you could only post the relevant minimal code snippet to reproduce the issue, rather than a bunch of scripts.\r\n\r\nAlso, it's better to ask such type of questions on the [forum](https://discuss.huggingface.co/) first. Here's our guide on [how to request support](https://discuss.huggingface.co/t/how-to-request-support/3128).\r\n\r\nThanks.", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "@sgugger ", "It's very hard to help you without being able to reproduce the bug. Could you share a small version of your csv file that reproduces it?", "Yes, no problem. I just tried with a sample created from the `head`of my `full_corpus.csv` file and got the same error. This is the head:\r\n\r\n```\r\nA tomada de posse já está marcada para esta quarta feira ao fim da tarde...\r\nLobo Xavier está infetado com Covid-19. Esteve no Conselho de Estado na terça-feira.\r\n\"Porque está descida é temporária. Se descessem agora, depois não poderiam explicar a necessidade de uma nova subida.\"\r\nEm acumulação com o Banco de Portugal.\r\n\"EUA: Há muitas maneiras de isto acabar mal. A newsletter Novo Normal do ⁦⁩ no ECO. Um guia do que pode suceder nas eleições americanas (sentem-se, é melhor)\"\r\nCosta vai substituir presidente do Tribunal de Contas via\r\nComo criar filhos felizes?\r\nUma economia a 90 por cento via\r\nApoio à Retoma Progressiva vai permitir suspender contratos via Falta saber qual o valor do salário e quem o paga.\r\nO perigo de esperar que o Estado nos salve\r\n```", "The problem is that you are not passing a `max_seq_length` so the script uses the tokenizer `model_lax_length`, which is in turn excessively large (1000000000000000019884624838656). So this results in all your texts not even being able to produce one batch.\r\n\r\nJust pass `--max_seq_length 512` or something else and you should be good.", "Ok, thanks. It's working now." ]