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DanielPFlorian

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[ "Hello!\r\n\r\nAt first, can you share your Transformers and TensorFlow version please?", "@jplu \r\nI use Transformers 4.1.1 and Tensorflow 2.4 in GCP VM, but I can change versions. I will train this model with v3-8 and v3-32.\r\nMy code and command are shown below. It is largely based on run_clm.py and run_tf_text_classification.py.\r\nThanks.\r\n\r\n```\r\nimport logging\r\nimport os\r\nfrom dataclasses import dataclass, field\r\nfrom typing import Dict, Optional\r\n\r\nimport datasets\r\nimport numpy as np\r\nimport tensorflow as tf\r\n\r\nfrom transformers import (\r\n AutoConfig,\r\n AutoTokenizer,\r\n TFAutoModel,\r\n\r\n GPT2Config,\r\n GPT2Tokenizer,\r\n BertTokenizer,\r\n TFGPT2LMHeadModel,\r\n HfArgumentParser,\r\n PreTrainedTokenizer,\r\n TFTrainer,\r\n TFTrainingArguments,\r\n)\r\n\r\ndef get_tfds(\r\n train_file: str,\r\n tokenizer: PreTrainedTokenizer,\r\n max_seq_length: Optional[int] = None,\r\n):\r\n files = {}\r\n\r\n if train_file is not None:\r\n files[datasets.Split.TRAIN] = [train_file]\r\n\r\n ds = datasets.load_dataset(\"csv\", data_files=files)\r\n features_name = 'content'\r\n transformed_ds = {}\r\n\r\n for k in files.keys():\r\n transformed_ds[k] = ds[k].map(\r\n lambda example: tokenizer.batch_encode_plus(\r\n example[features_name],\r\n truncation=True,\r\n max_length=max_seq_length\r\n ),\r\n batched=True,\r\n )\r\n\r\n def gen_train():\r\n for ex in transformed_ds[datasets.Split.TRAIN]:\r\n yield (\r\n {\r\n 'input_ids': ex[\"input_ids\"]\r\n },\r\n {\r\n 'labels': ex[\"input_ids\"]\r\n }\r\n )\r\n\r\n train_types = (\r\n {\r\n \"input_ids\": tf.int32\r\n },\r\n {\r\n \"labels\": tf.int32\r\n },\r\n )\r\n\r\n train_shapes = (\r\n {\r\n \"input_ids\": tf.TensorShape([None])\r\n },\r\n {\r\n \"labels\": tf.TensorShape([None])\r\n },\r\n )\r\n\r\n train_ds = tf.data.Dataset.from_generator(gen_train, train_types, train_shapes)\r\n\r\n\r\n if train_ds is not None:\r\n train_ds = train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN])))\r\n\r\n return train_ds\r\n\r\n\r\n@dataclass\r\nclass DataTrainingArguments:\r\n \"\"\"\r\n Arguments pertaining to what data we are going to input our model for training and eval.\r\n Using `HfArgumentParser` we can turn this class\r\n into argparse arguments to be able to specify them on\r\n the command line.\r\n \"\"\"\r\n\r\n train_file: str = field(default=None, metadata={\"help\": \"The path of the training file\"})\r\n dev_file: Optional[str] = field(default=None, metadata={\"help\": \"The path of the development file\"})\r\n test_file: Optional[str] = field(default=None, metadata={\"help\": \"The path of the test file\"})\r\n max_seq_length: int = field(\r\n default=128,\r\n metadata={\r\n \"help\": \"The maximum total input sequence length after tokenization. Sequences longer \"\r\n \"than this will be truncated, sequences shorter will be padded.\"\r\n },\r\n )\r\n overwrite_cache: bool = field(\r\n default=False, metadata={\"help\": \"Overwrite the cached training and evaluation sets\"}\r\n )\r\n\r\n\r\n@dataclass\r\nclass ModelArguments:\r\n \"\"\"\r\n Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.\r\n \"\"\"\r\n\r\n model_name_or_path: str = field(\r\n metadata={\"help\": \"Path to pretrained model or model identifier from huggingface.co/models\"}\r\n )\r\n config_name: Optional[str] = field(\r\n default=None, metadata={\"help\": \"Pretrained config name or path if not the same as model_name\"}\r\n )\r\n tokenizer_name: Optional[str] = field(\r\n default=None, metadata={\"help\": \"Pretrained tokenizer name or path if not the same as model_name\"}\r\n )\r\n use_fast: bool = field(default=False, metadata={\"help\": \"Set this flag to use fast tokenization.\"})\r\n # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,\r\n # or just modify its tokenizer_config.json.\r\n cache_dir: Optional[str] = field(\r\n default=None,\r\n metadata={\"help\": \"Where do you want to store the pretrained models downloaded from huggingface.co\"},\r\n )\r\n\r\n\r\n\r\n# See all possible arguments in src/transformers/training_args.py\r\n# or by passing the --help flag to this script.\r\n# We now keep distinct sets of args, for a cleaner separation of concerns.\r\nparser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))\r\nmodel_args, data_args, training_args = parser.parse_args_into_dataclasses()\r\n\r\nif (\r\n os.path.exists(training_args.output_dir)\r\n and os.listdir(training_args.output_dir)\r\n and training_args.do_train\r\n and not training_args.overwrite_output_dir\r\n):\r\n raise ValueError(\r\n f\"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome.\"\r\n )\r\n\r\n# tokenizer\r\ntokenizer = AutoTokenizer.from_pretrained(\r\n model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,\r\n cache_dir=model_args.cache_dir,\r\n)\r\n#tokenizer.add_special_tokens({'pad_token': '[PAD]'})\r\n\r\n# data\r\ntrain_dataset = get_tfds(\r\n train_file=data_args.train_file,\r\n tokenizer=tokenizer,\r\n max_seq_length=data_args.max_seq_length,\r\n)\r\n\r\n# config\r\nconfig_kwargs = {\r\n \"cache_dir\": model_args.cache_dir,\r\n #\"use_auth_token\": True if model_args.use_auth_token else None,\r\n}\r\n\r\nconfig = GPT2Config.from_pretrained(model_args.model_name_or_path, **config_kwargs)\r\n\r\n# model\r\nwith training_args.strategy.scope():\r\n model = TFGPT2LMHeadModel.from_pretrained(\r\n model_args.model_name_or_path,\r\n from_pt=bool(\".bin\" in model_args.model_name_or_path),\r\n config=config,\r\n cache_dir=model_args.cache_dir,\r\n )\r\n# model.resize_token_embeddings(len(tokenizer))\r\n\r\n# Initialize our Trainer\r\ntrainer = TFTrainer(\r\n model=model,\r\n args=training_args,\r\n train_dataset=train_dataset\r\n)\r\n\r\n# Training\r\nif training_args.do_train:\r\n model_path = (\r\n model_args.model_name_or_path\r\n if (model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path))\r\n else None\r\n )\r\n train_result = trainer.train()\r\n trainer.save_model(model_path) # Saves the tokenizer too for easy upload\r\n #tokenizer.save_pretrained(training_args.output_dir)\r\n\r\n output_train_file = os.path.join(training_args.output_dir, \"train_results.txt\")\r\n if trainer.is_world_process_zero():\r\n with open(output_train_file, \"w\") as writer:\r\n logger.info(\"***** Train results *****\")\r\n for key, value in sorted(train_result.metrics.items()):\r\n logger.info(f\" {key} = {value}\")\r\n writer.write(f\"{key} = {value}\\n\")\r\n\r\n # Need to save the state, since Trainer.save_model saves only the tokenizer with the model\r\n trainer.state.save_to_json(os.path.join(training_args.output_dir, \"trainer_state.json\"))\r\n```\r\n\r\n```\r\npython3 run_tftrainer.py \\\r\n --train_file datasets.csv \\\r\n --model_name_or_path gpt2 \\\r\n --do_train \\\r\n --output_dir model \\\r\n --num_train_epochs 4 \\\r\n --per_device_train_batch_size 4 \\\r\n --logging_steps 10 \\\r\n --save_steps 10 \\\r\n --overwrite_output_dir \\\r\n --max_seq_length 128\r\n```\r\n\r\n", "Ok, thanks a lot for sharing this!\r\n\r\nIf you are using the 4.1.1 release of Transformers, the `TFGPT2LMHeadModel` has a `labels` argument so the problem might come from elsewhere. The other thing to know is that it is currently not possible to train an LM from scratch with TF until the next release (coming very soon), only fine tuning is possible for now.\r\n\r\nWhat is the error you get exactly?", "I see.\r\nError message is shown below. I have tuned many things, but the loss shows nan at best.\r\nWill it take more than a week for the next release? Then I will use pytorch/xla.\r\n\r\n\r\n```\r\nAll the layers of TFGPT2LMHeadModel were initialized from the model checkpoint at gpt2.\r\nIf your task is similar to the task the model of the checkpoint was trained on, you can already use TFGPT2LMHeadModel for predictions without further training.\r\nTraceback (most recent call last):\r\n File \"/home/kykim/.local/lib/python3.6/site-packages/tensorflow/python/eager/context.py\", line 2102, in execution_mode\r\n yield\r\n File \"/home/kykim/.local/lib/python3.6/site-packages/tensorflow/python/data/ops/iterator_ops.py\", line 758, in _next_internal\r\n output_shapes=self._flat_output_shapes)\r\n File \"/home/kykim/.local/lib/python3.6/site-packages/tensorflow/python/ops/gen_dataset_ops.py\", line 2610, in iterator_get_next\r\n _ops.raise_from_not_ok_status(e, name)\r\n File \"/home/kykim/.local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py\", line 6843, in raise_from_not_ok_status\r\n six.raise_from(core._status_to_exception(e.code, message), None)\r\n File \"<string>\", line 3, in raise_from\r\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot batch tensors with different shapes in component 0. First element had shape [65] and element 1 had shape [31].\r\n [[{{node MultiDeviceIteratorGetNextFromShard}}]]\r\n [[RemoteCall]] [Op:IteratorGetNext]\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"run_tftrainer.py\", line 198, in <module>\r\n train_result = trainer.train()\r\n File \"/home/kykim/.local/lib/python3.6/site-packages/transformers/trainer_tf.py\", line 548, in train\r\n for step, batch in enumerate(train_ds):\r\n File \"/home/kykim/.local/lib/python3.6/site-packages/tensorflow/python/distribute/input_lib.py\", line 649, in __next__\r\n return self.get_next()\r\n File \"/home/kykim/.local/lib/python3.6/site-packages/tensorflow/python/distribute/input_lib.py\", line 694, in get_next\r\n self._iterators[i].get_next_as_list_static_shapes(new_name))\r\n File \"/home/kykim/.local/lib/python3.6/site-packages/tensorflow/python/distribute/input_lib.py\", line 1474, in get_next_as_list_static_shapes\r\n return self._iterator.get_next()\r\n File \"/home/kykim/.local/lib/python3.6/site-packages/tensorflow/python/data/ops/multi_device_iterator_ops.py\", line 581, in get_next\r\n result.append(self._device_iterators[i].get_next())\r\n File \"/home/kykim/.local/lib/python3.6/site-packages/tensorflow/python/data/ops/iterator_ops.py\", line 825, in get_next\r\n return self._next_internal()\r\n File \"/home/kykim/.local/lib/python3.6/site-packages/tensorflow/python/data/ops/iterator_ops.py\", line 764, in _next_internal\r\n return structure.from_compatible_tensor_list(self._element_spec, ret)\r\n File \"/usr/lib/python3.6/contextlib.py\", line 99, in __exit__\r\n self.gen.throw(type, value, traceback)\r\n File \"/home/kykim/.local/lib/python3.6/site-packages/tensorflow/python/eager/context.py\", line 2105, in execution_mode\r\n executor_new.wait()\r\n File \"/home/kykim/.local/lib/python3.6/site-packages/tensorflow/python/eager/executor.py\", line 67, in wait\r\n pywrap_tfe.TFE_ExecutorWaitForAllPendingNodes(self._handle)\r\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot batch tensors with different shapes in component 0. First element had shape [65] and element 1 had shape [31].\r\n [[{{node MultiDeviceIteratorGetNextFromShard}}]]\r\n [[RemoteCall]]\r\n2021-01-12 19:57:25.672632: W tensorflow/core/kernels/data/generator_dataset_op.cc:103] Error occurred when finalizing GeneratorDataset iterator: Failed precondition: Python interpreter state is not initialized. The process may be terminated.\r\n [[{{node PyFunc}}]]\r\n```", "I know that `tf.data.Dataset.from_generator` has some issues on TPUs, can you rewrite your data processing function to use `tf.data.Dataset.from_tensor_slices` instead?\r\n\r\nAlso, to be sure that your data are properly formatted you can add an assert to checks this. In order to know if the problems comes from there or not." ]

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[ "Hello, thanks for opening an issue! We try to keep the github issues for bugs/feature requests.\r\nCould you ask your question on the [forum](https://discusss.huggingface.co) instead?\r\n\r\nHave you checked the [T5 docs](https://huggingface.co/transformers/model_doc/t5.html) regarding the `decoder_inputs`? Are they unclear?\r\n\r\nThanks!", "This issue has been automatically marked as stale and been closed because it has not had recent activity. Thank you for your contributions.\n\nIf you think this still needs to be addressed please comment on this thread." ]

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[ "Is `blenderbot-small` a valid model_type?\r\n\r\nOr maybe this file https://huggingface.co/facebook/blenderbot_small-90M/blob/main/tokenizer_config.json is an issue?", "yeah, `blenderbot-small` is valid. One can download both the model and the config correctly:\r\n\r\n```python\r\nfrom transformers import BlenderbotSmallModel\r\n\r\nmodel = BlenderbotSmallModel.from_pretrained(\"facebook/blenderbot_small-90M\")\r\n```\r\n\r\nI think it has something to do with the tokenizers `vocab.json` file. But it's 1-to-1 the same file as in `\"facebook/blenderbot-90M\"` which can correctly be loaded...I'll have to check in more detail in the next days. There is probably a problem with the BlenderbotSmallTokenizer" ]

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[ "Linking Haystack issue https://github.com/deepset-ai/haystack/issues/719", "Frequent user of hugging face here, I'm a fan of this new publication and would love to see it implemented. Commenting here for the GitHub algorithm to ++", "Hi all, rather than waiting for the implementation in huggingface. Is there a simple way to utilize the pretrained model from the smith repo on our own dataset (to generate document embedding)?" ]

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[ "Hey @juand-r, \r\n\r\nThanks for the issue! I think this problem should be solved by now. We have done some major refactoring for MBart and removed the `_keys_to_ignore_on_save` for MBart. Can you check whether the error persists on current master? We will do a release tomorrow probably so that the fix should be included in the next pip version :-) ", "Thanks, @patrickvonplaten !\r\n\r\nI just checked the error is gone when using version 4.2.1.", "Hey @juand-r ,\r\n\r\nI am also trying to fine tune mBART for some non English corpus. Is there any sample script that I can follow for this task? ", "Hi @ozcangundes,\r\n\r\nThis could be helpful:\r\nhttps://github.com/GEM-benchmark/GEM-baseline-models/blob/main/examples/mbart_large_mlsum_ru.ipynb\r\n\r\n> Hey @juand-r ,\r\n> \r\n> I am also trying to fine tune mBART for some non English corpus. Is there any sample script that I can follow for this task?\r\n\r\n" ]

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[ "Could you run the slow tests of the models concerned?", "For reference, some discussions on why the method was added in the first place:\r\nhttps://github.com/huggingface/transformers/issues/6080\r\nhttps://github.com/huggingface/transformers/pull/6103", "In general, I agree very much with your approach here and I like the idea of a context manager. From a user perspective for Seq2Seq models these are the common practices IMO:\r\n\r\n**Inference in 99% of the time**: You use generate() in 99% of the time so you tokenize only your input_ids exactly the same way you'd do it for other models (like gpt2)\r\n \r\n**Inference in 1% of the time**: In case you just want to do a single forward pass, you will have to input `input_ids` and `decoder_input_ids` -> this is usually only for special cases so one can reasonably expect the user to know how the model works. However in this case we either do need the context-manager or a `prepare_seq2seq_batch` method since the start token for the decoder is very much different from the one of the encoder. This is actually such as special case that we don't even need any magic functions for that, but just assume that the user manually prepends the `decoder_start_token_id` to `decoder_input_ids`.\r\n\r\n**training**: All seq2seq models usually only require the `labels` and `input_ids` and then the `decoder_input_ids` are automatically generated, with a method that just shifts the `labels` one to the right and adds the `decoder_start_token_id`. So far there is not a single seq2seq model that does not have this mechanism and thus all seq2seq models can be trained by only passing `input_ids` and `labels`. So I think we can assume that all seq2seq models only require `input_ids` and `labels`. => this makes them then also very similar to how BERT-like models are trained since they also just need `input_ids` and `labels`. Here the `prepare_seq2seq_batch` method is useful because it tokenizes both inputs at once and has some additional features like `src_lang` and `tgt_lang` (useful for MBart and Marian only though) and `target_max_length`, etc....but as said in the issues referenced above and mentioned in this PR as well, I do think that those are some \"magic\" functionalities that should not have their origin in `src/transformers` but better in `examples` -> so I think we agree here @sgugger .", "So only thing, I'm a bit worried about is that some users got very accustomed to the `prepare_seq2seq_batch` method so that they won't be too happy about removing it (especially since it's also doing all the `max_length` and `max_target_length` automatically.\r\n\r\nBut very much in favor of this change", "Failure is independent, due to the new tokenziers release, so merging." ]

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[ "Hello @Skylixia,\r\n\r\nI believe the summarisation examples have been migrated to the `seq2seq` folder here: https://github.com/huggingface/transformers/tree/master/examples/seq2seq\r\n\r\nIn the README, you can find instructions on how to fine-tune a model for summarisation: https://github.com/huggingface/transformers/tree/master/examples/seq2seq#fine-tuning-using-seq2seqtrainer\r\n\r\nHTH!", "This issue has been automatically marked as stale and been closed because it has not had recent activity. Thank you for your contributions.\n\nIf you think this still needs to be addressed please comment on this thread." ]

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[ "Maybe @sgugger has an idea.", "Hi @maxie320,\r\n\r\nThe `Trainer` now removes unused keys from the dataset if the dataset is an instance of `datasets.Dataset`. By unused, it means all the keys which are not in the model's forward method's argument list. And since `target_ids` is not a argument expected by the forward it's getting removed by the `Trainer` and hence the `KeyError`\r\n\r\nYou can rename the `target_ids` key by `labels` and also change the collator accordingly which should fix this issue", "@patil-suraj It's now showing a key error on `target_attention_mask`. I'm guessing this name has been changed as well?", "Okay, figured it out, used the same name `decoder_attention_mask` in `T2TDataCollator` as given in `forward()` method argument list, thanks for the assist @patil-suraj", "Also note that you can set `remove_unused_columns=False` in your `TrainingArguments` to disable the behavior where Trainer drops the columns not in the model signature.", "Sure, thank you! @sgugger", "Closing this issue since it seems solved, don't hesitate to reopen if you have more problems!", "Hello everyone,\r\nI am trying to run the same notebook given in https://colab.research.google.com/github/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb and I have a similar problem mentioned here. I applied the possible changes mentioned here, but it could not solve my problem.\r\n\r\nThere was a problem in nightly version with import torch as mentioned https://stackoverflow.com/questions/67257008/oserror-libmkl-intel-lp64-so-1-cannot-open-shared-object-file-no-such-file-or/67479054#67479054. After I add the modifications, it throws the error below:\r\n\r\n/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)\r\n1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\r\n1050 or _global_forward_hooks or _global_forward_pre_hooks):\r\n-> 1051 return forward_call(*input, **kwargs)\r\n1052 # Do not call functions when jit is used\r\n1053 full_backward_hooks, non_full_backward_hooks = [], []\r\n\r\nTypeError: forward() got an unexpected keyword argument 'lm_labels'\r\n\r\nAny ideas?\r\nThanks", "T5 expects `labels` now, not `lm_labels`. You should replace that in the return statement of your data collator\r\n```\r\nclass T2TDataCollator(DataCollator):\r\n def collate_batch(self, batch: List) -> Dict[str, torch.Tensor]:\r\n \"\"\"\r\n Take a list of samples from a Dataset and collate them into a batch.\r\n Returns:\r\n A dictionary of tensors\r\n \"\"\"\r\n input_ids = torch.stack([example['input_ids'] for example in batch])\r\n lm_labels = torch.stack([example['target_ids'] for example in batch])\r\n lm_labels[lm_labels[:, :] == 0] = -100\r\n attention_mask = torch.stack([example['attention_mask'] for example in batch])\r\n decoder_attention_mask = torch.stack([example['target_attention_mask'] for example in batch])\r\n \r\n\r\n return {\r\n 'input_ids': input_ids, \r\n 'attention_mask': attention_mask,\r\n 'lm_labels': lm_labels, \r\n 'decoder_attention_mask': decoder_attention_mask\r\n }\r\n```\r\n\r\nAlso that data collator should have a `__call__` method, not a `collate_batch`.", "Thank you for your quick reply @sgugger, now it works!" ]

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[ "Do you have any idea why GitHub is not showing the diff properly?", "Looks like I'm having an issue with CRLFs :( I think I replaced all CRLFs by LFs\r\nI'm currently investigating this", "@sgugger problem solved 👌" ]

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[ "Adding some more info:\r\n\r\nThe api call returns to the model endpoint `503 (Service Unavailable)` and the error message\r\n```json\r\n {\"error\":\"Model Musixmatch/umberto-wikipedia-uncased-v1 is currently loading\",\"estimated_time\":10}\r\n```\r\n\r\nThen while the model is loading a new error comes out:\r\n\r\n```\r\nbundle.5e4ae99.js:1 Uncaught (in promise) TypeError: Failed to fetch\r\n```\r\n\r\nThank you!", "pinging @Narsil ! :)", "Hi @loretoparisi ,\r\n\r\nSorry for the delayed answer. The problem was linked to you tokenizer that somehow had a failure when it was transformed automatically into a Fast one. (Actually it worked well, but the result could not be saved properly). I fixed your tokenizer by adding the precomputed result for Fast tokenizer:\r\n\r\nhttps://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/commit/483eca5f6b781ddb811e590fb584cc2e1d2b662e\r\n\r\nEverything seems to be working properly now (and loads fast)\r\n", "@Narsil the inference outputs seem weird though, like the tokenizer doesn't uncase inputs: https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1?text=Roma+%C3%A8+la+%3Cmask%3E+d%27Italia\r\n\r\n<img width=\"709\" alt=\"Screenshot 2021-01-22 at 19 47 53\" src=\"https://user-images.githubusercontent.com/326577/105532172-79ac3980-5cb8-11eb-908d-9d9c7b3b80d9.png\">\r\n", "- <unk> are explainable because this model uses only lowercase, so all MAJs are unks.\r\n- c/a at start end was an error in the config (it might be because, there are some automatic fixed offsets for Camembert that might not actually be used by this model).\r\n- The fact that some output are different from others is simply hardcoded in the widget (and is not correct IMHO)", "@Narsil thank you for your help, there is anything that we can do/test by our side? cc @simonefrancia \r\nThanks!", "> * `<unk>` are explainable because this model uses only lowercase, so all MAJs are unks.\r\n\r\nSure, this means that there's some missing config for the tokenizer. See this model for example: https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France.\r\n\r\n\r\n\r\n> * The fact that some output are different from others is simply hardcoded in the widget (and is not correct IMHO)\r\n\r\nnot sure what you mean here. cc @n1t0 ", "> Sure, this means that there's some missing config for the tokenizer. See this model for example: https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France.\r\n\r\nI can't make any choice about what's more reasonable for the end model, the current tokenizer is exactly what `sentencepiece` would do (we export all variables from it, by using the `precompiled_charsmap`).\r\n@loretoparisi if you want to actually force lowercasing input you can by changing `normalizer` within `tokenizer.json` to `Sequence` with a `Lowercase` then the `precompiled_charsmap`. But be aware that you won't have the same results as the raw SPM tokenizer anymore. Let me know if you want to do that I can do it, but again be careful of the impacts it could have for the model.\r\n\r\n> not sure what you mean here. cc @n1t0\r\n\r\nThis: https://github.com/huggingface/moon-landing/blob/master/front/js-src/lib/widgets/text-classification.ts#L45\r\nAlso see PR on transformers that could solve this : https://github.com/huggingface/transformers/pull/9783", "@Narsil I think there are several different things going on here:\r\n - The input doesn't get lowercased. This is true for both the fast and slow tokenizers, so yes, the conversion from slow to fast went well, but there's still a question about whether this should be fixed somehow (since the config contains `do_lowercase=True`, I think it was expected). If yes, both slow and fast tokenizers should be fixed.\r\n - Even if we don't look at the `<unk>`, the output still seems weird. After digging a bit, it seems that the IDs generated by the fast version of the tokenizer are not aligned with the slow one:\r\n```python\r\nfrom transformers import AutoTokenizer, pipeline\r\n\r\n\r\ndef run_input(input):\r\n tok_slow = AutoTokenizer.from_pretrained(\"Musixmatch/umberto-wikipedia-uncased-v1\", use_fast=False)\r\n p_slow = pipeline(\"fill-mask\", model=\"Musixmatch/umberto-wikipedia-uncased-v1\", tokenizer=tok_slow)\r\n ids_slow = tok_slow.encode(input)\r\n p_output_slow = p_slow(input)\r\n\r\n tok_fast = AutoTokenizer.from_pretrained(\"Musixmatch/umberto-wikipedia-uncased-v1\", use_fast=True)\r\n p_fast = pipeline(\"fill-mask\", model=\"Musixmatch/umberto-wikipedia-uncased-v1\", tokenizer=tok_fast)\r\n ids_fast = tok_fast.encode(input)\r\n p_output_fast = p_fast(input)\r\n\r\n print(\"Running with input: \", input)\r\n print(\"SLOW:\")\r\n print(ids_slow)\r\n print(p_output_slow)\r\n\r\n print(\"FAST:\")\r\n print(ids_fast)\r\n print(p_output_fast)\r\n\r\n\r\nrun_input(\"Roma è la <mask> d'Italia\")\r\nrun_input(\"roma è la <mask> d'italia\")\r\n```\r\nGives the following output:\r\n```python\r\nRunning with input: Roma è la <mask> d'Italia\r\nSLOW:\r\n[5, 31908, 3, 31912, 79, 97, 51, 32004, 7, 31931, 3, 11007, 6]\r\n[\r\n{'sequence': \"<s> <unk>oma è la lingua d'<unk>talia</s>\", 'score': 0.04120568186044693, 'token': 1476, 'token_str': '▁lingua'}, \r\n{'sequence': \"<s> <unk>oma è la città d'<unk>talia</s>\", 'score': 0.023448798805475235, 'token': 521, 'token_str': '▁città'}, \r\n{'sequence': \"<s> <unk>oma è la dea d'<unk>talia</s>\", 'score': 0.022841867059469223, 'token': 4591, 'token_str': '▁dea'}, \r\n{'sequence': \"<s> <unk>oma è la terra d'<unk>talia</s>\", 'score': 0.02243848517537117, 'token': 1415, 'token_str': '▁terra'}, \r\n{'sequence': \"<s> <unk>oma è la capitale d'<unk>talia</s>\", 'score': 0.01755419932305813, 'token': 3152, 'token_str': '▁capitale'}\r\n]\r\nFAST:\r\n[1, 31904, 0, 31908, 75, 93, 47, 32001, 3, 31927, 0, 11003, 2]\r\n[\r\n{'sequence': \"<s> <unk>oma è laà d'<unk>talia</s>\", 'score': 0.4644460380077362, 'token': 31936, 'token_str': 'à'},\r\n{'sequence': \"<s> <unk>oma è la<mask> d'<unk>talia</s>\", 'score': 0.41339975595474243, 'token': 32001, 'token_str': '<mask>'},\r\n{'sequence': \"<s> <unk>oma è laena d'<unk>talia</s>\", 'score': 0.02151116542518139, 'token': 408, 'token_str': 'ena'},\r\n{'sequence': \"<s> <unk>oma è laè d'<unk>talia</s>\", 'score': 0.01422190386801958, 'token': 31935, 'token_str': 'è'},\r\n{'sequence': \"<s> <unk>oma è la ten d'<unk>talia</s>\", 'score': 0.0057907504960894585, 'token': 685, 'token_str': '▁ten'}\r\n]\r\n\r\nRunning with input: roma è la <mask> d'italia\r\nSLOW:\r\n[5, 764, 97, 51, 32004, 7, 31931, 31911, 11007, 6]\r\n[\r\n{'sequence': \"<s> roma è la bandiera d'italia</s>\", 'score': 0.13166911900043488, 'token': 3525, 'token_str': '▁bandiera'},\r\n{'sequence': \"<s> roma è la capitale d'italia</s>\", 'score': 0.0553407184779644, 'token': 3152, 'token_str': '▁capitale'},\r\n{'sequence': \"<s> roma è la nazionale d'italia</s>\", 'score': 0.04516282677650452, 'token': 918, 'token_str': '▁nazionale'},\r\n{'sequence': \"<s> roma è la zona d'italia</s>\", 'score': 0.022440679371356964, 'token': 1740, 'token_str': '▁zona'},\r\n{'sequence': \"<s> roma è la regione d'italia</s>\", 'score': 0.02204475924372673, 'token': 1472, 'token_str': '▁regione'}\r\n]\r\nFAST:\r\n[1, 760, 93, 47, 32001, 3, 31927, 31907, 11003, 2]\r\n[\r\n{'sequence': \"<s> roma è la<mask> d'italia</s>\", 'score': 0.9972749352455139, 'token': 32001, 'token_str': '<mask>'},\r\n{'sequence': \"<s> roma è laà d'italia</s>\", 'score': 0.001777052297256887, 'token': 31936, 'token_str': 'à'},\r\n{'sequence': \"<s> roma è la pai d'italia</s>\", 'score': 0.00022994846221990883, 'token': 14871, 'token_str': '▁pai'},\r\n{'sequence': \"<s> roma è la raffigura d'italia</s>\", 'score': 0.00011272338451817632, 'token': 15184, 'token_str': '▁raffigura'},\r\n{'sequence': \"<s> roma è la hiv d'italia</s>\", 'score': 0.00011238666047574952, 'token': 28952, 'token_str': '▁hiv'}\r\n]\r\n```\r\nAs you can see, the output using the slow tokenizer seems fine, while the other doesn't.", "Okay this is now fixed: https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/commit/713d59922ccb4b5fc31a527ce2d785c23533363b\r\n\r\nThis 4 offset in the tokens is hardcoded for Camembert based tokenizers :\r\nhttps://github.com/huggingface/transformers/blob/937f67074d6728f145d54d6ea87221a46303363d/src/transformers/models/camembert/tokenization_camembert.py#L241\r\n\r\nI'm doing a pass on all BPE based spm to check various behaviors.", "Hi @Narsil,\r\nthanks for your support in Umberto.\r\nthanks also for making Umberto wikipedia alive again.\r\n\r\nWe see something not usual that replaces <mask> token.\r\n\r\n\r\nIn this example mask token is not replaced by a single BPE token, but an entire sentence and that sounds strange.\r\nIf there is something that we can do on our side, let us know.\r\nThanks", "@simonefrancia are you referring to the third result in the screenshot?", "@julien-c yes. My doubt is that input sentence is repeated for the third result.", "I think it's the widget's intended behavior for BPE when we are not able to display the BPE token by itself. But we can take a look...\r\n\r\nHow are the other results, are they sensible?", "I confirm it's the widget because suggested result is len <2, it's trying to repeat the full sentence instead of just the token.\r\nAnd the first C is ignored because it's a `<unk>` from the tokenizer's standpoint. ", "I found other interesting cases, for example this one, when mask is at starting point.\r\n\r\n\r\n\r\nIn case we don't specify anything before `<mask>`, something goes wrong. My doubt is that in this case `<mask>` token is replaced by `<s>` token. I tried to insert `<s>` before `<mask>` token and it works.\r\n\r\n\r\n\r\nHope this can help you.\r\n", "@Narsil Ok, but is it possible to force output that would be `<unk>` (because uppercase) to lowercase, in order that `<unk>` tokens can't appear? wikipedia model is lower case, so we can force to treat only lowercase words.\r\nThanks", "Hi @simonefrancia.\r\nIn order to force lowercase, you can do it in the Fast tokenizer but that would lead to different results between Slow and Fast tokenizers again.\r\n\r\n> @loretoparisi if you want to actually force lowercasing input you can by changing normalizer within tokenizer.json to Sequence with a Lowercase then the precompiled_charsmap. But be aware that you won't have the same results as the raw SPM tokenizer anymore. Let me know if you want to do that I can do it, but again be careful of the impacts it could have for the model.\r\n\r\nAs for the widget, a fix is coming (it's really a display issue, if you look at the raw results it should make more sense).", "I opened a new issue to keep track of the lowercasing issue as this is something that would probably be helpful for many tokenizers. (cf #10121)\r\n\r\nI believe everything else has been fixed, has it?", "I think so but I'll let @simonefrancia confirm.", "for [umberto-wikipedia](https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1) I think that's all, guys. Thanks\r\nInstead, for [umberto-commoncrawl ](https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1)model keeps loading. Also there are same problems in tokenizer? \r\n\r\n\r\n", "Yes it's the same problem. Do you want me to fix it in the same way ? (Hopefully this time it works right off the bat.)\r\n\r\nAre there any other models that could be under the same flag ? (I detected only this one during my full sweep for your organization)", "For our organization, we have only two models, umberto-wikipedia ( the one you fixed) and umberto-commoncrawl ( the one to be fixed). \r\nUmberto commoncrawl is cased, so maybe it could be a different problem or a different way to be fixed, but we would like it works. \r\nthanks for your support", "It's fixed now : https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1?text=Lo+scopo+della+vita+%C3%A8+%3Cmask%3E.", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "Hi! Could you please put the information required in the issue template? I.e, everything related to your environment.", "@LysandreJik I have updated the original question based on your suggestion. ", "You're loading your configuration with:\r\n\r\n```py\r\nconfig = BertConfig.from_pretrained(\"models1/our_fine_tuned_model_definition+comment.pt\", output_hidden_states=True) \r\n```\r\n\r\nIs `models1/our_fine_tuned_model_definition+comment.pt` a directory containing a `config.json` file?", "No, that folder does not contain any `config.json` file. Actually, I took the pretrained SciBERT model and saved it in my local system using the following comment \r\n\r\n```\r\npath_to_model='models1/our_fine_tuned_model_definition+comment.pt'\r\ntorch.save(net_copy.state_dict(), path_to_model)\r\n```\r\n\r\nAs a novice, I am not sure how to save the `config.json` file. Please help me with that. \r\n\r\nThanks in advance. ", "I recommend reading the [quickstart (#using-the-model)](https://huggingface.co/transformers/quicktour.html#using-the-model) to understand the loading/saving of models!\r\n\r\nI guess you loaded the model this way:\r\n\r\n```py\r\nfrom transformers import AutoModel\r\n\r\nmodel = AutoModel.from_pretrained(\"allenai/scibert_scivocab_cased\")\r\n```\r\nyou should save the model like this:\r\n```py\r\nmodel.save_pretrained(\"directory\")\r\n```\r\n\r\nThis will create a directory containing the following:\r\n\r\n```py\r\n!ls directory\r\nconfig.json pytorch_model.bin\r\n```\r\n\r\nYou can then load your configuration from that very easily:\r\n\r\n```py\r\nBertConfig.from_pretrained(\"directory\")\r\n```\r\nor load the model directly using `AutoModel` or `BertModel`:\r\n```py\r\nAutoModel.from_pretrained(\"directory\")\r\n# or\r\nBertModel.from_pretrained(\"directory\")\r\n```", "Please note that when downloading the `allenai/scibert_scivocab_cased` model, it's cached in your system. You can then freely reload it with the same identifier without re-downloading the model.\r\n\r\nExcept if you modify the model, for example by fine-tuning, you shouldn't need to save it to disk manually.", "@LysandreJik Thanks a lot ", "My pleasure. Closing the issue as resolved." ]

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[ "If you install SentencePiece `pip install sentencepiece`, do you still get that error?", "> If you install SentencePiece `pip install sentencepiece`, do you still get that error?\r\n\r\nI had it already installed: `sentencepiece==0.1.91`", "FWIW: when `I import protoc` in e.g. `ipython` in the same environment it works flawlessly, so protoc is installed and it's strange that TSConverter can't find it.", "Ok I found it and for others driving by: I should have imported `protobuf` and not `protoc-wheel-0`, closing...", "`pip install protobuf` solved it for me", "> `pip install protobuf` solved it for me\r\n\r\nI too didn't have to downgrade it, just installed a missing `protobuf` (latest version). This can be reproduced in e.g. a Hugging Face example for e.g. DONUT document classifier using our latest CUDA 11.8 containers: `mirekphd/cuda-11.8-cudnn8-devel-ubuntu22.04:20230928`. Note that the official `nvidia/cuda/11.8.0-cudnn8-devel-ubuntu22.04` containers seem to come with `protobuf` already preinstalled, so you won't reproduce the bug there).\r\n\r\nPerhaps `protobuf` should be added explicitly as a dependency of `transformers`?", "Hi\r\n\r\n`transformers` has a lot of models involved, and if we put everything as direct dependency, it would be very long and heavy to install. Even `torch` is not a direct dependency :-)\r\n\r\nYou can always install it as `pip install transformers[dev]` and I believe you will get `protobuf` (along with a lot of stuff).", "I'm still facing the same error. I have fine tuned mistral model, but I'm trying to inference it, it's still giving me:\r\n\r\nCould not complete request to HuggingFace API, Status Code: 500, Error: \\nLlamaConverter requires the protobuf library but it was not found in your environment. Checkout the instructions on the\\ninstallation page of its repo: https://github.com/protocolbuffers/protobuf/tree/master/python#installation and follow the ones\\nthat match your environment. Please note that you may need to restart your runtime after installation.\\n\r\n\r\n\r\nI've done: pip install protobuf, in both env (fine tuning and inferencing)" ]

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[ "> Thank you @patrickvonplaten the changes look great! One last suggestion on my side since this PR does some renaming of the modules: I believe the naming of the `ProphetNetSelfAttention` is misleading, since it is used as a cross attention in the decoder layer:\r\n> \r\n> https://github.com/huggingface/transformers/blob/02e05fb0a532e572b56ba75dad6ba3db625bbdeb/src/transformers/models/prophetnet/modeling_prophetnet.py#L1082\r\n> \r\n> \r\n> Maybe a more appropriate name would be `ProphetNetBaseAttention` or simply `ProphetNetAttention` ?\r\n> There is also a typo in `ProhpetNetPositionalEmbeddings` and `ProhpetNetFeedForward`\r\n\r\nI agree with you! I should be more careful next time when naming the classes :-) " ]

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[ "cc @LysandreJik @sgugger @jplu ", "Thanks for fixing!" ]

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[ "These need to be applied manually; we could probably do some of these automatically but haven't thought about that yet. While we think of this we can apply the label manually as feature requests come up." ]

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[ "Hi @nbeuchat this is a fasttext model, not a `transformers` model, so you can't load it that way.\r\n\r\nI've updated the main button on the webpage to make it clearer that you need to use the model in fasttext:\r\n\r\n<img width=\"1067\" alt=\"Screenshot 2021-01-11 at 19 21 07\" src=\"https://user-images.githubusercontent.com/326577/104222282-11d03600-5410-11eb-9b03-307fc776f197.png\">\r\n<img width=\"885\" alt=\"Screenshot 2021-01-11 at 19 21 56\" src=\"https://user-images.githubusercontent.com/326577/104222284-14329000-5410-11eb-842a-05fa8e05c1ca.png\">\r\n\r\n\r\n", "Also cc'ing @thomwolf and @celebio <3", "Got it, thanks for the info and for the quick update! ", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "Hi @jianqianzhou,\r\n\r\nThanks for raising this issue.\r\n\r\nI would remove the `OMP_NUM_THREADS` environment variable to fully exploit all the cores/threads you have on your machine. Also, tests were run on a machine with 56 cores so it might impact final performances. \r\n\r\nAlso, it might be possible to further optimize the model / quantized model through the ONNX Runtime optimizer tool.", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "The tests look ok to me!", "@NielsRogge removed the `drop_rows_to_fit` attribute in the last commit." ]

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[ "Can we add those `pip install -e .[ja]` dependencies to the hosted Inference API @Narsil?", "@julien-c I'm not sure if that is possible, but perhaps this can even be derived from the model card? If a model card specifies Japanese, then the env could include the `[ja]` option.", "If I'm not mistaken all models run in the same env so it's probably not an issue to add a dependency, but I'll let @Narsil answer!", "Yes I created a patch for this, should be up soon.", "and up !", "Confirmed it works, thanks for the quick fix!" ]

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[ "Thanks for fixing!" ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.", "Hi @Mounika2405. Were you able to find a solution for this issue? I am facing a similar issue with another torch_script model", "Facing the same issue. " ]

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[ "You are inspecting an input of the training datalaoder, which has been shuffled. Therefore you do not have the beginning of one of your original documents since by default, the script concatenates all your texts (after adding the special tokens at the beginning and the end) then splits the result in contiguous chunks of length `max_seq_length` (unspecified here so the default of a roberta-base model).\r\n\r\nSo the text you are inspecting is inside one of your original documents, which is why it doesn't have that <s> and </s>\r\n\r\nYou can use the `line_by_line` option to change the script preprocessing to consider each line of your dataset as a separate entry (and apply padding or truncation to always have them of `max_seq_length`), in which case every input will have that `</s>` at the beginning.", "Thanks for the information, this makes sense!" ]

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[ "HI @rezhv , that's a great question, I would suggest you ask such general questions on the forum https://discuss.huggingface.co/ and use issues to report bugs and to discuss new features :-)", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "hi @xiaolin-cheng \r\n\r\n`run_glue.py` fine-tunes the whole model, it doesn't freeze anything. You would need to manually freeze the base model, you could do this after loading the `ForSequenceClassification` and then freeze the base model. For example for `BertForSequenceClassification` you can access the base model using `model.bert`.\r\n\r\n```python\r\nmodel = BertForSequenceClassification.from_pretrained('bert-base-uncased')\r\n\r\nfor param in model.bert.parameters():\r\n param.requires_grad = False\r\n```", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "Hi @hadifar \r\n\r\nThe `AutoTokenizer` needs to know the model type to load the correct `Tokenizer` class, and that information is stored in the `config` file, so if `config.json` is not present it can not load the correct class. And `config.json` is saved when saving the model using `.save_pretrained` method. To load a separately saved tokenizer you should use the respective tokenizer class " ]

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[ "> Awesome work!! Just left few smalll comments. I think we should first find a proper fix #9478 and then merging this one. Switching on/off some tests everytime we touch a model is really not a long term solution, I think a proper template as to be stated first and then afterwards we do the models.\r\n\r\nIMO this PR should be merged and the s2s fix should be applied afterward as said offline. This PR is blocking a new release currently", "> IMO this PR should be merged and the s2s fix should be applied afterward as said offline. This PR is blocking a new release currently\r\n\r\nOk, nevermind, I didn't know you wanted to have it in the next release." ]

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[ "I am not aware of anything that could make life easier on this as I would have implemented it/documented it if I knew of it. Your solution of creating a project with just two files does not work, as it would then very likely be impossible to import the file in question and sphinx needs to do that.\r\n\r\nI'm not aware of any software that lints properly the .rst. Happy to add any new functionality the doc styler that could help here (as this one runs fast and can be run on a given .py/.rst file) though it's medium priority in terms of development of the project (we do want users to be able to build the documentation smoothly and easily but there are other things more important).", "Thank you for feedback, @sgugger \r\n\r\n-------------------\r\n\r\nSo to disable `Warning, treated as error:` I need to drop `-W` in:\r\n\r\n```\r\ncd docs && make html SPHINXOPTS=\"-W\"\r\n```\r\n\r\nand then need to figure out how to skip highlighting as it re-works all files on every run.\r\n\r\n------------------\r\n\r\nI started looking at finding a single page linter that supports sphinx's custom parser. I will post my findings here:\r\n- https://pypi.org/project/restructuredtext-lint/ - says partially supports sphinx\r\n- https://pypi.org/project/doc8/ - supports sphinx, but may have its own demands\r\n\r\n", "Also did you know sphinx has parallel processing with `-j`?\r\n\r\nI added `-a -E`, which forces a full rebuild, just for the test so that we are comparing the same things.\r\n\r\n```\r\ntime make html SPHINXOPTS=\"-a -E\"\r\nreal 1m15.265s\r\nuser 1m15.114s\r\nsys 0m1.790s\r\n```\r\n\r\n```\r\ntime make html SPHINXOPTS=\"-a -E -j 6\"\r\nreal 0m39.555s\r\nuser 1m31.551s\r\nsys 0m7.608s\r\n```\r\n\r\nthis is almost twice as fast! \r\n\r\nIt seems that on my setup `-j 5` is just as fast but less heat get generated (41 sec).\r\n\r\nIt has `-j auto` - to use all cpu cores, but it's a bad idea, since it won't get any faster with 12 or more workers. Any number of workers beyond 5 on my setup provides a tiny speedup.\r\n\r\nDo you think it'd be a good idea to add say `SPHINXOPTS=\"-j 4\"` as the default?\r\n\r\n\r\n", "I filed a bug report https://github.com/sphinx-doc/sphinx/issues/8681 since if that `re-highlighting of all modules` stage gets fixed to not re-run on all modules when only 1 files is modified and I drop `-W` temporarily - then the rebuild should be almost instantaneous for a single modified file and thus we won't need to look for an outside linter.\r\n", "Last time I tried ot use multiprocessing I didn't get any speed up, but it might have been because I was trying the auto option. We can certainly try with 4 cores to begin with.", "Excellent!\r\n\r\nIn general `auto` is almost never a good option w/o knowing the user's setup. That's why I never use `make test`, which runs `pytest -n auto` - I have 12 cpu cores and it can't possibly run 12 workers on 2 gpus - the outcome is really bad.", "> I filed a bug report [sphinx-doc/sphinx#8681](https://github.com/sphinx-doc/sphinx/issues/8681) since if that `re-highlighting of all modules` stage gets fixed to not re-run on all modules when only 1 files is modified and I drop `-W` temporarily - then the rebuild should be almost instantaneous for a single modified file and thus we won't need to look for an outside linter.\r\n\r\nsphinx dev has fixed this issue in master, so now `make docs` for one modified file is blazingly fast - ~5sec.\r\n\r\nMost of the overhead is loading tf+pt I think.\r\n```\r\ntime python -c \"import torch, tensorflow\"\r\n2021-01-18 09:54:49.558444: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\r\n\r\nreal 0m1.810s\r\nuser 0m2.375s\r\nsys 0m1.219s\r\n```\r\n\r\nI personally am pretty happy with this outcome, so closing this ticket.", "Oh, that's very nice!" ]

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[ "I can confirm! Good catch!", "I resume the work on creating `test_trainer_tf.py`, I promise I will finish it this time. After that, it might be easier to catch the errors in `tf_trainer.py`.", "I take care of this!", "OK, @jplu . Thank you for letting me know about it (I did some check and didn't found it on master, so I thought it was not done yet).", "I encountered this issue while I was running the model.\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError Traceback (most recent call last)\r\n[/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py](https://localhost:8080/#) in __getattr__(self, item)\r\n 265 try:\r\n--> 266 return self.data[item]\r\n 267 except KeyError:\r\n\r\nKeyError: 'cardinality'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n```\r\n\r\nBelow is the code:\r\n\r\n```\r\nfrom transformers import TFAutoModelForSequenceClassification\r\n\r\nmodel = TFAutoModelForSequenceClassification.from_pretrained(\r\n \"distilbert-base-uncased\")\r\n\r\nmodel.compile(optimizer=optimizer) # No loss argument!\r\n\r\nfrom transformers import TFTrainer, TFTrainingArguments\r\n\r\n\r\ntraining_args = TFTrainingArguments(\r\n output_dir=\"./sentiment_model\",\r\n per_device_train_batch_size=32,\r\n per_device_eval_batch_size=32,\r\n num_train_epochs=3,\r\n evaluation_strategy=\"steps\",\r\n eval_steps=500, # Adjust as needed\r\n save_total_limit=2,\r\n)\r\n\r\ntrainer = TFTrainer(\r\n model=model,\r\n args=training_args,\r\n train_dataset=train_tokenized,\r\n eval_dataset=val_tokenized,\r\n)\r\n\r\ntrainer.train() # <- where the error occured\r\n```", "It doesn't look the same issue as the original one. Could you open a new issue, and provide a simple/small dataset along the code to show the issue. Thank you.", "@ydshieh https://github.com/huggingface/transformers/issues/26632#issue-1929838712 " ]

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[ "Failing Test is fixed on master I believe" ]

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[ "The tutorial has issues due old code. Will make another pull request with new code." ]

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[ "Hey @joy20182018,\r\n\r\nWe cannot guarantee that our library is in sync with other libraries like `https://github.com/allenai/longformer`. Please make sure you follow the advice as written on: https://huggingface.co/transformers/model_doc/longformer.html", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "There is an open PR by @patil-suraj for T5 FP16 https://github.com/huggingface/transformers/pull/9487\r\n\r\nAnd here is an open PR for deepspeed Integration by @stas00 https://github.com/huggingface/transformers/pull/9211", "Thank you!", "Moving my answers from https://github.com/huggingface/transformers/pull/9487 as they were irrelevant to the PR itself:\r\n\r\nContext: @exelents struggles with making rtx-3090 work with pytorch and getting:\r\n```\r\nnvcc fatal : Unsupported gpu architecture 'compute_86\r\n```\r\n\r\nI explained how I made it to work.\r\n\r\n------------------------------\r\n\r\nSo I just did:\r\n```\r\npip install --pre torch torchvision -f https://download.pytorch.org/whl/nightly/cu110/torch_nightly.html -U\r\n```\r\nThen inside deepspeed github clone:\r\n```\r\nrm -rf build\r\nTORCH_CUDA_ARCH_LIST=\"6.1;8.6\" DS_BUILD_OPS=1 pip install --no-cache -v --disable-pip-version-check -e .\r\n```\r\n\r\n8.6 corresponds to rtx-3090 arch.\r\n\r\nYou can remove 6.1, this is just my 2nd 1070 card's arch.\r\n\r\nAnd you can remove `-e` if you don't want the develop install.\r\n\r\nYou can install it normally from pypi too:\r\n```\r\npip install deepspeed\r\n```\r\nit'll use PTX/JIT - I tested it to work just fine - the explicit way from source repo just builds the most optimal specific version for my hardware and pre-compiles all features, which takes much longer to build.\r\n\r\nNow inside the deepspeed PR brach https://github.com/huggingface/transformers/pull/9211, I run against a small t5 model:\r\n```\r\nexport BS=20; rm -r output_dir; CUDA_VISIBLE_DEVICES=0,1 PYTHONPATH=../../src USE_TF=0 deepspeed --num_gpus=2 ./finetune_trainer.py --model_name_or_path t5-small --output_dir output_dir --adam_eps 1e-06 --data_dir wmt_en_ro --do_eval --do_predict --do_train --evaluation_strategy=steps --freeze_embeds --label_smoothing 0.1 --learning_rate 3e-5 --logging_first_step --logging_steps 1000 --max_source_length 128 --max_target_length 128 --num_train_epochs 1 --overwrite_output_dir --per_device_eval_batch_size $BS --per_device_train_batch_size $BS --predict_with_generate --eval_steps 25000 --sortish_sampler --task translation_en_to_ro --test_max_target_length 128 --val_max_target_length 128 --warmup_steps 5 --n_train 100 --n_val 100 --n_test 100 --deepspeed ds_config.json --fp16 --save_steps 1\r\n```\r\n\r\nAll works. t5-base works too.\r\n\r\nFollow my steps and see if you can use your rtx-3090 card first. Then compare to what you are doing differently.", "I have been using pt-nightly w/ rtx-3090 for the last 2 months, so yes it works. pt-1.7.1 doesn't work.\r\n\r\nFor building modules that build pytorch extensions like deepspeed, and apex and fairscale I use cuda-11.1. Let me know what you're trying to build and I will tell you how to do it. \r\n\r\nI'm still on 11.1 for building extensions, while I know 11.2 is out since I'm not sure 11.1 is compatible with 11.2. 11.0 is compatible with 11.1 so one can use it to build against pt-nightly w/ 11.0 after hacking the build script.", "> I change current cuda in my system to 11.0 version (cuda-toolkit-11-0 package in Ubuntu)\r\n> Then I install latest pytorch nighty by a command which you propose.\r\n\r\nI think the difference is that you need cuda-11.1 and not cuda-11.0 system-wide. This is where our setups diverge I think.\r\n\r\nCareful though, there is 11.2 out there. I'm on ubuntu, I'm not at all sure it'd work w/ pt-nightly, that's why I'm not upgrading mine.\r\n\r\nfor rtx-3090 to work\r\n- tf requires cuda-11.1\r\n- pt works with cuda-11.0\r\n- pt extensions need cuda-11.1: apex, fairscale, deepspeed, The first 2 require hacking their build script to support 11.1 w/ pt built w/ 11.0. deepspeed works out of box.\r\n\r\nnote: If someone reads this at a later time this will probably become incorrect once pt-nightly builds w/ cuda-11.2 - then you should be able to install 11.2 system-wide and hopefully the extensions will just work.", "Moving my posts from PR #9487 due to they are irrelevant.\r\n\r\nI don't know what is my problem. I even tried solution made by @stas00 in #9211 but I still have the same problem.\r\nMaybe problem is I built Pytorch from source and forgot some option? I did it because pypi's version don't support Cuda 11.2 and supported cuda 11.0 don't support my gpu (rtx 3090)?\r\nMaybe I need install something to enable fp16 support?", "> I use pytorch-nightly w/ cuda-11.0 which works with rtx-3090:\r\n> \r\n> ```\r\n> pip install --pre torch torchvision -f https://download.pytorch.org/whl/nightly/cu110/torch_nightly.html -U\r\n> ```\r\n> \r\n> pt-nightly w/ cuda-11.2 should be released really soon now. You can track it here [pytorch/pytorch#50232](https://github.com/pytorch/pytorch/issues/50232)\r\n\r\nIt doesn't work. Pytorch nighty gets an error:\r\n`nvcc fatal : Unsupported gpu architecture 'compute_86`\r\nThat's mean that Cuda 11.0 doesn't support RTX 3090", "> I have been using it for the last 2 months, so yes it works. pt-1.7.1 doesn't work.\r\n> \r\n> Chances are that you have more than one pytorch installed and you have the non-nightly version loaded, check your:\r\n> \r\n> ```\r\n> print(torch.__version__)\r\n> ```\r\nhere is installed version: 1.8.0.dev20210109+cu110\r\n\r\n> For building extensions like deepspeed, and apex and fairscale I use cuda-11.1\r\nOkay, I'll try cuda 11.1, maybe it'll help.\r\n\r\n", "> Excellent. so what exactly do you do when you get that error?\r\n\r\nI change current cuda in my system to 11.0 version (cuda-toolkit-11-0 package in Ubuntu)\r\nThen I install latest pytorch nighty by a command which you propose.\r\nLatest, I run deepspeed training script proposed in #9211 issue. But in parameters there I change model name to t5-large and remove language parameters from parameters fed to model\r\n\r\nThis code in utils.py:\r\n```\r\n if data_args.src_lang is not None:\r\n self.dataset_kwargs[\"src_lang\"] = data_args.src_lang\r\n if data_args.tgt_lang is not None:\r\n self.dataset_kwargs[\"tgt_lang\"] = data_args.tgt_lang\r\n```\r\nThis way I get a minimal training code that should run T5-large. It don't take my dataset like in two examples I have shown before, but it should work. What I see is only error \"platform not supported\" on pt nighty build, or NaNs in output tensors in version which I installed from source.\r\n\r\nHere is training script:\r\nhttps://gist.github.com/exelents/9dd3e6dec64dc0d640b85a7e0cfa53e9", "Thank you for making this extra effort, @exelents! We got out of the PR's way now.\r\n\r\nPlease let me know if you had success with: https://github.com/huggingface/transformers/issues/9490#issuecomment-757346485 taking into account this: https://github.com/huggingface/transformers/issues/9490#issuecomment-757346664\r\n", "Okay, @stas00 \r\nI have installed cuda-toolkit-11-1, installed Pytorch nighty version:\r\n`pip install --pre torch torchvision -f https://download.pytorch.org/whl/nightly/cu110/torch_nightly.html -U`\r\nThen I compiled from source DeepSpeed with Cuda 11.1 and Nvidia 8.6 computing platform:\r\n`TORCH_CUDA_ARCH_LIST=\"8.6\" DS_BUILD_OPS=1 pip install --no-cache -v --disable-pip-version-check -e .`\r\nNow everything works well, on the loss scale 256.0 \r\n\r\nupd: Also, I have installed Huggingface Transformers library from source, on brach from PR #9211 merged with branch from PR #9487\r\n\r\nIt seems that maybe DeepSpeed doesn't support new Cuda 11.2, or because I compiled PyTorch and deepspeed on Cuda 11.2 with TORCH_CUDA_ARCH_LIST=8.0 instead of 8.6.", "Glad to hear it works now!\r\n\r\n> TORCH_CUDA_ARCH_LIST=8.0\r\n\r\nMost likely this!\r\n\r\nWhen you build from source you need to add +PTX `8.0+PTX` for this newer arch to work if you don't specify 8.6 explicitly. It basically tells the cuda compiler to allow newer archs to be supported as well and will compile the extension during first run-time via JIT and cache and re-use it.\r\n\r\nThis is how pytorch nightly is built (i.e. it includes `+PTX`).\r\n\r\nThe whole PTX has been recently documented in https://pytorch.org/docs/master/cpp_extension.html#torch.utils.cpp_extension.CUDAExtension" ]

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[ "Failures in the tests are unrelated so merging." ]

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[ "This is great!", "Dear @patil-suraj \r\nYour PR works well for t5 model, thank you for your work.\r\nBut now I tried new t5 model version released recently by Google: google/t5-v1_1-xl\r\nThe same code after loading google/t5-v1_1-xl instead of t5-3b is going to return a lot \"overflow\" errors.\r\n\r\nCan you tell me, should your code fix fp16 on google/t5-v1_1-xl model? \r\nHere is training code:\r\nhttps://github.com/exelents/try_t5_qa\r\nRun ./run-qa-3b.sh\r\n\r\nUpd: I run my code on Transformers's branch from your current PR #9487 merged with PR #9211 needed for deepspeed integration.\r\nCan you confirm a problem, or it's just mine?", "> Dear @patil-suraj\r\n> Your PR works well for t5 model, thank you for your work.\r\n> But now I tried new t5 model version released recently by Google: google/t5-v1_1-xl\r\n> The same code after loading google/t5-v1_1-xl instead of t5-3b is going to return a lot \"overflow\" errors.\r\n> \r\n> Can you tell me, should your code fix fp16 on google/t5-v1_1-xl model?\r\n> Here is training code:\r\n> https://github.com/exelents/try_t5_qa\r\n> Run ./run-qa-3b.sh\r\n> \r\n> Upd: I run my code on Transformers's branch from your current PR #9487 merged with PR #9211 needed for deepspeed integration.\r\n> Can you confirm a problem, or it's just mine?\r\n\r\nHey @exelents, can you include a code snippet to reproduce your error as well as the full stack trace of your error?", "@patrickvonplaten , @exelents \r\n\r\nas stated in #9432 \r\n\r\nThis fix works for following models and versions, with apex `01` and `native amp`\r\n- T5v1: t5-small, t5-base, t5-large\r\n- T5v1_1: google/t5-v1_1-small, google/t5-v1_1-base\r\n- MT5: google/mt5-small, google/mt5-base\r\n\r\nJust did a small experiment with `t5-v1_1-large` and it still gives `nan` loss after 200 steps, so might not work for `xl`, \r\n\r\nalso, @exelents by overflow error do you mean the gradient overflow warning thrown by `apex` ?", "> @patrickvonplaten , @exelents\r\n> \r\n> as stated in #9432\r\n> \r\n> This fix works for following models and versions, with apex `01` and `native amp`\r\n> \r\n> * T5v1: t5-small, t5-base, t5-large\r\n> * T5v1_1: google/t5-v1_1-small, google/t5-v1_1-base\r\n> * MT5: google/mt5-small, google/mt5-base\r\n> \r\n> Just did a small experiment with `t5-v1_1-large` and it still gives `nan` loss after 200 steps, so might not work for `xl`,\r\n> \r\n> also, @exelents by overflow error do you mean the gradient overflow warning thrown by `apex` ?\r\n\r\nAh ok, we still see `nan's` with `t5-v1_1-large` then :-/ Do you think this could be fixed by adding one more clamp statement? @patil-suraj ", "> Hey @exelents, can you include a code snippet to reproduce your error as well as the full stack trace of your error?\r\nMy code is here:\r\nhttps://github.com/exelents/try_t5_qa\r\nIt requires deepspeed to run, as well as code from #9211 PR (deepspeed integration) be merged. Use run-qa-3b.sh to test.\r\n\r\nHere is error stack:\r\nhttps://gist.github.com/exelents/10f1d03e61059ddf2dfba7068114c93a\r\nLook at the end - we have a message after every step:\r\n`[2021-01-11 16:58:18,163] [INFO] [stage2.py:1361:step] [deepscale] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 256.0, reducing to 128.0`\r\nWait a second, I'll try to check loss value tensor.", "> Do you think this could be fixed by adding one more clamp statement?\r\n\r\nI'm again trying to locate where exactly in the model this happen. In case it's the same as above (first `inf` then `nan` ) then we could fix it by adding one more clamp", "I have checked a loss value, and it seems in is not NaN. It got values like \"48.7500\" or \"40.9688\" but there are vaild values. Despite that I see messages like \"OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1024.0, reducing to 512.0\", that it seems means that something bad happened with model's loss.", "> Attempted loss scale: 1024.0, reducing to 512.0\", that it seems means that something bad happened with model's loss.\r\n\r\nThose warnings don't mean anything went wrong, it's logical with dynamic loss scaling that some loss scale values are too big at the beginning of training." ]

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[ "Error messages of the CircleCI are:\r\n\r\n```\r\n-- Docs: https://docs.pytest.org/en/stable/warnings.html\r\n=========================== short test summary info ============================\r\nFAILED tests/test_pipelines_conversational.py::SimpleConversationPipelineTests::test_history_cache\r\nFAILED tests/test_pipelines_conversational.py::SimpleConversationPipelineTests::test_integration_torch_conversation\r\n==== 2 failed, 4207 passed, 1744 skipped, 734 warnings in 190.84s (0:03:10) ====\r\n```\r\n\r\n```\r\nFAILED tests/test_pipelines_conversational.py::SimpleConversationPipelineTests::test_history_cache\r\n==== 1 failed, 4178 passed, 1774 skipped, 735 warnings in 260.31s (0:04:20) ====\r\n```\r\n\r\nI'm sorry but I'd like to ask you if `run_glue.py` is related to the conversation pipeline.\r\n", "@sgugger @LysandreJik \r\nThank you for reviewing and merging!" ]

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[ "Hey @guillaume-be,\r\n\r\nyou're 100% correct about both the naming: We should remove one `ProphetNet` and also about the config. Thanks a lot for reporting this. I'll open a PR " ]

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[ "Will wait until https://github.com/huggingface/transformers/pull/10775 is merged, then rebase and then merge.", "@patrickvonplaten \r\n\r\nI like the new structure but it seems this PR broke the flax example: https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm_flax.py\r\n\r\n- This line (https://github.com/huggingface/transformers/blob/896d7be97401a85dc0ffc5460afd707e8e092781/examples/language-modeling/run_mlm_flax.py#L577) will raise the error\r\n```\r\nTypeError: __init__() got an unexpected keyword argument 'dropout_rate'\r\n```\r\n\r\n- In addition, this line https://github.com/huggingface/transformers/blob/896d7be97401a85dc0ffc5460afd707e8e092781/src/transformers/models/bert/modeling_flax_bert.py#L254 uses an undefined variable `self.dropout_rate`.\r\n\r\nI think we should make more test cases and make sure the examples are runnable. \r\n", "I am very interested in the jax/flax integration. Could you also take a look at my PR? https://github.com/huggingface/transformers/pull/10796\r\nIf you are collaborative and welcome contributions from me, I can contribute more and improve the flax examples." ]

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[ "I don't understand this: `\"60 started to trigger a new warning, saying that the input_ids length was longer than model max_length.\"`", "Would be nice to find what triggered this to be sure we didn't introduce a bug no?", "@patrickvonplaten I think we're good.\r\n\r\nIt's this commit 79bbcc5260c3acde3e7156966ba836afcbfd8808 that triggered the extra warning." ]

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[ "Merged PR to unblock TF Bart - Split PR. However merge to master made tf templates test fail, see: https://github.com/huggingface/transformers/runs/1676878391 . @jplu, I think they need some updating." ]

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[ "The `model_parallel` argument has nothing to do with training in a parallel fashion (and is going to be deleted very soon since you're not the first user its name confuses). To use parallel training with:\r\n- PyTorch DataParallel, there is nothing to do, the Trainer does it automatically\r\n- PyTorch DistributedDataParallel, you should launch your script with the `python -m torch.distributed.launch` command (see the examples).", "Glad to hear about the name.\r\n\r\nI was aware that DataParallel was used by default.\r\n\r\nBy examples you must refer to: https://huggingface.co/transformers/examples.html\r\nbut it doesn't seem to provide any information on `torch.distributed.launch` am I missing something?\r\n\r\nwill try to run with flag, but would love if I could find some documentation on this\r\n\r\nThanks for the quick response\r\n", "you can find the commands/docs to launch distributed training in the examples [readme](https://github.com/huggingface/transformers/tree/master/examples#distributed-training-and-mixed-precision)", "Thanks, I will try this out as soon as our GPU's are available again!", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "Hi! The `run_glue.py` script does what you're looking for.", "> Hi! The `run_glue.py` script does what you're looking for.\r\n\r\nLet's say I have train.csv, dev.csv and test.csv, how can I do it without modify the code? Thank you for your patience.", "Please use the [forums](https://discuss.huggingface.co/) for questions around the script. Running it with the -h option will give you the list of arguments it accepts, in particular `--train_file` and `--validation_file`.", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "LGTM" ]

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[ "@patrickvonplaten Should I remove the following hack in BART?\r\n```python\r\nif inputs[\"decoder_input_ids\"] is None and inputs[\"input_ids\"] is not None:\r\n inputs[\"decoder_input_ids\"] = shift_tokens_right(\r\n inputs[\"input_ids\"], self.config.pad_token_id, self.config.eos_token_id\r\n )\r\n```", "> In general I really don't like the tf.cond(condition, do_fn_one, do_fn_two) design. I think I understand that it is sometimes necessary, but I really like to keep the usage of this function to a minimum in general. The functional approach is very different to our general library design and make the code much much harder to read. It always creates an abstraction by having to wrap parts of the code into a function with no args, like def attn_mask_from_inp_ids() which is not easy to follow and to me always looks like a hack. In Bart we manage to do this part of the code without the usage of tf.cond and Bart has the same exact logic as LED has there -> so we can make it easier I think.\r\n\r\nI understand your point and agree with you and I share you opinion on this, and unfortunately if you come to control flow (conditions and loops) there are some strict rules that one cannot overcome. `tf.cond` is somehow mandatory for autograph.\r\n\r\nA solution I think that should work would be to force the `layer.call` function to be run in graph mode with `@tf.function` which takes care of making itself the translation of all these conditions and loops. This work in some cases, let's see if it works... Does it sounds a proper solution for you?", "> @patrickvonplaten Should I remove the following hack in BART?\r\n> \r\n> ```python\r\n> if inputs[\"decoder_input_ids\"] is None and inputs[\"input_ids\"] is not None:\r\n> inputs[\"decoder_input_ids\"] = shift_tokens_right(\r\n> inputs[\"input_ids\"], self.config.pad_token_id, self.config.eos_token_id\r\n> )\r\n> ```\r\n\r\nPlease don't - it's needed for some use-cases in Bart and for backward comp", "Actually, one thing I'd like to know more in general about our models in TF is the following: \r\n\r\n\"Can we use normal if-else statements in the forward pass\"? \r\n\r\nI always thought that the answer is:\r\n\r\n\"Yes we can as long as the output type and shape of each case is the same\" \r\n\r\nSo for me statements like:\r\n\r\n```python \r\nif shape_list(input_ids) > n:\r\n attention_mask = torch.zeros(shape_list(input_ids))\r\nelse:\r\n attention_mask = torch.ones(shape_list(input_ids))\r\n```\r\n(this code snippet doesn't exist -> it's just an example)\r\n\r\nare totally fine. Is the assumption correct @jplu ?\r\n\r\nOr can we in general **never** use normal if-else statements in TF's forward pass and have to rely on `tf.cond(....)`? This would really surprise me as we're having tons of if statements everywhere in the TF code...\r\n\r\n", "The general answer is yes, but it has some conditions. If you run this condition in eager mode, it will works by default (you can basically do almost anything in eager mode)\r\n\r\nIf you run this condition in graph mode you have two solution to make it works:\r\n1. Either use `tf.cond`\r\n2. Or to wrap your condition into a function decorated with `tf.function`. This will have to effect to apply the Autograph library over the content of your decorated function. Autograph will automatically converts `if-then` clauses, loops, `break`, `return`, `continue`, and more. You can have more information here https://www.tensorflow.org/guide/function#autograph_transformations", "Now that we all agree on a solution, I will apply it for all the models 👍 ", "Ok, LGTM!! Feel free to merge whenever you feel it^^" ]

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[ "Could you run the code quality scripts for the code quality test? `make style && make quality`, after installing the latest code quality versions: `pip install -U .[quality]`", "Looks like there is some styling issue. Could you run `make style` on your branch?", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "Thanks for the reviews, I've addressed all comments. There are 2 things remaining:\r\n\r\n- in the code examples, I use both `tokenize()` and `convert_tokens_to_ids` as the bounding boxes (which are at word-level) need to be converted to token-level. Is there a better solution?\r\n```\r\nwords = [\"Hello\", \"world\"]\r\nnormalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]\r\ntokens = []\r\ntoken_boxes = []\r\nfor word, box in zip(words, normalized_word_boxes):\r\n word_tokens = tokenizer.tokenize(word)\r\n tokens.extend(word_tokens)\r\n token_boxes.extend([box] * len(word_tokens))\r\n```\r\n- according to @sgugger the input data on which the integration tests are ran are maybe too long, and black formatting causes them to be flattened vertically. Could you maybe fix this @LysandreJik? ", "I pushed the reformat you asked for @NielsRogge, make sure to pull before doing any more changes!", "Ok thank you, so the only thing remaining is make the code examples more efficient? Is there a way to make the code block (see comment above) better?" ]

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[ "The fraction (and float) `'epoch': 0.3333333333333333` comes from here:\r\n\r\nhttps://github.com/huggingface/transformers/blob/1c19b423bf274a465f95725a79819bf82f71329e/src/transformers/trainer.py#L899\r\n\r\n@sgugger - is this by design \r\n\r\nor should it be `ceil`: `'epoch': 1`\r\n```\r\n self.state.epoch = math.ceil( epoch + (step + 1) / steps_in_epoch)\r\n```\r\nor `floor`: `'epoch': 0`\r\n```\r\n self.state.epoch = math.floor( epoch + (step + 1) / steps_in_epoch) \r\n```\r\n\r\n`'epoch': 0.3333333333333333` is telling me it's somewhere in the first epoch but isn't done yet?\r\n\r\nPerhaps it's just fine, it's just very odd to see epoch not being an int.\r\n\r\nThanks.\r\n", "This is not a bug, it's completely normal. See the [dcoumentation](https://huggingface.co/transformers/main_classes/callback.html#trainerstate) of `TrainerState.epoch`.", "Ah, OK. Some rounding then perhaps - `0.3333333333333333` is just too loud. 2 decimals?", "Sure, there is no formatting at all for those results, but we can add some.", "Anything else to format while I'm at it? loss I guess - 4 decimals, right?\r\n\r\n`{'loss': 14.846837043762207, 'learning_rate': 6e-06, 'epoch': 0.3333333333333333} `", "I don't see why not." ]

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[ "Just noticed that generation tests are completely irrelevant for the overall testing time...no generation test takes more than 0.5 seconds and 95 % of the generation tests take less than 0.05 seconds" ]

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[ "Also checked that slow tests are passing" ]

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[ "Just realize that I used a single input... Issued closed", "Thanks, your post helped me so much!\r\nI'm using BloomModel on AWS Lambda Function but Lambda doesn't support GPU. \r\nSo I write the code like that:\r\ndevice = 'cpu'\r\n#topic variable is already given\r\nprompt = f' About {Topic} is what I think: '\r\ninputs = tokenizer(prompt, return_tensors='pt')\r\n\r\ninputs = inputs['input_ids'].to(device)\r\nmodel = model.to(device)\r\nsample = model.generate(inputs, max_length=100, temperature=0.9, repetition_penalty = 2.0)\r\noutput = tokenizer.decode(sample[0], truncate_before_pattern=[r\"\\n\\n^#\", \"^'''\", \"\\n\\n\\n\"])\r\n" ]

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[ "This doesn't seem to be related to your length, but rather to this:\r\n\r\n```py\r\ndata['labels'].squeeze(): 60\r\n---------------------------------------------------------------------------\r\nIndexError Traceback (most recent call last)\r\n<ipython-input-12-33cc96af719d> in <module>()\r\n 2 input = tokenizer.decode(data['input_ids'].squeeze(), skip_special_tokens=True)\r\n 3 print(\"data['labels'].squeeze(): \", len(data['labels'].squeeze()))\r\n----> 4 label = tokenizer.decode(data['labels'].squeeze(), skip_special_tokens=True)\r\n 5 print(\"data keys: \", data.keys(),\"\\n\")\r\n 6 lines = textwrap.wrap(\"Query:\\n%s\\n\" % data['question'], width=150)\r\n\r\n5 frames\r\n/usr/local/lib/python3.6/dist-packages/sentencepiece/__init__.py in _func(v, n)\r\n 492 def _func(v, n):\r\n 493 if type(n) is int and (n < 0 or n >= v.piece_size()):\r\n--> 494 raise IndexError('piece id is out of range.')\r\n 495 return func(v, n)\r\n 496 \r\n\r\nIndexError: piece id is out of range.\r\n```\r\n\r\nYour tokenizer doesn't manage to decode your label.", "@LysandreJik yes understand that buy I did not understand what I did wrong first. Then I realized that labels that are -100 should be 0 again.\r\n\r\nAnyway good to figure out late than not doing that" ]

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[ "Hi there, I would like to help you, but the code you are providing is not runable on my side (MLMTrainer, MLMArguments, train_dataset, eval_dataset and rt_model for instance are not defined). Could you please post a complete and short reproducer of the bug?" ]

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[ "@patrickvonplaten, @lhoestq Any feedback on this? ", "Feel free to open a PR indeed :) \r\nWhat do you think @patrickvonplaten ? I guess it can be part of the RetrievAugLMMarginOutput attributes.", "Hey @dblakely,\r\n\r\nIt would be great if you could open a PR. Both Bart and T5 already return the `cross_attentions`, so it should be a pretty easy change by just adding \r\n\r\n```python\r\ngenerator_cross_attentions=gen_outputs.cross_attentions,\r\n```\r\nhere:\r\n\r\nhttps://github.com/huggingface/transformers/blob/fac7cfb16a437a97584f6a14c3856b2e06bf0eaa/src/transformers/models/rag/modeling_rag.py#L657\r\n\r\nand then adding `generator_cross_attentions` to all output classes as suggested by @lhoestq " ]

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[ "Hello!\r\n\r\nThis is because you do not instantiate your model in the created strategy. You already have an example on how to train such models in the [repo](https://github.com/huggingface/transformers/tree/master/examples/text-classification)", "Hi @jplu,\r\nAre you referring to this: https://github.com/huggingface/transformers/blob/f33a6f34461fea61b579a7ec732fcd174b2b41cd/examples/text-classification/run_tf_text_classification.py#L263\r\ni.e. do i just need to wrap `load_model` in the above code in the context manager `with training_args.strategy.scope():` resulting in \r\n```\r\n# train model\r\ndef train_model(\r\n model_args, data_dir, model_dir, logs_dir, batch_size=32, num_epochs=10\r\n):\r\n training_args = TFTrainingArguments(\r\n output_dir=model_dir,\r\n num_train_epochs=num_epochs,\r\n do_train=True,\r\n per_device_train_batch_size=batch_size,\r\n per_device_eval_batch_size=batch_size * 2,\r\n evaluation_strategy=\"steps\",\r\n warmup_steps=500,\r\n weight_decay=0.01,\r\n logging_dir=logs_dir,\r\n dataloader_num_workers=15,\r\n )\r\n datasets = {\r\n \"train\": load_dataset(data_dir=data_dir, split=\"train\", batch_size=batch_size),\r\n \"val\": load_dataset(\r\n data_dir=data_dir, split=\"validation\", batch_size=batch_size\r\n ),\r\n }\r\n \r\n with training_args.strategy.scope():\r\n model = load_model(**model_args)\r\n\r\n trainer = TFTrainer(\r\n model=model,\r\n args=training_args,\r\n train_dataset=datasets[\"train\"],\r\n eval_dataset=datasets[\"val\"],\r\n compute_metrics=compute_metrics,\r\n )\r\n trainer.train()\r\n return trainer\r\n```\r\n\r\nIf not could you point me to the right place?", "Yes this is what I meant :)", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "Hey @albusdemens, could you maybe update your transformers version to 4.0.0?", "Thanks @patrickvonplaten, that fixed the issue! " ]

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[ "Hey @miguelvictor, the problem is that `max_length` is set to a value that is too small. You need to increase either,\r\n\r\n```model.config.max_length``` or pass a `max_length` parameter to `generate()` that is longer than your input_ids.", "Ohh... my bad. Thank you!" ]