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DanielPFlorian

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Transformers-Github-Issues

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[ "Are you able to successfully load your model with e.g. `AutoModel.from_pretrained(local_path)`?", "> Are you able to successfully load your model with e.g. `AutoModel.from_pretrained(local_path)`?\r\n\r\nNo, same error.", "Hi @nithinreddyy, could you share information about your setup so that we may investigate? Thanks.\r\nWhat happens if you remove the backward slash in your path? Is it a local path or is `Huggingface-Sentiment-Pipeline` in the root directory?", "The code is working. The actual code is \r\n\r\n```\r\nfrom transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline\r\nimport transformers\r\n\r\nmodel = AutoModelForSequenceClassification.from_pretrained('directory')\r\ntoken = AutoTokenizer.from_pretrained('directory')\r\n\r\nclassifier = pipeline(task='sentiment-analysis', model=model, tokenizer=token)\r\n\r\nclassifier(\"my name is nithin\") #Run this code and it returns the output\r\n```\r\n\r\nTo save the 'sentiment-analysis' pipeline, try the below code\r\n\r\n```\r\nfrom transformers import pipeline\r\n\r\nclassifier = pipeline('sentiment-analysis')\r\nclassifier.save_pretrained('directory')\r\n```\r\n\r\nThat's it. Thank you.\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! Sorry to hear the example doesn't work well for you. To be honest, it doesn't really make sense to try and generate a single token like it is done in that example. I have slightly modified the example so that it generates the 20 following tokens.\r\n\r\nAlso, I've removed the space at the end of the sequence because I believe it is there by mistake:\r\n\r\n```py\r\nfrom transformers import AutoModelWithLMHead, AutoTokenizer, top_k_top_p_filtering\r\nimport torch\r\nfrom torch.nn import functional as F\r\ntokenizer = AutoTokenizer.from_pretrained(\"gpt2\")\r\nmodel = AutoModelWithLMHead.from_pretrained(\"gpt2\")\r\nsequence = f\"Hugging Face is based in DUMBO, New York City, and\"\r\ninput_ids = tokenizer.encode(sequence, return_tensors=\"pt\")\r\n# get logits of last hidden state\r\ngenerated = input_ids\r\nfor i in range(20):\r\n next_token_logits = model(generated).logits[:, -1, :]\r\n # filter\r\n filtered_next_token_logits = top_k_top_p_filtering(next_token_logits, top_k=50, top_p=1.0)\r\n # sample\r\n probs = F.softmax(filtered_next_token_logits, dim=-1)\r\n next_token = torch.multinomial(probs, num_samples=1)\r\n generated = torch.cat([generated, next_token], dim=-1)\r\n\r\nresulting_string = tokenizer.decode(generated.tolist()[0])\r\n\r\nprint(resulting_string)\r\n```\r\n\r\nRunning this gives me the following examples (not cherry-picked):\r\n\r\n```\r\nHugging Face is based in DUMBO, New York City, and is produced by Eltas & Co., Inc. (a wholly owned subsidiary of Eltas\r\nHugging Face is based in DUMBO, New York City, and focuses primarily on the music and entertainment industry, and is funded by the Hudson River Chamber of Commerce.\r\nHugging Face is based in DUMBO, New York City, and has aired in dozens of local, national and foreign programs, including The Brady Bunch, The Colbert\r\n\r\n```", "Thanks a lot for your help Lysandre!\r\n\r\nRemoving the space at the end of the example sequence solves the issue. Now I am getting normal results. It would be great if you could update the website since I imagine other people will run into the same issue at some point!\r\n\r\nAlso, thanks for adding the code to generate 20 tokens. That is helpful as well, although I believe the main problem was the space at the end of the input sequence.\r\n\r\nThanks again for your prompt reply. Feel free to close the issue whenever you want", "Great, nice to hear this fixes the issue! I've updated the docs on the `master` branch." ]

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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.", "I am also having the same issue and I don't have input/output longer than 1024/.", "Experiencing the same problem using the 2048 size with GPT-J." ]

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[ "You can filter the model hub on 'ru' (Russian) and 'fill-mask' (masked language modeling): https://huggingface.co/models?filter=ru&pipeline_tag=fill-mask\r\n\r\nrobert-base was trained on English text only, so it will not work for Russian. \r\n\r\nMaybe a good choice is this model: https://huggingface.co/blinoff/roberta-base-russian-v0", "How about XLM-RoBERTa? That should be trained with multiple languages, but I got similar issues. \r\n\r\n```\r\nThe specified target token ` courageuse` does not exist in the model vocabulary. Replacing with `▁courage`.\r\n[{'sequence': '<s> Cette femme est belle.</s>', 'score': 0.002463364042341709, 'token': 21525, 'token_str': '▁belle'}, {'sequence': '<s> Cette femme est courage.</s>', 'score': 4.064602762809955e-06, 'token': 116252, 'token_str': '▁courage'}]\r\n```\r\n\r\n> You can filter the model hub on 'ru' (Russian) and 'fill-mask' (masked language modeling): https://huggingface.co/models?filter=ru&pipeline_tag=fill-mask\r\n> \r\n> robert-base was trained on English text only, so it will not work for Russian.\r\n> \r\n> Maybe a good choice is this model: https://huggingface.co/blinoff/roberta-base-russian-v0\r\n\r\n", "Thank you for the reply! I second the comment above regarding XLM-Roberta. ", "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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[ "Hello, thanks for opening an issue! We try to keep the github issues for bugs/feature requests.\r\nCould you ask your question on the [forum](https://discusss.huggingface.co) instead?\r\n\r\nThanks!" ]

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[ "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 suggestion @sgugger!" ]

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[ "Did you try to use `run_clm.py`?", "@LysandreJik Yea, it's working. But i cannot fine-tune it using \"GPU\" in colab and the \"TPU\" gives me this error ```exception: process 0 terminated with signal SIGKILL```, I tried the solutions in the issues but still ", "SIGKILL is probably an out of memory error, meaning you're running out of RAM. I'm not entirely sure colab gives you the hardware necessary to do such big trainings, even when using DeepSpeed.", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "@sgugger Awesome, I'll create a new PR for it.", "Merging this one then, thanks again!" ]

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[ "Hi, could you give a reproducible code example?", "With pleasure!\r\n\r\n```\r\nimport tensorflow as tf\r\nfrom transformers import GPT2Config, TFGPT2LMHeadModel\r\nimport numpy as np\r\n\r\n# Prepare the dataset.\r\ninputs = np.random.randint(0, 128, (1025, 256))\r\nlabels = np.random.randint(0, 128, (1025, 256))\r\ndataset = tf.data.Dataset.from_tensor_slices((inputs, labels))\r\ndataset = dataset.shuffle(1000).batch(32, drop_remainder=True)\r\n\r\n# Create the model configuration.\r\nmodel_config = GPT2Config(\r\n vocab_size=128,\r\n bos_token_id=0,\r\n eos_token_id=127,\r\n n_head=8,\r\n n_layer=6,\r\n n_embd=512,\r\n n_positions=2048\r\n)\r\nprint(model_config)\r\n\r\n# Create the model.\r\nmodel = TFGPT2LMHeadModel(model_config)\r\n\r\n# Compile the model.\r\noptimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0)\r\nloss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\r\nmetric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')\r\nmodel.compile(\r\n optimizer=optimizer,\r\n loss=[loss, *[None] * model.config.n_layer],\r\n metrics=[metric]\r\n)\r\n\r\n# Train the model.\r\nhistory = model.fit(\r\n dataset, \r\n epochs=10,\r\n)\r\n\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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[ "Please tell us which version you are using and also check [your SO question](https://stackoverflow.com/questions/66906652/how-to-download-hugging-face-sentiment-analysis-pipeline-to-use-it-offline/66907181#comment118269245_66907181) again because it was revised.\r\n\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.", "I am trying the below codes\r\n\r\n```\r\nfrom transformers import AutoTokenizer\r\nfrom transformers import AutoModelForSequenceClassification\r\nfrom scipy.special import softmax\r\n```\r\n\r\nhowever, getting the below errors in jupyter notebook\r\n\r\n`---------------------------------------------------------------------------\r\nImportError Traceback (most recent call last)\r\nCell In[49], line 2\r\n 1 from transformers import AutoTokenizer\r\n----> 2 from transformers import AutoModelForSequenceClassification\r\n 3 from scipy.special import softmax\r\n\r\nImportError: cannot import name 'AutoModelForSequenceClassification' from 'transformers' (C:\\Users\\Abhik\\anaconda3\\Lib\\site-packages\\transformers\\__init__.py)`\r\n\r\n\r\nare there any changes to it? I am using jupyter notebbok 7" ]

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[ "@ashleylew were you able to fix this? Could you please share what did?\r\n", "I was just loading the data incorrectly. I misunderstood how to load custom data in and was treating it like one of Hugging Face's datasets rather than a custom one, but once I fixed that, everything was fine! I can give more details if necessary.", "I'll check that part out in my script then, thanks!\r\n", "@ashleylew Hi I'm getting the same error, can you share the data loading script? I tried doing stuff but it doesn't work for me. \r\nThanks\r\n", "Yeah! So in my command script that I posted here, I used ` --dataset_name data/output.jsonl \\ ` but that command is for pre-loaded data sets, whereas mine is a custom one. So instead you'll want to use: \r\n\r\n```\r\n --train_file train.json \\\r\n --validation_file validation.json \\\r\n```" ]

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[ "Hi! This is a weird error, I haven't seen it before. I don't know how you've setup your installation and which CUDA version is torch using, but I believe it still has incompatibilities with CUDA 11.2. If you installed it as a wheel I think CUDA is included and it doesn't really matter, but as the only thing I see from your report is CUDA version 11.2 I can't help but wonder if that's the issue.\r\n\r\nIn any case I doubt that it's linked to `transformers`, have you checked the following issues on the PyTorch github? https://github.com/pytorch/pytorch/issues/31702 and https://github.com/pytorch/pytorch/issues/27837\r\n\r\nIt seems that memory can be an issue, but given the size of a Quadro 8000 it doubt that's the issue here ...", "@LysandreJik Thank you for getting back to me so quickly. I just checked which CUDA version torch is seeing:\r\n\r\n>>> torch.__version__\r\n'1.7.1'\r\n>>> torch.version.cuda\r\n'11.1'\r\n\r\nI'm surprised that it's not CUDA 11.2 which is what nvidia-smi shows. Does this information help? \r\n\r\nThis doesn't seem like a GPU memory issues because the example script runs fine for a few minutes and I see (using nvidia-smi) that GPU memory is not being fully used.", "> but I believe it still has incompatibilities with CUDA 11.2.\r\n\r\nCorrect: https://github.com/pytorch/pytorch/issues/50232#issuecomment-777703998\r\n\r\n> RuntimeError: CUDA error: unspecified launch failure\r\n\r\nDue to its async nature, often the only way to get to see the real error is to run pytorch with env var: `CUDA_LAUNCH_BLOCKING=1`\r\n\r\nPerhaps if you do that you will get better information.\r\n\r\nAlso please check the output of `dmesg -T` - sometimes the `nvidia` kernel module throws a kernel-level traceback in system logs.", "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.", "Responding to avoid making this issue stale: \r\n\r\nThis issue has not been resolved. I tried a number of different configurations including different versions of pytorch, but it didn't help.", "Care to try with CUDA_LAUNCH_BLOCKING as suggested in https://github.com/huggingface/transformers/issues/11020#issuecomment-812960095", "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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[ "cc @sgugger ", "This would require quite a lot of changes in the main Trainer for a use case that is a bit unique. You should subclass the `Trainer` for this problem and just add a second `evaluate` in the training loop for your second dataset.", "@sgugger thanks for the reply! Any chance you could provide a little guidance of how this would look like? Looking at the source code of `Trainer` I admit I feel a little bit lost..." ]

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[ "Hey @dorost1234,\r\n\r\nyou're right to be fully correct we should set the `<extra_id_0>` to `-100`. Maybe we could add the following lines to the doc\r\n\r\n```python\r\n# make sure no loss is computed on sentinel tokens. Sentinel tokens are in `additional_special_tokens`.\r\nlabels = [label if label not in tokenizer.additional_special_tokens else -100]\r\n```\r\n\r\nWould you like (and have the time) to open a PR for this to improve the docs maybe? Otherwise I can do it as well! :-) ", "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 @donno2048. the GPT Neo is available on the master branch, and is not yet in a release. I invite you to install transformers from source, with the following:\r\n```\r\npip install git+https://github.com/huggingface/transformers\r\n```", "Thanks", "Does the min_length param work for you? I do the same as above and it doesn't seem to change anything " ]

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[ "Update on that: model and checkpoints are released:\r\n\r\nhttps://github.com/google-research/language/tree/master/language/canine\r\n\r\n:hugs: ", "Hi @stefan-it, thanks for the update.\r\nDo you know how we can use those pre-trained tensorflow checkpoints to get the pooled text representations from CANINE model?\r\nThanks", "any updates on this ? ", "Hi, \r\n\r\nI've started working on this. Forward pass in PyTorch is working, and giving me the same output tensors as the TF implementation on the same input data.\r\n\r\nWill open a PR soon", "Hi @dhgarrette,\r\n\r\nI don't want to spam the CANINE PR with this question/discussion, so I'm asking it here in this issue 😅\r\n\r\nSo I would like to use CANINE for token classification (I'm currently implementing it into Flair framwork...), and for that reason tokenized input is passed to the model. For token classification using e.g. BERT one would use the first subword as \"pooling strategy\". But when using CANINE and following the subword \"analogy\", using the embedding of the first - let's say - character is a good strategy (instead of e.g. `mean`) 🤔 " ]

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[ "Can you `ls` what's in `/content/wav2vec2-large-xlsr-asr-demo`?", "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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[ "Which model are you using? There is no reason the predictions for QQP should OOM even your GPU, unless the model is outputting more than the logits.", "Thank you very much for your reply! My model config is as below:\r\n```JSON\r\n{\r\n \"architectures\": [\r\n \"AlbertForMaskedLM\"\r\n ],\r\n \"attention_probs_dropout_prob\": 0,\r\n \"bos_token_id\": 2,\r\n \"classifier_dropout_prob\": 0.1,\r\n \"down_scale_factor\": 1,\r\n \"embedding_size\": 128,\r\n \"eos_token_id\": 3,\r\n \"gap_size\": 0,\r\n \"hidden_act\": \"gelu_new\",\r\n \"hidden_dropout_prob\": 0,\r\n \"hidden_size\": 2048,\r\n \"initializer_range\": 0.02,\r\n \"inner_group_num\": 1,\r\n \"intermediate_size\": 3072,\r\n \"layer_norm_eps\": 1e-12,\r\n \"max_position_embeddings\": 512,\r\n \"model_type\": \"albert\",\r\n \"net_structure_type\": 0,\r\n \"num_attention_heads\": 16,\r\n \"num_hidden_groups\": 1,\r\n \"num_hidden_layers\": 12,\r\n \"num_memory_blocks\": 0,\r\n \"pad_token_id\": 0,\r\n \"type_vocab_size\": 2,\r\n \"vocab_size\": 30000\r\n}\r\n```\r\n\r\nAnd I'm loading my model like this:\r\n```Python\r\nstate_dict = torch.load(os.path.join(model_args.model_name_or_path, \"checkpoint.pth\"))\r\nmodel = AutoModelForSequenceClassification.from_pretrained(\r\n pretrained_model_name_or_path=None,\r\n config=config,\r\n state_dict=state_dict,\r\n use_auth_token=True if model_args.use_auth_token else None\r\n)\r\n```\r\n\r\nAnd these are all the named parameters:\r\n```\r\nalbert.embeddings.position_ids torch.Size([1, 512])\r\nalbert.embeddings.word_embeddings.weight torch.Size([30000, 128])\r\nalbert.embeddings.position_embeddings.weight torch.Size([512, 128])\r\nalbert.embeddings.token_type_embeddings.weight torch.Size([2, 128])\r\nalbert.embeddings.LayerNorm.weight torch.Size([128])\r\nalbert.embeddings.LayerNorm.bias torch.Size([128])\r\nalbert.encoder.embedding_hidden_mapping_in.weight torch.Size([2048, 128])\r\nalbert.encoder.embedding_hidden_mapping_in.bias torch.Size([2048])\r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.full_layer_layer_norm.weight torch.Size([2048])\r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.full_layer_layer_norm.bias torch.Size([2048])\r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.attention.query.weight torch.Size([2048, 2048])\r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.attention.query.bias torch.Size([2048])\r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.attention.key.weight torch.Size([2048, 2048])\r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.attention.key.bias torch.Size([2048])\r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.attention.value.weight torch.Size([2048, 2048])\r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.attention.value.bias torch.Size([2048])\r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.attention.dense.weight torch.Size([2048, 2048])\r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.attention.dense.bias torch.Size([2048])\r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.attention.LayerNorm.weight torch.Size([2048])\r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.attention.LayerNorm.bias torch.Size([2048])\r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.ffn.weight torch.Size([3072, 2048])\r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.ffn.bias torch.Size([3072])\r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.ffn_output.weight torch.Size([2048, 3072])\r\nalbert.embeddings.LayerNorm.weight torch.Size([128]) \r\nalbert.embeddings.LayerNorm.bias torch.Size([128]) \r\nalbert.encoder.embedding_hidden_mapping_in.weight torch.Size([2048, 128]) \r\nalbert.encoder.embedding_hidden_mapping_in.bias torch.Size([2048]) \r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.full_layer_layer_norm.weight torch.Size([2048]) \r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.full_layer_layer_norm.bias torch.Size([2048]) \r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.attention.query.weight torch.Size([2048, 2048]) \r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.attention.query.bias torch.Size([2048]) \r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.attention.key.weight torch.Size([2048, 2048]) \r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.attention.key.bias torch.Size([2048]) \r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.attention.value.weight torch.Size([2048, 2048]) \r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.attention.value.bias torch.Size([2048]) \r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.attention.dense.weight torch.Size([2048, 2048]) \r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.attention.dense.bias torch.Size([2048]) \r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.attention.LayerNorm.weight torch.Size([2048]) \r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.attention.LayerNorm.bias torch.Size([2048]) \r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.ffn.weight torch.Size([3072, 2048]) \r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.ffn.bias torch.Size([3072]) \r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.ffn_output.weight torch.Size([2048, 3072]) \r\nalbert.encoder.albert_layer_groups.0.albert_layers.0.ffn_output.bias torch.Size([2048]) \r\nalbert.pooler.weight torch.Size([2048, 2048]) \r\nalbert.pooler.bias torch.Size([2048]) \r\nclassifier.weight torch.Size([2, 2048]) \r\nclassifier.bias torch.Size([2]) \r\n```\r\nThe training and evaluation code is run_glue.py.", "By the way, my model is pretrained(distilled) in a distributed manner(distributedDataParallel). I'm wondering if it is ok to run GLUE tasks this way? I will be grateful for any help you can provide. @sgugger ", "I find these logs and I guess this is why my training and evaluation failed.\r\n```\r\n[INFO|trainer.py:472] 2021-04-04 23:14:56,386 >> The following columns in the training set don't have a corresponding argument in `AlbertForSequenceClassification.forward` and have been ignored: question1, question2, idx.\r\n[INFO|trainer.py:472] 2021-04-04 23:14:56,389 >> The following columns in the evaluation set don't have a corresponding argument in `AlbertForSequenceClassification.forward` and have been ignored: question1, question2, idx.\r\n```\r\nBut I am confused about what happened inside the trainer.", "The fact the model has been trained in a distributed manner is not relevant and shouldn't impact this. The warning you get is also not related and normal if you're running the `run_glue` script: it's just informing you that the `Trainer` is dropping those columns after the preprocessing, since they is no model argument matching.\r\n\r\nI'm trying to reproduce but everything is working fine on my side. If you just use a randomly initialized ALBERT with this config do you have the same problem? (I can run evaluation without problem on my side for that)", "Thank you for your reply. I double-check my config today and find that I am reusing the config from distillation and the output_hidden_states is set to true......I am very sorry for my carelessness and thank you so much for your time and attention.", "Ah I understand better now :-) " ]

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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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[ "My workaround was to manually download checkpoints using `cached_file` function to local, and instantiate a model from the download file using `from_pretrained`.", "Yes that's indeed the preferred workaround. Thanks!" ]

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[ "Could you share the command you are using to launch the script? I'm trying to reproduce but it works fine for me.\r\nAlso your error seems like a CUDA setup error, so is the script running properly without the change?", "@sgugger \r\n\r\n```\r\nexport BASE_PATH=/data/huyong/code/socialbert\r\nexport CUDA_VISIBLE_DEVICES=1\r\npython run_mlm.py \\\r\n --config_name $BASE_PATH/pretrained_models/bert\\\r\n --model_type bert \\\r\n --max_seq_length 128 \\\r\n --preprocessing_num_workers 20 \\\r\n --model_name_or_path $BASE_PATH/pretrained_models/bert \\\r\n --train_file $BASE_PATH/data/mini.txt \\\r\n --line_by_line \\\r\n --do_train \\\r\n --save_total_limit 3 \\\r\n --per_device_train_batch_size 8 \\\r\n --max_train_samples 100000 \\\r\n --output_dir $BASE_PATH/checkpoint/bert\r\n```", "Thanks, but no one will be able to help you if you're using a personal model you don't share, as we can't debug something we can't reproduce. Also, you did not tell us if the script was running fine before the change.", "@sgugger \r\nThanks. \r\nActually, I don't use my personal model, and the model I use to continue pre-train is the [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext). And I manually download three files: `vocab.txt`,`config.json` and `pytorch_model.bin`, and run the script by specifying the model dir and get wrong. But when I directly use the model name like the following, and it works!\r\n```bash\r\nexport BASE_PATH=/data/huyong/code/socialbert\r\nexport CUDA_VISIBLE_DEVICES=1\r\npython run_mlm.py \\\r\n --config_name hfl/chinese-roberta-wwm-ext \\\r\n --model_name_or_path hfl/chinese-roberta-wwm-ext \\\r\n --model_type bert \\\r\n --max_seq_length 128 \\\r\n --preprocessing_num_workers 20 \\\r\n --train_file $BASE_PATH/data/mini.txt \\\r\n --line_by_line \\\r\n --do_train \\\r\n --save_total_limit 3 \\\r\n --per_device_train_batch_size 8 \\\r\n --max_train_samples 100000 \\\r\n --output_dir $BASE_PATH/checkpoint/bert\r\n```\r\nThanks a lot !" ]

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[ "Hi, could you provide the location of the wrong links? Without additional information we cannot help you.", "This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.\n\nPlease note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored." ]

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[ "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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[ { "id": 1897896961, "node_id": "MDU6TGFiZWwxODk3ODk2OTYx", "url": "https://api.github.com/repos/huggingface/transformers/labels/Migration", "name": "Migration", "color": "e99695", "default": false, "description": "" } ]

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[ "Hello! Do you have a reproducible code example so that we can try to understand what's happening here? Thank you!", "I have generated a simplified version of the original Oscar (https://github.com/microsoft/Oscar) codebase here - https://github.com/gsrivas4/Oscar_latest. The branch `old_transformers` -https://github.com/gsrivas4/Oscar_latest/tree/old_transformers uses an old version of hugging face without an issue. However, the branch `latest_transformers` - https://github.com/gsrivas4/Oscar_latest/tree/latest_transformer gets below error when I run the command `oscar/run_captioning.py --model_name_or_path pretrained_models/base-vg-labels/ep_67_588997 --do_train --do_lower_case --evaluate_during_training --add_od_labels --learning_rate 0.00003 --per_gpu_train_batch_size 64 --num_train_epochs 30 --save_steps 5000 --output_dir output/`:\r\n```\r\nTraceback (most recent call last):\r\n File \"oscar/run_captioning.py\", line 1010, in <module>\r\n main()\r\n File \"oscar/run_captioning.py\", line 966, in main\r\n from_tf=bool('.ckpt' in args.model_name_or_path), config=config)\r\n File \"/usr/local/lib/python3.6/site-packages/transformers/modeling_utils.py\", line 1185, in from_pretrained\r\n model.tie_weights()\r\n File \"/usr/local/lib/python3.6/site-packages/transformers/modeling_utils.py\", line 497, in tie_weights\r\n self._tie_or_clone_weights(output_embeddings, self.get_input_embeddings())\r\n File \"/usr/local/lib/python3.6/site-packages/transformers/modeling_utils.py\", line 462, in get_input_embeddings\r\n return base_model.get_input_embeddings()\r\n File \"/usr/local/lib/python3.6/site-packages/transformers/modeling_utils.py\", line 464, in get_input_embeddings\r\n raise NotImplementedError\r\nNotImplementedError\r\n``` \r\n\r\nTo replicate the experiment, follow the [README.md](https://github.com/gsrivas4/Oscar_latest/blob/old_transformers/README.md) file to use old version of transformers - https://github.com/huggingface/transformers/tree/067923d3267325f525f4e46f357360c191ba562e. Follow the [README.md](https://github.com/gsrivas4/Oscar_latest/blob/latest_transformer/README.md) to run the code with latest transformers. \r\n\r\nThe platform information is below:\r\nPlatform: x86_64 GNU/Linux\r\nPython version: 3.6.8\r\nPyTorch version (GPU?): 1.7.0+cu101 (GPU)\r\nTensorflow version (GPU?): 2.3.0 (GPU)\r\nUsing GPU in script?: yes\r\nUsing distributed or parallel set-up in script?: No\r\n\r\nLet me know if you have any issues generating the setup.\r\n", "It seems your `BertForImageCaptioning` doesn't have a `get_input_embeddings()` method, and neither does your `CaptionPreTrainedModel`. \r\n\r\nYou should implement that method on either of those in order to be able to resize them, like it is done in the `BertModel` for example:\r\n\r\nhttps://github.com/huggingface/transformers/blob/6c25f5228e7fb48a520f63ee82dd9ce25b27d6df/src/transformers/models/bert/modeling_bert.py#L853-L854\r\n\r\nSorry for the inconvenience!\r\n", "@LysandreJik I understand that I have to define the function `get_input_embeddings()` and I have also looked at the sample example where this function is defined - https://github.com/huggingface/transformers/blob/master/examples/research_projects/movement-pruning/emmental/modeling_bert_masked.py#L487-L488. It would be great if some description is given about the inputs and outputs of this function in a bit more detailed way. It would be also beneficial if the details about this function are documented in the migration document.\r\n ", "@LysandreJik I could resolve the issue by adding definition for the function at following lines in my code - https://github.com/gsrivas4/Oscar_latest/blob/latest_transformer/oscar/modeling/modeling_bert.py#L190-L191. Thanks for the help.", "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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[ "Hello! From what I'm seeing, the error comes from the `tokenizers` library instead:\r\n```\r\n[...]\r\n File \"/homes/gws/hcybay/miniconda3/envs/test2/lib/python3.8/site-packages/transformers-4.4.2-py3.8.egg/transformers/tokenization_utils_fast.py\", line 25, in <module>\r\n File \"/homes/gws/hcybay/miniconda3/envs/test2/lib/python3.8/site-packages/tokenizers/__init__.py\", line 79, in <module>\r\n from .tokenizers import (\r\nImportError: /lib64/libc.so.6: version `GLIBC_2.18' not found (required by /homes/gws/hcybay/miniconda3/envs/test2/lib/python3.8/site-packages/tokenizers/tokenizers.cpython-38-x86_64-linux-gnu.so)\r\n```\r\n\r\nDo you mind opening an issue there? They'll probably be able to help out better.", "Sure--sorry, didn't know which to open it in", "Looks like I definitely should've searched the issues there first... https://github.com/huggingface/tokenizers/issues/585" ]

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[ "Hello! GPT Neo is available on the master branch, while you're installing the version v4.2.2.\r\n\r\nYou should change `pip install transformers` to `pip install git+https://github.com/huggingface/transformers` and reload your kernel", "Doing so results in this error: \r\n\r\n```\r\n>>> generator = pipeline('text-generation', model='EleutherAI/gpt-neo-2.7B')\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"C:\\Users\\jerkm\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\transformers\\pipelines\\__init__.py\", line 540, in pipeline\r\n framework, model = infer_framework_load_model(\r\n File \"C:\\Users\\jerkm\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\transformers\\pipelines\\base.py\", line 235, in infer_framework_load_model\r\n raise ValueError(f\"Could not load model {model} with any of the following classes: {class_tuple}.\")\r\nValueError: Could not load model EleutherAI/gpt-neo-2.7B with any of the following classes: (<class 'transformers.models.auto.modeling_tf_auto.TFAutoModelForCausalLM'>,).\r\n```", "I think this was resolved when I installed pytorch" ]

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[ "Thanks again!", "How to distributed training?~~~ In no trainer mlm", "The same way as any other scripts: `python -m torch.distributed.launch --nproc_per_node xxx run_mlm_no_trainer.py --script_args`.", "hello@sgugger , when I used multi-gpu, I got this error message:\r\n(basic_dl) root@PM00011093:/data/zhaoyichen/workplace/transformers-master/examples# python -m torch.distributed.launch \\\r\n> --nproc_per_node 2 pytorch/language-modeling/run_mlm_no_trainer.py \\\r\n> --dataset_name wikitext \\\r\n> --dataset_config_name wikitext-2-raw-v1 \\\r\n> --model_name_or_path roberta-base \\\r\n> --output_dir /tmp/test-mlm\r\n*****************************************\r\nSetting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. \r\n*****************************************\r\nusage: run_mlm_no_trainer.py [-h] [--dataset_name DATASET_NAME]\r\n [--dataset_config_name DATASET_CONFIG_NAME]\r\n [--train_file TRAIN_FILE]\r\n [--validation_file VALIDATION_FILE]\r\n [--validation_split_percentage VALIDATION_SPLIT_PERCENTAGE]\r\n [--pad_to_max_length] --model_name_or_path\r\n MODEL_NAME_OR_PATH [--config_name CONFIG_NAME]\r\n [--tokenizer_name TOKENIZER_NAME]\r\n [--use_slow_tokenizer]\r\n [--per_device_train_batch_size PER_DEVICE_TRAIN_BATCH_SIZE]\r\n [--per_device_eval_batch_size PER_DEVICE_EVAL_BATCH_SIZE]\r\n [--learning_rate LEARNING_RATE]\r\n [--weight_decay WEIGHT_DECAY]\r\n [--num_train_epochs NUM_TRAIN_EPOCHS]\r\n [--max_train_steps MAX_TRAIN_STEPS]\r\n [--gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS]\r\n [--lr_scheduler_type {linear,cosine,cosine_with_restarts,polynomial,constant,constant_with_warmup}]\r\n [--num_warmup_steps NUM_WARMUP_STEPS]\r\n [--output_dir OUTPUT_DIR] [--seed SEED]\r\n [--model_type {clip,bigbird_pegasus,deit,luke,gpt_neo,big_bird,speech_to_text,vit,wav2vec2,m2m_100,convbert,led,blenderbot-small,retribert,mt5,t5,pegasus,marian,mbart,blenderbot,distilbert,albert,camembert,xlm-roberta,bart,longformer,roberta,layoutlm,squeezebert,bert,openai-gpt,gpt2,megatron-bert,mobilebert,transfo-xl,xlnet,flaubert,fsmt,xlm,ctrl,electra,reformer,funnel,lxmert,bert-generation,deberta,deberta-v2,dpr,xlm-prophetnet,prophetnet,mpnet,tapas,ibert}]\r\n [--max_seq_length MAX_SEQ_LENGTH]\r\n [--line_by_line LINE_BY_LINE]\r\n [--preprocessing_num_workers PREPROCESSING_NUM_WORKERS]\r\n [--overwrite_cache OVERWRITE_CACHE]\r\n [--mlm_probability MLM_PROBABILITY]\r\nrun_mlm_no_trainer.py: error: unrecognized arguments: --local_rank=0\r\nusage: run_mlm_no_trainer.py [-h] [--dataset_name DATASET_NAME]\r\n [--dataset_config_name DATASET_CONFIG_NAME]\r\n [--train_file TRAIN_FILE]\r\n [--validation_file VALIDATION_FILE]\r\n [--validation_split_percentage VALIDATION_SPLIT_PERCENTAGE]\r\n [--pad_to_max_length] --model_name_or_path\r\n MODEL_NAME_OR_PATH [--config_name CONFIG_NAME]\r\n [--tokenizer_name TOKENIZER_NAME]\r\n [--use_slow_tokenizer]\r\n [--per_device_train_batch_size PER_DEVICE_TRAIN_BATCH_SIZE]\r\n [--per_device_eval_batch_size PER_DEVICE_EVAL_BATCH_SIZE]\r\n [--learning_rate LEARNING_RATE]\r\n [--weight_decay WEIGHT_DECAY]\r\n [--num_train_epochs NUM_TRAIN_EPOCHS]\r\n [--max_train_steps MAX_TRAIN_STEPS]\r\n [--gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS]\r\n [--lr_scheduler_type {linear,cosine,cosine_with_restarts,polynomial,constant,constant_with_warmup}]\r\n [--num_warmup_steps NUM_WARMUP_STEPS]\r\n [--output_dir OUTPUT_DIR] [--seed SEED]\r\n [--model_type {clip,bigbird_pegasus,deit,luke,gpt_neo,big_bird,speech_to_text,vit,wav2vec2,m2m_100,convbert,led,blenderbot-small,retribert,mt5,t5,pegasus,marian,mbart,blenderbot,distilbert,albert,camembert,xlm-roberta,bart,longformer,roberta,layoutlm,squeezebert,bert,openai-gpt,gpt2,megatron-bert,mobilebert,transfo-xl,xlnet,flaubert,fsmt,xlm,ctrl,electra,reformer,funnel,lxmert,bert-generation,deberta,deberta-v2,dpr,xlm-prophetnet,prophetnet,mpnet,tapas,ibert}]\r\n [--max_seq_length MAX_SEQ_LENGTH]\r\n [--line_by_line LINE_BY_LINE]\r\n [--preprocessing_num_workers PREPROCESSING_NUM_WORKERS]\r\n [--overwrite_cache OVERWRITE_CACHE]\r\n [--mlm_probability MLM_PROBABILITY]\r\nrun_mlm_no_trainer.py: error: unrecognized arguments: --local_rank=1\r\nTraceback (most recent call last):\r\n File \"/root/anaconda3/lib/python3.6/runpy.py\", line 193, in _run_module_as_main\r\n \"__main__\", mod_spec)\r\n File \"/root/anaconda3/lib/python3.6/runpy.py\", line 85, in _run_code\r\n exec(code, run_globals)\r\n File \"/data/zhaoyichen/pyvenv/basic_dl/lib/python3.6/site-packages/torch/distributed/launch.py\", line 261, in <module>\r\n main()\r\n File \"/data/zhaoyichen/pyvenv/basic_dl/lib/python3.6/site-packages/torch/distributed/launch.py\", line 257, in main\r\n cmd=cmd)\r\nsubprocess.CalledProcessError: Command '['/data/zhaoyichen/pyvenv/basic_dl/bin/python', '-u', 'pytorch/language-modeling/run_mlm_no_trainer.py', '--local_rank=1', '--dataset_name', 'wikitext', '--dataset_config_name', 'wikitext-2-raw-v1', '--model_name_or_path', 'roberta-base', '--output_dir', '/tmp/test-mlm']' returned non-zero exit status 2.\r\n\r\n", "You need to launch it with `--use_env` when using the PyTorch launcher (or use `accelerate launch`)." ]

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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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[ "Hi @mandareln \r\n\r\nYou should use `BertLMHeadModel` class if you want to use bert as decoder, here you are using `BertForMaskedLM` which is the reason for this error as it does not have the `use_cache` argument.", "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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[ "cc @sgugger " ]

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[ "You're doing something wrong here:\r\n```py\r\nlogits, past = model(myinput, past_key_values = past)\r\n```\r\n\r\nThe model returns a dict. Your `logits` and `past` are the keys of the dicts.\r\n\r\nIf you want the values, then either do:\r\n```py\r\noutput = model(myinput, past_key_values = past)\r\nlogits = output.logits\r\npast = output.past_key_values\r\n```\r\nor\r\n```py\r\nlogits, past = model(myinput, past_key_values = past, return_dict=False)\r\n```\r\n\r\nThis code must have worked with versions <=3. Please read the migration guide relative to switching to version 4 [here](https://huggingface.co/transformers/migration.html#switching-the-return-dict-argument-to-true-by-default)", "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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[ "For running conversion script for `BigBirdPegasus`:\r\n\r\n```shell\r\npython3 src/transformers/models/bigbird_pegasus/convert_bigbird_pegasus_tf_to_pytorch.py --tf_ckpt_path src/tf_ckpt/bigbird-pegasus-large-arxiv/model.ckpt-300000 --save_dir src/google/bigbird-pegasus-large-arxiv\r\n```\r\n\r\nFor running conversion script for bigbird-roberta `EncoderDecoderModel`:\r\n\r\n```shell\r\npython3 src/transformers/models/bigbird_pegasus/convert_bigbird_roberta_tf_to_pytorch.py --tf_ckpt_path src/tf_ckpt/bigbird-roberta-arxiv/model.ckpt-300000 --save_dir src/google/bigbird-roberta-arxiv\r\n```", "@LysandreJik, yes we are planning to add this [notebook](https://colab.research.google.com/github/vasudevgupta7/bigbird/blob/main/notebooks/bigbird_pegasus_evaluation.ipynb) with a few modifications.", "@patrickvonplaten, Test failing on CircleCi: `tests/test_modeling_bigbird_pegasus.py::BigBirdPegasusStandaloneDecoderModelTest::test_decoder_model_attn_mask_past` is passing for me locally.\r\n\r\nEverything else is fixed!!" ]

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[ "Hi @shon-otmazgin, here's the model: https://huggingface.co/kssteven/ibert-roberta-base\r\n\r\nThe documentation is unfortunately wrong. I'm updating it.", "Fixing it in https://github.com/huggingface/transformers/pull/10994", "Hello @LysandreJik, \r\nWe should specify `kssteven/ibert-roberta-base` in `from_pretrained` function?", "Yes, that's right! That's the checkpoint you're looking for.\r\n\r\nThe docs are now updated on `master`, and the next release (next few days) will have them. ", "Thanks @LysandreJik\r\n\r\nAfter reinstalling from source:\r\n\r\npython\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/sotmazgi/PycharmProjects/s2e-coref/ibert_test.py\", line 3, in <module>\r\n tokenizer = RobertaTokenizer.from_pretrained('kssteven/ibert-roberta-base')\r\n File \"/home/sotmazgi/PycharmProjects/s2e-coref/venv/lib/python3.6/site-packages/transformers/tokenization_utils_base.py\", line 1705, in from_pretrained\r\n resolved_vocab_files, pretrained_model_name_or_path, init_configuration, *init_inputs, **kwargs\r\n File \"/home/sotmazgi/PycharmProjects/s2e-coref/venv/lib/python3.6/site-packages/transformers/tokenization_utils_base.py\", line 1776, in _from_pretrained\r\n tokenizer = cls(*init_inputs, **init_kwargs)\r\n File \"/home/sotmazgi/PycharmProjects/s2e-coref/venv/lib/python3.6/site-packages/transformers/models/roberta/tokenization_roberta.py\", line 171, in __init__\r\n **kwargs,\r\n File \"/home/sotmazgi/PycharmProjects/s2e-coref/venv/lib/python3.6/site-packages/transformers/models/gpt2/tokenization_gpt2.py\", line 179, in __init__\r\n with open(vocab_file, encoding=\"utf-8\") as vocab_handle:\r\nTypeError: expected str, bytes or os.PathLike object, not NoneType\r\n```", "Ah, it seems that the I-BERT authors have not uploaded some slow tokenizer files. Can you try it with a `RobertaTokenizerFast` instead of a `RobertaTokenizer` and let me know if it works for you?", "Yes thank you" ]

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[ "Thanks a lot for doing this!" ]

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[ "That's correct. \r\n\r\nYou need a separate process for each gpu under DeepSpeed for communications to work. I will update the docs to make this clear.\r\n\r\nIf you want to use multiple gpus you must use the launcher. So you can still use the notebook to set things up, but the training must happen in external process, e.g. see:\r\n\r\nhttps://github.com/stas00/porting/blob/master/transformers/deepspeed/DeepSpeed_on_colab_CLI.ipynb\r\n\r\nbut edit the launcher line to use `deepspeed --num_gpus 2`\r\n\r\nI will close this for now as it's a clear \"this is not possible due to the DeepSpeed design\", but if you have some further questions please don't hesitate to follow up." ]

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[ "This is not an issue with Transformers but with using Apex with the \"O3\" opt-level. This changes your model during the training and results in the error you're seeing. The best is to re-instantiate a clean `Trainer` for evaluation after you're done with training.", "Thank you for your reply, I just tried it and it actually only works with Apex \"01\" opt-level !" ]

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[ "As explained in https://github.com/huggingface/transformers/issues/11076#issuecomment-814218202, the loss did decrease over time on this small sample so it looks like there are no regressions w.r.t training.\r\n\r\n~Merge when ready @patil-suraj.~ :point_down: ", "It seems I'm not passing the slow tests locally, `test_gpt_neo_sample` fails with:\r\n\r\n```\r\nAssertionError: 'Today is a nice day and a wonderful time to be in Rome, though the sun won’' != 'Today is a nice day and if you don’t get the memo here is what you can'\r\n- Today is a nice day and a wonderful time to be in Rome, though the sun won’\r\n+ Today is a nice day and if you don’t get the memo here is what you can\r\n```", "As seen with @patil-suraj, this is due to a wrongly initialized seed; and the other tests ensure that we have a correct attention mask and generation. Merging!" ]

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[ "Same issue for me. \r\n\r\nWhen running\r\n```python\r\npython run_benchmark.py --no_speed --models a-ware/roberta-large-squad-classification --sequence_lengths 32 --batch_sizes 32\r\n```\r\nI get:\r\n```\r\nAttributeError: Can't pickle local object 'separate_process_wrapper_fn.<locals>.multi_process_func.<locals>.wrapper_func'\r\n```", "Anyone from the `transformers` team got a solution for this?", "Can you try setting `multi_process=False`?", "@patrickvonplaten If I remember correctly the error disappeared when setting `multi_process` to false. However, I figured I should set it to true in order to obtain performance estimates which are as close as possible to reality?", "For PyTorch it's totally fine to set `multi_process=False` -> it's only in TF where the memory consumption is a bit off then", "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.", "This issue is still present in the latest version (4.11.3). I ran the example benchmark shown [here](https://huggingface.co/transformers/benchmarks.html) in TensorFlow (2.6.0) and got the same error:\r\n\r\n> AttributeError: Can't pickle local object 'separate_process_wrapper_fn.<locals>.multi_process_func.<locals>.wrapper_func\r\n" ]

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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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[ "for error fixation" ]

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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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[ "I think you're using it wrong indeed. As said in the [docs](https://huggingface.co/transformers/model_sharing.html#model-versioning), you can either specify a tag name, branch name, or commit hash.", "To reiterate over what @NielsRogge already said, this is the revision of the *model*, not the repository. You can check the model commits here: https://huggingface.co/roberta-large/commits/main\r\n\r\nRevisions also include branches and tags, but this particular model only has a single branch and no tag.", "Ah, that makes sense. Thanks for the prompt reply!" ]

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[ "Very much needed!" ]

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[ "> It is so similar to the PyTorch implementation it seems a script could take care of the implementation by copying the PyTorch one and replacing a few strings!\r\n\r\n@marcvanzee and I were also wondering about this in general -- is there a 80/20 solution that requires user input in some cases? It would have to not introduce silent errors (e.g. a model that seems to run the same but differs in some hard-to-find way).\r\n" ]

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[ "I re-launched the app.\r\n\r\nThe app itself doesn't have much information to help recreate the system though (and is not designed for heavy use :) )\r\n\r\nI'd recommend reading through the blog post instead: https://yjernite.github.io/lfqa.html", "Thank you so much @yjernite " ]

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[ "I tested run all our `sagemaker/tests` and a few additional `model_parallel` tests. ✅ \r\nI also tested everything with the upcoming `pytorch 1.7.1` image with the new `smp 1.3.0` (their model parallelism library)✅\r\n\r\nWhat is still open for me is how does this behaves with `Seq2SeqTrainer`. Can users now use the `Seq2SeqTrainer` for model parallelism too? Data parallelism works already. Tested with `BART`\r\n\r\nAfter we have merged the `SageMakerTrainer` with the `Trainer` I would update the docs for sagemaker/model parallelism and the tests in tests/sagemaker. \r\n" ]

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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\nYou can ping @VictorSanh as he'll be the most helpful regarding distillation.\r\n\r\nThanks!", "Apologies, I've posted to the forum [here](https://discuss.huggingface.co/t/reproducing-distilroberta/5217?u=davidharrison).\r\n\r\nThanks!" ]

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[ "Thanks a lot for adding this!" ]

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