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DanielPFlorian

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Transformers-Github-Issues

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6621?src=pr&el=h1) Report\n> Merging [#6621](https://codecov.io/gh/huggingface/transformers/pull/6621?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/573bdb0a5d2897ff6c7520ebb38693c7acfbf17e?el=desc) will **increase** coverage by `0.85%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6621?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6621 +/- ##\n==========================================\n+ Coverage 79.16% 80.02% +0.85% \n==========================================\n Files 156 156 \n Lines 28217 28217 \n==========================================\n+ Hits 22339 22581 +242 \n+ Misses 5878 5636 -242 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6621?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_xlm.py](https://codecov.io/gh/huggingface/transformers/pull/6621/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl94bG0ucHk=) | `18.94% <0.00%> (-74.32%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_flaubert.py](https://codecov.io/gh/huggingface/transformers/pull/6621/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9mbGF1YmVydC5weQ==) | `24.53% <0.00%> (-63.81%)` | :arrow_down: |\n| [src/transformers/modeling\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6621/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19yb2JlcnRhLnB5) | `77.37% <0.00%> (-19.71%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6621/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `82.18% <0.00%> (-0.26%)` | :arrow_down: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6621/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `88.05% <0.00%> (+0.55%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6621/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `87.29% <0.00%> (+2.60%)` | :arrow_up: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6621/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.46% <0.00%> (+2.75%)` | :arrow_up: |\n| [src/transformers/configuration\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/6621/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3Q1LnB5) | `96.42% <0.00%> (+10.71%)` | :arrow_up: |\n| [src/transformers/modeling\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/6621/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190NS5weQ==) | `83.83% <0.00%> (+12.21%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/6621/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl90NS5weQ==) | `90.93% <0.00%> (+64.09%)` | :arrow_up: |\n| ... and [1 more](https://codecov.io/gh/huggingface/transformers/pull/6621/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6621?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6621?src=pr&el=footer). Last update [573bdb0...d61cbf8](https://codecov.io/gh/huggingface/transformers/pull/6621?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "works for me in in torch 1.5.1. and torch 1.6.\r\nMaybe this is a one off s3 failure?\r\nCan anybody else replicate?\r\n\r\n```\r\nfrom transformers import PegasusForConditionalGeneration\r\nmodel = PegasusForConditionalGeneration.from_pretrained(model_name)\r\n```", "I set ```force_download=True``` and it worked. Thanks!", "> I set `force_download=True` and it worked. Thanks!\r\n\r\ncan you describe in detail how did you solved the problem\r\n\r\n", "Just upgrading the PyTorch and TensorFlow version solved the problem for me. ", "torch==1.6.0\r\ntensorflow==2.3.1\r\ntransformers==3.5.1\r\n\r\nAnd I'm trying to load my model trained on gpt2-small named train-on-test1. But I get the an OSERROR: \r\n\r\n Unable to load weights from pytorch checkpoint file for '{pretrained_model_name_or_path}' OSError: Unable to load weights \r\n from pytorch checkpoint file for '/mounted/models/train-on-test1/' at '/mounted/models/train-on-test1/pytorch_model.bin' If you \r\n tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True.\r\n", "I'm getting the same using torch == 1.7.0 and transformers == 4.1.1 and a xlnet localy downloaded model :\r\n\r\n```\r\nfrom transformers import XLNetForSequenceClassification\r\nmodel = XLNetForSequenceClassification.from_pretrained('/../../models/xlnet/', num_labels = 3)\r\n\r\nOSError: Unable to load weights from pytorch checkpoint file. If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True. \r\n```", "I keep getting the same error too. I have pretrained a distilbertmodel named amazon-distilbert. When I am trying to load it using from pretrained it is throwing the same error.\r\n\r\n```\r\nfrom transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification\r\n\r\ntokenizer = DistilBertTokenizerFast.from_pretrained(\"distilbert-base-cased\");\r\nmodel = DistilBertForSequenceClassification.from_pretrained(\"../models/amazon-distilbert\")\r\n```\r\n\r\nAnd the error\r\n```\r\n f\"Unable to load weights from pytorch checkpoint file for '{pretrained_model_name_or_path}' \"\r\nOSError: Unable to load weights from pytorch checkpoint file for '/GitHub/TextSentimentAnalysis/models/amazon-distilbert' at '//GitHub/TextSentimentAnalysis/models/amazon-distilbert/pytorch_model.bin'If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True. \r\n```", "same error with torch 1.5.0, TensorFlow 2.4.1, and transformers 4.2.2. Does anyone know how to solve such a problem?", "Same error with torch 1.8.1, transformers 4.5.1 when trying to call \r\n```\r\nfrom transformers import AutoTokenizer, AutoModelForSeq2SeqLM\r\n \r\nAutoModelForSeq2SeqLM.from_pretrained('Helsinki-NLP/opus-mt-ja-en').save_pretrained('pretrained_models/opus-mt-ja-en')\r\n```", "can anyone share how to solve this error to me? plz , i have met this problem too", "Same issue here ..", "> > I set `force_download=True` and it worked. Thanks!\r\n> \r\n> can you describe in detail how did you solved the problem\r\n\r\nIn the **from_pretrained** function, set one of the parameters as **force_download=True**\r\nEg. - model = LayoutLMForTokenClassification.from_pretrained(\"microsoft/layoutlm-base-uncased\", num_labels=num_labels, **force_download=True**)", "In my case, I was running on a cpu only compute, and this issue got solved when installing a cpu version of PyTorch. For example: http://download.pytorch.org/whl/cpu/torch-1.13.0%2Bcpu-cp39-cp39-linux_x86_64.whl\r\n" ]

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[ "Will investigate now @sgugger @VictorSanh . My first guess is that it because of \"inf\" values because of masking because this error does not happen when the attention mask is not passed to forward. " ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6618?src=pr&el=h1) Report\n> Merging [#6618](https://codecov.io/gh/huggingface/transformers/pull/6618?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/039d8d65fc19ac74a8c7917233eb2828c46c0fa7?el=desc) will **decrease** coverage by `0.93%`.\n> The diff coverage is `0.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6618?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6618 +/- ##\n==========================================\n- Coverage 79.79% 78.86% -0.94% \n==========================================\n Files 156 156 \n Lines 28213 28213 \n==========================================\n- Hits 22513 22250 -263 \n- Misses 5700 5963 +263 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6618?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/trainer\\_tf.py](https://codecov.io/gh/huggingface/transformers/pull/6618/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyX3RmLnB5) | `12.25% <0.00%> (ø)` | |\n| [src/transformers/modeling\\_tf\\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/6618/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9lbGVjdHJhLnB5) | `25.13% <0.00%> (-73.83%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_mobilebert.py](https://codecov.io/gh/huggingface/transformers/pull/6618/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9tb2JpbGViZXJ0LnB5) | `24.55% <0.00%> (-72.36%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6618/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `85.99% <0.00%> (-1.31%)` | :arrow_down: |\n| [src/transformers/tokenization\\_dpr.py](https://codecov.io/gh/huggingface/transformers/pull/6618/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fZHByLnB5) | `57.65% <0.00%> (+4.50%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_flaubert.py](https://codecov.io/gh/huggingface/transformers/pull/6618/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9mbGF1YmVydC5weQ==) | `88.34% <0.00%> (+63.80%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_albert.py](https://codecov.io/gh/huggingface/transformers/pull/6618/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9hbGJlcnQucHk=) | `90.90% <0.00%> (+69.43%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6618?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6618?src=pr&el=footer). Last update [039d8d6...76afd14](https://codecov.io/gh/huggingface/transformers/pull/6618?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "Hey @BCWang93, can you post a reproducible code snippet? It would be great if we could just copy paste your code and see the same error you post here :-) ", "> Hey @BCWang93, can you post a reproducible code snippet? It would be great if we could just copy paste your code and see the same error you post here :-)\r\n\r\nBecause this is an entire project, I can't paste the full code. But I found the difference between transformers 3 and 2. Transformers2 maps ID to \"[unk]\" when dealing with characters like '\\n','\\r' et.al, but transformers3 discards all such characters when dealing with such cases. Like this:\r\n\r\nThanks very much!", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "same question", "same issues coming for me as well", "same issue", "Does this still happen in the latest transformers version? Could you put the output of `transformers-cli` env here? Thanks.", "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.", "Reopening. Still having same issue?" ]

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[ "Hello!\r\n\r\nWhat is the command line you use to train the model?", "Hello @jplu \r\nthat's the command line I tried to use to train the model (you added that command to the model description on huggingface community models)\r\n\r\n\r\n```\r\ncd examples/ner\r\npython run_tf_ner.py \\\r\n--data_dir . \\\r\n--labels ./labels.txt \\\r\n--model_name_or_path jplu/tf-xlm-roberta-base \\\r\n--output_dir model \\\r\n--max-seq-length 128 \\\r\n--num_train_epochs 2 \\\r\n--per_gpu_train_batch_size 16 \\\r\n--per_gpu_eval_batch_size 32 \\\r\n--do_train \\\r\n--do_eval \\\r\n--logging_dir logs \\\r\n--mode token-classification \\\r\n--evaluate_during_training \\\r\n--optimizer_name adamw\r\n```", "Can you use the last version of the trainer, with th ecommand line I used:\r\n\r\n```\r\npython run_tf_ner.py \\\r\n--data_dir . \\\r\n--labels ./labels.txt \\\r\n--model_name_or_path jplu/tf-xlm-roberta-base \\\r\n--output_dir model \\\r\n--num_train_epochs 8 \\\r\n--per_gpu_train_batch_size 32 \\\r\n--per_gpu_eval_batch_size 64 \\\r\n--do_train \\\r\n--do_eval \\\r\n--do_predict \\\r\n--logging_steps 10 \\\r\n--evaluate_during_training \\\r\n--save_steps 100 \\\r\n--overwrite_output_dir\r\n```", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "You may be right but maybe there is a reason? @sgugger ", "I'm guessing it was used at some point and then I forgot to remove it when it wasn't used anymore. Thanks for fixing!" ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6612?src=pr&el=h1) Report\n> Merging [#6612](https://codecov.io/gh/huggingface/transformers/pull/6612?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/18ca0e91402d17950b870d7c9f67ddb7fd573817&el=desc) will **decrease** coverage by `0.61%`.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6612?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6612 +/- ##\n==========================================\n- Coverage 79.89% 79.28% -0.62% \n==========================================\n Files 156 156 \n Lines 28213 28219 +6 \n==========================================\n- Hits 22542 22374 -168 \n- Misses 5671 5845 +174 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6612?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/tokenization\\_mbart.py](https://codecov.io/gh/huggingface/transformers/pull/6612/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fbWJhcnQucHk=) | `97.14% <100.00%> (+1.83%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_xlnet.py](https://codecov.io/gh/huggingface/transformers/pull/6612/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl94bG5ldC5weQ==) | `21.12% <0.00%> (-71.05%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_flaubert.py](https://codecov.io/gh/huggingface/transformers/pull/6612/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9mbGF1YmVydC5weQ==) | `24.53% <0.00%> (-63.81%)` | :arrow_down: |\n| [src/transformers/modeling\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6612/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19yb2JlcnRhLnB5) | `77.37% <0.00%> (-19.71%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6612/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `86.64% <0.00%> (-0.33%)` | :arrow_down: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6612/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `88.05% <0.00%> (+0.55%)` | :arrow_up: |\n| [src/transformers/modeling\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/6612/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190NS5weQ==) | `83.83% <0.00%> (+6.20%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6612/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9yb2JlcnRhLnB5) | `93.22% <0.00%> (+47.80%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/6612/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9lbGVjdHJhLnB5) | `98.95% <0.00%> (+73.82%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6612?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6612?src=pr&el=footer). Last update [18ca0e9...2207e5d](https://codecov.io/gh/huggingface/transformers/pull/6612?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "Hey @pranavpsv, \r\n\r\nYes, I think we can switch the `TextGenerationPipeline` to `AutoModelForCausalLM` and `AutoModelForSeq2SeqLM`. So check if the model is in one of the above and then use it, instead of using `AutoModelWithLMHead`. Also once we have a `ConditionalTextGenerationPipeline` we can remove the `AutoModelForSeq2SeqLM` dependency. Feel free to open a PR about this :-) ", "@patrickvonplaten Got it, thank you for the info :)\r\n\r\nMy model is just a GPT2 model, so I believe it should use AutoModelForCausalLM.\r\n\r\nI just checked on the transformers master branch in the pipelines.py source code [file](https://github.com/huggingface/transformers/blob/d0e42a7bed3de9271ae39c575d7eeb54cf985921/src/transformers/pipelines.py#L2432). \r\nIt shows that the pipelines.py file from master branch doesn't use AutoModelWithLMHead. \r\n\r\nHowever, for some reason, when pip installing the latest version of transformers, the Pipeline object (with task as text-generation) still gives the AutoModelWithLMHead deprecated warning (indicating that it might be importing AutoModelWithLMHead). \r\n\r\n To confirm, I found that the installed transformers (3.0.2) pipelines.py file imports AutoModelWithLMHead (which is what could be causing this warning):\r\n\r\n```\r\n# The pipelines.py file\r\nif is_torch_available():\r\n import torch\r\n from .modeling_auto import (\r\n AutoModel,\r\n AutoModelForSequenceClassification,\r\n AutoModelForQuestionAnswering,\r\n AutoModelForTokenClassification,\r\n AutoModelWithLMHead,\r\n AutoModelForSeq2SeqLM,\r\n )\r\n\r\n```\r\nThere seems to be a discrepancy between master branch and the 3.0.2 release pipelines object.\r\n\r\n\r\nFor now, I'm doing the following to avoid the warning.\r\n\r\n\r\n```\r\nmodel = GPT2LMHeadModel.from_pretrained(checkpoint)\r\ntokenizer = AutoTokenizer.from_pretrained(checkpoint)\r\ntext_generator = TextGenerationPipeline(model=model, tokenizer=tokenizer)\r\n\r\n```", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "what are your versions? This doesn't break for me.\r\nTry running \r\n```\r\ntransformers-cli env\r\n```", "The latest version from PyPI, v3.0.2. It doesn't happen while running locally but does break on remote executions, though.\r\n\r\nLooking through the documentation, it made sense to me that an error would pop up since [`.generate()`](https://huggingface.co/transformers/model_doc/bart.html#transformers.BartForConditionalGeneration.generate) outputs a `torch.Tensor`, but [`tokenizer.decode()`](https://huggingface.co/transformers/main_classes/tokenizer.html#transformers.PreTrainedTokenizer.decode) (and batch_decode) expect `List[int]`", "PyTorch 1.6.0 and Python 3.6, if that helps.", "Can I see your traceback?\r\n\r\nYou can loop over tensors just like lists.", "I have a custom `summarization_trainer.py` in there that calls the `main` from `finetune.py`.\r\n\r\nTraceback:\r\n```\r\nFile \"summarization_trainer.py\", line 157, in main\r\n return finetune_main(args, model)\r\n File \"/opt/ml/code/finetune.py\", line 460, in main\r\n logger=logger,\r\n File \"/opt/ml/code/lightning_base.py\", line 448, in generic_train\r\n trainer.fit(model)\r\n File \"/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py\", line 1044, in fit\r\n results = self.run_pretrain_routine(model)\r\n File \"/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py\", line 1196, in run_pretrain_routine\r\n False)\r\n File \"/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/evaluation_loop.py\", line 293, in _evaluate\r\n output = self.evaluation_forward(model, batch, batch_idx, dataloader_idx, test_mode)\r\n File \"/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/evaluation_loop.py\", line 470, in evaluation_forward\r\n output = model.validation_step(*args)\r\n File \"/opt/ml/code/finetune.py\", line 211, in validation_step\r\n return self._generative_step(batch)\r\n File \"/opt/ml/code/finetune.py\", line 255, in _generative_step\r\n preds: List[str] = self.ids_to_clean_text(generated_ids)\r\n File \"/opt/ml/code/finetune.py\", line 153, in ids_to_clean_text\r\n generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True\r\n File \"/opt/conda/lib/python3.6/site-packages/transformers/tokenization_utils_base.py\", line 2254, in batch_decode\r\n return [self.decode(seq, **kwargs) for seq in sequences]\r\n File \"/opt/conda/lib/python3.6/site-packages/transformers/tokenization_utils_base.py\", line 2254, in <listcomp>\r\n return [self.decode(seq, **kwargs) for seq in sequences]\r\n File \"/opt/conda/lib/python3.6/site-packages/transformers/tokenization_utils_fast.py\", line 439, in decode\r\n text = self._tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)\r\n File \"/opt/conda/lib/python3.6/site-packages/tokenizers/implementations/base_tokenizer.py\", line 267, in decode\r\n return self._tokenizer.decode(ids, skip_special_tokens=skip_special_tokens)\r\nTypeError\r\n```", "is there anything after TypeError?\r\nI guess this is a torch 1.6 issue.", "No, it doesn't say anything after `TypeError`. Could be a 1.6 issue, let me try to downgrade and report back.", "Hmm, something's off. Now I'm seeing `TypeError` when trying to save the tokenizer during a checkpoint.\r\n```\r\nTraceback (most recent call last):\r\n File \"/root/ds-sandbox/projects/abstractive_summarization/finetuning-bart/finetuning_bart/summarization_trainer.py\", line 214, in <module>\r\n _ = slack_wrapper(args)\r\n File \"/opt/conda/lib/python3.6/site-packages/knockknock/slack_sender.py\", line 105, in wrapper_sender\r\n raise ex\r\n File \"/opt/conda/lib/python3.6/site-packages/knockknock/slack_sender.py\", line 63, in wrapper_sender\r\n value = func(*args, **kwargs)\r\n File \"/root/ds-sandbox/projects/abstractive_summarization/finetuning-bart/finetuning_bart/summarization_trainer.py\", line 159, in main\r\n return finetune_main(args, model)\r\n File \"/root/ds-sandbox/projects/abstractive_summarization/finetuning-bart/finetuning_bart/finetune.py\", line 463, in main\r\n logger=logger,\r\n File \"/root/ds-sandbox/projects/abstractive_summarization/finetuning-bart/finetuning_bart/lightning_base.py\", line 448, in generic_train\r\n trainer.fit(model)\r\n File \"/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py\", line 1003, in fit\r\n results = self.single_gpu_train(model)\r\n File \"/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/distrib_parts.py\", line 186, in single_gpu_train\r\n results = self.run_pretrain_routine(model)\r\n File \"/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py\", line 1213, in run_pretrain_routine\r\n self.train()\r\n File \"/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py\", line 370, in train\r\n self.run_training_epoch()\r\n File \"/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py\", line 470, in run_training_epoch\r\n self.run_evaluation(test_mode=False)\r\n File \"/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/evaluation_loop.py\", line 430, in run_evaluation\r\n self.on_validation_end()\r\n File \"/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/callback_hook.py\", line 112, in on_validation_end\r\n callback.on_validation_end(self, self.get_model())\r\n File \"/opt/conda/lib/python3.6/site-packages/pytorch_lightning/utilities/distributed.py\", line 12, in wrapped_fn\r\n return fn(*args, **kwargs)\r\n File \"/opt/conda/lib/python3.6/site-packages/pytorch_lightning/callbacks/model_checkpoint.py\", line 309, in on_validation_end\r\n self._do_check_save(filepath, current, epoch)\r\n File \"/opt/conda/lib/python3.6/site-packages/pytorch_lightning/callbacks/model_checkpoint.py\", line 346, in _do_check_save\r\n self._save_model(filepath)\r\n File \"/opt/conda/lib/python3.6/site-packages/pytorch_lightning/callbacks/model_checkpoint.py\", line 168, in _save_model\r\n self.save_function(filepath, self.save_weights_owandb: Program failed with code 1. Press ctrl-c to abort syncing.\r\nnly)\r\n File \"/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_io.py\", line 268, in save_checkpoint\r\n checkpoint = self.dump_checkpoint(weights_only)\r\n File \"/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_io.py\", line 379, in dump_checkpoint\r\n model.on_save_checkpoint(checkpoint)\r\n File \"/opt/conda/lib/python3.6/site-packages/pytorch_lightning/utilities/distributed.py\", line 12, in wrapped_fn\r\n return fn(*args, **kwargs)\r\n File \"/root/ds-sandbox/projects/abstractive_summarization/finetuning-bart/finetuning_bart/lightning_base.py\", line 223, in on_save_checkpoint\r\n self.tokenizer.save_pretrained(save_path)\r\n File \"/opt/conda/lib/python3.6/site-packages/transformers/tokenization_utils_base.py\", line 1379, in save_pretrained\r\n vocab_files = self.save_vocabulary(save_directory)\r\n File \"/opt/conda/lib/python3.6/site-packages/transformers/tokenization_utils_fast.py\", line 449, in save_vocabulary\r\n files = self._tokenizer.save_model(save_directory)\r\n File \"/opt/conda/lib/python3.6/site-packages/tokenizers/implementations/base_tokenizer.py\", line 323, in save_model\r\n return self._tokenizer.model.save(directory, name=name)\r\nTypeError\r\n```\r\n\r\nI have the changes proposed in this PR in my code so it is clearly not the same.", "^ is occurring on torch 1.5.1 and 1.6.0 both.", "Update: I was creating a Fast tokenizer and passing it in during the creation of `SummarizationModule`. Switching to the one automatically created by the `BaseTransformer` avoids the last error.", "The change in the PR is also related to the above error and creating a new tokenizer instead of an automatic initialization. Letting `SummarizationModule` and `BaseTransformer` deal with the creation of tokenizer instead doesn't raise those errors, however, that also means I can't use the Rust-based tokenizers.", "Great catch! Would you mind making a new issue with a broken snippet that doesn't use PL? and tag @sshleifer \r\nE.g.\r\n```\r\nfrom transformers import BartTokenizerFast, BartForConditionalGeneration\r\n...\r\n\r\n```\r\n?\r\nThen we can try to fix the bug on the proper level of abstraction.", "Thanks! Yes, agree that the right place for this is an issue. I'll try to create an issue that doesn't use PL later in the day." ]

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[ "I just fixed. Can you try again. Should produce a warning but no error.", "> I just fixed. Can you try again. Should produce a warning but no error.\r\n\r\nI am still getting the below error. It does not seem to even go to the tokenizer. It throws the error right when we acquire the pretrained model. It seems as though something about the checkpoint of the pre-trained model has changed\r\n\r\nRuntimeError: Error(s) in loading state_dict for PegasusForConditionalGeneration:\r\n\tsize mismatch for model.encoder.embed_positions.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 1024]).\r\n\tsize mismatch for model.decoder.embed_positions.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 1024]).", "I changed the state dict and deleted `model.encoder.embed_positions.weight` at 6pm EST.\r\n\r\nYour code works for me on master (93c5c9a5) \r\n\r\ncommand: `transformers-cli env`\r\n\r\n```\r\n- `transformers` version: 3.0.2\r\n- Platform: Darwin-19.5.0-x86_64-i386-64bit\r\n- Python version: 3.7.7\r\n- PyTorch version (GPU?): 1.5.1 (False)\r\n- Tensorflow version (GPU?): 2.2.0 (False)\r\n- Using GPU in script?: <fill in>\r\n- Using distributed or parallel set-up in script?: <fill in>\r\n```\r\n", "> `model.encoder.embed_positions.weight`\r\n\r\nI completely removed transformers and took a fresh git and I am still getting the same error. Is there some other information I could provide that will help resolve this issue for me?", "> I changed the state dict and deleted `model.encoder.embed_positions.weight` at 6pm EST.\r\n> \r\n> Your code works for me on master ([93c5c9a](https://github.com/huggingface/transformers/commit/93c5c9a528475db73c2b481131578b8dd903efba))\r\n> \r\n> command: `transformers-cli env`\r\n> \r\n> ```\r\n> - `transformers` version: 3.0.2\r\n> - Platform: Darwin-19.5.0-x86_64-i386-64bit\r\n> - Python version: 3.7.7\r\n> - PyTorch version (GPU?): 1.5.1 (False)\r\n> - Tensorflow version (GPU?): 2.2.0 (False)\r\n> - Using GPU in script?: <fill in>\r\n> - Using distributed or parallel set-up in script?: <fill in>\r\n> ```\r\n\r\nThe error occurs only for pegasus-arxiv. It works with warning for pegasus-pubmed and pegasus-large. I need help with pegasus-arxiv.\r\n" ]

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[ "you could input the `input_embeds` tokens instead of the `input_ids` to the forward pass and adjust the `hidden_size` accordingly. Or you just tweak the model files yourself and you remove `nn.Embeddings` and replace it by a dense layer. Btw, normally you get much better answers for these kind of questions when you post it on https://discuss.huggingface.co/ . We try to move \"non-bug\" questions to this forum :-) ", "@patrickvonplaten Thank you for the reply, Sure from onwards, I'll post my doubts there.\r\nIf you could provide any quick template to start with continuous values, that’d be helpful.", "Hi, @patrickvonplaten I asked this question on discussion forum but didn't get any update yet. Can you provide any starter code/ Template where I can feed continuous values directly? \r\n\r\nhttps://discuss.huggingface.co/t/how-to-use-huggingface-model-for-continuous-values-directly/816", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6606?src=pr&el=h1) Report\n> Merging [#6606](https://codecov.io/gh/huggingface/transformers/pull/6606?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/18ca0e91402d17950b870d7c9f67ddb7fd573817?el=desc) will **decrease** coverage by `0.61%`.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6606?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6606 +/- ##\n==========================================\n- Coverage 79.89% 79.28% -0.62% \n==========================================\n Files 156 156 \n Lines 28213 28215 +2 \n==========================================\n- Hits 22542 22370 -172 \n- Misses 5671 5845 +174 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6606?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/configuration\\_pegasus.py](https://codecov.io/gh/huggingface/transformers/pull/6606/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3BlZ2FzdXMucHk=) | `100.00% <100.00%> (+9.09%)` | :arrow_up: |\n| [src/transformers/modeling\\_pegasus.py](https://codecov.io/gh/huggingface/transformers/pull/6606/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19wZWdhc3VzLnB5) | `100.00% <100.00%> (ø)` | |\n| [src/transformers/modeling\\_tf\\_xlnet.py](https://codecov.io/gh/huggingface/transformers/pull/6606/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl94bG5ldC5weQ==) | `21.12% <0.00%> (-71.05%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_flaubert.py](https://codecov.io/gh/huggingface/transformers/pull/6606/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9mbGF1YmVydC5weQ==) | `24.53% <0.00%> (-63.81%)` | :arrow_down: |\n| [src/transformers/modeling\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6606/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19yb2JlcnRhLnB5) | `77.37% <0.00%> (-19.71%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6606/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `86.64% <0.00%> (-0.33%)` | :arrow_down: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6606/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `88.05% <0.00%> (+0.55%)` | :arrow_up: |\n| [src/transformers/modeling\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/6606/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190NS5weQ==) | `83.83% <0.00%> (+6.20%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6606/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9yb2JlcnRhLnB5) | `93.22% <0.00%> (+47.80%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/6606/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9lbGVjdHJhLnB5) | `98.95% <0.00%> (+73.82%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6606?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6606?src=pr&el=footer). Last update [18ca0e9...68ace1e](https://codecov.io/gh/huggingface/transformers/pull/6606?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Just to get some context, why can't we fix the tokenizer's default value?", "correct max_model_length differs between checkpoints\r\n", "But isn't each tokenizer instantiated with a checkpoint?\r\nI'd expect this to be automatically done by\r\n```\r\ntokenizer1 = AutoTokenizer.from_pretrained(\"checkpoint_with_one_max_len\")\r\ntokenizer2 = AutoTokenizer.from_pretrained(\"checkpoint_with_another_max_len\")\r\n```", "Yeah. The tokenizer defaults have always been correct.\r\n\r\nThe issue was that fixing the model defaults to match them created an inconsistency with the state_dict on s3.\r\n\r\nFor cnn...\r\n\r\n- Broken converter creates model, config with 512 positional embeddings. Tokenizer is good -- says max_model_length=1024.\r\n- User reports IndexError bug. #6599 \r\n- Sam adjusts model config to position_embeddings=1024. \r\n- This creates a new error: at __init__ the cnn model will allocate space for 1024 positional embeddings, but the state dict on s3 will only have 512. #6909\r\n- Sam deletes positional embeddings on S3. Code works but gives missing key warning.\r\n- Sam sends this PR to suppress warning, add regression test that checks that #6599 can't happen again.\r\n\r\n\r\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6605?src=pr&el=h1) Report\n> Merging [#6605](https://codecov.io/gh/huggingface/transformers/pull/6605?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/fe0b85e77a6af041471657069bbb9c21a880cd5c?el=desc) will **increase** coverage by `0.08%`.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6605?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6605 +/- ##\n==========================================\n+ Coverage 80.21% 80.30% +0.08% \n==========================================\n Files 156 156 \n Lines 28178 28205 +27 \n==========================================\n+ Hits 22604 22650 +46 \n+ Misses 5574 5555 -19 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6605?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/configuration\\_reformer.py](https://codecov.io/gh/huggingface/transformers/pull/6605/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3JlZm9ybWVyLnB5) | `100.00% <ø> (ø)` | |\n| [src/transformers/modeling\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/6605/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iZXJ0LnB5) | `88.42% <ø> (ø)` | |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6605/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `88.05% <ø> (ø)` | |\n| [src/transformers/training\\_args.py](https://codecov.io/gh/huggingface/transformers/pull/6605/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmluZ19hcmdzLnB5) | `91.26% <ø> (+10.67%)` | :arrow_up: |\n| [src/transformers/configuration\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6605/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3V0aWxzLnB5) | `95.97% <100.00%> (+0.02%)` | :arrow_up: |\n| [src/transformers/data/data\\_collator.py](https://codecov.io/gh/huggingface/transformers/pull/6605/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL2RhdGFfY29sbGF0b3IucHk=) | `90.90% <100.00%> (ø)` | |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6605/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.21% <100.00%> (-0.26%)` | :arrow_down: |\n| [src/transformers/modeling\\_albert.py](https://codecov.io/gh/huggingface/transformers/pull/6605/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19hbGJlcnQucHk=) | `83.50% <100.00%> (+0.13%)` | :arrow_up: |\n| [src/transformers/modeling\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/6605/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19kaXN0aWxiZXJ0LnB5) | `97.84% <100.00%> (+0.02%)` | :arrow_up: |\n| [src/transformers/modeling\\_longformer.py](https://codecov.io/gh/huggingface/transformers/pull/6605/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19sb25nZm9ybWVyLnB5) | `92.02% <100.00%> (+0.07%)` | :arrow_up: |\n| ... and [15 more](https://codecov.io/gh/huggingface/transformers/pull/6605/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6605?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6605?src=pr&el=footer). Last update [9a86321...3f89b2d](https://codecov.io/gh/huggingface/transformers/pull/6605?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Re. the eval loss, did you also run [test_trainer_distributed.py](https://github.com/huggingface/transformers/blob/master/tests/test_trainer_distributed.py) on a multi-gpu machine?", "No, I don't have a multi-GPU machine setup. It does not seem like this test uses the eval_loss anywhere, it only computes a metric.", "> No, I don't have a multi-GPU machine setup.\r\n\r\nYou can use the office machines! Not necessarily related to this PR but to keep in mind to run this test once in a while", "> Not necessarily related to this PR but to keep in mind to run this test once in a while\r\n\r\nIt was indeed broken not due to this PR, fixed. Know how to run it periodically now :-)" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6604?src=pr&el=h1) Report\n> Merging [#6604](https://codecov.io/gh/huggingface/transformers/pull/6604?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/18ca0e91402d17950b870d7c9f67ddb7fd573817?el=desc) will **decrease** coverage by `1.15%`.\n> The diff coverage is `66.66%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6604?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6604 +/- ##\n==========================================\n- Coverage 79.89% 78.74% -1.16% \n==========================================\n Files 156 156 \n Lines 28213 28213 \n==========================================\n- Hits 22542 22216 -326 \n- Misses 5671 5997 +326 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6604?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_pytorch\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6604/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9weXRvcmNoX3V0aWxzLnB5) | `89.30% <66.66%> (ø)` | |\n| [src/transformers/modeling\\_tf\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/6604/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl90NS5weQ==) | `26.84% <0.00%> (-64.10%)` | :arrow_down: |\n| [src/transformers/configuration\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/6604/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3Q1LnB5) | `85.71% <0.00%> (-10.72%)` | :arrow_down: |\n| [src/transformers/modeling\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/6604/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190NS5weQ==) | `71.61% <0.00%> (-6.02%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6604/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `80.70% <0.00%> (-5.77%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6604/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `84.69% <0.00%> (-2.29%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6604/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `82.44% <0.00%> (+0.25%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6604/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9yb2JlcnRhLnB5) | `93.22% <0.00%> (+47.80%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6604?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6604?src=pr&el=footer). Last update [18ca0e9...6ebffb5](https://codecov.io/gh/huggingface/transformers/pull/6604?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "This fixes #5588 ", "This pull request has been replaced with #6623 " ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6603?src=pr&el=h1) Report\n> Merging [#6603](https://codecov.io/gh/huggingface/transformers/pull/6603?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/18ca0e91402d17950b870d7c9f67ddb7fd573817&el=desc) will **decrease** coverage by `1.12%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6603?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6603 +/- ##\n==========================================\n- Coverage 79.89% 78.77% -1.13% \n==========================================\n Files 156 156 \n Lines 28213 28213 \n==========================================\n- Hits 22542 22226 -316 \n- Misses 5671 5987 +316 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6603?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/generation\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6603/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3V0aWxzLnB5) | `96.94% <ø> (ø)` | |\n| [src/transformers/modeling\\_tf\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/6603/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl90NS5weQ==) | `26.84% <0.00%> (-64.10%)` | :arrow_down: |\n| [src/transformers/configuration\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/6603/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3Q1LnB5) | `85.71% <0.00%> (-10.72%)` | :arrow_down: |\n| [src/transformers/modeling\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/6603/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190NS5weQ==) | `71.61% <0.00%> (-6.02%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6603/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `83.20% <0.00%> (-3.26%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6603/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `84.69% <0.00%> (-2.29%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6603/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `82.44% <0.00%> (+0.25%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6603/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9yb2JlcnRhLnB5) | `93.22% <0.00%> (+47.80%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6603?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6603?src=pr&el=footer). Last update [18ca0e9...1ac2967](https://codecov.io/gh/huggingface/transformers/pull/6603?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Next time, try writing a PR title that provides more context.\r\nLike \"remove confusing newline\".\r\nThen your current title could go in the description.\r\n\r\nAnyways, thanks for the contribution!", "I'll try to be more clear next time, thanks." ]

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[ "I do not know what happened on your PR, any way you can restore or rebase/force-push for the diff to be readable?", "force-pushed", "@julien-c Rebased onto latest commit in master (https://github.com/huggingface/transformers/commit/f72fe1f31aca235c7f675680832cc364efe4088e)", "Don't think the change to `past[-1]` is applicable to all models. We should enforce the use of `return_dict=True` in TF as well to simplify this behaviour", "> Don't think the change to `past[-1]` is applicable to all models.\r\n\r\n@patrickvonplaten removed this commit.", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6601?src=pr&el=h1) Report\n> Merging [#6601](https://codecov.io/gh/huggingface/transformers/pull/6601?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/26d5475d4b6644528956df3020dbaa436b443706?el=desc) will **increase** coverage by `5.22%`.\n> The diff coverage is `80.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6601?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6601 +/- ##\n==========================================\n+ Coverage 75.91% 81.13% +5.22% \n==========================================\n Files 195 168 -27 \n Lines 39827 32288 -7539 \n==========================================\n- Hits 30233 26198 -4035 \n+ Misses 9594 6090 -3504 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6601?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/6601/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19ncHQyLnB5) | `86.66% <80.00%> (-1.32%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/6601/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9lbGVjdHJhLnB5) | `25.13% <0.00%> (-71.56%)` | :arrow_down: |\n| [src/transformers/tokenization\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/6601/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYmFydC5weQ==) | `42.10% <0.00%> (-57.90%)` | :arrow_down: |\n| [src/transformers/tokenization\\_camembert.py](https://codecov.io/gh/huggingface/transformers/pull/6601/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fY2FtZW1iZXJ0LnB5) | `37.03% <0.00%> (-53.13%)` | :arrow_down: |\n| [src/transformers/tokenization\\_mbart.py](https://codecov.io/gh/huggingface/transformers/pull/6601/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fbWJhcnQucHk=) | `36.50% <0.00%> (-39.33%)` | :arrow_down: |\n| [src/transformers/tokenization\\_funnel.py](https://codecov.io/gh/huggingface/transformers/pull/6601/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fZnVubmVsLnB5) | `62.79% <0.00%> (-37.21%)` | :arrow_down: |\n| [src/transformers/modeling\\_lxmert.py](https://codecov.io/gh/huggingface/transformers/pull/6601/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19seG1lcnQucHk=) | `70.01% <0.00%> (-20.72%)` | :arrow_down: |\n| [src/transformers/trainer\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6601/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyX3V0aWxzLnB5) | `60.29% <0.00%> (-20.48%)` | :arrow_down: |\n| [src/transformers/modeling\\_mobilebert.py](https://codecov.io/gh/huggingface/transformers/pull/6601/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19tb2JpbGViZXJ0LnB5) | `79.21% <0.00%> (-10.22%)` | :arrow_down: |\n| [src/transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6601/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19vcGVuYWkucHk=) | `72.25% <0.00%> (-10.00%)` | :arrow_down: |\n| ... and [157 more](https://codecov.io/gh/huggingface/transformers/pull/6601/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6601?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6601?src=pr&el=footer). Last update [26d5475...8ffb4c0](https://codecov.io/gh/huggingface/transformers/pull/6601?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Hey @LSinev, \r\n\r\nI think the `token_type_ids` would have to be increased by 1 at each generation step. With the current generation implementing this logic is quite tedious - I think we should put this PR on hold until generation is properly refactored. Will try to get to this within the next 3,4 weeks. Hope that's fine!", "\r\n@LSinev I think this implementation is what I need! (haven't tried it)\r\nFor my purpose, I would have a `context` (`token_type_ids`=0), a `prompt token` (`token_type_ids`=1) followed by prediction (`token_type_ids`=1).\r\nWith this PR, seems like I can pass in `context` + `prompt token` with `token_type_ids` and the last `token_type_ids` will be reused in the generated, which is what I want.\r\n\r\n> I think the `token_type_ids` would have to be increased by 1 at each generation step\r\n\r\n @patrickvonplaten Do you mean `position_ids`?\r\n I just got an idea. What if we allow users to pass in a function for `prepare_inputs_for_generation`, so users can easily customize the behavior? 🤔 💥", "> like\r\n\r\nThat would work with `past`, but would break if you set `use_cache=False` because `token_type_ids` length's is not increased. I can see that this feature is required. However, I want to wait until the refactoring is done before adding more functionality to the general `generate()` method. \r\n\r\nAdding a function to `prepare_input_ids` is a bit tooo hacky for me as well (we would need to add a function arg to `generate()`)", "Was just curious what was the status of this PR? It seems all the checks have passed", "> > like\r\n> \r\n> That would work with `past`, but would break if you set `use_cache=False` because `token_type_ids` length's is not increased. I can see that this feature is required. However, I want to wait until the refactoring is done before adding more functionality to the general `generate()` method.\r\n> \r\n> Adding a function to `prepare_input_ids` is a bit tooo hacky for me as well (we would need to add a function arg to `generate()` which I don't really like\r\n\r\nSorry to only get back you guys now. So having refactored the `generate()` method, I think we can should add an `if token_type_ids in model_kwargs` here: https://github.com/huggingface/transformers/blob/70708cca1a2f3e01d9d72a3aaa7ab078dfef639e/src/transformers/generation_utils.py#L184 to deal with this case. The way it's implemented now does not really work as explained in the message above.", "Maybe I should add changes to `GPT2ForSequenceClassification` too? Or maybe `GPT2PreTrainedModel` should have widest `prepare_inputs_for_generation` for all inherited models?", "Tests added. Not a good showcase (because the model is not finetuned to use `token_type_ids` in the way like https://github.com/huggingface/transfer-learning-conv-ai), but at least it tests that `token_type_ids` are used (and expanded if `num_return_sequences` passed) and affect output. Not sure if slow tests are tested online at PR submission, though.", "Tests are great! It's fine if it's not a good showcase - as long as we make sure the functionality works and will work in the future this is great! \r\n\r\nGreat job @LSinev and thanks for bearing with me throughout this PR :-) " ]

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[ "Found bug, will be fix by EOD.", "This is a coincidence. I found the same issue with pegasus-arxiv and pegasus-pubmed today. Glad this will be fixed", "> \r\n> ## Information\r\n> Model I am using: google/pegasus-cnn_dailymail\r\n> \r\n> The problem arises when using:\r\n> \r\n> ```\r\n> import torch\r\n> from transformers import AutoModelWithLMHead, AutoTokenizer\r\n> \r\n> torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'\r\n> \r\n> model = AutoModelWithLMHead.from_pretrained(\"google/pegasus-cnn_dailymail\")\r\n> tokenizer = AutoTokenizer.from_pretrained(\"google/pegasus-cnn_dailymail\")\r\n> \r\n> src_text = [\r\n> '(CNN)James Best, best known for his portrayal of bumbling sheriff Rosco P. Coltrane on TV\\'s \"The Dukes of Hazzard,\" died Monday after a brief illness. He was 88. Best died in hospice in Hickory, North Carolina, of complications from pneumonia, said Steve Latshaw, a longtime friend and Hollywood colleague. Although he\\'d been a busy actor for decades in theater and in Hollywood, Best didn\\'t become famous until 1979, when \"The Dukes of Hazzard\\'s\" cornpone charms began beaming into millions of American homes almost every Friday night. For seven seasons, Best\\'s Rosco P. Coltrane chased the moonshine-running Duke boys back and forth across the back roads of fictitious Hazzard County, Georgia, although his \"hot pursuit\" usually ended with him crashing his patrol car. Although Rosco was slow-witted and corrupt, Best gave him a childlike enthusiasm that got laughs and made him endearing. His character became known for his distinctive \"kew-kew-kew\" chuckle and for goofy catchphrases such as \"cuff \\'em and stuff \\'em!\" upon making an arrest. Among the most popular shows on TV in the early \\'80s, \"The Dukes of Hazzard\" ran until 1985 and spawned TV movies, an animated series and video games. Several of Best\\'s \"Hazzard\" co-stars paid tribute to the late actor on social media. \"I laughed and learned more from Jimmie in one hour than from anyone else in a whole year,\" co-star John Schneider, who played Bo Duke, said on Twitter. \"Give Uncle Jesse my love when you see him dear friend.\" \"Jimmy Best was the most constantly creative person I have ever known,\" said Ben Jones, who played mechanic Cooter on the show, in a Facebook post. \"Every minute of his long life was spent acting, writing, producing, painting, teaching, fishing, or involved in another of his life\\'s many passions.\" Born Jewel Guy on July 26, 1926, in Powderly, Kentucky, Best was orphaned at 3 and adopted by Armen and Essa Best, who renamed him James and raised him in rural Indiana. Best served in the Army during World War II before launching his acting career. In the 1950s and 1960s, he accumulated scores of credits, playing a range of colorful supporting characters in such TV shows as \"The Twilight Zone,\" \"Bonanza,\" \"The Andy Griffith Show\" and \"Gunsmoke.\" He later appeared in a handful of Burt Reynolds\\' movies, including \"Hooper\" and \"The End.\" But Best will always be best known for his \"Hazzard\" role, which lives on in reruns. \"Jimmie was my teacher, mentor, close friend and collaborator for 26 years,\" Latshaw said. \"I directed two of his feature films, including the recent \\'Return of the Killer Shrews,\\' a sequel he co-wrote and was quite proud of as he had made the first one more than 50 years earlier.\" People we\\'ve lost in 2015 . CNN\\'s Stella Chan contributed to this story.'\r\n> ]\r\n> batch = tokenizer.prepare_seq2seq_batch(src_text, truncation=True, padding='longest').to(torch_device)\r\n> translated = model.generate(**batch)\r\n> tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)\r\n> ```\r\n> \r\nCurrently I am overcoming this by passing additional parameter max_length=512 in prepare_seq2seq_batch. \r\n\r\nThis is not what I want. I want to expand the length to atleast 2048 if possible. But I don't see a way to do it", "The bug should be fixed. Please try again.\r\n\r\nThe following max_lengths are recommended:\r\n```\r\nmax_input_length = {\r\n \"xsum\": 512,\r\n \"cnn_dailymail\": 1024,\r\n \"newsroom\": 512,\r\n \"wikihow\": 512,\r\n \"multi_news\": 1024,\r\n \"reddit_tifu\": 512,\r\n \"big_patent\": 1024,\r\n \"arxiv\": 1024,\r\n \"pubmed\": 1024,\r\n \"gigaword\": 128,\r\n \"aeslc\": 512,\r\n \"billsum\": 1024,\r\n \"large\": 1024,\r\n}\r\n```\r\n\r\n@suchig You can probably do longer with the current code by passing `max_position_embeddings=2048`, but no model was finetuned on sequences of that length so it might (a) OOM or (b) perform poorly.", "> The bug should be fixed. Please try again.\r\n> \r\n> The following max_lengths are recommended:\r\n> \r\n> ```\r\n> max_input_length = {\r\n> \"xsum\": 512,\r\n> \"cnn_dailymail\": 1024,\r\n> \"newsroom\": 512,\r\n> \"wikihow\": 512,\r\n> \"multi_news\": 1024,\r\n> \"reddit_tifu\": 512,\r\n> \"big_patent\": 1024,\r\n> \"arxiv\": 1024,\r\n> \"pubmed\": 1024,\r\n> \"gigaword\": 128,\r\n> \"aeslc\": 512,\r\n> \"billsum\": 1024,\r\n> \"large\": 1024,\r\n> }\r\n> ```\r\n> \r\n> @suchig You can probably do longer with the current code by passing `max_position_embeddings=2048`, but no model was finetuned on sequences of that length so it might (a) OOM or (b) perform poorly.\r\n\r\nAt this junction, I am not even able to go beyond PegasusForConditionalGeneration.from_pretrained(mname) statement. It was working fine earlier in the morning. It feels as though something has changed in the pretrained model checkpoint this afternoon.", "@suchig please stick to one issue. ", "Hi @sshleifer , I want to a train a model for 2048 input length. My idea is to use a distilled teacher model and change the source length to 2048. How can I change the `max_position_embeddings`? \r\n\r\nI'm using the following command to train the model on CNN-DM dataset.\r\n\r\n```python examples/seq2seq/distillation.py --teacher sshleifer/distill-pegasus-cnn-16-4 --data_dir ./data/cnn_dm/ --student_decoder_layers 3 --student_encoder_layers 12 --freeze_embeds --learning_rate=3e-4 --do_train --do_predict --val_check_interval 0.1 --n_val 1000 --eval_beams 2 --length_penalty=0.5 --val_metric rouge2 --max_source_length 2048 --max_target_length=142 --val_max_target_length=142 --test_max_target_length=142 --model_name_or_path IGNORED --alpha_hid=3. --train_batch_size=1 --eval_batch_size=1 --gradient_accumulation_steps=256 --sortish_sampler --num_train_epochs=6 --warmup_steps 500 --output_dir distilpegasus_xsum_12_3 --gpus 1 --tokenizer_name sshleifer/distill-pegasus-cnn-16-4 --num_workers=0 --adafactor```", "That command will be very slow, but you can achieve your goal by passing `max_position_embeddings=2048` [here](https://github.com/huggingface/transformers/blob/master/examples/seq2seq/distillation.py#L41)" ]

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[ "It should be `.cache/torch/transformers` I think (even though \"torch\" is in the name).", "Probably that's the case. Thanks." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6595?src=pr&el=h1) Report\n> Merging [#6595](https://codecov.io/gh/huggingface/transformers/pull/6595?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/ab42d74850233cff9df87701d257d9b975435f66&el=desc) will **increase** coverage by `1.10%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6595?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6595 +/- ##\n==========================================\n+ Coverage 79.42% 80.52% +1.10% \n==========================================\n Files 156 156 \n Lines 28127 28127 \n==========================================\n+ Hits 22339 22650 +311 \n+ Misses 5788 5477 -311 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6595?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/tokenization\\_albert.py](https://codecov.io/gh/huggingface/transformers/pull/6595/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYWxiZXJ0LnB5) | `28.84% <0.00%> (-58.66%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/6595/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9ncHQyLnB5) | `71.84% <0.00%> (-23.17%)` | :arrow_down: |\n| [src/transformers/pipelines.py](https://codecov.io/gh/huggingface/transformers/pull/6595/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9waXBlbGluZXMucHk=) | `79.69% <0.00%> (-0.26%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6595/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `87.29% <0.00%> (+0.97%)` | :arrow_up: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6595/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.46% <0.00%> (+1.25%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_mobilebert.py](https://codecov.io/gh/huggingface/transformers/pull/6595/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9tb2JpbGViZXJ0LnB5) | `96.91% <0.00%> (+72.35%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6595?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6595?src=pr&el=footer). Last update [ab42d74...a49a870](https://codecov.io/gh/huggingface/transformers/pull/6595?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "If I understand correctly, the BERT model used here is slightly different because:\r\n- It doesn't use token type IDs\r\n- It's tying its word embedding layer to its LM head\r\n- No pooling layer\r\n\r\nDoesn't that just mean we could use an additional architecture instead of an entire model class? Something like the following, in `modeling_bert.py`:\r\n\r\n```py\r\n@add_start_docstrings(\r\n \"\"\"Bert Model with a `language modeling` head on top that acts as a decoder in a seq2seq setting.\"\"\", BERT_START_DOCSTRING\r\n)\r\nclass CausalBertModel(BertPreTrainedModel):\r\n def __init__(self, config):\r\n super().__init__(config)\r\n\r\n if not config.is_decoder:\r\n logger.warning(\"If you want to use `BertLMHeadModel` as a standalone, add `is_decoder=True.`\")\r\n\r\n self.bert = BertModel(config)\r\n self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\r\n\r\n self.init_weights()\r\n```\r\n\r\nAnd this module wouldn't accept token type IDs as an input.\r\n\r\nI don't know what to do regarding the tokenizer though. This ^ approach could probably leverage @julien-c's https://github.com/huggingface/transformers/pull/6995", "Naming ideas: `BertFor{Conditional}Generation`, `BertEncoder`, `BertDecoder`. \r\n\r\nReasoning:\r\n\r\n+ `CausalBert` doesn't make sense if the encoder wasn't trained with a causal mask. \r\n+ I think in class naming it's more important to give someone a sense of how to use something than how that thing was trained, but that's not an opinion I hold strongly.\r\n\r\nAnyways, your signatures look super clean, easy and consistent!\r\nExcited to try these out+happy to help check metrics.", "@LysandreJik, \r\n\r\nThere are a couple of problems with that: \r\n\r\n1) I also need a different `BertEmbeddings` or manually set `self.token_type_embeddings` to a zero matrix. Even if `token_type_ids` is set to `None` in Bert, the `self.token_type_embeddings` is always used. This model just does not have the embeddings (and should not have them IMO). I could set the `self.token_type_embedding` matrix just to 0, but then people using this class for training would not realize that a `self.token_type_embedding` matrix is trained which it shouldn't. So, here I think either way, I will need a separete `BertEmbeddings` class.\r\n\r\n2) A bigger problem is the config class. Because I need both the new `CausalBertForCausalLM` and `BertLMHeadModel` in the `AUTO_MODELS_FOR_CAUSAL_LM` class (to leverage both models with the `EncoderDecoder` framework), the two models have to have different config classes. I guess we could also create a separate config class and overwrite the inherited config class from `BertPretrainedModel`, but then IMO, it's cleaner to just create a new `PretrainedModelClass` and in this case we can directly create a completely new model class\r\n\r\nSo overall, it seems to me that a separate model class is the cleaner way to go - what do you think?\r\n\r\n@sshleifer - very much agree here! Think the naming should be different...`BertEncoder` is already taken though. I could go for `BertForGenerationEncoder` and `BertForGenerationDecoder` and `BertForGenerationConfig` - No need for `BertForConditionalGeneration` as the `EncoderDecoderModel will be used for this", "BertForGenerationEncoder and BertForGenerationDecoder and BertForGenerationConfig 👍 \r\n\r\nI do see lysandre's point though and would be fine with you setting `token_type` matrix to 0 if it's small (which I think it is).\r\n", "summarization models seem to function and are uploaded here: \r\n\r\nhttps://huggingface.co/models?search=google%2Froberta2roberta", "**UPDATE**: PR is ready for review @sshleifer @LysandreJik @sgugger . \r\n\r\nWould be awesome if you could take a look", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6594?src=pr&el=h1) Report\n> Merging [#6594](https://codecov.io/gh/huggingface/transformers/pull/6594?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/15478c1287a4e7b52c01730ffb0718243d153600?el=desc) will **increase** coverage by `2.11%`.\n> The diff coverage is `75.76%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6594?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6594 +/- ##\n==========================================\n+ Coverage 78.37% 80.49% +2.11% \n==========================================\n Files 164 167 +3 \n Lines 31026 31314 +288 \n==========================================\n+ Hits 24318 25207 +889 \n+ Misses 6708 6107 -601 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6594?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/tokenization\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/6594/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdDUucHk=) | `95.23% <ø> (-0.05%)` | :arrow_down: |\n| [src/transformers/tokenization\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6594/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYXV0by5weQ==) | `91.52% <40.00%> (-4.78%)` | :arrow_down: |\n| [src/transformers/modeling\\_encoder\\_decoder.py](https://codecov.io/gh/huggingface/transformers/pull/6594/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19lbmNvZGVyX2RlY29kZXIucHk=) | `88.78% <50.00%> (-3.22%)` | :arrow_down: |\n| [src/transformers/modeling\\_bert\\_generation.py](https://codecov.io/gh/huggingface/transformers/pull/6594/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iZXJ0X2dlbmVyYXRpb24ucHk=) | `69.19% <69.19%> (ø)` | |\n| [src/transformers/tokenization\\_bert\\_generation.py](https://codecov.io/gh/huggingface/transformers/pull/6594/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYmVydF9nZW5lcmF0aW9uLnB5) | `94.64% <94.64%> (ø)` | |\n| [src/transformers/\\_\\_init\\_\\_.py](https://codecov.io/gh/huggingface/transformers/pull/6594/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9fX2luaXRfXy5weQ==) | `99.33% <100.00%> (+0.01%)` | :arrow_up: |\n| [src/transformers/configuration\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6594/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2F1dG8ucHk=) | `93.61% <100.00%> (+0.13%)` | :arrow_up: |\n| [src/transformers/configuration\\_bert\\_generation.py](https://codecov.io/gh/huggingface/transformers/pull/6594/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2JlcnRfZ2VuZXJhdGlvbi5weQ==) | `100.00% <100.00%> (ø)` | |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6594/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `82.41% <100.00%> (-0.26%)` | :arrow_down: |\n| [src/transformers/generation\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6594/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3V0aWxzLnB5) | `96.92% <100.00%> (-0.28%)` | :arrow_down: |\n| ... and [22 more](https://codecov.io/gh/huggingface/transformers/pull/6594/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6594?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6594?src=pr&el=footer). Last update [15478c1...aa953cb](https://codecov.io/gh/huggingface/transformers/pull/6594?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "> Looks great to me! I mostly have annoying nits about the docs, cause I'm an annoying person.\r\n\r\nHaha, no you are 100% right - sorry for being so sloppy with the docs! I should have learnt it by now ....", "@sshleifer @sgugger - thanks a lot for your suggestions. I went for the name `BertGenerationEncoder` and `BertGenerationDecoder` now. I think it's the best trade-off between short and concise name that is not confusing.", "Have the \"share\" models been implemented? In the paper, in many tasks they achieve the best results.", "Yes you can find then under google/roberta2roberta", "Thank you. How to tie weights in the code for training own model?", "`tie_encoder_decoder=True` -> The code in this model card should show you how to do it :-) https://huggingface.co/patrickvonplaten/roberta2roberta-share-cnn_dailymail-fp16" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6593?src=pr&el=h1) Report\n> Merging [#6593](https://codecov.io/gh/huggingface/transformers/pull/6593?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/ab42d74850233cff9df87701d257d9b975435f66&el=desc) will **increase** coverage by `0.01%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6593?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6593 +/- ##\n==========================================\n+ Coverage 79.42% 79.43% +0.01% \n==========================================\n Files 156 156 \n Lines 28127 28127 \n==========================================\n+ Hits 22339 22343 +4 \n+ Misses 5788 5784 -4 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6593?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_albert.py](https://codecov.io/gh/huggingface/transformers/pull/6593/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9hbGJlcnQucHk=) | `21.47% <0.00%> (-69.44%)` | :arrow_down: |\n| [src/transformers/tokenization\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6593/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fcm9iZXJ0YS5weQ==) | `76.71% <0.00%> (-21.92%)` | :arrow_down: |\n| [src/transformers/tokenization\\_utils\\_base.py](https://codecov.io/gh/huggingface/transformers/pull/6593/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfYmFzZS5weQ==) | `86.58% <0.00%> (-7.19%)` | :arrow_down: |\n| [src/transformers/tokenization\\_transfo\\_xl.py](https://codecov.io/gh/huggingface/transformers/pull/6593/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdHJhbnNmb194bC5weQ==) | `38.73% <0.00%> (-3.76%)` | :arrow_down: |\n| [src/transformers/tokenization\\_utils\\_fast.py](https://codecov.io/gh/huggingface/transformers/pull/6593/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfZmFzdC5weQ==) | `92.14% <0.00%> (-2.15%)` | :arrow_down: |\n| [src/transformers/tokenization\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6593/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fb3BlbmFpLnB5) | `82.57% <0.00%> (-1.52%)` | :arrow_down: |\n| [src/transformers/tokenization\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/6593/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYmVydC5weQ==) | `91.07% <0.00%> (-0.45%)` | :arrow_down: |\n| [src/transformers/tokenization\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6593/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHMucHk=) | `90.00% <0.00%> (-0.41%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6593/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `82.44% <0.00%> (+0.25%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6593/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `87.29% <0.00%> (+0.97%)` | :arrow_up: |\n| ... and [2 more](https://codecov.io/gh/huggingface/transformers/pull/6593/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6593?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6593?src=pr&el=footer). Last update [ab42d74...d243891](https://codecov.io/gh/huggingface/transformers/pull/6593?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Long term, I would rather fill nan with a sentinel value like -11.27 (my negative birthday, dont forget!) and check equality but I am also fine with the temp fix.", "Ok pinging @LysandreJik here that we should take a look next week. Will note it down as well. " ]

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[ "Hey @kiruthiga-A,\r\n\r\nCould you post a complete code example so that we can reproduce the error? If the complete code is too long, a google colab would be super useful.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "I have this same exact issue importing a model from HuggingFace" ]

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[ { "login": "sshleifer", "id": 6045025, "node_id": "MDQ6VXNlcjYwNDUwMjU=", "avatar_url": "https://avatars.githubusercontent.com/u/6045025?v=4", "gravatar_id": "", "url": "https://api.github.com/users/sshleifer", "html_url": "https://github.com/sshleifer", "followers_url": "https://api.github.com/users/sshleifer/followers", "following_url": "https://api.github.com/users/sshleifer/following{/other_user}", "gists_url": "https://api.github.com/users/sshleifer/gists{/gist_id}", "starred_url": "https://api.github.com/users/sshleifer/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sshleifer/subscriptions", "organizations_url": "https://api.github.com/users/sshleifer/orgs", "repos_url": "https://api.github.com/users/sshleifer/repos", "events_url": "https://api.github.com/users/sshleifer/events{/privacy}", "received_events_url": "https://api.github.com/users/sshleifer/received_events", "type": "User", "site_admin": false } ]

[ "try passing `--fp16 --fp_16_opt_level=O1` \r\nthat is a relevant default that has changed. I have also experienced some torch 1.6 issues, so would love to know if that helps.\r\n\r\n\r\nSemi-relatedly, a good practice is to run \r\n```\r\n!pip freeze | grep transformers\r\n!pip freeze | grep torch\r\n```\r\nat the top of colab so that when you go back you can know what version you were on.", "Thanks for the quick reply! I tried this and it didn't work though :(\r\nRemoving the fp16 parameter for now and fine-tuning.\r\nWill keep the colab advice in mind :)", "+1 on this. Using `fp16` with level `O1` or `O2` both causes OOM even for batch size 1. Without `fp16` fine-tuning works.\r\n\r\nTorch 1.6.0, transformers 3.0.2, Linux, V100 GPU.", "This is a torch 1.6 issue.\r\nI haven't gotten anything working well with torch 1.6 + fp16.\r\ntorch 1.5.1 with apex installed works well for me.\r\n", "I tried running fp16 training with `amp_backend=apex` and `amp_backend=native` (passing them as additional args) and the latter does much better in terms power consumption, but memory consumption is same for both (wandb GPU graphs). However, both of them OOM during the validation step. May have something to do with beam search since my validation batch size is 1.\r\n\r\n<img width=\"1564\" alt=\"Screen Shot 2020-08-31 at 12 47 28\" src=\"https://user-images.githubusercontent.com/1833708/91760533-60c7ae00-eb88-11ea-888b-550e3bb28967.png\">\r\n", "Can you try torch 1.5.1 ?\r\n", "Succeeds with 1.5.1, and power and temperature are in-line with native.", "However, the process failing during generation for 1.6.0 suggests there's some optimization missing during the generation steps which causes OOM.", "Another thing to note which might be related: Validation (400 samples) takes 5x time for 1 epoch of training (2400 samples). Even if accounting for beam size (4x), it is much slower.", "Interesting! I would definitely be open to a PR here if you have a fix in mind!", "Thanks! I have a couple ideas and will try them out and create a PR if any of them works.", "I think the problem is that the `_generative_step` method calls `_step` in it, causing 2x forward steps within each validation step. Also, `model.generate` steps are inherently slower than an eval forward pass, even with `num_beams=1`, about 30-60x slower. But this is a different problem than the OOM issue on 1.6.0. Maybe should split this up into a different issue?", "The problem is with `model.generate` that causes OOM on PyTorch 1.6. I switched out to using a custom `validation_step` that only uses `_step` and does not make a call to `model.generate`; it succeeds and is fast. The drawback is that I cannot use beam search for the validation step and keep `do_predict` set to `False` to ensure the test step does not execute. All of which are acceptable limitations to me for faster val, val not running into OOM and being able to use native fp16 with PyTorch 1.6.0.\r\n\r\nI'm happy to create a PR for it if it makes sense to check it in.", "That PR would be interesting. More interesting would be figuring out why generate OOMs in these conditions.", "Definitely the question for why generate OOMs is interesting but one I haven't found an answer for yet. I suggested a workaround in #7004 using the fix I described earlier.", "OK, I'm gunna try to fix the underlying issue today/tomorrow and if I fail, we'll move to your PR.\r\nThanks!", "Does anyone have a snippet that replicates the OOM outside of colab?\r\nI have no trouble running `examples/seq2seq/test_bash_script.py` on self hosted hardware in torch 1.6.\r\n", "The issue wasn't on Colab but on AWS.", "What was your command/hardware?", "Command: `python script.py ...` with a bunch of args (I have a custom wrapper that inherits `SummarizationModule` for initialization and adds extra args. I did not modify train / eval / test in that so should be identical to running `python fine-tune.py` from `finetune.sh`).\r\nGPU: V100-SXM2-16GB.", "I can replicate on v100 with cuda 10.1, torch 1.6, python 3.7.\r\nThe problem is that during the first call to `generate` (during the validation sanity check) the untrained model generates `config.max_length` tokens, causing OOM.\r\n\r\nEasiest fix is adding `--num_sanity_val_steps=0` to your command. LMK if that works.\r\nThe linked PR above allows the user to limit how many tokens are generating during validation, which may be independently helpful.\r\n\r\n\r\n", "Hmm, that makes sense. I'll also say that in the screenshots I had attached earlier it occurred at the end of the first epoch during validation, so setting that new flag should help with that. It is a tricky choice between setting a `max_length` for generate steps that is different from the model's expected output. I do prefer using a forward pass' output (my PR #7004) as a substitute for the runtime output when it is with the correct `max_length` instead of a shorter output that fits within the memory at that time.\r\n\r\nHowever, this still does not explain the avg gen time being 30-60x time per batch (with equal batch sizes for training and validation).\r\n\r\nLastly, the same model does generate without producing an OOM at run-time on similar hardware with the model producing upto `max_length`, which continues to baffle me.", "I don't have any slow down after the validation sanity check in my replication. Maybe I haven't found your bug.", "I don't understand \r\n> Lastly, the same model does generate without producing an OOM at run-time on similar hardware with the model producing upto max_length, which continues to baffle me.\r\n\r\nDo you have a snippet that does not involve finetune.py (just calls `generate`) that OOMs/is way slower?", "> I don't have any slow down after the validation sanity check in my replication. Maybe I haven't found your bug.\r\n\r\nOr maybe it got resolved between when I tested it and this version. No worries.\r\n\r\n> Do you have a snippet that does not involve finetune.py (just calls generate) that OOMs/is way slower?\r\n\r\nThis might not matter anymore if the previous is fixed during training since this is specifically at runtime. Regardless, here's an example I have that is much slower.\r\n```python\r\n%%timeit\r\nwith torch.no_grad():\r\n generated_ids = model.generate(tokenized_input[\"input_ids\"].to(\"cuda\"), skip_special_tokens=True, clean_up_tokenization_spaces=False, \r\n num_beams=3, top_p=0.9, repetition_penalty=10, decoder_start_token_id=model.config.decoder_start_token_id, max_length=model.config.max_length)\r\n```\r\nwhich produces:\r\n`10.4 s ± 7.89 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)`", "The sequence produced is of length 311. While the output sequence length is long (max possible is 768), 10 seconds is still quite a lot.", "can you send a full working example that I can copy paste and try in different torch versions?", "Sure! I'm using a finetuned model and a custom dataset so changed the below to `bart-large` and removed the lines where a dataset is queried. Everything else is the same.\r\n\r\n```python\r\nfrom transformers import BartTokenizer, BartForConditionalGeneration, BartConfig\r\ntokenizer = BartTokenizer.from_pretrained(\"facebook/bart-large\")\r\nmodel = BartForConditionalGeneration.from_pretrained(\"facebook/bart-large\")\r\nmodel = model.to(\"cuda\")\r\nmodel = model.eval()\r\ntokenized_input = tokenizer(..., return_tensors=\"pt\", max_length=model.config.max_position_embeddings)\r\nwith torch.no_grad():\r\n generated_ids = model.generate(tokenized_input[\"input_ids\"].to(\"cuda\"), skip_special_tokens=True, clean_up_tokenization_spaces=False, \r\n num_beams=3, top_p=0.9, repetition_penalty=10, decoder_start_token_id=model.config.decoder_start_token_id, max_length=model.config.max_length)\r\n```\r\n\r\nI'm running this in a notebook so I can time profile the generate step.", "I've spent a quite some time today focused on trying various combinations of `generate`. The increased delay arises from a `num_beams` count that is large, which leads to the model producing longer outputs, thus compounding the generate time (`num_beams` * `max_length`).\r\n\r\nIn conclusion, it doesn't appear to be a bug but a property of generate being more memory intensive.", "@sshleifer , I have the same issue, and I am using the latest version that includes the PR you provided. I set eval_max_gen_length to 30 and still getting OOM during the sanity check. Do I also have to set num_sanity_val_steps=0 ?" ]

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[ "My bad, I ignore some code in the file. " ]

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[ "spurious" ]

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[ "Let me know if/when you need me to review this!", "Do you think it's a good investment of time to clean up automated marian model cards/conversion?\r\n\r\nFor first 1000 models from `OPUS-MT-Train` repo, the process was basically \r\ndownload and convert everything, if there is a '+' in the model name try to find a matching group name.\r\nAutomated model card is an f string + Jorg's README.\r\n\r\nFor the next 500 models, the process was:\r\ndecide whether we don't want the model (\"the blacklisting process\"):\r\n- if it's \"short pair\" (as in en-es/en-zh) identical to one we have and the score (which we get from https://github.com/Helsinki-NLP/Tatoeba-Challenge/blob/master/models/released-models.txt) is worse than the BLEU of the previous model (which we extract disgustingly from the README.md).\r\n- if either side is \"jpx\" (see slack: \"you can ignore jpx\"\r\n\r\nAssuming we DO want the model:\r\n- identical state dict conversion to the previous iteration\r\n- Automated model card = yaml front matter with language tags (incl. group expansion for multilingual, look at all the tags on this guy: https://huggingface.co/Helsinki-NLP/opus-mt-roa-en) + f string + Jorg's README.\r\n- also write `metadata.json` to S3 so that, in the future, blacklisting need not involved extracting floats from markdown. \r\n\r\n\r\n(The `api-inference` issue is a disk space one that @mfuntowicz hopes to resolve tomorrow, curl-requests come back 👍 )\r\n\r\nThis process works well but is obviously not as simple as it could/should be. The state dict automation that other projects have is simple and done, but the automated model carding needs and blacklisting needs to be at least partially rewritten.\r\n\r\nI also suspect that if we do this again in a third repo we will need to add more model card/blacklisting logic.\r\n\r\n### Proposed next steps\r\n- add metadata.json to every opus-mt model in the hub\r\n- strip the opus-mt model prefix from every model in the hub. What's the point of it?\r\n- some one off deletions of old capital letter group names as discussed in #fixme \r\n- get this code clean enough that it runs on future models for the current `Tatoeba-Challenge` repo.\r\n\r\nDid any of that make sense? What do you think?", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6586?src=pr&el=h1) Report\n> Merging [#6586](https://codecov.io/gh/huggingface/transformers/pull/6586?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/28cf873036d078b47fb9dd38ac3421a7c874da44?el=desc) will **increase** coverage by `1.02%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6586?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6586 +/- ##\n==========================================\n+ Coverage 76.58% 77.61% +1.02% \n==========================================\n Files 181 181 \n Lines 34828 34828 \n==========================================\n+ Hits 26674 27032 +358 \n+ Misses 8154 7796 -358 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6586?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_mobilebert.py](https://codecov.io/gh/huggingface/transformers/pull/6586/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9tb2JpbGViZXJ0LnB5) | `24.46% <0.00%> (-72.60%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_flaubert.py](https://codecov.io/gh/huggingface/transformers/pull/6586/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9mbGF1YmVydC5weQ==) | `24.53% <0.00%> (-63.81%)` | :arrow_down: |\n| [src/transformers/modeling\\_marian.py](https://codecov.io/gh/huggingface/transformers/pull/6586/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19tYXJpYW4ucHk=) | `60.00% <0.00%> (-30.00%)` | :arrow_down: |\n| [src/transformers/activations.py](https://codecov.io/gh/huggingface/transformers/pull/6586/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9hY3RpdmF0aW9ucy5weQ==) | `81.81% <0.00%> (-4.55%)` | :arrow_down: |\n| [src/transformers/modeling\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/6586/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iYXJ0LnB5) | `93.93% <0.00%> (-0.34%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6586/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `83.13% <0.00%> (-0.25%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6586/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `87.01% <0.00%> (+0.32%)` | :arrow_up: |\n| [src/transformers/configuration\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6586/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3V0aWxzLnB5) | `96.66% <0.00%> (+0.66%)` | :arrow_up: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6586/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.46% <0.00%> (+2.25%)` | :arrow_up: |\n| [src/transformers/tokenization\\_utils\\_base.py](https://codecov.io/gh/huggingface/transformers/pull/6586/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfYmFzZS5weQ==) | `93.64% <0.00%> (+3.24%)` | :arrow_up: |\n| ... and [7 more](https://codecov.io/gh/huggingface/transformers/pull/6586/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6586?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6586?src=pr&el=footer). Last update [28cf873...ab93341](https://codecov.io/gh/huggingface/transformers/pull/6586?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "@sshleifer Is it still a failing test? I can have a look", "Still failing! See https://github.com/huggingface/transformers/runs/1024137280?check_suite_focus=true", "On it 💪 ", "attempt at: #6716" ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "Any interest in fixing this?", "Hello! Indeed, there doesn't seem to be any type cast. Do you want to open a PR with a fix?", "Do you think the fix we have is appropriate? I'm worried it'll break something elsewhere which is expecting the to method to do casting.", "I think here we could do something better than what is currently implemented. We could do it like it's done in the PyTorch implementation, as it takes all `args` and `kwargs`. Something like this:\r\n\r\n```py\r\n def to(self, *args, **kwargs) -> \"BatchEncoding\":\r\n \"\"\"\r\n *Appropriate docstring here*\r\n\r\n Args:\r\n *Appropriate Args here*\r\n\r\n Returns:\r\n :class:`~transformers.BatchEncoding`:\r\n The same instance of :class:`~transformers.BatchEncoding` after modification.\r\n \"\"\"\r\n self.data = {k: v.to(*args, **kwargs) for k, v in self.data.items()}\r\n return self\r\n\r\n```\r\nWhat do you think?", "Won't that pass the casts down? It looks like it would directly pass everything through, but we wouldn't usually want to cast the `LongTensor` that lives in a `BatchEncoding`.", "Ah, then I misunderstood. Let me try to understand the issue better to see how to work it out: as Apex casts from `LongTensor` to `HalfTensor` when using `BatchEncoding` which raises this issue, doesn't the issue happen if the inputs are `torch.Tensor`s instead of a dict/`BatchEncoding`? These inputs would be converted to `HalfTensor`s as well, resulting in the error, wouldn't it?", "Apex.amp blindly calls a `to()` method on anything that gets passed into a module if it's a type it doesn't know about, passing it arguments that cast a tensor to `HalfTensor`. `BatchEncoding` exposes a `to()` method which is designed for moving things between devices, and not for casting, yet it passes the casts down to the tensors despite the docs saying it's only for moving between devices. We don't want to cast the tensors inside `BatchEncoding` to `HalfTensor` as that truncates and rounds the vocabulary to 65k and every 16th word in the high end, even if we cast the indices back to `LongTensor` at the point they are looked up in the embedding. This is a mostly silent failure as if you blindly insert the cast back in that the embedding error suggests you lose a lot of vocabulary items.\r\n\r\nThe workaround I posted makes `BatchEncoding` ignore any casts that are passed into it.\r\n\r\nHonestly the issue is that pytorch overloaded the `to` method with two completely different functions, casting and device transfer, and python doesn't have a type system that allows you to select an appropriate method overloading based on the inbound method arguments.", "Thanks for taking the time to write such a detailed explanation. I think your proposal makes complete sense. The way the `to` method is implemented in `BatchEncoding` is only intended for use with devices, not casting, so it won't break the current implementation.\r\n\r\nOn top of what you have proposed, I think logging a warning in the case that it's not a device would be appropriate (if it doesn't get caught in the `if` statement). We would want to warn users that their cast did not go through if they attempt doing something like this, rather than silently doing nothing.", "Sure. I can log a warning in there and make up a PR." ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6580?src=pr&el=h1) Report\n> Merging [#6580](https://codecov.io/gh/huggingface/transformers/pull/6580?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/7516bcf27319a2aea9bbe927f8e4d8e501e23c99&el=desc) will **decrease** coverage by `0.57%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6580?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6580 +/- ##\n==========================================\n- Coverage 80.53% 79.95% -0.58% \n==========================================\n Files 156 156 \n Lines 28130 28130 \n==========================================\n- Hits 22654 22492 -162 \n- Misses 5476 5638 +162 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6580?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6580/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9vcGVuYWkucHk=) | `22.58% <0.00%> (-72.26%)` | :arrow_down: |\n| [src/transformers/tokenization\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/6580/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdDUucHk=) | `61.95% <0.00%> (-34.79%)` | :arrow_down: |\n| [src/transformers/tokenization\\_marian.py](https://codecov.io/gh/huggingface/transformers/pull/6580/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fbWFyaWFuLnB5) | `66.66% <0.00%> (-32.50%)` | :arrow_down: |\n| [src/transformers/tokenization\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6580/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYXV0by5weQ==) | `95.55% <0.00%> (-2.23%)` | :arrow_down: |\n| [src/transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6580/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19vcGVuYWkucHk=) | `80.96% <0.00%> (-1.30%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6580/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `86.31% <0.00%> (-0.98%)` | :arrow_down: |\n| [src/transformers/configuration\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6580/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3V0aWxzLnB5) | `95.91% <0.00%> (-0.69%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6580/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.21% <0.00%> (-0.26%)` | :arrow_down: |\n| [src/transformers/pipelines.py](https://codecov.io/gh/huggingface/transformers/pull/6580/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9waXBlbGluZXMucHk=) | `79.94% <0.00%> (+0.25%)` | :arrow_up: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6580/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `82.44% <0.00%> (+0.25%)` | :arrow_up: |\n| ... and [2 more](https://codecov.io/gh/huggingface/transformers/pull/6580/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6580?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6580?src=pr&el=footer). Last update [7516bcf...3ececb0](https://codecov.io/gh/huggingface/transformers/pull/6580?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6579?src=pr&el=h1) Report\n> Merging [#6579](https://codecov.io/gh/huggingface/transformers/pull/6579?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/7516bcf27319a2aea9bbe927f8e4d8e501e23c99&el=desc) will **decrease** coverage by `0.52%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6579?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6579 +/- ##\n==========================================\n- Coverage 80.53% 80.00% -0.53% \n==========================================\n Files 156 156 \n Lines 28130 28130 \n==========================================\n- Hits 22654 22506 -148 \n- Misses 5476 5624 +148 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6579?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/optimization.py](https://codecov.io/gh/huggingface/transformers/pull/6579/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9vcHRpbWl6YXRpb24ucHk=) | `25.55% <0.00%> (-70.00%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/6579/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9iZXJ0LnB5) | `69.06% <0.00%> (-29.32%)` | :arrow_down: |\n| [src/transformers/optimization\\_tf.py](https://codecov.io/gh/huggingface/transformers/pull/6579/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9vcHRpbWl6YXRpb25fdGYucHk=) | `33.33% <0.00%> (-24.33%)` | :arrow_down: |\n| [src/transformers/modeling\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/6579/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iZXJ0LnB5) | `88.26% <0.00%> (-0.17%)` | :arrow_down: |\n| [src/transformers/pipelines.py](https://codecov.io/gh/huggingface/transformers/pull/6579/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9waXBlbGluZXMucHk=) | `79.94% <0.00%> (+0.25%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_flaubert.py](https://codecov.io/gh/huggingface/transformers/pull/6579/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9mbGF1YmVydC5weQ==) | `88.34% <0.00%> (+63.80%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6579?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6579?src=pr&el=footer). Last update [7516bcf...1e290b6](https://codecov.io/gh/huggingface/transformers/pull/6579?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "Hey @jusjosgra, \r\n\r\nCan you post a complete code snippet to produce the error. I'm using `master` and running this code does not give me any errors:\r\n\r\n```python\r\nfrom transformers import pipeline\r\nqa = pipeline(\"question-answering\", model=\"bert-large-uncased-whole-word-masking-finetuned-squad\", tokenizer=\"bert-large-uncased-whole-word-masking-finetuned-squad\")\r\noutput = qa(question=question, context=context, max_seq_len=512, max_answer_len=50)\r\n```\r\n\r\nDo you use batches as an input in your example? ", "Using @patrickvonplaten code I am experiencing the same problem here. Is truncation & padding of sqad examples not working correctly?\r\nIn my case, I'm using batches as an input, therefore I introduce a list of questions (the same repeated n times) and n contexts for the model to search the answer in. ", "@alexvaca0 are you running on a pypi version or on the current `master` branch?", "@LysandreJik on the current master branch", "Okay, pinging @mfuntowicz to see if he has any insights.", "I'm starting to suspect this has something to do with certain tokens... Look, this is one of the texts that the pipeline tokenizes to 485 instead of 512:\r\n\r\n**For a city containing multiple hospitals such as NYC, we defined a 102 proximity metric in this study as population normalized number of hospital beds within Prior to analysis of potential predictors, we considered multiple base regression models. 110 Given the significant spatial correlation in the present case data as evidenced by the 111 Moran Index, I (176) = 0.481, p < 0.0005 [24] , we explored potential regression models 112 both with and without spatial effects. We compared four base models (no predictors): 113 1) a Poisson model with random intercept; 2) a Poisson Besag-York-Mollié (BYM) by Eq 1:\r\nwhere υ i has an intrinsic conditional autoregressive (ICAR) structure [27] . We used the 119 reparameterization of the BYM model proposed by Riebler et al. [28] , known as the 120 BYM2 model and shown in Eq 2:\r\nwhere τ γ is the overall precision hyperparameter, ϕ ∈ [0, 1] is the mixing hyperparameter 122 representing the proportional division of variance between the spatial and nonspatial 123 effects, υ * is the spatial (ICAR) effect with a scaling factor such that Var (υ * ) ≈ 1, and 124 ν * is the nonspatial random-effect with ν * ∼ N (0, 1). Penalized complexity (PC) priors 125 are applied to hyperparameters τ γ and ϕ (compared to log-gamma priors in the random 126 intercept model) [29] . All four models used ZCTA population as the exposure and a 127 log-link function. We selected the model with the lowest Deviance Information Criterion 128 (DIC) [30] , representing the best trade-off between model fit and complexity. Characteristics for the four base models examined, including hyperparameters, are 130 shown in \r\ni , scaled nonspatial random-effect; υ * i , scaled spatial random-effect with intrinsic conditional autoregressive structure; τ ν , precision for nonspatial random effect, log-Gamma prior; τ γ , overall precision, penalized complexity (PC) prior; ϕ, mixing parameter, PC prior; n, overdispersion parameter, PC Gamma prior. Multiple regression models were built using a method adjusted from Nikolopoulos et 136 al.**\r\n\r\nI don't know if it's a coincidence that it has some mathematical signs that the tokenizer might not be processing correctly...", "Facing the same issue when dealing with multiple examples and padding strategy=\"longest\". I think it might have to do with the fact that the features are created per example and hence the padding is done per example. The padding code does not get to see all examples to find the longest example.\r\n\r\nhttps://github.com/huggingface/transformers/blob/master/src/transformers/pipelines.py#L1753-L1764\r\n\r\nSame issue with `squad.py`\r\nhttps://github.com/huggingface/transformers/blob/master/src/transformers/data/processors/squad.py#L354-L362", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "@patrickvonplaten didn't add any tests since it subclasses `RobertaForCausalLM` which is already tested.", "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6577?src=pr&el=h1) Report\n> Merging [#6577](https://codecov.io/gh/huggingface/transformers/pull/6577?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/12d7624199e727f37bef7f53d527df7fabdb1fd6&el=desc) will **increase** coverage by `1.14%`.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6577?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6577 +/- ##\n==========================================\n+ Coverage 79.18% 80.33% +1.14% \n==========================================\n Files 156 156 \n Lines 28129 28132 +3 \n==========================================\n+ Hits 22275 22599 +324 \n+ Misses 5854 5533 -321 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6577?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/\\_\\_init\\_\\_.py](https://codecov.io/gh/huggingface/transformers/pull/6577/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9fX2luaXRfXy5weQ==) | `99.28% <ø> (ø)` | |\n| [src/transformers/modeling\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6577/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19hdXRvLnB5) | `78.73% <ø> (ø)` | |\n| [src/transformers/modeling\\_camembert.py](https://codecov.io/gh/huggingface/transformers/pull/6577/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19jYW1lbWJlcnQucHk=) | `100.00% <100.00%> (ø)` | |\n| [src/transformers/tokenization\\_albert.py](https://codecov.io/gh/huggingface/transformers/pull/6577/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYWxiZXJ0LnB5) | `28.84% <0.00%> (-58.66%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/6577/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9iZXJ0LnB5) | `69.06% <0.00%> (-29.32%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/6577/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9ncHQyLnB5) | `71.84% <0.00%> (-23.17%)` | :arrow_down: |\n| [src/transformers/modeling\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/6577/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iZXJ0LnB5) | `88.26% <0.00%> (-0.17%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6577/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.46% <0.00%> (+0.25%)` | :arrow_up: |\n| [src/transformers/configuration\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6577/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3V0aWxzLnB5) | `96.59% <0.00%> (+0.68%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6577/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `87.29% <0.00%> (+0.97%)` | :arrow_up: |\n| ... and [9 more](https://codecov.io/gh/huggingface/transformers/pull/6577/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6577?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6577?src=pr&el=footer). Last update [12d7624...1e68a94](https://codecov.io/gh/huggingface/transformers/pull/6577?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "hi @patrickvonplaten , could you take a look ? Thanks !", "Yes, right. But this intended for the `EncoderDecoder` model class which can use pre-trained encoder for seq2seq tasks.\r\nSee PR #6411 and #6538 . Also Patrick shared this awesome paper which explores this idea and has shown some good results on seq2seq tasks. https://arxiv.org/pdf/1907.12461.pdf", "Thanks for the explanation! Yes it makes perfect sense to use these models for seq2seq but I still think maybe we should add a note somewhere in the document to explain this?", "Definitely! Will update the docs to explicitly state that this is intended for seq2seq and might not perform well on just causal modelling. Will also link the paper in `EncoderDecoder` doc page, so people will know what to choose.", "Btw, @patil-suraj did you start doing some Roberta2Roberta experiments? Wanted to start running some experiments next week - wanted to check if you already had some interesting results", "@patrickvonplaten Just started one experiment for qg and going to run cnn/dm after that, will let you know the results. \r\nSince Roberta has `bos`, what's the `decoder_start_token_id` for `Roberta2Roberta`, is it `bos` or `pad` token ?", "In my experiments with bert2bert, I used the same token for encoder bos and decoder bos, but it's up to you! `bos` makes more sense to me in the case of Roberta2Roberta.", "Okay, Thanks Patrick!" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6576?src=pr&el=h1) Report\n> Merging [#6576](https://codecov.io/gh/huggingface/transformers/pull/6576?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/d9cbc0350deaa7e146a8c8dbb6ad4dc9bd6afc4f?el=desc) will **decrease** coverage by `0.92%`.\n> The diff coverage is `62.88%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6576?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6576 +/- ##\n==========================================\n- Coverage 80.37% 79.45% -0.93% \n==========================================\n Files 156 156 \n Lines 28058 28380 +322 \n==========================================\n- Hits 22552 22548 -4 \n- Misses 5506 5832 +326 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6576?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/configuration\\_reformer.py](https://codecov.io/gh/huggingface/transformers/pull/6576/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3JlZm9ybWVyLnB5) | `100.00% <ø> (ø)` | |\n| [src/transformers/data/processors/glue.py](https://codecov.io/gh/huggingface/transformers/pull/6576/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL3Byb2Nlc3NvcnMvZ2x1ZS5weQ==) | `48.91% <0.00%> (-0.18%)` | :arrow_down: |\n| [src/transformers/generation\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6576/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3V0aWxzLnB5) | `96.94% <ø> (ø)` | |\n| [src/transformers/hf\\_argparser.py](https://codecov.io/gh/huggingface/transformers/pull/6576/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9oZl9hcmdwYXJzZXIucHk=) | `67.74% <0.00%> (-1.49%)` | :arrow_down: |\n| [src/transformers/modeling\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6576/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19hdXRvLnB5) | `78.73% <ø> (ø)` | |\n| [src/transformers/modeling\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/6576/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iZXJ0LnB5) | `88.42% <ø> (+0.16%)` | :arrow_up: |\n| [src/transformers/modeling\\_mbart.py](https://codecov.io/gh/huggingface/transformers/pull/6576/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19tYmFydC5weQ==) | `100.00% <ø> (ø)` | |\n| [src/transformers/optimization.py](https://codecov.io/gh/huggingface/transformers/pull/6576/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9vcHRpbWl6YXRpb24ucHk=) | `95.55% <ø> (ø)` | |\n| [src/transformers/tokenization\\_utils\\_base.py](https://codecov.io/gh/huggingface/transformers/pull/6576/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfYmFzZS5weQ==) | `93.76% <ø> (ø)` | |\n| [src/transformers/trainer\\_tf.py](https://codecov.io/gh/huggingface/transformers/pull/6576/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyX3RmLnB5) | `12.25% <0.00%> (ø)` | |\n| ... and [43 more](https://codecov.io/gh/huggingface/transformers/pull/6576/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6576?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6576?src=pr&el=footer). Last update [d329c9b...44e9543](https://codecov.io/gh/huggingface/transformers/pull/6576?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Neat implem! I was initially wondering whether we should just have a `HyperTrainer` whose only job would be to spawn multiple instances of Trainers, given that the adherence between hp search and everything else in this class is rather small.\r\n\r\nThoughts? Others feel free to chime in ", "> I was initially wondering whether we should just have a `HyperTrainer` whose only job would be to spawn multiple instances of Trainers\r\n\r\nWe can also take that road, my reasoning was that since the `HyperTrainer` would need to take all the same arguments as `Trainer` and then some, it made sense to just add it to the class. I think it's easier for a user to just have one class as an access point." ]

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[ "Hi,\r\n\r\n`is_pretokenized=True` actually means that you are providing a list of **words** as strings instead of a full sentence or paragraph not sub-words. The step which is skipped in this case is the *pre* tokenization step, not the tokenization step.\r\n\r\nThis is useful for NER or token classification for instance but I understand that the wording can be confusing, we will try to make it more clear in the docstring and the page of the doc ([here](https://huggingface.co/transformers/preprocessing.html#pre-tokenized-inputs)) cc @sgugger and @LysandreJik ", "Adding this to my TODO.", "Thanks for making this clear! :)" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6574?src=pr&el=h1) Report\n> Merging [#6574](https://codecov.io/gh/huggingface/transformers/pull/6574?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/cfa26d2b412ac3494eef06506004ca857c115ad9&el=desc) will **decrease** coverage by `1.09%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6574?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6574 +/- ##\n==========================================\n- Coverage 79.24% 78.15% -1.10% \n==========================================\n Files 156 156 \n Lines 28130 28130 \n==========================================\n- Hits 22292 21985 -307 \n- Misses 5838 6145 +307 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6574?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/optimization.py](https://codecov.io/gh/huggingface/transformers/pull/6574/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9vcHRpbWl6YXRpb24ucHk=) | `25.55% <0.00%> (-70.00%)` | :arrow_down: |\n| [src/transformers/pipelines.py](https://codecov.io/gh/huggingface/transformers/pull/6574/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9waXBlbGluZXMucHk=) | `26.26% <0.00%> (-53.69%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/6574/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9iZXJ0LnB5) | `66.00% <0.00%> (-32.38%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/6574/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9ncHQyLnB5) | `65.68% <0.00%> (-29.33%)` | :arrow_down: |\n| [src/transformers/optimization\\_tf.py](https://codecov.io/gh/huggingface/transformers/pull/6574/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9vcHRpbWl6YXRpb25fdGYucHk=) | `33.33% <0.00%> (-24.33%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6574/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9hdXRvLnB5) | `48.79% <0.00%> (-18.08%)` | :arrow_down: |\n| [src/transformers/data/processors/squad.py](https://codecov.io/gh/huggingface/transformers/pull/6574/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL3Byb2Nlc3NvcnMvc3F1YWQucHk=) | `13.76% <0.00%> (-14.38%)` | :arrow_down: |\n| [src/transformers/modeling\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6574/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19hdXRvLnB5) | `64.36% <0.00%> (-14.37%)` | :arrow_down: |\n| [src/transformers/modelcard.py](https://codecov.io/gh/huggingface/transformers/pull/6574/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGNhcmQucHk=) | `82.71% <0.00%> (-2.47%)` | :arrow_down: |\n| [src/transformers/modeling\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/6574/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19kaXN0aWxiZXJ0LnB5) | `96.19% <0.00%> (-1.64%)` | :arrow_down: |\n| ... and [17 more](https://codecov.io/gh/huggingface/transformers/pull/6574/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6574?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6574?src=pr&el=footer). Last update [cfa26d2...5e35d96](https://codecov.io/gh/huggingface/transformers/pull/6574?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Thanks!" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6573?src=pr&el=h1) Report\n> Merging [#6573](https://codecov.io/gh/huggingface/transformers/pull/6573?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/cfa26d2b412ac3494eef06506004ca857c115ad9&el=desc) will **increase** coverage by `0.14%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6573?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6573 +/- ##\n==========================================\n+ Coverage 79.24% 79.38% +0.14% \n==========================================\n Files 156 156 \n Lines 28130 28130 \n==========================================\n+ Hits 22292 22332 +40 \n+ Misses 5838 5798 -40 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6573?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/tokenization\\_mbart.py](https://codecov.io/gh/huggingface/transformers/pull/6573/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fbWJhcnQucHk=) | `56.25% <0.00%> (-39.07%)` | :arrow_down: |\n| [src/transformers/tokenization\\_utils\\_base.py](https://codecov.io/gh/huggingface/transformers/pull/6573/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfYmFzZS5weQ==) | `93.76% <0.00%> (+3.52%)` | :arrow_up: |\n| [src/transformers/tokenization\\_marian.py](https://codecov.io/gh/huggingface/transformers/pull/6573/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fbWFyaWFuLnB5) | `99.16% <0.00%> (+32.50%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6573?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6573?src=pr&el=footer). Last update [cfa26d2...0a20942](https://codecov.io/gh/huggingface/transformers/pull/6573?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Thanks!" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6572?src=pr&el=h1) Report\n> Merging [#6572](https://codecov.io/gh/huggingface/transformers/pull/6572?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/cfa26d2b412ac3494eef06506004ca857c115ad9?el=desc) will **increase** coverage by `1.01%`.\n> The diff coverage is `13.15%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6572?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6572 +/- ##\n==========================================\n+ Coverage 79.24% 80.26% +1.01% \n==========================================\n Files 156 156 \n Lines 28130 28243 +113 \n==========================================\n+ Hits 22292 22668 +376 \n+ Misses 5838 5575 -263 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6572?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/\\_\\_init\\_\\_.py](https://codecov.io/gh/huggingface/transformers/pull/6572/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9fX2luaXRfXy5weQ==) | `99.28% <ø> (ø)` | |\n| [...rc/transformers/data/datasets/language\\_modeling.py](https://codecov.io/gh/huggingface/transformers/pull/6572/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL2RhdGFzZXRzL2xhbmd1YWdlX21vZGVsaW5nLnB5) | `56.32% <10.52%> (-35.52%)` | :arrow_down: |\n| [src/transformers/data/data\\_collator.py](https://codecov.io/gh/huggingface/transformers/pull/6572/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL2RhdGFfY29sbGF0b3IucHk=) | `62.80% <13.33%> (-28.11%)` | :arrow_down: |\n| [src/transformers/data/datasets/\\_\\_init\\_\\_.py](https://codecov.io/gh/huggingface/transformers/pull/6572/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL2RhdGFzZXRzL19faW5pdF9fLnB5) | `100.00% <100.00%> (ø)` | |\n| [src/transformers/tokenization\\_albert.py](https://codecov.io/gh/huggingface/transformers/pull/6572/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYWxiZXJ0LnB5) | `28.84% <0.00%> (-58.66%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/6572/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9ncHQyLnB5) | `71.84% <0.00%> (-23.17%)` | :arrow_down: |\n| [src/transformers/pipelines.py](https://codecov.io/gh/huggingface/transformers/pull/6572/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9waXBlbGluZXMucHk=) | `79.69% <0.00%> (-0.26%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6572/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.46% <0.00%> (ø)` | |\n| [src/transformers/configuration\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6572/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3V0aWxzLnB5) | `96.59% <0.00%> (+0.68%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6572/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `87.29% <0.00%> (+0.97%)` | :arrow_up: |\n| ... and [7 more](https://codecov.io/gh/huggingface/transformers/pull/6572/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6572?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6572?src=pr&el=footer). Last update [cfa26d2...e924e84](https://codecov.io/gh/huggingface/transformers/pull/6572?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Thanks for reply and carefully review!\r\nI understand what you and @JetRunner mean now. \r\nThe purpose of submitting this script is to provide a tool that can test `DataCollator` and `TextDataset` (I don't know if I am doing this right). \r\nI agree with you and think it is not elegant to add a `run_bert_nsp.py` script alone. I will remove this script and support NSP together with MLM objective later.", "Mmmm, your rebase makes the PR touch way too many files now and quite unreadable. You should close and reopen it (once ready) so we can see the diff a little bit better.", "ok i will reopen the pr. thanks~", "I’m very sorry, I didn’t know that pr should be closed before rebase, which resulted in PR touch way too many files.\r\nI didn't notice you have already implement the `BertForPreTraining` in the modeling_bert file before, so I modified `BertForNextSencencePrediction` to support both mlm and nsp objectives. \r\nThese changes have now been revert.", "@sgugger `DataCollatorForNextSencencePrediction` and `TextDatasetForNextSencencePrediction` now support mlm and nsp together. ", "You need to reopen a new PR, this one will forever have all those files diff with all the old commits you rebased, so we can't review it.", "got it." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6571?src=pr&el=h1) Report\n> Merging [#6571](https://codecov.io/gh/huggingface/transformers/pull/6571?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/7719ecd19f63876fdc2d31699977c7ced3643417?el=desc) will **decrease** coverage by `0.28%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6571?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6571 +/- ##\n==========================================\n- Coverage 82.26% 81.98% -0.29% \n==========================================\n Files 172 172 \n Lines 33077 33077 \n==========================================\n- Hits 27211 27118 -93 \n- Misses 5866 5959 +93 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6571?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/6571/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9kaXN0aWxiZXJ0LnB5) | `64.52% <0.00%> (-34.77%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/6571/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9ncHQyLnB5) | `71.59% <0.00%> (-23.38%)` | :arrow_down: |\n| [src/transformers/tokenization\\_albert.py](https://codecov.io/gh/huggingface/transformers/pull/6571/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYWxiZXJ0LnB5) | `70.19% <0.00%> (-23.08%)` | :arrow_down: |\n| [src/transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6571/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19vcGVuYWkucHk=) | `72.25% <0.00%> (-10.00%)` | :arrow_down: |\n| [src/transformers/modeling\\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/6571/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19ncHQyLnB5) | `72.31% <0.00%> (-6.73%)` | :arrow_down: |\n| [src/transformers/configuration\\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/6571/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2dwdDIucHk=) | `91.89% <0.00%> (-5.41%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_xlm.py](https://codecov.io/gh/huggingface/transformers/pull/6571/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl94bG0ucHk=) | `88.37% <0.00%> (-4.87%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6571/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `79.19% <0.00%> (-3.76%)` | :arrow_down: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6571/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `84.17% <0.00%> (-2.70%)` | :arrow_down: |\n| [src/transformers/data/data\\_collator.py](https://codecov.io/gh/huggingface/transformers/pull/6571/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL2RhdGFfY29sbGF0b3IucHk=) | `92.83% <0.00%> (-0.72%)` | :arrow_down: |\n| ... and [5 more](https://codecov.io/gh/huggingface/transformers/pull/6571/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6571?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6571?src=pr&el=footer). Last update [5c1d5ea...debfb81](https://codecov.io/gh/huggingface/transformers/pull/6571?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "This one is obsolete, correct @stefan-it ? Please confirm and close if so.", "Hi @vblagoje ,\r\n\r\nthe PR would also update the readme example 😅\r\n\r\nI will resolve the merge conflicts now!", "Resolved 🤓" ]

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[ "same issues here. please help!", "same issue", "Same issue with Camembert torch model\r\n\r\npython transformers/convert_graph_to_onnx.py --model camembert-base --framework pt /tmp/camembert-base.onnx\r\n\r\n====== Converting model to ONNX ======\r\nONNX opset version set to: 11\r\nLoading pipeline (model: camembert-base, tokenizer: camembert-base)\r\nUsing framework PyTorch: 1.4.0\r\nFound input input_ids with shape: {0: 'batch', 1: 'sequence'}\r\nFound input attention_mask with shape: {0: 'batch', 1: 'sequence'}\r\nFound output output_0 with shape: {0: 'batch', 1: 'sequence'}\r\nFound output output_1 with shape: {0: 'batch'}\r\nEnsuring inputs are in correct order\r\ntoken_type_ids is not present in the generated input list.\r\nGenerated inputs order: ['input_ids', 'attention_mask']\r\nError while converting the model: export() got an unexpected keyword argument 'use_external_data_format'\r\n", "My issue was due to an older version of pytorch v1.4 where export function arguments are not the same as in version v1.6", "Any Solutions for this issue ?", "Hello @junkgear, we're doing a rework of the ONNX implementation, you can take a look at the proposal here: https://github.com/huggingface/transformers/pull/11786\r\n\r\nLet me know if that works for you!", "I dumped the vocab and config file in model folder and was able to convert\r\nGave model folder as input instead of bin file to convert_graph_to_onnx.py script\r\n\r\n```\r\nfrom transformers import (WEIGHTS_NAME, AdamW, get_linear_schedule_with_warmup,\r\n RobertaConfig, RobertaModel, RobertaTokenizer)\r\n\r\nMODEL_CLASSES = {'roberta': (RobertaConfig, RobertaModel, RobertaTokenizer)}\r\n\r\nconfig_class, model_class, tokenizer_class = MODEL_CLASSES['roberta']\r\n\r\nconfig = config_class.from_pretrained('microsoft/codebert-base')\r\n\r\ntokenizer = tokenizer_class.from_pretrained('microsoft/codebert-base')\r\n\r\n#convert config file to json and save in model folder\r\nconfig.to_json_file('model/config.json')\r\n\r\n#save the vocabulary\r\ntokenizer.save_vocabulary('model/')\r\n```", "> I dumped the vocab and config file in model folder and was able to convert\r\n> Gave model folder as input instead of bin file to convert_graph_to_onnx.py script\r\n> \r\n> ```\r\n> from transformers import (WEIGHTS_NAME, AdamW, get_linear_schedule_with_warmup,\r\n> RobertaConfig, RobertaModel, RobertaTokenizer)\r\n> \r\n> MODEL_CLASSES = {'roberta': (RobertaConfig, RobertaModel, RobertaTokenizer)}\r\n> \r\n> config_class, model_class, tokenizer_class = MODEL_CLASSES['roberta']\r\n> \r\n> config = config_class.from_pretrained('microsoft/codebert-base')\r\n> \r\n> tokenizer = tokenizer_class.from_pretrained('microsoft/codebert-base')\r\n> \r\n> #convert config file to json and save in model folder\r\n> config.to_json_file('model/config.json')\r\n> \r\n> #save the vocabulary\r\n> tokenizer.save_vocabulary('model/')\r\n> ```\r\nThis reply saved me an hour or two. If you look into convert_graph_to_onnx.py, --model parameter can only be HuggingFace's model id or a path. So anything like my_model.ckpt won't work. It should be a path containing vocab.txt, config.json, and pytorch_model.bin. Also the tokenizer should be either a tokenizer/model id or a folder containing the tokenizer files. Thanks a lot @junkgear ! " ]

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[ "Hi @LysandreJik and @lhoestq ,\r\nFYI, there is still this error on Transformers.\r\nHave you planned to support DPR into pipeline API?\r\n\r\nThanks", "Thanks for reporting !\r\nThe reader is currently not configured as a model for question answering indeed, but that's definitely something we'll change.\r\nWe'll also make it loadable as an an open-domain question answering Pipeline object.", "Hi @lhoestq ,\r\nI got it.\r\nIs there any approximate release date?\r\nI believe that open-domain QA Pipeline will be really useful, thank you. \r\n\r\n", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6567?src=pr&el=h1) Report\n> Merging [#6567](https://codecov.io/gh/huggingface/transformers/pull/6567?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/12d7624199e727f37bef7f53d527df7fabdb1fd6?el=desc) will **decrease** coverage by `0.77%`.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6567?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6567 +/- ##\n==========================================\n- Coverage 79.18% 78.41% -0.78% \n==========================================\n Files 156 156 \n Lines 28129 28129 \n==========================================\n- Hits 22275 22056 -219 \n- Misses 5854 6073 +219 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6567?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_xlnet.py](https://codecov.io/gh/huggingface/transformers/pull/6567/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ194bG5ldC5weQ==) | `83.30% <100.00%> (ø)` | |\n| [src/transformers/optimization.py](https://codecov.io/gh/huggingface/transformers/pull/6567/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9vcHRpbWl6YXRpb24ucHk=) | `25.55% <0.00%> (-70.00%)` | :arrow_down: |\n| [src/transformers/pipelines.py](https://codecov.io/gh/huggingface/transformers/pull/6567/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9waXBlbGluZXMucHk=) | `25.63% <0.00%> (-54.32%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/6567/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9ncHQyLnB5) | `65.68% <0.00%> (-29.33%)` | :arrow_down: |\n| [src/transformers/optimization\\_tf.py](https://codecov.io/gh/huggingface/transformers/pull/6567/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9vcHRpbWl6YXRpb25fdGYucHk=) | `33.33% <0.00%> (-24.33%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6567/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9hdXRvLnB5) | `48.79% <0.00%> (-18.08%)` | :arrow_down: |\n| [src/transformers/data/processors/squad.py](https://codecov.io/gh/huggingface/transformers/pull/6567/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL3Byb2Nlc3NvcnMvc3F1YWQucHk=) | `13.76% <0.00%> (-14.38%)` | :arrow_down: |\n| [src/transformers/modeling\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6567/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19hdXRvLnB5) | `64.36% <0.00%> (-14.37%)` | :arrow_down: |\n| [src/transformers/modelcard.py](https://codecov.io/gh/huggingface/transformers/pull/6567/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGNhcmQucHk=) | `82.71% <0.00%> (-2.47%)` | :arrow_down: |\n| [src/transformers/modeling\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/6567/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19kaXN0aWxiZXJ0LnB5) | `96.19% <0.00%> (-1.64%)` | :arrow_down: |\n| ... and [15 more](https://codecov.io/gh/huggingface/transformers/pull/6567/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6567?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6567?src=pr&el=footer). Last update [12d7624...1c18ddf](https://codecov.io/gh/huggingface/transformers/pull/6567?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Of course, @JetRunner, here it is \r\n\r\n", "@JetRunner, done", "Merging since the CI error looks unrelated." ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "The first step would be refactoring our current implementation with the priority queue. I am on it.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Also pinging @LysandreJik for notification in case this is easy to implement", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "Keep it a live :)", "That's an important feature indeed! Will try to tackle this with @LysandreJik @VictorSanh in the new year :-) ", "Hi, I'm not too familiar with T5 internals but I crudely tried modifying `modeling_t5.py` as OP suggested, but I ran into some issues with unsupported return values for `torch.utils.checkpoint.checkpoint`, so it seems like there might be something else other than that block that needs changing?\r\n```\r\n File \"/data/sauravkadavath/miniconda3/envs/transformers-4.0.0/lib/python3.7/site-packages/torch/utils/checkpoint.py\", line 163, in checkpoint\r\n return CheckpointFunction.apply(function, preserve, *args)\r\nTypeError: CheckpointFunctionBackward.forward: expected Tensor or tuple of Tensor (got NoneType) for return value 1\r\n```", "Hey @ssss1029, \r\n\r\nThanks for playing around with the feature! Would you mind using your code to open a PR? I'll help you get it merged. It is very well possible that we might have to change some more code in T5 to make it work. Ideally, I'd try to base the T5 gradient checkpointing's code as much as possible on how Bart does it.", "Lots of people have been asking for T5 checkpointing, so your PR would be a great contribution if you want to give it a try :-) ", "Hi Patrick, unfortunately, I'm pretty new to Huggingface internals and I won't have the bandwidth to implement this.", "@patrickvonplaten @ssss1029\r\nJust a straightforward workaround, but not for PR.\r\nI modify the torch.utils.checkpoint file to overcome its limitation. See the code below, all the modificaitons are with comments.\r\nTraining with t5-base, I obverse the loss is droping down as same as with gradient_checkpointing off and the memory usage drops down as well. But don't have time to do full verification now.\r\n\r\n### 1. checkpoint.CheckpointFunction\r\n```python\r\nclass CheckpointFunction(torch.autograd.Function):\r\n\r\n @staticmethod\r\n def forward(ctx, run_function, preserve_rng_state, *args):\r\n check_backward_validity(args)\r\n ctx.run_function = run_function\r\n ctx.preserve_rng_state = preserve_rng_state\r\n if preserve_rng_state:\r\n ctx.fwd_cpu_state = torch.get_rng_state()\r\n ctx.had_cuda_in_fwd = False\r\n if torch.cuda._initialized:\r\n ctx.had_cuda_in_fwd = True\r\n ctx.fwd_gpu_devices, ctx.fwd_gpu_states = get_device_states(*args)\r\n ctx.save_for_backward(*args)\r\n with torch.no_grad():\r\n outputs = run_function(*args)\r\n # return outputs\r\n\r\n #\r\n # Lie to torch we have no None items, to avoid the assert\r\n #\r\n result = []\r\n for o in outputs:\r\n if o is None:\r\n o = torch.zeros(0).cuda()\r\n result.append(o)\r\n\r\n return tuple(result)\r\n\r\n @staticmethod\r\n def backward(ctx, *args):\r\n if not torch.autograd._is_checkpoint_valid():\r\n raise RuntimeError(\"Checkpointing is not compatible with .grad(), please use .backward() if possible\")\r\n inputs = ctx.saved_tensors\r\n rng_devices = []\r\n if ctx.preserve_rng_state and ctx.had_cuda_in_fwd:\r\n rng_devices = ctx.fwd_gpu_devices\r\n with torch.random.fork_rng(devices=rng_devices, enabled=ctx.preserve_rng_state):\r\n if ctx.preserve_rng_state:\r\n torch.set_rng_state(ctx.fwd_cpu_state)\r\n if ctx.had_cuda_in_fwd:\r\n set_device_states(ctx.fwd_gpu_devices, ctx.fwd_gpu_states)\r\n detached_inputs = detach_variable(inputs)\r\n with torch.enable_grad():\r\n outputs = ctx.run_function(*detached_inputs)\r\n\r\n if isinstance(outputs, torch.Tensor):\r\n outputs = (outputs,)\r\n \r\n #\r\n # Skip None items and tensors which requires_grad are False when do backward\r\n #\r\n backward_outputs = []\r\n backward_args = []\r\n for o, a in zip(outputs, args):\r\n if o is not None and o.requires_grad:\r\n backward_outputs.append(o)\r\n backward_args.append(a)\r\n torch.autograd.backward(backward_outputs, backward_args)\r\n\r\n # torch.autograd.backward(outputs, args)\r\n grads = tuple(inp.grad if isinstance(inp, torch.Tensor) else inp\r\n for inp in detached_inputs)\r\n return (None, None) + grads\r\n```\r\n\r\n### 2. checkpoint.checkpoint()\r\n```python\r\ndef checkpoint(function, *args, **kwargs):\r\n preserve = kwargs.pop('preserve_rng_state', True)\r\n if kwargs:\r\n raise ValueError(\"Unexpected keyword arguments: \" + \",\".join(arg for arg in kwargs))\r\n\r\n outputs = CheckpointFunction.apply(function, preserve, *args)\r\n\r\n #\r\n # Resotre None items to result\r\n #\r\n result = []\r\n for o in outputs:\r\n if len(o) == 0:\r\n o = None\r\n result.append(o)\r\n\r\n return tuple(result)\r\n```\r\n\r\n### 3. modeling_t5.T5Stack.forward(), just the common way\r\n```python\r\n if getattr(self.config, \"gradient_checkpointing\", False):\r\n\r\n def create_custom_forward(module):\r\n def custom_forward(*inputs):\r\n return tuple(module(*inputs, use_cache, output_attentions))\r\n\r\n return custom_forward\r\n\r\n layer_outputs = checkpoint(\r\n create_custom_forward(layer_module),\r\n hidden_states,\r\n extended_attention_mask,\r\n position_bias,\r\n encoder_hidden_states,\r\n encoder_extended_attention_mask,\r\n encoder_decoder_position_bias,\r\n head_mask[i],\r\n past_key_value,\r\n )\r\n\r\n else:\r\n layer_outputs = layer_module(\r\n hidden_states,\r\n attention_mask=extended_attention_mask,\r\n position_bias=position_bias,\r\n encoder_hidden_states=encoder_hidden_states,\r\n encoder_attention_mask=encoder_extended_attention_mask,\r\n encoder_decoder_position_bias=encoder_decoder_position_bias,\r\n head_mask=head_mask[i],\r\n past_key_value=past_key_value,\r\n use_cache=use_cache,\r\n output_attentions=output_attentions,\r\n )\r\n```\r\n\r\n", "Hey @xFinal , \r\n\r\nYour 3rd approach is definitely the one we'd be super happy to integrate into Transformers. Thanks a mille for you contribution already. If anyone in the community wants to give it a shot to add @xFinal's 3rd proposed solution to `modeling_t5.py` that would be awesome :-)", "Hi @patrickvonplaten ,\r\n\r\nGlad to hear it's helpful! But I have two worries about the integration:\r\n1. It's not tested by a full train yet.\r\n2. The approch now is to modify the **torch.utils.checkpoint** file which is a part of Pytorch. Maybe not suitable for integration I think. Maybe there will be more elegant way, like adjust t5 itself?\r\n", "Hi @xFinal ,\r\nI tried your solution and got the following error:\r\n\r\n`TypeError('CheckpointFunctionBackward.forward: expected Tensor or tuple of Tensor (got tuple) for return value 1') \r\n> /share/home/dwaydwaydway/t5/src/transformers/src/transformers/models/t5/modified_gradient_ckpt.py(124)checkpoint() \r\n 123 \r\n--> 124 outputs = CheckpointFunction.apply(function, preserve, *args) \r\n 125 `\r\n\r\nMay I ask which pytorch version did you use?", "@dwaydwaydway,\r\nThe verison is 1.7.1\r\nMake sure return tuple type in CheckpointFunction.forward()", "This issue has been stale for 1 month.", "Inspired by @xFinal's solution, I implemented [another workaround](https://github.com/veritable-tech/transformers/commit/cdbc6d52ff07fe41c9585fdae017279ea1a4cf6b) that doesn't require modifying the `Checkpoint` class (by returning a dummy Tensor instead of None in `T5Block.forward`).\r\n\r\nIt seems to work, but my tests might not be comprehensive enough.", "Hey @ceshine - do you mind opening a PR for it? :-)", "> Hey @ceshine - do you mind opening a PR for it? :-)\r\n\r\nNot at all. I'll open a PR after a bit more polishing.", "@ceshine that's great! :)" ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "the new embeddings are initialized with N(0, 0.02) by default\r\n\r\nin `_get_resized_embeddings`\r\nhttps://github.com/huggingface/transformers/blob/9c2b2db2cdf0af968aae58d6075b6654224fb760/src/transformers/modeling_utils.py#L650-L651\r\n\r\ncalling `_init_weights`\r\nhttps://github.com/huggingface/transformers/blob/9c2b2db2cdf0af968aae58d6075b6654224fb760/src/transformers/modeling_bert.py#L592-L597", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "For open-ended questions like this you should try https://discuss.huggingface.co\r\n\r\nDid you read https://huggingface.co/blog/how-to-train?", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "It seems that keras is passing `experimental_aggregate_gradients` to `apply_gradients`, but the *transformers* TF2 optimizer does not have this argument (see https://github.com/huggingface/transformers/blob/master/src/transformers/optimization_tf.py#L224).\r\n\r\nOne workaround right now is to set `optimizer._HAS_AGGREGATE_GRAD = False`, which prevents keras from passing this argument.", "Thanks for the analysis @volker42maru. @jplu when you're back from vacation, we should fix this optimizer to accept this argument.", "Hello!\r\n\r\nI was aware of this, and this was on purpose to make the trainer compliant with all the TF 2.X versions. Now that the trainer is fixed to v 2.2 min, I will modify accordingly the method. Thanks @volker42maru to raise this and make me remember I had to update this.", "The PR #6717 should fix the problem." ]

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[ "looks similar to #6192", "Hey @aclifton314, \r\n\r\nCould you post a complete code example here so that we can reproduce the error?", "@patil-suraj thanks for your reply. I checked the vocab and input embed size and they are the same:\r\n```python\r\nmodel.transformer.wte.weight.shape[0] = 50257\r\nlen(model.tokenizer) = 50257\r\n```\r\nThe OP in the issue you linked to figured out that, for him, the issue was coming from the cross entropy loss:\r\n\r\n> I figured it out, it was due to the change in ignore_index=-100 instead of -1 in the cross entropy loss which was causing the issue. I'll close this.\r\n\r\nFor me, I calculate a loss with respect to an n-grams model I made so I'm not entirely sure that is the source of the problem. What's more, doing a test on the training with a dataset that has 1024 examples completes, while using a dataset with 5426 examples produces the `IndexError: index out of range in self` error.", "@patrickvonplaten sure, let me see what I can put together.", "@patrickvonplaten I'm having problems coming up with some sample code that reproduces the problem exactly. Since I am generating text during the training process, my hunch is that I generate a sequence that is longer than what GPT2 allows. It looks like another user posted some code that might serve as a sample to reproduce the error here: https://github.com/huggingface/transformers/issues/6599", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "One way is to use `config.initializer_range`\r\n\r\n#6562", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6557?src=pr&el=h1) Report\n> Merging [#6557](https://codecov.io/gh/huggingface/transformers/pull/6557?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/12d7624199e727f37bef7f53d527df7fabdb1fd6&el=desc) will **increase** coverage by `1.14%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6557?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6557 +/- ##\n==========================================\n+ Coverage 79.18% 80.33% +1.14% \n==========================================\n Files 156 156 \n Lines 28129 28129 \n==========================================\n+ Hits 22275 22597 +322 \n+ Misses 5854 5532 -322 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6557?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/tokenization\\_albert.py](https://codecov.io/gh/huggingface/transformers/pull/6557/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYWxiZXJ0LnB5) | `28.84% <0.00%> (-58.66%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/6557/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9iZXJ0LnB5) | `69.06% <0.00%> (-29.32%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/6557/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9ncHQyLnB5) | `71.84% <0.00%> (-23.17%)` | :arrow_down: |\n| [src/transformers/modeling\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/6557/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iZXJ0LnB5) | `88.26% <0.00%> (-0.17%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6557/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.71% <0.00%> (+0.50%)` | :arrow_up: |\n| [src/transformers/configuration\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6557/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3V0aWxzLnB5) | `96.59% <0.00%> (+0.68%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6557/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `87.29% <0.00%> (+0.97%)` | :arrow_up: |\n| [src/transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6557/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19vcGVuYWkucHk=) | `82.25% <0.00%> (+1.29%)` | :arrow_up: |\n| [src/transformers/tokenization\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6557/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYXV0by5weQ==) | `97.77% <0.00%> (+2.22%)` | :arrow_up: |\n| [src/transformers/tokenization\\_utils\\_base.py](https://codecov.io/gh/huggingface/transformers/pull/6557/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfYmFzZS5weQ==) | `93.76% <0.00%> (+3.79%)` | :arrow_up: |\n| ... and [6 more](https://codecov.io/gh/huggingface/transformers/pull/6557/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6557?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6557?src=pr&el=footer). Last update [12d7624...ce57708](https://codecov.io/gh/huggingface/transformers/pull/6557?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Cool dataset from AllenAI" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6556?src=pr&el=h1) Report\n> Merging [#6556](https://codecov.io/gh/huggingface/transformers/pull/6556?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/12d7624199e727f37bef7f53d527df7fabdb1fd6&el=desc) will **decrease** coverage by `0.77%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6556?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6556 +/- ##\n==========================================\n- Coverage 79.18% 78.41% -0.78% \n==========================================\n Files 156 156 \n Lines 28129 28129 \n==========================================\n- Hits 22275 22057 -218 \n- Misses 5854 6072 +218 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6556?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/optimization.py](https://codecov.io/gh/huggingface/transformers/pull/6556/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9vcHRpbWl6YXRpb24ucHk=) | `25.55% <0.00%> (-70.00%)` | :arrow_down: |\n| [src/transformers/pipelines.py](https://codecov.io/gh/huggingface/transformers/pull/6556/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9waXBlbGluZXMucHk=) | `25.63% <0.00%> (-54.32%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/6556/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9ncHQyLnB5) | `65.68% <0.00%> (-29.33%)` | :arrow_down: |\n| [src/transformers/optimization\\_tf.py](https://codecov.io/gh/huggingface/transformers/pull/6556/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9vcHRpbWl6YXRpb25fdGYucHk=) | `33.33% <0.00%> (-24.33%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6556/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9hdXRvLnB5) | `48.79% <0.00%> (-18.08%)` | :arrow_down: |\n| [src/transformers/data/processors/squad.py](https://codecov.io/gh/huggingface/transformers/pull/6556/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL3Byb2Nlc3NvcnMvc3F1YWQucHk=) | `13.76% <0.00%> (-14.38%)` | :arrow_down: |\n| [src/transformers/modeling\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6556/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19hdXRvLnB5) | `64.36% <0.00%> (-14.37%)` | :arrow_down: |\n| [src/transformers/modelcard.py](https://codecov.io/gh/huggingface/transformers/pull/6556/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGNhcmQucHk=) | `82.71% <0.00%> (-2.47%)` | :arrow_down: |\n| [src/transformers/modeling\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/6556/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19kaXN0aWxiZXJ0LnB5) | `96.19% <0.00%> (-1.64%)` | :arrow_down: |\n| [src/transformers/generation\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6556/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3V0aWxzLnB5) | `96.11% <0.00%> (-0.84%)` | :arrow_down: |\n| ... and [15 more](https://codecov.io/gh/huggingface/transformers/pull/6556/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6556?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6556?src=pr&el=footer). Last update [12d7624...6a0e0c9](https://codecov.io/gh/huggingface/transformers/pull/6556?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]