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DanielPFlorian

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Transformers-Github-Issues

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[ "Hi @stevhliu and @gisturiz, yes happy to help🤗. In general is very good, I would comment the following:\r\n\r\n* Using `\"Subtítulos de imagen\"` instead of `\"Subtitulación de Imágenes\"` when translating `\"Image captioning\"`, as it sounds more natural.\r\n* In the same way using `\"el proceso de preprocesamiento\"` instead of `\"la canalización de preprocesamiento\"` in the line 111.\r\n* And translate this part `[this guide]` in line 153.\r\n\r\nRemember to add this new documentation in the file `es/_toctree.yml` . You can read [this guide](https://github.com/huggingface/transformers/blob/main/docs/TRANSLATING.md) for more information.", "Hi @aaronjimv thanks for your review, I've pushed the updates and should be good now. I'd appreciate any feedback. And thanks to you as well @stevhliu ", "Hi @gisturiz, LGTM. \r\nMy final comments would be remove the final `\"g\"` in `Subtítulos de imágenesg` on the `es/_toctree.yml` file. \r\nChange the times that `\"la subtítulos de imágenes\"` appears in the first paragraph to `\"los subtítulos de imágenes\"`. \r\nAnd remember to keep the tag `<Tip>` as original in English, since it is a part of the doc-builder's syntax.", "Thanks again, love to see the collaboration here! 🔥 \r\n\r\nHey @gisturiz, a fix has been deployed on the `main` branch that should get the test to pass. Would you mind rebasing on `main` to resolve the error? 🙂 " ]

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[ "Would you like to open a PR to have a nicer failure?", "Sure! I will do it." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28958). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28957). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "I'll bump this I have the same issue", "I haven't had the time to actually look into this properly, but my intuition is that perhaps this is a design choice since evaluating across multiple devices may result in erroneous results (e.g., errors when gathering). That's why I usually use all devices for evaluating on the validation set but only use one device for evaluating on the test set. ", "But it only uses one device when on the validation set as well. How do you get it to differentiate between test and validation in the trainer? How do you know there'd be errors when gathering?", "I don't think that you can do that with the current HuggingFace API since it only appears to be using a `train_dataset` and `eval_dataset`. I believe that if you want to use a third dataset (i.e., a test set) then you'd have to create a separate Trainer object because you would need to prepare your data with Accelerate again.\r\n\r\n> How do you know there'd be errors when gathering?\r\n\r\nI don't think you can know ahead of time 100%, but I have experienced cases where the gathering has resulted in erroneous values for the number of samples (e.g., the `support` of scikit-learn's classification report showing a different number, etc.).\r\n\r\nI'm personally planning on taking a better look at this some time this week when I have more time outside of work.", "I fixed the problem by making `eval_accumulation_steps` equal to the number of GPU devices.", "Glad you fixed it but that still seems a bit counterintuitive to me. I don't recall seeing anything about that in the documentation either. I'll close this for now and reopen it later when something comes up.", "@seanswyi It's OK to leave this open if you think there's still changes needed in the documentation for clarification. ", "@amyeroberts This isn't related to this issue itself, but do you know if there is a particular reason behind the design choice to only use a train and eval dataset rather than allowing the option to include a test set? I'm wondering if including that option would go against some sort of design principle.", "@seanswyi I don't know tbh. My guess would be for cleanliness and to prevent peeking behaviour. Evaluating on a test set is separate from the training process, which we can use both training and validation metrics to track the model performance and make decisions on e.g. hyperparam sweeps. So it might not make sense to be part of a \"trainer\".\r\n\r\ncc @pacman100 @muellerzr to confirm and address the docstrings. " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28955). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "Thanks for the great suggestions!", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28954). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "> # What does this PR do?\n> \n> This PR adds a German translation for the CONTRIBUTING.md.\n> \n> ## Before submitting\n> - [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).\n> \n> ## Who can review?\n> \n> Documentation: @stevhliu\n> Contributors: @flozi00\n> \n\nb44567538b48e63354ecd0a87ba0492888bcfbeb" ]

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[ "cc @zucchini-nlp ", "Hey, @Hannibal046 . Thanks for pointing this out! I added a PR to fix this behavior. When it gets merged, you can install `transformers` from `main` to get the correct generation." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28952). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@amyeroberts I assume the \"build PR documentation\" check is failing but unrelated to this PR, so I can merge?", "@NielsRogge Yes, if you look on main you can see that the build is failing for lots of (unrelated) PRs at the moment. I'm happy for you to merge as all the other CIs are passing. ", "Hi @NielsRogge and @amyeroberts,\r\n\r\nThanks for your contribution to the community @NielsRogge! I am wondering if `\"do_rescale\": true` is necessary for `SiglipImageProcessor`? Let me know if I miss anything!\r\n\r\nAlso, did you encounter the same problem as in https://github.com/huggingface/transformers/issues/28968?\r\n\r\nThanks!\r\n\r\nBest,", "@zhjohnchan `do_rescale=True` is necessary if the input images are in the range `[0, 255]`. If they've already been rescaled to `[0, 1]` you can set it to `False`. ", "@amyeroberts Thank you so much! Just realized it should be rescaled for all Transformers' preprocessors.", "@zhjohnchan I haven't encountered the same problem, made a fine-tuning notebook for SigLIP here: https://github.com/NielsRogge/Transformers-Tutorials/blob/master/SigLIP/Fine_tuning_SigLIP_and_friends_for_multi_label_image_classification.ipynb.", "@NielsRogge Got it! Thank you so much!" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28951). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

[ { "id": 2648621985, "node_id": "MDU6TGFiZWwyNjQ4NjIxOTg1", "url": "https://api.github.com/repos/huggingface/transformers/labels/Feature%20request", "name": "Feature request", "color": "FBCA04", "default": false, "description": "Request for a new feature" } ]

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[ "cc @Rocketknight1 @ArthurZucker ", "Hi @yonigottesman - this would be a useful feature, but how do you plan to implement it?", "> Hi @yonigottesman - this would be a useful feature, but how do you plan to implement it?\r\n\r\nindeed, great feature!\r\n\r\npossible approach: in `apply_chat_template` how about looping through the messages and calling `compiled_template.render` for each message, knowing what is user and non-user and thereby building `0`/`1` mask that is returned by `apply_chat_template` ?" ]

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[ "Can HF folks point me on how to add test case in this case and also how to update the documentation?", "cc @yeounoh @jonb377", "Tests should be added in the `tests/trainer/test_trainer.py` file. You should find similar tests! ", "> As @ArthurZucker hinted at, we now don't handle things like this in the trainer directly. I would rather see this code over in accelerate which we can then bring into Trainer automatically since it relies on it for preparation. Especially as this deals with the dataloaders. Would that be possible please! :)\r\n\r\nCan you elaborate it a bit more? I can move the `model = model.to(xm.xla_device())` logic. But for the dataloader logic, i.e., tpu_spmd_dataloader, where do you suggest me to move it to? ", "> Tests should be added in the `tests/trainer/test_trainer.py` file. You should find similar tests!\r\n\r\nSpeaking of adding tests, what should I test? I mean do you have TPU CI?", "The test failures don't seem to be related. I tried rebasing as well.", "Thanks @ArthurZucker and @muellerzr for approving the change.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28949). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "It's all green. Can HF folks help with landing the PR? Appreciate it.", "I can merge :) Thanks for adding this support @alanwaketan! " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28948). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "Hi, I caused the bug. Thanks for catching it! Can confirm this is replicable, very interested in why this wasn't caught in my pull request's CI runs or CI runs after my commit was added.\r\n\r\nCan also confirm that this PR in its current state doesn't undo the improvement to the whisper-v3 loading time that the bug-introducing-commit aimed to add.", "Cool, thanks for checking that it doesn't affect the improvement!\r\n\r\nI think a lot of continuous integration testing frameworks tend to skip tests that \"seem unaffected\" by a change, but that's done using heuristics and can go wrong from time to time (such as the one we're seeing now). \r\n\r\nWith that said, many teams tend to have a separate process that continuously runs the full gamut of tests (fast, slow, integration, etc) and reports on any build breakage/test failures it encounters. I imagine HuggingFace should have something like this as well, but it might not be working right now for some reason.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28947). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "It looks like the CI skipped some tests, and tie_weights() still need to be added some models (ex. BertGeneration, Realm). I'm trying to run the full range of tests now to find all these cases.\r\n\r\nAside: seems like there's already quite a bit of test failures even running on main in HEAD because there's no CI that runs over all the tests (even the non-slow ones). Feels like these failures won't really be uncovered unless someone modifies a specific model, but in that case, they will encounter failures unrelated to their change. \r\n\r\nPerhaps it's worth adding a CI that continuously runs all the tests for all the models?\r\n\r\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28946). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@amyeroberts \r\nCommitted change, thanks!\r\n", "Thanks Amy!" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28945). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "(and yes we should remove the old `ConversationalPipeline` sooner rather than later given it already doesn't work anymore due to `conversational` pipeline-type being removed from the Hub, IIUC)", "@julien-c Done! This PR now adds a `DeprecationWarning` to `ConversationalPipeline`. I also updated the chat template docs for the new pipeline.", "very nice!", "One question for people, maybe @gante: Are you okay with the return format I'm using? Right now, if you pass a chat like this:\r\n\r\n```python\r\n[ \r\n {\"role\": \"system\", \"content\": \"This is a system message.\"},\r\n {\"role\": \"user\", \"content\": \"This is a test\"},\r\n]\r\n```\r\n\r\nYou get a response that's the same chat, continued:\r\n\r\n```python\r\n[\r\n {\"role\": \"system\", \"content\": \"This is a system message.\"},\r\n {\"role\": \"user\", \"content\": \"This is a test\"},\r\n {\"role\": \"assistant\", \"content\": \"This is a reply\"},\r\n]\r\n```\r\n\r\nI think this is the right thing to do, because it matches the behaviour of the existing `text-generation` pipeline (it returns the prompt at the start of the generated string). Let me know if you have a different opinion, though!", "IMO it looks good to me", "Cool!", "In that case, I think we're ready for final review (cc @amyeroberts) - I'm leaving the KV cache to another PR.", "cc @LysandreJik @julien-c as well if there's anything else you want me to add before we merge this!" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28944). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "I have checked that there were changes to change black to ruff, so I made sure I had the right version of ruff, but make style and make quality doesn't seem to make any changes for some reason.", "I made the variable a public one yet it's not importable from what I see in CI.", "@merveenoyan In order to be able to import the objects, it's necessary to add them to the `auto` and `transformers` init. \r\n\r\nEssentially, any place you find `\"MODEL_MAPPING\"` in the inits, you'll need to add the equivalent for `\"MODEL_FOR_IMAGE_MAPPING\"`:\r\n* E.g. [here](https://github.com/huggingface/transformers/blob/3f4e79d29ce32d9f8f75b082836b01ee180d0966/src/transformers/models/auto/__init__.py#L42) and [here](https://github.com/huggingface/transformers/blob/3f4e79d29ce32d9f8f75b082836b01ee180d0966/src/transformers/models/auto/__init__.py#L226) in `src/transformers/models/auto/__init__.py`\r\n* [here](https://github.com/huggingface/transformers/blob/3f4e79d29ce32d9f8f75b082836b01ee180d0966/src/transformers/__init__.py#L6181) and [here](https://github.com/huggingface/transformers/blob/3f4e79d29ce32d9f8f75b082836b01ee180d0966/src/transformers/__init__.py#L1450) for `src/transformers/__init__.py` \r\n\r\nYou'll also need to add the required `_LazyAutoMapping` class in `modeling_auto.py` e.g. [here](https://github.com/huggingface/transformers/blob/3f4e79d29ce32d9f8f75b082836b01ee180d0966/src/transformers/models/auto/modeling_auto.py#L1206C17-L1206C33)\r\n\r\nThat being said - this means this object is exposed at the top level of `transformers`, which I think is OK. Another alternative would be to have a list of objects to skip in the `check_repo.py` script.", "@amyeroberts I think CI was broken from another PR and I merged main into this branch now, it seems to work. Can you review this? " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28943). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28942). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28941). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "Hi @megiandoni, thanks for raising an issue! \r\n\r\nSo that we can best help you could you please: \r\n* Provide information about the running environment: run `transformers-cli env` in the terminal and copy-paste the output\r\n* Provide a minimal code snippet to reproduce the error? At the moment, important information is missing from the example, in particular all instance attributes of `self` e.g. `self.compute_metrics`, `self.tokenizer` or `self.IntervalStrategy.STEPS`. \r\n* Some more information about the issue. What is the behaviour observed - do it just hang without executing? Or completely stop i.e. exits out of the evaluation loop? Could you share the logging output of the run? \r\n\r\ncc @muellerzr @pacman100 ", "Hey @amyeroberts, thank you so much for your quick response! \r\nI'll do my best at answering your questions:\r\n\r\n**Info about environment:**\r\n- `transformers` version: 4.37.2\r\n- Platform: Linux-6.1.0-13-amd64-x86_64-with-glibc2.36\r\n- Python version: 3.11.6\r\n- Huggingface_hub version: 0.20.3\r\n- Safetensors version: 0.4.2\r\n- Accelerate version: 0.26.1\r\n- Accelerate config: not found\r\n- PyTorch version (GPU?): 2.2.0+cu121 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\r\n- Jax version: not installed\r\n- JaxLib version: not installed\r\n- Using GPU in script?: Yes\r\n- Using distributed or parallel set-up in script?: No\r\n\r\n**Info about my code:**\r\nI've defined a \"runner\" class, and the tokenizer, trainer, metric functions and so on are it's attributes. If you want to reproduce my issue just ignore the \"self\". \r\nI'm using this model and tokenizer https://huggingface.co/cekal/mpt-7b-peft-compatible/tree/main\r\n\r\nHere's the compute metrics function\r\n\r\ndef compute_metrics(self, eval_pred):\r\n print(\"Compute accuracy for GSM8K dataset.\")\r\n preds, labels = eval_pred\r\n if isinstance(preds, tuple):\r\n preds = preds[0]\r\n preds = np.argmax(preds, axis=-1)\r\n preds= np.where(preds != -100, preds, self.tokenizer.pad_token_id)\r\n labels = np.where(labels != -100, labels, self.tokenizer.pad_token_id)\r\n decoded_results = self.tokenizer.batch_decode(preds, skip_special_tokens=True)\r\n decoded_labels = self.tokenizer.batch_decode(labels, skip_special_tokens=True)\r\n correct = [self.is_correct(pred, label) for pred, label in zip(decoded_results, decoded_labels)]\r\n accuracy = sum(correct) / len(correct)\r\n metrics = {'accuracy': accuracy}\r\n return metrics\r\n\r\nHere is the code that creates the issue for me:\r\n\r\n `# Get tokenizer and model\r\n self.tokenizer = AutoTokenizer.from_pretrained(\r\n self.model_path,\r\n padding_side=\"right\",\r\n use_fast=True\r\n )\r\n #if self.tokenizer.pad_token is None:\r\n self.tokenizer.pad_token = self.tokenizer.eos_token\r\n \r\n self.model = MptForCausalLM.from_pretrained(self.model_path, trust_remote_code=True, low_cpu_mem_usage=True, device_map=\"auto\", torch_dtype=torch.float16)\r\n self.model.sequence_len = self.model.config.max_position_embeddings if hasattr(self.model.config, 'max_position_embeddings') else None\r\n self.model.resize_token_embeddings(len(self.tokenizer))\r\n\r\n # Get data\r\n dataset = load_dataset(\"gsm8k\", \"main\", cache_dir=self.dataset_dir) \r\n train, val = dataset[\"train\"], dataset[\"test\"]\r\n\r\n self.training_args = TrainingArguments(do_train=True,\r\n do_eval=True,\r\n logging_strategy=IntervalStrategy.STEPS,\r\n logging_steps=50,\r\n logging_first_step=True,\r\n evaluation_strategy=IntervalStrategy.STEPS,\r\n eval_steps=100,\r\n learning_rate=2e-4,\r\n output_dir=self.tmp_dir,\r\n overwrite_output_dir=True,\r\n auto_find_batch_size=True,\r\n per_device_eval_batch_size=1,\r\n max_steps=10,\r\n warmup_ratio=0.05,\r\n weight_decay=0.01,\r\n optim=\"paged_adamw_8bit\",\r\n report_to=\"wandb\",\r\n debug=\"underflow_overflow\",\r\n save_strategy='no',\r\n gradient_accumulation_steps=4, \r\n dataloader_num_workers=4,\r\n fp16=True, \r\n eval_accumulation_steps=1,\r\n )\r\n\r\n # Initialize Trainer\r\n lora_config = LoraConfig(r=8,\r\n lora_alpha=16,\r\n target_modules=[\"Wqkv\", \"out_proj\", \"up_proj\", \"down_proj\"],\r\n lora_dropout=0.1,\r\n bias=\"all\",\r\n modules_to_save=[\"classifier\"],\r\n task_type=\"CAUSAL_LM\")\r\n\r\n response_template = \" ### Answer:\"\r\n collator = DataCollatorForCompletionOnlyLM(tokenizer=self.tokenizer, response_template=response_template)\r\n trainer = SFTTrainer(\r\n model=self.model,\r\n args=self.training_args,\r\n train_dataset=train,\r\n eval_dataset=val,\r\n formatting_func=formatting_prompts_func,\r\n tokenizer=self.tokenizer,\r\n max_seq_length=512,\r\n peft_config=lora_config,\r\n data_collator=collator,\r\n compute_metrics=self.compute_metrics\r\n )\r\n \r\n # Train and evaluate\r\n if self.do_train:\r\n # Converts params with gradients to float32\r\n ModelUtils.convert_active_params_to_fp32(self.model) \r\n trainer.train()\r\n # Convert params back to float 16\r\n for param in self.model.parameters():\r\n if param.dtype == torch.float32:\r\n param.data = param.data.to(torch.float16)\r\n if self.do_eval:\r\n trainer.evaluate(metric_key_prefix=\"eval\")`\r\n\r\n`IntervalStrategy.STEPS` comes from the IntervalStrategy class in transformers.trainer_utils. I've been using that instead of just writing \"steps\" cause that caused some other issues in the past.\r\n\r\n**Issue**\r\nThe script execution is interrupted, no evaluation metrics are calculated or logged, and no error is thrown. \r\n<img width=\"671\" alt=\"image\" src=\"https://github.com/huggingface/transformers/assets/21020497/de0a9d3f-79f3-4ce4-9099-d97a76cb7adb\">\r\n\r\nI hope this helps, I'll gladly answer any additional questions!" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28937). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "I'm not sure adding a new argument `cache_position` to the forward call of the model is strictly backwards compatible. Here's an example to motivate this.\r\n\r\nThe following works on `transformers==4.37.2`:\r\n```python\r\nimport torch\r\nfrom transformers import AutoModelForCausalLM, LlamaTokenizer\r\n\r\nmodel = AutoModelForCausalLM.from_pretrained(\"trl-internal-testing/tiny-random-LlamaForCausalLM\", attn_implementation=\"eager\")\r\ntokenizer = LlamaTokenizer.from_pretrained(\"trl-internal-testing/tiny-random-LlamaForCausalLM\")\r\n\r\n# random input id\r\ninputs = tokenizer(\"Hey there\", return_tensors=\"pt\", return_attention_mask=True)\r\n\r\nposition_ids = inputs.attention_mask.long().cumsum(-1) - 1\r\nposition_ids.masked_fill_(inputs.attention_mask == 0, 1)\r\n\r\nwith torch.no_grad():\r\n logits = model.forward(**inputs, position_ids=position_ids).logits\r\n```\r\n\r\nIf we run the same code on this PR, we get the following error:\r\n```\r\n File \"/Users/sanchitgandhi/transformers/src/transformers/models/llama/modeling_llama.py\", line 352, in forward\r\n attn_weights = attn_weights + causal_mask\r\n ~~~~~~~~~~~~~^~~~~~~~~~~~~\r\nRuntimeError: The size of tensor a (3) must match the size of tensor b (2048) at non-singleton dimension 4\r\n```\r\n\r\n<details>\r\n\r\n<summary> Full traceback: </summary>\r\n\r\n```\r\n File \"/Users/sanchitgandhi/transformers/debug_llama.py\", line 14, in <module>\r\n logits = model.forward(**inputs, position_ids=position_ids).logits\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/sanchitgandhi/transformers/src/transformers/models/llama/modeling_llama.py\", line 1106, in forward\r\n outputs = self.model(\r\n ^^^^^^^^^^^\r\n File \"/Users/sanchitgandhi/venv/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1518, in _wrapped_call_impl\r\n return self._call_impl(*args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/sanchitgandhi/venv/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1527, in _call_impl\r\n return forward_call(*args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/sanchitgandhi/transformers/src/transformers/models/llama/modeling_llama.py\", line 950, in forward\r\n layer_outputs = decoder_layer(\r\n ^^^^^^^^^^^^^^\r\n File \"/Users/sanchitgandhi/venv/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1518, in _wrapped_call_impl\r\n return self._call_impl(*args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/sanchitgandhi/venv/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1527, in _call_impl\r\n return forward_call(*args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/sanchitgandhi/transformers/src/transformers/models/llama/modeling_llama.py\", line 694, in forward\r\n hidden_states, self_attn_weights, present_key_value = self.self_attn(\r\n ^^^^^^^^^^^^^^^\r\n File \"/Users/sanchitgandhi/venv/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1518, in _wrapped_call_impl\r\n return self._call_impl(*args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/sanchitgandhi/venv/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1527, in _call_impl\r\n return forward_call(*args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/sanchitgandhi/transformers/src/transformers/models/llama/modeling_llama.py\", line 352, in forward\r\n attn_weights = attn_weights + causal_mask\r\n ~~~~~~~~~~~~~^~~~~~~~~~~~~\r\nRuntimeError: The size of tensor a (3) must match the size of tensor b (2048) at non-singleton dimension 4\r\n```\r\n\r\n</details>\r\n\r\nThis is because `cache_positions` is not specified to the forward call, and so defaults to `None`. When we do our reshape in the attention layer:\r\nhttps://github.com/huggingface/transformers/blob/56768a028b52290ff55f0ad3902679f6dafc568e/src/transformers/models/llama/modeling_llama.py#L352-L353\r\ninstead of reshaping to `[ :, :, cache_position, : key_states.shape[-2]]`, we reshape to `[ :, :, None, : key_states.shape[-2]]`. So instead of slicing, we insert an extra dimension! This gives the size mismatch when we add the attention mask to the weights. The user needs to specify `cache_position` as an argument to the forward call in order for this to work.\r\n\r\nOverall, I think we should avoid adding extra arguments that require code changes from the user, especially to the top-level modules which are already highly-used. What about a design more like Flax where we keep track of the `cache_position` internally in the `StaticCache` abstraction? This then requires no changes from the user", "We can make it BC! this PR is not ready yet, but generate should check the past key value class and if signature can take cache_position, give them. Something like that. \r\n\r\nI'll work on making it BC! :) ", "Thanks, merging asap ", "<img width=\"1795\" alt=\"image\" src=\"https://github.com/huggingface/transformers/assets/48595927/78692611-934d-4035-99f6-3399cbbfd90a\">\r\n\r\nSlow tests are happy", "Example of a breaking behaviour that I introduced while working on FA2: https://github.com/huggingface/transformers/pull/25598#issuecomment-1743338628 so we should be careful when adding new args in our modules", "Hey @ArthurZucker, I discovered that this change actually breaks TPU...\r\n\r\nNow, TPU training with FSDPv2 will produce loss with NaN. I haven't looked into your PR so I'm not sure why. Just bisecting til this change.", "Mmm this might be a ROPE issue? #29109 might also play" ]

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[ "Happy to help, I can begin with the 4 files under Multimodal", "Hey @stevhliu, I would love to translate chat_templating.md, trainer.md, and torchscript.md under Developer guides to Spanish.", "Awesome! Thanks for your interest, I've assigned those docs to you. Happy translating! 🤗 \r\n\r\nWould it be ok with you @gisturiz @aaronjimv for @njackman-2344 to ping y'all for a review of the content since you're both active in the translation efforts?", "> Awesome! Thanks for your interest, I've assigned those docs to you. Happy translating! 🤗\r\n> \r\n> Would it be ok with you @gisturiz @aaronjimv for @njackman-2344 to ping y'all for a review of the content since you're both active in the translation efforts?\r\n\r\nHi, sure happy to help 🤗.", "Sure thing @stevhliu " ]

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[ "cc @gante ", "Hi @John-Boccio 👋 \r\n\r\nWe don't need `transformers` changes to enable your request :D You can parameterize an arbitrary function with a mutable input and use that mutable input as the state in your `prefix_allowed_tokens_fn`.\r\n\r\nHere's an example of how to create a function with state:\r\n```py\r\nfrom functools import partial\r\n\r\n# A function with a `state` input, assumed to be a dictionary\r\ndef some_fn(foo, state=None):\r\n if state is None or not isinstance(state, dict):\r\n raise ValueError('`state` must be provided as a dictionary.')\r\n else:\r\n if 'bar' in state:\r\n state['bar'] += 1\r\n else:\r\n state['bar'] = 1\r\n return foo + state['bar']\r\n\r\n# partial() allows us to create a new function from `some_fn` with a fixed value for `state`.\r\n# Because the `state` input is mutable, the new function will keep track of the changes to `state`.\r\nparameterized_fn = partial(some_fn, state={'bar': 0})\r\n\r\nprint(parameterized_fn(0)) # 1\r\nprint(parameterized_fn(0)) # 2\r\nprint(parameterized_fn(0)) # 3\r\n```", "Hi @gante ! Thank you for the suggestion. I actually had a similar idea and it does work well but has one catch - you must be performing greedy decoding. As soon as you add more than 1 beam, then all the beams will be sharing the objects passed into the partial function (i.e. all beams share the same state with no way to distinguish which beam you're operating on currently).\r\n\r\nI think there will have to be some sort of new parameter to `generate` along the lines of `prefix_allowed_tokens_cls` which allows you to pass in a class that should be created for each beam that is used during generation.", "For beam search to track specific beams, you would have to change a few things indeed -- including the API of the `LogitsProcessors`, to pass the previous beam indices so it could be passed to `prefix_allowed_tokens_fn`.\r\n\r\nThis falls outside the scope of what we want to support in `transformers`, at least for now 🤗 My suggestion would be to fork the library and change the generation loop to your needs :)" ]

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[ "* are you using the library inside a docker container ? \r\n* do you have a `https_proxy` or a `HTTPS_PROXY` envirenmental variables set ?\r\n* which python version of `urllib3` are you using\r\n* do you have VPN enabled ? ", "@not-lain \r\n\r\n- it's not inside docker, just a vent\r\n- I have a http_proxy set but not https.\r\n- 1.26.18\r\n- yes", "@nikhilajoshy \r\ntry these fixes : \r\n* upgrade the requests library to a stable version\r\n```\r\npip install requests==2.27.1 \r\n```\r\n* or set the following envirenmental variable to an empty string\r\n```python\r\nimport os\r\nos.environ['CURL_CA_BUNDLE'] = ''\r\n```\r\n\r\nif none of the above did not work for you try checking this https://stackoverflow.com/questions/56016210/proxyerrorcannot-connect-to-proxy-newconnectionerror \r\n" ]

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[ "transferring you to the transformers repo instead!", "@AdityaKane2001 Indeed! Thanks for flagging this. Would you like to open a PR to fix this? This way you get the github contribution. ", "Sure" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28933). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "I don't know anyone in particular, I'm afraid.\r\n\r\nMaybe @flozi00 can help me since he contributed to the existing German translations? :grin:", "I remember for an discussion about the form if we should \"du\" or \"sie\"\r\nLast time the hugging face team decided for the more formulary \"sie\"\r\n\r\nThe rest LGTM", "Thanks, I will update the translation accordingly.\r\n\r\nIt seems that the German translation for the Hugging Face Hub also uses the formal 'Sie', while the Hugging Face NLP Course is the outlier here.", "@flozi00 I made the appropriate changes and did some minor edits. Ready for another review." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28932). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@Rocketknight1 , hey! I liked the feature, a very useful one I think. Just a couple questions, since I am not sure what was the intended behavior initially", "This should be ready for review now @gante @amy! The core code is totally incomprehensible tensor operations - don't stress if you can't follow them, because I wrote them in one caffeine-fuelled afternoon and I also forget what they're doing if I look away for more than 20 minutes. We're kind of trusting in the tests.\r\n\r\nThe main problem I encountered is I don't have a clean way to get the tokenizer's vocabulary - I'm handling the two common cases of replacing `Ġ` and `▁` with spaces, but there doesn't seem to be any universal method to get the actual string each token will yield. This will probably work for most tokenizers, though, and most stop strings don't contain spaces anyway.", "Nice! I'll let @gante review first to confirm it's all aligned with the current logic processors. \r\n\r\nJust skimming my main comment is that we need tests for the criterion's methods, in particular `get_matching_positions`. ", "@amyeroberts those are purely internal methods - maybe I should just mark them as private with a leading `_` instead?", "@Rocketknight1 Request for tests is to verify the logic rather than them being public or private. `test_stop_string_criteria` is good, but the logic in `get_matching_positions` is quite complex. I'd like for this to be properly covered so that: \r\n1) we can be certain this method is doing the right thing - not just all the pieces as a whole\r\n2) We can modify safely it if needed. ", "@amyeroberts tests for the sub-methods are in!" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28931). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "I have a fully functional working solution for static shaped Whisper, which we have extensively tested on librispeech dataset and get same accuracy as original model. ", "@sanchit-gandhi FYI I'm going to change the `Cache` structure a bit, while it's not widespread in the codebase. In a nutshell, given the hard constraints of the static cache (and its obvious benefits), all caches will have an interface similar to the new static cache (which differs from the original `Cache` implementation).\r\n\r\nPR in progress here: #29005 \r\n\r\nAfter this PR is done, then we can expand its usage using the same interface, e.g. for encoder-decoder models 🤗 " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28930). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "> no matter what the text is put I will accept :-)\r\n\r\n@ydshieh \r\n\r\n```\r\n@is_flaky(description=\"is flaky\")\r\n```\r\n\r\n;) ", "FYI:\r\n\r\n> Hi! The following are the extra failures your branch rebased on ae0c27ad against CI on ae0c27ad (main).", "> > no matter what the text is put I will accept :-)\r\n> \r\n> @ydshieh\r\n> \r\n> ```\r\n> @is_flaky(description=\"is flaky\")\r\n> ```\r\n> \r\n> ;)\r\n\r\nYou are a debug master now @amyeroberts !" ]

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[ "Hi @yuragenetika, thanks for raising this issue! \r\n\r\nIn order for you to be able to use your custom class in pipelines, you have to register it with its corresponding auto class, such that the following would load your model:\r\n\r\n```py\r\nfrom transformers import AutoModelForImageClassification\r\n\r\nmodel = AutoModelForImageClassification.from_pretrained(\"my_checkpoint\")\r\n```\r\n\r\nYou can find instructions on this [here in the docs ](https://huggingface.co/docs/transformers/custom_models#registering-a-model-with-custom-code-to-the-auto-classes)", "@yuragenetika, do you mind sharing your repo_id, I'll take a look", "in case that `YuraGenetika/roi-classifier-20x-GFP-scenes_large-mpl-weights_label_smoothing` is your repo, you need to add your custom architecture to the hub first, this problem is not related to pipeline at all.\r\nas for the model kindly confim if that is your repo, and I'll fix it for you.\r\nyou might also consider reading this blogpost about using custom architecture when working with huggingface : https://huggingface.co/blog/not-lain/custom-architectures-with-huggingface", "Thanks for the response.\r\nI followed exactly the instructions and I get the following error on reading the model from hf remote repository:\r\n\r\nfrom transformers import AutoImageProcessor, AutoModelForImageClassification\r\n\r\nprocessor = AutoImageProcessor.from_pretrained(MODEL_REPO_ID, trust_remote_code=True)\r\nmodel = AutoModelForImageClassification.from_pretrained(MODEL_REPO_ID, trust_remote_code=True)\r\n\r\nOSError: YuraGenetika/sampler-test does not appear to have a file named preprocessor_config.json. Checkout 'https://huggingface.co/YuraGenetika/sampler-test/main' for available files.", "Another question,\r\nI am basically want to overrider classifier head of Dinov2ForImageClassification class but the instructions in above don't provide clear description how to load appropriate weights for dinov2 different sizes\r\n\r\nfor example I want to reuse wights from facebook/dinov2-large-imagenet1k-1-layer", "@yuragenetika did you setup your repos to private ? \r\n\r\n\r\n", "since you changed the architecture for the model you need to add the new architecture to your repo by adding the following files: \r\n* `config.py` \r\n* `custom_model_architecture.py` \r\nthese 2 files need to be defined in orfer for the transformers library to download your weights and instantiate your model ", "again, the transformers library cannot load a custom architecture unless it was ___defined in the hub___ and the `config.json` is pointing at it. defining the architecture in __main__ does not fix this.\r\ntry checking these resources to find out more about custom architectures : \r\n* https://huggingface.co/blog/not-lain/custom-architectures-with-huggingface\r\n* https://huggingface.co/docs/transformers/custom_models\r\n* https://huggingface.co/docs/transformers/en/add_new_pipeline", "there is another work around this, but not recommended since there is too much manual changes in this : \r\n```python\r\n(...)\r\n\r\nmymodel = CustomDinov2Classifier(...)\r\nmodel.load( \r\n # load weights\r\n)\r\n\r\n# Use a pipeline as a high-level helper\r\nfrom transformers import pipeline\r\n\r\npipe = pipeline(\"image-classification\", model=\"facebook/dinov2-small-imagenet1k-1-layer\")\r\n\r\n# override pipeline parameters\r\npipe.model = mymodel\r\npipe.model.config.label2id = ....\r\npipe.model.config.id2label = ...\r\n\r\npipe(new_input)\r\n(...)\r\n```\r\n\r\nbut this is **NOT RECOMMENDED METHOD**\r\n", "thanks for your help\r\n\r\nwhat really worked for me and was the following:\r\n\r\nBACKBONE_REPO_ID = 'facebook/dinov2-small-imagenet1k-1-layer'\r\n\r\nprocessor = AutoImageProcessor.from_pretrained(BACKBONE_REPO_ID)\r\nmodel = CustomDinov2ForImageClassification.from_pretrained(BACKBONE_REPO_ID, id2label=id2label, label2id=label2id, num_labels=len(id2label), ignore_mismatched_sizes = True)\r\n**model.register_for_auto_class(\"AutoModelForImageClassification\")**\r\n\r\nno need to redefine config and just define any parameter on class \r\n \r\nmodel.loss_weights=[10.0, 4.0, 2.0, 5.0]\r\nmodel.label_smoothing = 0.0\r\n\r\ntrain and save\r\n\r\n**processor.push_to_hub(MODEL_REPO_ID)**\r\nmodel.push_to_hub(MODEL_REPO_ID)\r\ntrainer.push_to_hub(MODEL_REPO_ID)\r\n\r\nafter that everything worked as designed\r\n\r\n\r\nfrom transformers import pipeline\r\n\r\npipe = pipeline(\"image-classification\", model= MODEL_REPO_ID, trust_remote_code=True)\r\n\r\nor \r\n\r\nfrom transformers import AutoImageProcessor, AutoModelForImageClassification\r\n\r\nprocessor = AutoImageProcessor.from_pretrained(MODEL_REPO_ID, trust_remote_code=True)\r\nclassifier = AutoModelForImageClassification.from_pretrained(MODEL_REPO_ID, trust_remote_code=True)\r\n\r\n\r\n\r\n", "this is my custom model\r\n\r\nclass CustomDinov2ForImageClassification(Dinov2ForImageClassification):\r\n # config_class = CustomDinov2Config\r\n def __init__(self, config):\r\n super().__init__(config)\r\n layers = []\r\n config.num_layers = 1\r\n input_size, hidden_size = 2 * config.hidden_size, config.hidden_size\r\n for _ in range(config.num_layers):\r\n layers.append(nn.Linear(input_size, hidden_size))\r\n layers.append(nn.GELU())\r\n layers.append(nn.LayerNorm(config.hidden_size))\r\n input_size = hidden_size\r\n self.embeddings = nn.Sequential(*layers)\r\n self.classifier = nn.Linear(config.hidden_size, config.num_labels)\r\n\r\n def forward(self, pixel_values, output_hidden_states=False, output_attentions=False, labels=None):\r\n outputs = self.dinov2(\r\n pixel_values,\r\n output_attentions=output_attentions,\r\n output_hidden_states=output_hidden_states\r\n )\r\n sequence_output = outputs[0] # batch_size, sequence_length, hidden_size\r\n\r\n cls_token = sequence_output[:, 0]\r\n patch_tokens = sequence_output[:, 1:]\r\n\r\n linear_input = torch.cat([cls_token, patch_tokens.mean(dim=1)], dim=1)\r\n \r\n embeddings = self.embeddings(linear_input)\r\n\r\n logits = self.classifier(embeddings)\r\n \r\n loss = None\r\n if labels is not None:\r\n criterion = nn.CrossEntropyLoss(\r\n weight=torch.tensor(self.loss_weights).to(self.device), \r\n label_smoothing=self.label_smoothing)\r\n loss = criterion(logits, labels)\r\n\r\n return ImageClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)" ]

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[ "A model to test it: [BlackSamorez/Mixtral-8x7b-AQLM-2Bit-1x16-hf-test-dispatch](https://huggingface.co/BlackSamorez/Mixtral-8x7b-AQLM-2Bit-1x16-hf-test-dispatch)", "A Google Colab demo: [Mixtral in 2 bits](https://colab.research.google.com/drive/1-xZmBRXT5Fm3Ghn4Mwa2KRypORXb855X?usp=sharing).", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28928). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "cc @oobabooga this might be of your interest ! ", "I updated the docked recipe and added tests, but they are skipped because `aqlm` is not installed in the testing environment.", "The tests are still getting skipped. Is putting `aqlm` in the dockerfile enough for the testing environment to get it?", "I'm pretty sure tests failing has nothing to do with my PR", "@BlackSamorez on a google colab env the inference script works great, however on my VM, on a python 3.10 env with latest torch + cuda11.8 I constantly get:\r\n```bash\r\nTraceback (most recent call last):\r\n File \"/transformers/scratch.py\", line 11, in <module>\r\n output = quantized_model.generate(tokenizer(\"\", return_tensors=\"pt\")[\"input_ids\"].cuda(), max_new_tokens=10)\r\n File \"/miniconda3/envs/aqlm/lib/python3.10/site-packages/torch/utils/_contextlib.py\", line 115, in decorate_context\r\n return func(*args, **kwargs)\r\n File \"/transformers/src/transformers/generation/utils.py\", line 1495, in generate\r\n return self.greedy_search(\r\n File \"/transformers/src/transformers/generation/utils.py\", line 2366, in greedy_search\r\n next_tokens = torch.argmax(next_tokens_scores, dim=-1)\r\nRuntimeError: CUDA error: device kernel image is invalid\r\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\r\n```\r\nDo you have an idea what might be wrong here?", "The only difference I see between the colab instance and mine is the CUDA version, I'll update it to 12.1 and loop back here", "@younesbelkada \r\nThe kernel is compiled in runtime with \r\n```python\r\nimport os\r\nfrom typing import Optional\r\n\r\nimport torch\r\nfrom torch.utils.cpp_extension import load\r\n\r\nCUDA_FOLDER = os.path.dirname(os.path.abspath(__file__))\r\nCUDA_KERNEL = load(\r\n name=\"codebook_cuda\",\r\n sources=[os.path.join(CUDA_FOLDER, \"cuda_kernel.cpp\"), os.path.join(CUDA_FOLDER, \"cuda_kernel.cu\")],\r\n)\r\n```\r\n\r\nMaybe your `nvcc` is sourced from an incorrect cuda installment. I'm not really sure how to test it. Maybe you could try specifying an `nvcc` path somehow with an environmental variable.\r\nI'll try to reproduce it as well.", "CUDA 11.8 seems to work fine on my machine on an a100 GPU.", "FYI: I've released `aqlm` version `1.0.1` where I added device guards to fix CUDA errors when running in the multi-gpu setup. I've added the corresponding tests similar to `autoawq` ones", "Looks like some network error occured ", "🤗 🚀 ", "Hi @BlackSamorez ! \r\nThanks again for your great work ! I was wondering if you could update the installation cell on the shared notebook to install transformers from source instead of your fork - that way we could catch potential bugs in the future before the release 🙏 ", "@younesbelkada \r\nLooks like [this commit](https://github.com/huggingface/transformers/commit/164bdef8cc5143a0766cee448e97166682a722b1) outside of the PR broke something.\r\n```python\r\nAttributeError Traceback (most recent call last)\r\n\r\n[<ipython-input-2-68b1b199d504>](https://localhost:8080/#) in <cell line: 3>()\r\n 1 from transformers import AutoTokenizer, AutoModelForCausalLM\r\n 2 \r\n----> 3 quantized_model = AutoModelForCausalLM.from_pretrained(\r\n 4 \"BlackSamorez/Mixtral-8x7b-AQLM-2Bit-1x16-hf-test-dispatch\",\r\n 5 torch_dtype=\"auto\", device_map=\"auto\", low_cpu_mem_usage=True,\r\n\r\n4 frames\r\n\r\n[/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py](https://localhost:8080/#) in from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)\r\n 565 elif type(config) in cls._model_mapping.keys():\r\n 566 model_class = _get_model_class(config, cls._model_mapping)\r\n--> 567 return model_class.from_pretrained(\r\n 568 pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs\r\n 569 )\r\n\r\n[/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py](https://localhost:8080/#) in from_pretrained(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)\r\n 3561 \r\n 3562 if hf_quantizer is not None:\r\n-> 3563 hf_quantizer.postprocess_model(model)\r\n 3564 model.hf_quantizer = hf_quantizer\r\n 3565 \r\n\r\n[/usr/local/lib/python3.10/dist-packages/transformers/quantizers/base.py](https://localhost:8080/#) in postprocess_model(self, model, **kwargs)\r\n 177 The keyword arguments that are passed along `_process_model_after_weight_loading`.\r\n 178 \"\"\"\r\n--> 179 return self._process_model_after_weight_loading(model, **kwargs)\r\n 180 \r\n 181 @abstractmethod\r\n\r\n[/usr/local/lib/python3.10/dist-packages/transformers/quantizers/quantizer_aqlm.py](https://localhost:8080/#) in _process_model_after_weight_loading(self, model, **kwargs)\r\n 78 \r\n 79 def _process_model_after_weight_loading(self, model: \"PreTrainedModel\", **kwargs):\r\n---> 80 model._is_quantized_training_enabled = False\r\n 81 return model\r\n 82 \r\n\r\n[/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in __setattr__(self, name, value)\r\n 1745 buffers[name] = value\r\n 1746 else:\r\n-> 1747 super().__setattr__(name, value)\r\n 1748 \r\n 1749 def __delattr__(self, name):\r\n\r\nAttributeError: can't set attribute '_is_quantized_training_enabled'\r\n```" ]

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[ "@zucchini-nlp Thanks for opening this PR! Can you provide some more context on why this is necessary? Passing around kwargs is a behaviour we try to avoid if possible. ", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28927). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@amyeroberts the original API, designed before we joined, expects `**kwargs` to be passed ([e.g.](https://github.com/huggingface/transformers/blob/0b693e90e0748e16427a2764d516e9f5ba801bcc/src/transformers/generation/stopping_criteria.py#L65)). In other words, if individual stopping criteria classes support it, so should the class that wraps them as a list :)\r\n\r\n@michaelbenayoun pointed it out today, I'm assuming he's planning to use it :p" ]

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[ "I don't think those are major concerns - they're not in the public API, so the only way to exploit that code would be to get users to run those scripts manually. In general, I think `pickle.load` is fine in:\r\n\r\n- The test suite\r\n- Conversion scripts like these\r\n- Scripts and examples\r\n\r\nIn all of those cases, attackers can't just modify a repo to insert malicious code that will get silently executed.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28926). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "Hi @lidingsnyk, thanks for raising this issue! \r\n\r\nThere was a similar issue posted - #28891, which was resolved. Could you try [installing from source](https://huggingface.co/docs/transformers/en/installation#install-from-source) to confirm if this resolves your issue? ", "Thanks a lot @amyeroberts . Indeed the issue is fixed. I'm getting the exact same metrics in our batch inference with flash attention 2 enabled. Looking forward to next released version." ]

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[ "Hi, thanks for raising an issue! \r\n\r\nThis is a question best placed in our [forums](https://discuss.huggingface.co/). We try to reserve the github issues for feature requests and bug reports.", "@amyeroberts Totally makes sense! Will close with a comment! \r\n\r\nIn case anyone else comes searching for it in the future, here's a link to the topic on the transformers forum: https://discuss.huggingface.co/t/how-to-disable-log-history-from-getting-printed-every-logging-steps/72470 " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28923). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "Hi @Chanwhistle, thanks for opening this issue! \r\n\r\nIn terms of reproduction, could you provide a minimal code snippet which reproduces the error? We get many issues and requests per day, so that we can address them in a timely manner, we need you to help us help you 🤗 \r\n\r\ncc @gante ", "Thank you!\r\n\r\ncode is like below.\r\n\r\n```\r\n outputs = self.generate(\r\n **input_args,\r\n min_length=0,\r\n max_new_tokens=256,\r\n num_beams=num_beams,\r\n temperature=0.1,\r\n num_return_sequences=num_return_sequences,\r\n output_scores=False,\r\n return_dict_in_generate=True,\r\n forced_bos_token_id=None,\r\n prefix_allowed_tokens_fn=end_to_end_prefix_allowed_tokens_fn()\r\n )\r\n```\r\n\r\ninput is \r\n\"In a South African girl of Xhosa stock with severe { piebaldism } [ <D> ] and profound congenital { sensorineural deafness } [ <D> ]\"\r\n\r\nand expected output is\r\n\r\n\"In a South African girl of Xhosa stock with severe { piebaldism } [ <D> (words from consrtained decoding) ] and profound congenital { sensorineural deafness } [ <D> (words from consrtained decoding) ]\"\r\n\r\nBut when I do this with my code, real output is \r\n\r\n\"In a South African girl of Xhosa stock with severe { piebaldism } [ <D> piebaldism ] and profound congenital { sensorineural deafness } [ with with with with with ..\"\r\n\r\nConstrained decoding doesn't done when the second constrained decoding happens. And also This happens only in Bart like model.\r\n\r\n\r\nprefix_allowed_tokens function is like below.\r\nThose code are from ([Facebook/Genre](https://github.com/facebookresearch/GENRE))\r\n\r\n```\r\ndef end_to_end_prefix_allowed_tokens_fn(\r\n model,\r\n encode_fn,\r\n decode_fn,\r\n bos_token_id,\r\n pad_token_id,\r\n eos_token_id,\r\n vocabulary_length,\r\n task,\r\n type_dictionary,\r\n sentences: List[str],\r\n start_mention_token=\"{\",\r\n end_mention_token=\"}\",\r\n start_entity_token=\"[\",\r\n end_entity_token=\"]\",\r\n mention_trie: Trie = None,\r\n candidates_trie: Trie = None,\r\n mention_to_candidates_dict: Dict[str, List[str]] = None,\r\n):\r\n\r\n assert not (\r\n candidates_trie is not None and mention_to_candidates_dict is not None\r\n ), \"`candidates_trie` and `mention_to_candidates_dict` cannot be both != `None`\"\r\n\r\n dict4ner = {}\r\n\r\n type_dictionary = {\r\n \"disease_type_token\" : \"<D>\",\r\n }\r\n \r\n model_type = str(model)\r\n codes = {\r\n n: encode_fn(\" {}\".format(c))[1] \r\n for n, c in zip(\r\n (\r\n \"start_mention_token\",\r\n \"end_mention_token\",\r\n \"start_entity_token\",\r\n \"end_entity_token\",\r\n ),\r\n (\r\n start_mention_token,\r\n end_mention_token,\r\n start_entity_token,\r\n end_entity_token,\r\n ),\r\n )\r\n }\r\n codes[\"EOS\"] = eos_token_id\r\n \r\n for key, value in type_dictionary.items():\r\n codes[key] = encode_fn(value)[1]\r\n \r\n dict4ner = {\r\n v: encode_fn(\" }} [ {} ]\".format(v))[1:] \r\n for k, v in type_dictionary.items()\r\n }\r\n \r\n sent_origs = [[codes[\"EOS\"]] + encode_fn(sent)[1:] for sent in sentences]\r\n \r\n if mention_trie is None:\r\n mention_trie = DummyTrieMention(\r\n [\r\n i\r\n for i in range(vocabulary_length)\r\n if i not in (bos_token_id, pad_token_id,)\r\n ]\r\n )\r\n\r\n if candidates_trie is None and mention_to_candidates_dict is None:\r\n candidates_trie = DummyTrieEntity(\r\n [\r\n i\r\n for i in range(vocabulary_length)\r\n if i not in (bos_token_id, pad_token_id,)\r\n ],\r\n codes,\r\n )\r\n \r\n def prefix_allowed_tokens_fn_EL(batch_id, sent):\r\n\r\n sent = sent.tolist()\r\n status = get_status(sent)\r\n sent_orig = sent_origs[batch_id]\r\n \r\n if status == \"o\" or status == \"m\":\r\n trie_out = get_trie_outside_EL(sent, sent_orig)\r\n elif status == \"e\":\r\n trie_out = get_trie_entity(sent, sent_orig)\r\n else:\r\n raise RuntimeError\r\n \r\n return trie_out\r\n \r\n\r\n def get_status(sent):\r\n c = [\r\n codes[e]\r\n for e in (\r\n \"start_mention_token\",\r\n \"end_mention_token\",\r\n \"start_entity_token\",\r\n \"end_entity_token\",\r\n )\r\n ]\r\n status = sum(e in c for e in sent) % 4\r\n \r\n if status == 0:\r\n return \"o\"\r\n elif status == 1:\r\n return \"m\"\r\n else:\r\n return \"e\"\r\n \r\n\r\n def get_trie_outside_EL(sent, sent_orig):\r\n pointer_end = get_pointer_end_EL(sent, sent_orig)\r\n\r\n if pointer_end:\r\n return [sent_orig[pointer_end]]\r\n else:\r\n return []\r\n\r\n\r\n def get_pointer_end(sent, sent_orig):\r\n i = 0\r\n j = 0\r\n \r\n while i < len(sent):\r\n if sent[i] == sent_orig[j]:\r\n i += 1\r\n j += 1\r\n elif (\r\n sent[i] == codes[\"start_mention_token\"] or \r\n sent[i] == codes[\"end_mention_token\"] \r\n ):\r\n i += 1\r\n elif sent[i] == codes[\"start_entity_token\"]:\r\n i += 1\r\n while sent[i] != codes[\"end_entity_token\"]:\r\n i += 1\r\n i += 1\r\n else:\r\n return None\r\n \r\n return j if j != len(sent_orig) else None\r\n \r\n def get_pointer_end_EL(sent, sent_orig):\r\n i = 0\r\n j = 0\r\n \r\n while i < len(sent):\r\n if sent[i] == sent_orig[j]:\r\n i += 1\r\n j += 1\r\n elif sent[i] != codes[\"end_entity_token\"]:\r\n i += 1\r\n else:\r\n return None\r\n \r\n return j if j != len(sent_orig) else None\r\n \r\n \r\n def get_trie_mention(sent, sent_orig):\r\n \r\n pointer_start, _ = get_pointer_mention(sent)\r\n if pointer_start + 1 < len(sent):\r\n ment_next = mention_trie.get(sent[pointer_start + 1 :])\r\n else:\r\n ment_next = mention_trie.get([])\r\n\r\n pointer_end = get_pointer_end(sent, sent_orig)\r\n\r\n if pointer_end:\r\n if sent_orig[pointer_end] != codes[\"EOS\"]:\r\n if sent_orig[pointer_end] in ment_next:\r\n if codes[\"EOS\"] in ment_next:\r\n return [sent_orig[pointer_end], codes[\"end_mention_token\"]]\r\n else:\r\n return [sent_orig[pointer_end]]\r\n elif codes[\"EOS\"] in ment_next:\r\n return [codes[\"end_mention_token\"]]\r\n else:\r\n return []\r\n else:\r\n return [codes[\"end_mention_token\"]]\r\n else:\r\n return []\r\n \r\n def get_pointer_mention(sent):\r\n pointer_end = -1\r\n pointer_start = None\r\n \r\n for i, e in enumerate(sent):\r\n if e == codes[\"start_mention_token\"]:\r\n pointer_start = i\r\n elif e == codes[\"end_mention_token\"]:\r\n pointer_end = i\r\n # for debug\r\n if pointer_start == None:\r\n import pdb;pdb.set_trace()\r\n \r\n return pointer_start, pointer_end\r\n\r\n def get_trie_entity(sent, sent_orig):\r\n pointer_start, pointer_end = get_pointer_mention(sent)\r\n\r\n if pointer_start + 1 != pointer_end:\r\n mention = decode_fn(sent[pointer_start + 1 : pointer_end]).strip()\r\n\r\n if task == \"NER\":\r\n candidates_trie_tmp = Trie(dict4ner.values())\r\n elif candidates_trie is not None:\r\n candidates_trie_tmp = candidates_trie\r\n elif mention_to_candidates_dict is not None:\r\n candidates_trie_tmp = Trie(\r\n [\r\n encode_fn(\r\n \" {} {} {} {}\".format(\r\n end_mention_token,\r\n start_entity_token,\r\n e,\r\n end_entity_token,\r\n )\r\n )[1:]\r\n for e in mention_to_candidates_dict.get(mention, [\"CUI-less\"])\r\n ]\r\n )\r\n else:\r\n raise RuntimeError()\r\n \r\n return candidates_trie_tmp.get(sent[pointer_end:])\r\n \r\n return []\r\n \r\n return prefix_allowed_tokens_fn_EL\r\n```\r\n\r\nThanks!", "Hi @Chanwhistle . I had similar problems with `prefix_allowed_tokens_fn` in the past. It's hard to say from your code what went wrong, but given that generation fails only in BART-like models, I would advise to check the tokenization. \r\n\r\nYou can set a breakpoint to verify if the trie can correctly get the `allowed_tokens` given the generated sentence. Also, ensure that the special start and end of mention/entity tokens are tokenized separately, without being merged with adjacent tokens.", "Hi @Chanwhistle 👋 Having a popular project like `transformers` means we get many support and feature requests — if we want to maximize how much we help the community, the community has to help us stay productive 🙏\r\n\r\nTo that end, please share a *short stand-alone script* where the issue is clearly reproducible on *any* computer. Thank you 🤗\r\n\r\n(If this is your first issue with us, check [this guide](https://huggingface.co/course/chapter8/5?fw=pt).)", "@gante Thank you for answer.\r\n\r\nI read given webpage and made short stand-alone script, and checked same error occured.\r\n\r\n- `transformers` version: 4.35.2\r\n- Platform: Linux-5.4.0-169-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.18\r\n- Huggingface_hub version: 0.19.4\r\n- Safetensors version: 0.4.1\r\n- Accelerate version: 0.25.0\r\n- Accelerate config: not found\r\n- PyTorch version (GPU?): 2.1.0 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\r\n- Jax version: not installed\r\n- JaxLib version: not installed\r\n- Using GPU in script?: No\r\n- Using distributed or parallel set-up in script?: No\r\n\r\n\r\n\r\nHere is the code.\r\n\r\n\r\n```\r\nfrom typing import Dict, List\r\nimport torch\r\nfrom transformers import (\r\n BartTokenizer,\r\n BartForConditionalGeneration,\r\n)\r\n\r\nclass Trie(object):\r\n def __init__(self, sequences: List[List[int]] = []):\r\n self.trie_dict = {}\r\n self.len = 0\r\n if sequences:\r\n for sequence in sequences:\r\n Trie._add_to_trie(sequence, self.trie_dict)\r\n self.len += 1\r\n\r\n self.append_trie = None\r\n self.bos_token_id = None\r\n\r\n def get(self, prefix_sequence: List[int]):\r\n return Trie._get_from_trie(\r\n prefix_sequence, self.trie_dict, self.append_trie, self.bos_token_id\r\n )\r\n\r\n @staticmethod\r\n def _add_to_trie(sequence: List[int], trie_dict: Dict):\r\n if sequence:\r\n if sequence[0] not in trie_dict:\r\n trie_dict[sequence[0]] = {}\r\n Trie._add_to_trie(sequence[1:], trie_dict[sequence[0]])\r\n\r\n @staticmethod\r\n def _get_from_trie(\r\n prefix_sequence: List[int],\r\n trie_dict: Dict,\r\n append_trie=None,\r\n bos_token_id: int = None,\r\n ):\r\n if len(prefix_sequence) == 0:\r\n output = list(trie_dict.keys())\r\n if append_trie and bos_token_id in output:\r\n output.remove(bos_token_id)\r\n output += list(append_trie.trie_dict.keys())\r\n return output\r\n elif prefix_sequence[0] in trie_dict:\r\n return Trie._get_from_trie(\r\n prefix_sequence[1:],\r\n trie_dict[prefix_sequence[0]],\r\n append_trie,\r\n bos_token_id,\r\n )\r\n else:\r\n if append_trie:\r\n return append_trie.get(prefix_sequence)\r\n else:\r\n return []\r\n\r\n def __len__(self):\r\n return self.len\r\n\r\n def __getitem__(self, value):\r\n return self.get(value)\r\n \r\ndef get_end_to_end_prefix_allowed_tokens_fn_hf(\r\n model,\r\n sentences: List[str],\r\n start_mention_token=\"{\",\r\n end_mention_token=\"}\",\r\n start_entity_token=\"[\",\r\n end_entity_token=\"]\",\r\n mention_trie: Trie = None,\r\n candidates_trie: Trie = None,\r\n mention_to_candidates_dict: Dict[str, List[str]] = None,\r\n task = None\r\n):\r\n return _get_end_to_end_prefix_allowed_tokens_fn(\r\n model,\r\n lambda x: model.tokenizer.encode(x),\r\n lambda x: model.tokenizer.decode(torch.tensor(x)),\r\n model.tokenizer.bos_token_id,\r\n model.tokenizer.pad_token_id,\r\n model.tokenizer.eos_token_id,\r\n len(model.tokenizer) - 1,\r\n task,\r\n sentences,\r\n start_mention_token,\r\n end_mention_token,\r\n start_entity_token,\r\n end_entity_token,\r\n mention_trie,\r\n candidates_trie,\r\n mention_to_candidates_dict,\r\n )\r\n\r\n\r\ndef _get_end_to_end_prefix_allowed_tokens_fn(\r\n model,\r\n encode_fn,\r\n decode_fn,\r\n bos_token_id,\r\n pad_token_id,\r\n eos_token_id,\r\n vocabulary_length,\r\n task,\r\n sentences: List[str],\r\n start_mention_token=\"{\",\r\n end_mention_token=\"}\",\r\n start_entity_token=\"[\",\r\n end_entity_token=\"]\",\r\n mention_trie: Trie = None,\r\n candidates_trie: Trie = None,\r\n mention_to_candidates_dict: Dict[str, List[str]] = None,\r\n):\r\n \r\n codes = {\r\n n: encode_fn(\" {}\".format(c))[1] \r\n for n, c in zip(\r\n (\r\n \"start_mention_token\",\r\n \"end_mention_token\",\r\n \"start_entity_token\",\r\n \"end_entity_token\",\r\n ),\r\n (\r\n start_mention_token,\r\n end_mention_token,\r\n start_entity_token,\r\n end_entity_token,\r\n ),\r\n )\r\n }\r\n codes[\"EOS\"] = eos_token_id\r\n codes[\"disease_type_token\"] = encode_fn(\"<D>\")[1]\r\n \r\n sent_origs = [[codes[\"EOS\"]] + encode_fn(sent)[1:] for sent in sentences]\r\n\r\n def prefix_allowed_tokens_fn_EL(batch_id, sent):\r\n\r\n sent = sent.tolist()\r\n status = get_status(sent)\r\n sent_orig = sent_origs[batch_id]\r\n \r\n if status == \"o\" or status == \"m\":\r\n trie_out = get_trie_outside_EL(sent, sent_orig)\r\n elif status == \"e\":\r\n trie_out = get_trie_entity(sent, sent_orig)\r\n if trie_out == codes[\"EOS\"]:\r\n trie_out = get_trie_outside_EL(sent, sent_orig)\r\n else:\r\n raise RuntimeError\r\n \r\n return trie_out\r\n \r\n\r\n def get_status(sent):\r\n c = [\r\n codes[e]\r\n for e in (\r\n \"start_mention_token\",\r\n \"end_mention_token\",\r\n \"start_entity_token\",\r\n \"end_entity_token\",\r\n )\r\n ]\r\n status = sum(e in c for e in sent) % 4\r\n \r\n if status == 0:\r\n return \"o\"\r\n elif status == 1:\r\n return \"m\"\r\n else:\r\n return \"e\"\r\n \r\n def get_trie_outside_EL(sent, sent_orig):\r\n pointer_end = get_pointer_end_EL(sent, sent_orig)\r\n\r\n if pointer_end:\r\n return [sent_orig[pointer_end]]\r\n else:\r\n return []\r\n \r\n def get_pointer_end_EL(sent, sent_orig):\r\n i = 0\r\n j = 0\r\n \r\n while i < len(sent):\r\n if sent[i] == sent_orig[j]:\r\n i += 1\r\n j += 1\r\n elif sent[i] != codes[\"end_entity_token\"]:\r\n i += 1\r\n else:\r\n return None\r\n \r\n return j if j != len(sent_orig) else None\r\n \r\n def get_pointer_mention(sent):\r\n pointer_end = -1\r\n \r\n for i, e in enumerate(sent):\r\n if e == codes[\"start_mention_token\"]:\r\n pointer_start = i\r\n elif e == codes[\"end_mention_token\"]:\r\n pointer_end = i\r\n \r\n return pointer_start, pointer_end\r\n\r\n def get_trie_entity(sent, sent_orig):\r\n pointer_start, pointer_end = get_pointer_mention(sent)\r\n\r\n if pointer_start + 1 != pointer_end:\r\n mention = decode_fn(sent[pointer_start + 1 : pointer_end]).strip()\r\n\r\n if candidates_trie is not None:\r\n candidates_trie_tmp = candidates_trie\r\n elif mention_to_candidates_dict is not None:\r\n candidates_trie_tmp = Trie(\r\n [\r\n encode_fn(\r\n \" {} {} {} {}\".format(\r\n end_mention_token,\r\n start_entity_token,\r\n e,\r\n end_entity_token,\r\n )\r\n )[1:]\r\n for e in mention_to_candidates_dict.get(mention, [\"CUI-less\"])\r\n ]\r\n )\r\n else:\r\n raise RuntimeError()\r\n \r\n return candidates_trie_tmp.get(sent[pointer_end:])\r\n \r\n return []\r\n \r\n return prefix_allowed_tokens_fn_EL\r\n \r\n\r\nclass _GENREHubInterface:\r\n def sample(\r\n self,\r\n sentences: List[str],\r\n num_beams: int = 5,\r\n num_return_sequences=1,\r\n text_to_id: Dict[str, str] = None,\r\n marginalize: bool = False,\r\n **kwargs\r\n ) -> List[str]:\r\n \r\n input_args = {\r\n k: v.to(self.device) if isinstance(v, torch.Tensor) else v\r\n for k, v in self.tokenizer.batch_encode_plus(\r\n sentences, padding=True, return_tensors=\"pt\"\r\n ).items()\r\n }\r\n \r\n outputs = self.generate(\r\n **input_args,\r\n min_length=0,\r\n max_new_tokens=128,\r\n num_beams=num_beams,\r\n num_return_sequences=num_return_sequences,\r\n early_stopping=False,\r\n output_scores=False,\r\n return_dict_in_generate=False,\r\n forced_bos_token_id=None,\r\n **kwargs\r\n )\r\n \r\n return outputs\r\n\r\n def encode(self, sentence):\r\n return self.tokenizer.encode(sentence, return_tensors=\"pt\")[0]\r\n\r\nclass GENREHubInterface(_GENREHubInterface, BartForConditionalGeneration):\r\n pass\r\n \r\n \r\nclass GENRE(BartForConditionalGeneration):\r\n @classmethod\r\n def from_pretrained(cls, model_name_or_path):\r\n model = GENREHubInterface.from_pretrained(model_name_or_path)\r\n tokenizer = BartTokenizer.from_pretrained(model_name_or_path)\r\n start_disease_token = \"<D>\"\r\n tokenizer.add_tokens([start_disease_token])\r\n model.resize_token_embeddings(len(tokenizer))\r\n model.tokenizer = tokenizer\r\n \r\n return model\r\n```\r\n\r\nand Here is the script.\r\n\r\n```\r\nsentence = [\"Germ-line and somatic truncating mutations of the { APC } [ <D> ] gene are thought to initiate { colorectal tumor } [ <D> ] formation in { familial adenomatous polyposis syndrome } [ <D> ] and sporadic colorectal carcinogenesis, respectively.\"]\r\n\r\nmodel = GENRE.from_pretrained(\"facebook/bart-base\")\r\n\r\nprefix_allowed_tokens_fn = get_end_to_end_prefix_allowed_tokens_fn_hf(\r\n model,\r\n sentence,\r\n candidates_trie=Trie([\r\n model.encode(\" }} [ <D> {} ]\".format(e))[1:].tolist()\r\n for e in [\"APC\", \"colorectal tumor\", \"familial adenomatous polyposis syndrome\"]\r\n ])\r\n)\r\n\r\noutput = model.sample(\r\n sentence,\r\n prefix_allowed_tokens_fn=prefix_allowed_tokens_fn ,\r\n)\r\n\r\nprint(model.tokenizer.batch_decode(output, skip_special_tokens=True))\r\n\r\n```\r\n \r\nMy expected output is \r\n\r\n[\"Germ-line and somatic truncating mutations of the { APC } [ <D> APC ] gene are thought to initiate { colorectal tumor } [ <D> colorectal tumor ] formation in { familial adenomatous polyposis syndrome } [ <D> familial adenomatous polyposis syndrome ] and sporadic colorectal carcinogenesis, respectively.\"]\r\n\r\nbut this script's output is \r\n\r\n['Germ-line and somatic truncating mutations of the { APC } [ <D> familial adenomatous polyposis syndrome ] gene are thought to initiate { colorectal tumor } [ and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and the']\r\n", "Yeah no, that's not a short script 😅 I'm sorry, we will only dedicate resources with a short reproducer of the issue, without significant external code.\r\n\r\n" ]

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[ "Hello @andstor, \r\n\r\nThe model is saved in the selected half-precision when using mixed-precision training, i.e., `fp16` if mixed-precision is using `fp16` else `bf16` if mixed-precision is using `bf16`. Earlier there was logic to convert the state_dict to FP32 but users complained about the increase in the ckpt size and hence the current logic. Is this a limitation for your use case", "I would expect that if I use a FP32 model with mixed-precision, the “master weights” in the optimizer would be extracted on save. If not, the saving method is lossy, as it discards valuable precision. This lossy saving seems to only happen when using DeepSpeed with mixed-precision. If users have a problem with the size, they should simply ensure the model is loaded in half-precision before starting training." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28920). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "cc @amyeroberts " ]

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[ "cc @Rocketknight1 I can do it if you are low on bandwidth! Think it makes sense as a lot of models have `2.5B` or such names! ", "I can take this one, I think!", "to anyone reading this in the future: \r\nI found a work around this, **if you cannot rename your repo and remove the dot from its name**, you can follow these steps. it's not technically a fix but I did the following to go around this issue (checkout this pull request to find out more : https://huggingface.co/briaai/RMBG-1.4/discussions/9 ) \r\nwhat I did is : \r\n* create another repo that does not have a dot in its name. Example : `not-lain/CustomCodeForRMBG`\r\n* put all code for custom model in `not-lain/CustomCodeForRMBG`\r\n* push only the weights and the config.json to repo with dot in its name (checkout the pull request mentioned above) .\r\n* make sure that the `config.json` points out at the repo without dot in its name here's an example of what I did : \r\n```json\r\n{\r\n \"_name_or_path\": \"not-lain/CustomCodeForRMBG\",\r\n \"architectures\": [\r\n \"BriaRMBG\"\r\n ],\r\n \"auto_map\": {\r\n \"AutoConfig\": \"not-lain/CustomCodeForRMBG--MyConfig.RMBGConfig\",\r\n \"AutoModelForImageSegmentation\": \"not-lain/CustomCodeForRMBG--briarmbg.BriaRMBG\"\r\n },\r\n \"custom_pipelines\": {\r\n \"image-segmentation\": {\r\n \"impl\": \"not-lain/CustomCodeForRMBG--MyPipe.RMBGPipe\",\r\n \"pt\": [\r\n \"AutoModelForImageSegmentation\"\r\n ],\r\n \"tf\": [],\r\n \"type\": \"image\"\r\n }\r\n },\r\n \"in_ch\": 3,\r\n \"model_type\": \"SegformerForSemanticSegmentation\",\r\n \"out_ch\": 1,\r\n \"torch_dtype\": \"float32\",\r\n \"transformers_version\": \"4.38.0.dev0\"\r\n}\r\n```", "Hi @not-lain - I'm a bit confused by this issue. I investigated and I saw the bug you reported for the `briaai/RMBG-1.4` repo. However, many repos in Transformers put a `.` in their name. In fact, using a naming convention like `-v0.1` is extremely common. This makes it surprising that we've never seen this issue before.\r\n\r\nBefore we make a PR, can you investigate to determine exactly which combinations of model classes and repo names trigger the bug? The issue may be specific to the custom code in the `RMBG-1.4` repo, rather than a general issue in `transformers`.", "@Rocketknight1 those repos don't have custom architectures in them, they are using predifined architectures in the transformers library.\r\nthe problem is due to the configuration file wrongly parsed when importing the model class.\r\n\r\nI'll try to recreate another repo with a dot in its name that has a custom architecture for you to experiment with.\r\nshould be ready in a bit.", "@Rocketknight1 these 2 repos have identical code inside of them.\r\n* `not-lain/MyRepo`\r\n* `not-lain/MyRepo1.0`\r\n\r\ntry running the following code : \r\n```python\r\nfrom transformers import AutoModelForImageClassification\r\nmodel = AutoModelForImageClassification.from_pretrained(\"not-lain/MyRepo\", trust_remote_code=True) # works\r\nmodel = AutoModelForImageClassification.from_pretrained(\"not-lain/MyRepo1.0\", trust_remote_code=True) # doesn't work\r\n```\r\niteratively \r\n```python\r\nfrom transformers import pipeline\r\npipe = pipeline(model=\"not-lain/MyRepo\", trust_remote_code=True) # works\r\npipe = pipeline(model=\"not-lain/MyRepo1.0\", trust_remote_code=True) # doesn't work\r\n```\r\n", "Hi @not-lain - I understand it's only triggered when the repo has remote code, I'm just surprised that the issue has only surfaced now! That said, your reproducer repos are helpful - let me see if I can figure out the cause and a fix.", "I'm also seeing this with `AutoModel.from_pretrained('.')` on transformers v4.37.2:\r\n```\r\nModuleNotFoundError: No module named 'transformers_modules.'\r\n```\r\nfinal_module becomes `transformers_modules/./my_file.py`, and the naive replacement of `/` with `.` to get the import name is not sufficient here.", "@cebtenzzre \r\ntry this instead, this should in theory fix it : \r\n```python\r\nAutoModel.from_pretrained('./')\r\n```" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28918). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

[ { "id": 2648621985, "node_id": "MDU6TGFiZWwyNjQ4NjIxOTg1", "url": "https://api.github.com/repos/huggingface/transformers/labels/Feature%20request", "name": "Feature request", "color": "FBCA04", "default": false, "description": "Request for a new feature" } ]

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[ "The main Flash Attention 2 discussion tracking issue and discussion seems to be https://github.com/huggingface/transformers/issues/26350" ]

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[ { "id": 2648621985, "node_id": "MDU6TGFiZWwyNjQ4NjIxOTg1", "url": "https://api.github.com/repos/huggingface/transformers/labels/Feature%20request", "name": "Feature request", "color": "FBCA04", "default": false, "description": "Request for a new feature" } ]

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[ "Hey! This can be easily done with a custom `SlidingWindowLlamaAttention` that you register to the `LLAMA_ATTENTION_CLASSES` 😉 ", "Hey @ArthurZucker,\r\n\r\nDo you think this feature is actually useful? If we just replace the standard attention with SWA as a drop-in replacement, without any finetuning; won't the performance drop?\r\n\r\nWhat are your thoughts?", "I don't think perf should drop that much, as it would be kind of like SinkCache in a way. But we don't know until we try! \r\nClosing as completed since you can use the `LLAMA_ATTENTION_CLASSES` 😉 ", "Hi @ArthurZucker, \r\n\r\nHere's what I tried out (in `transformers/models/llama/modeling_llama.py`)\r\n\r\n```\r\ndef _make_causal_mask(\r\n input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0,\r\n window_size: int=0):\r\n \"\"\"\r\n Make causal mask used for bi-directional self-attention.\r\n \"\"\"\r\n bsz, tgt_len = input_ids_shape\r\n mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)\r\n # standard causal attention!\r\n # mask_cond = torch.arange(mask.size(-1), device=device)\r\n # mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)\r\n\r\n # slided window attention mask!!\r\n for i in range(tgt_len):\r\n start = max(0, i - window_size + 1)\r\n end = min(tgt_len, i + 1)\r\n mask[i, start:end] = 0\r\n mask = mask.to(dtype)\r\n if past_key_values_length > 0:\r\n mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)\r\n return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)\r\n```\r\n\r\nNow this function creates a mask that slides according to the window size. Assuming the window_size is 3 and length of input is 6, the mask looks like: \r\n\r\n```\r\n[0, -65534, -65534, -65534, -65534, -65534],\r\n[0, 0, -65534, -65534, -65534, -65534],\r\n[0, 0, 0, -65534, -65534, -65534],\r\n[-65534, 0, 0, 0, -65534, -65534],\r\n[-65534, -65504, 0, 0, 0, -65504],\r\n[-65504, -65504, -65504, 0, 0, 0]]\r\n```\r\n\r\nwhere as the standard causal mask attention looks like the below:\r\n\r\n```\r\n[0, -65534, -65534, -65534, -65534, -65534],\r\n[0, 0, -65534, -65534, -65534, -65534],\r\n[0, 0, 0, -65534, -65534, -65534],\r\n[0, 0, 0, 0, -65534, -65534],\r\n[0, 0, 0, 0, 0, -65504],\r\n[0, 0, 0, 0, 0, 0]]\r\n```\r\nI am wondering if this change is enough for the plain vanilla SWA implementation. \r\nI am not interfering with the `position_ids` because I recollect they are not changed from the original ones i.e the positions will be referred from original one not the window!\r\n\r\nPlease share your thoughts on this, really appreciate it!", "Hi @ArthurZucker,\r\n\r\nI tried this drop-in replacement on a sample of \"BillSum\" dataset and the normal attention performs way better compared to the drop-in SWA! The drop-in doesn't even produce a fluent text, so I am not sure if this implementation is actually correct or if I am missing some details!\r\n\r\nOn the other hand, I fine-tuned the Llama-7B with Guanaco with SWA for 5 epochs and it is generating some text (still gibberish) compared to the drop-in replacement, but still it is also way off compared to the normal attention!\r\n\r\nHere are few observations:\r\n\r\n1. The normal attention is faster compared to the SWA ones (measured using time.time()), but the theory says otherwise for the long text!! (and billsum is a long-text!)\r\n2. For long text summarization, all the 3 variations (vanilla attention, drop-in SWA and finetuned SWA) produces mostly gibberish, but for some reason the SWA suffers significantly higher (gibberish/no-text) compared to vanilla model!\r\n\r\nHere's the code snippet used for generating the output:\r\n```\r\n output = model.generate(input_ids, \r\n max_new_tokens=300, \r\n num_beams=3,\r\n do_sample=do_sample_flag, #which I set to True and False to see different effects, usually sampling helps)\r\n top_k=100,\r\n temperature=10.0,\r\n no_repeat_ngram_size=5,\r\n attention_mask=attn_masks)\r\n```\r\n\r\nAnd here's the preprocessing function\r\n\r\n```\r\nprefix = \"Summarize the following bill. Focus your summary on the most important aspects of the bill. You do not have to summarize everything. Particularly focus on questions related to appropriation and the effects and impacts of the bill. However, you do not need to go into complex details, it is acceptable to provide ranges. Use active verbs to describe the bill, like 'amends' or 'changes'. Do not use ambivalent verbs like 'proposes' or 'suggests.'\"\r\n\r\ndef preprocess_function(examples):\r\n inputs = [prefix + doc for doc in examples['text']]\r\n model_inputs = tokenizer(inputs, padding=True, truncation=False, \r\n max_length=1024, return_tensors='pt')\r\n model_inputs[\"labels\"] = examples[\"summary\"]\r\n return model_inputs\r\n```\r\n\r\nI've referred to Summarization article from here for most of the details: https://huggingface.co/docs/transformers/tasks/summarization#inference \r\n\r\nIncase you have any thoughts, feel free to let me know! TIA :)" ]

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[ "Sorry, wrong mueller. Can't really help you😄 But thanks for suggesting me!", "Oops, sorry. I hope I fixed it now.", "cc @niels as well 🤗 ", "Hi @dparr005, thanks for opening this issue! \r\n\r\nCould you share a minimal code snippet to reproduce the error? \r\n\r\ncc @kashif \r\n\r\n", "checking thanks!", "@dparr005 can you try running the training on a single GPU to see the issue? and since your data has somewhat sane magnitudes, perhaps also set your `scaling=None` in the config", "I just used the basis from: https://huggingface.co/blog/time-series-transformers with my own data. I am not sure what portion of the code you are asking for.\r\n\r\nIn addition, I was able to run the code on a single GPU already (using a local Jupyter Notebook). But when I run it on the HPC cluster using multi-GPU, it does not work. My hypothesis is that somehow it is seeding the samples differently perhaps and that is why it runs on Jupyter Notebook and not using the multi-GPU configuration.", "Actually, the error more looks like the following:\r\n```python\r\nfuture_observed_mask=batch[\"future_observed_mask\"].to(device)\r\npackages/torch/nn/modules/module.py\", line 1194, in _call_impl\r\n return forward_call(*input, **kwargs)\r\naccelerate/utils/operations.py\", line 553, in forward\r\n return model_forward(*args, **kwargs)\r\npackages/accelerate/utils/operations.py\", line 541, in __call__\r\n return convert_to_fp32(self.model_forward(*args, **kwargs))\r\ntorch/amp/autocast_mode.py\", line 14, in decorate_autocast\r\n return func(*args, **kwargs)\r\npackages/torch/nn/parallel/distributed.py\", line 1026, in forward\r\n if torch.is_grad_enabled() and self.reducer._rebuild_buckets():\r\nRuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing the keyword argument `find_unused_parameters=True` to `torch.nn.parallel.DistributedDataParallel`, and by \r\nmaking sure all `forward` function outputs participate in calculating loss. \r\n```\r\nIf you already have done the above, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's `forward` function. Please include the loss function and the structure of the return value of `forward` of your module when reporting this issue (e.g. list, dict, iterable).\r\nParameter indices which did not receive grad for rank 0: 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49\r\n In addition, you can set the environment variable TORCH_DISTRIBUTED_DEBUG to either INFO or DETAIL to print out information about which particular parameters did not receive gradient on this rank as part of this error", "i see @dparr005 so the issue is multi-gpu training... perhaps i need to gather the losses etc., I have a multi-gpu setup now so can finally test", "Yes. That was my other hypothesis, that somehow the code is expecting a gather statement (from all GPU's once a single training epoch is done) before going to the next epoch.\r\n\r\nWhat do you need from me to test this hypothesis?", "I just ran the given code (from gitlab) in a multiple GPU environment but it gives the same type of errors.\r\n\r\nThe distribution environment is:\r\n```\r\npartialState: Distributed environment: MULTI_GPU Backend: nccl\r\nNum processes: 3\r\nProcess index: 0\r\nLocal process index: 0\r\nDevice: cuda:0\r\n```\r\n\r\nI converted the Jupyter Notebook into a .py script and am calling it from SLURM. Via:\r\n`crun -p ~/envs/ts_tnn python -m accelerate.commands.launch --config_file config.yaml --num_processes=3 --multi_gpu fromGithub.py`\r\n\r\nCan anyone help me? It seems to me that it is an issue with implementing accelerate.", "What's your exact code look like here?", "```\r\nfrom data_utils import *\r\nfrom datasets import load_dataset\r\nfrom functools import partial\r\nfrom gluonts.time_feature import get_lags_for_frequency\r\nfrom gluonts.time_feature import time_features_from_frequency_str\r\nfrom transformers import TimeSeriesTransformerConfig, TimeSeriesTransformerForPrediction\r\nfrom accelerate import Accelerator\r\nfrom torch.optim import AdamW\r\nfrom evaluate import load\r\nfrom gluonts.time_feature import get_seasonality\r\nimport matplotlib.dates as mdates\r\nfrom accelerate.state import PartialState\r\n\r\n\r\n\r\ndef getModel(prediction_length, lags_sequence, time_features_len, train_dataset_len):\r\n config = TimeSeriesTransformerConfig(\r\n prediction_length=prediction_length,\r\n # context length:\r\n context_length=prediction_length * 2,\r\n # lags coming from helper given the freq:\r\n lags_sequence=lags_sequence,\r\n # we'll add 2 time features (\"month of year\" and \"age\", see further):\r\n num_time_features=time_features_len + 1,\r\n # we have a single static categorical feature, namely time series ID:\r\n num_static_categorical_features=1,\r\n # it has 366 possible values:\r\n cardinality=[train_dataset_len],\r\n # the model will learn an embedding of size 2 for each of the 366 possible values:\r\n embedding_dimension=[2],\r\n\r\n # transformer params:\r\n encoder_layers=4,\r\n decoder_layers=4,\r\n d_model=32,\r\n )\r\n model = TimeSeriesTransformerForPrediction(config)\r\n return config, model\r\n\r\n\r\n\r\n \r\n\r\n\r\ndef main():\r\n ### set up data\r\n dataset = load_dataset(\"monash_tsf\", \"tourism_monthly\")\r\n train_example = dataset[\"train\"][0]\r\n validation_example = dataset[\"validation\"][0]\r\n freq = \"1M\"\r\n prediction_length = 24\r\n\r\n assert len(train_example[\"target\"]) + prediction_length == len(\r\n validation_example[\"target\"]\r\n )\r\n train_dataset = dataset[\"train\"]\r\n test_dataset = dataset[\"test\"]\r\n \r\n ### make sure that the data is in the correct form\r\n train_dataset.set_transform(partial(transform_start_field, freq=freq))\r\n test_dataset.set_transform(partial(transform_start_field, freq=freq))\r\n lags_sequence = get_lags_for_frequency(freq)\r\n time_features = time_features_from_frequency_str(freq)\r\n config, model = getModel(prediction_length, lags_sequence, len(time_features), len(train_dataset))\r\n \r\n # get data loaders:\r\n train_dataloader = create_train_dataloader(\r\n config=config,\r\n freq=freq,\r\n data=train_dataset,\r\n batch_size=256,\r\n num_batches_per_epoch=100,\r\n )\r\n\r\n test_dataloader = create_backtest_dataloader(\r\n config=config,\r\n freq=freq,\r\n data=test_dataset,\r\n batch_size=64,\r\n )\r\n \r\n ### Init accelerator\r\n accelerator = Accelerator()\r\n device = accelerator.device\r\n model.to(device)\r\n ps = PartialState()\r\n print(\"partialState: \", ps)\r\n optimizer = AdamW(model.parameters(), lr=6e-4, betas=(0.9, 0.95), weight_decay=1e-1)\r\n\r\n model, optimizer, train_dataloader = accelerator.prepare(\r\n model,\r\n optimizer,\r\n train_dataloader,\r\n )\r\n\r\n model.train()\r\n for epoch in range(40):\r\n for idx, batch in enumerate(train_dataloader):\r\n optimizer.zero_grad()\r\n outputs = model(\r\n static_categorical_features=batch[\"static_categorical_features\"].to(device)\r\n if config.num_static_categorical_features > 0\r\n else None,\r\n static_real_features=batch[\"static_real_features\"].to(device)\r\n if config.num_static_real_features > 0\r\n else None,\r\n past_time_features=batch[\"past_time_features\"].to(device),\r\n past_values=batch[\"past_values\"].to(device),\r\n future_time_features=batch[\"future_time_features\"].to(device),\r\n future_values=batch[\"future_values\"].to(device),\r\n past_observed_mask=batch[\"past_observed_mask\"].to(device),\r\n future_observed_mask=batch[\"future_observed_mask\"].to(device),\r\n )\r\n loss = outputs.loss\r\n\r\n # Backpropagation\r\n accelerator.backward(loss)\r\n optimizer.step()\r\n if idx % 100 == 0:\r\n print(\"loss:\", loss.item())\r\n \r\n ### Inference\r\n model.eval()\r\n\r\n forecasts = []\r\n\r\n for batch in test_dataloader:\r\n outputs = model.generate(\r\n static_categorical_features=batch[\"static_categorical_features\"].to(device)\r\n if config.num_static_categorical_features > 0\r\n else None,\r\n static_real_features=batch[\"static_real_features\"].to(device)\r\n if config.num_static_real_features > 0\r\n else None,\r\n past_time_features=batch[\"past_time_features\"].to(device),\r\n past_values=batch[\"past_values\"].to(device),\r\n future_time_features=batch[\"future_time_features\"].to(device),\r\n past_observed_mask=batch[\"past_observed_mask\"].to(device),\r\n )\r\n forecasts.append(outputs.sequences.cpu().numpy())\r\n forecasts = np.vstack(forecasts)\r\n mase_metric = load(\"evaluate-metric/mase\")\r\n smape_metric = load(\"evaluate-metric/smape\")\r\n\r\n forecast_median = np.median(forecasts, 1)\r\n\r\n mase_metrics = []\r\n smape_metrics = []\r\n for item_id, ts in enumerate(test_dataset):\r\n training_data = ts[\"target\"][:-prediction_length]\r\n ground_truth = ts[\"target\"][-prediction_length:]\r\n mase = mase_metric.compute(\r\n predictions=forecast_median[item_id],\r\n references=np.array(ground_truth),\r\n training=np.array(training_data),\r\n periodicity=get_seasonality(freq),\r\n )\r\n mase_metrics.append(mase[\"mase\"])\r\n\r\n smape = smape_metric.compute(\r\n predictions=forecast_median[item_id],\r\n references=np.array(ground_truth),\r\n )\r\n smape_metrics.append(smape[\"smape\"])\r\n \r\n ### print results of the evaluation\r\n print(f\"MASE: {np.mean(mase_metrics)}\")\r\n print(f\"sMAPE: {np.mean(smape_metrics)}\")\r\n \r\n plt.scatter(mase_metrics, smape_metrics, alpha=0.3)\r\n plt.xlabel(\"MASE\")\r\n plt.ylabel(\"sMAPE\")\r\n plt.savefig(\"figures/github_results.pdf\")\r\n plt.show()\r\n \r\n \r\n def plot(ts_index):\r\n fig, ax = plt.subplots()\r\n\r\n index = pd.period_range(\r\n start=test_dataset[ts_index][FieldName.START],\r\n periods=len(test_dataset[ts_index][FieldName.TARGET]),\r\n freq=freq,\r\n ).to_timestamp()\r\n\r\n # Major ticks every half year, minor ticks every month,\r\n ax.xaxis.set_major_locator(mdates.MonthLocator(bymonth=(1, 7)))\r\n ax.xaxis.set_minor_locator(mdates.MonthLocator())\r\n\r\n ax.plot(\r\n index[-2 * prediction_length :],\r\n test_dataset[ts_index][\"target\"][-2 * prediction_length :],\r\n label=\"actual\",\r\n )\r\n\r\n plt.plot(\r\n index[-prediction_length:],\r\n np.median(forecasts[ts_index], axis=0),\r\n label=\"median\",\r\n )\r\n\r\n plt.fill_between(\r\n index[-prediction_length:],\r\n forecasts[ts_index].mean(0) - forecasts[ts_index].std(axis=0),\r\n forecasts[ts_index].mean(0) + forecasts[ts_index].std(axis=0),\r\n alpha=0.3,\r\n interpolate=True,\r\n label=\"+/- 1-std\",\r\n )\r\n plt.legend()\r\n plt.show()\r\n \r\nmain()\r\n```", "This is the code from data_utils.py. The above code was fromGithub.py\r\n\r\n```\r\nfrom datasets import DatasetDict\r\nfrom gluonts.itertools import Map\r\nfrom datasets import Dataset, Features, Value, Sequence\r\nfrom gluonts.dataset.pandas import PandasDataset\r\nimport datasets\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom datetime import datetime\r\nimport matplotlib.pyplot as plt\r\nfrom functools import lru_cache\r\nfrom functools import partial\r\nfrom gluonts.time_feature import get_lags_for_frequency\r\nfrom gluonts.time_feature import time_features_from_frequency_str\r\nfrom transformers import TimeSeriesTransformerConfig, TimeSeriesTransformerForPrediction\r\nimport os\r\nfrom os.path import exists\r\nfrom torch.nn.parallel import DistributedDataParallel as DDP\r\nfrom torch.distributed import init_process_group, destroy_process_group\r\nimport torch\r\nimport torch.distributed as dist\r\nfrom accelerate import Accelerator\r\nfrom torch.optim import AdamW\r\nimport sys\r\nfrom gluonts.time_feature import (\r\n time_features_from_frequency_str,\r\n TimeFeature,\r\n get_lags_for_frequency,\r\n)\r\nfrom gluonts.dataset.field_names import FieldName\r\nfrom gluonts.transform import (\r\n AddAgeFeature,\r\n AddObservedValuesIndicator,\r\n AddTimeFeatures,\r\n AsNumpyArray,\r\n Chain,\r\n ExpectedNumInstanceSampler,\r\n InstanceSplitter,\r\n RemoveFields,\r\n SelectFields,\r\n SetField,\r\n TestSplitSampler,\r\n Transformation,\r\n ValidationSplitSampler,\r\n VstackFeatures,\r\n RenameFields,\r\n)\r\n\r\nfrom transformers import PretrainedConfig\r\nfrom gluonts.transform.sampler import InstanceSampler\r\nfrom typing import Optional\r\nfrom typing import Iterable\r\nimport torch\r\nfrom gluonts.itertools import Cyclic, Cached\r\nfrom gluonts.dataset.loader import as_stacked_batches\r\nimport matplotlib.dates as mdates\r\n\r\n\r\ndef getRank():\r\n try:\r\n local_rank = int(os.environ[\"LOCAL_RANK\"])\r\n except KeyError:\r\n local_rank = 0\r\n return local_rank\r\n\r\nclass ProcessStartField():\r\n ts_id = 0\r\n def __call__(self, data):\r\n data[\"start\"] = data[\"start\"].to_timestamp()\r\n data[\"feat_static_cat\"] = [self.ts_id]\r\n self.ts_id += 1\r\n return data\r\n\r\n@lru_cache(10_000)\r\ndef convert_to_pandas_period(date, freq):\r\n return pd.Period(date, freq)\r\n\r\n\r\ndef transform_start_field(batch, freq):\r\n batch[\"start\"] = [convert_to_pandas_period(date, freq) for date in batch[\"start\"]]\r\n return batch\r\ndef create_transformation(freq: str, config: PretrainedConfig) -> Transformation:\r\n remove_field_names = []\r\n if config.num_static_real_features == 0:\r\n remove_field_names.append(FieldName.FEAT_STATIC_REAL)\r\n if config.num_dynamic_real_features == 0:\r\n remove_field_names.append(FieldName.FEAT_DYNAMIC_REAL)\r\n if config.num_static_categorical_features == 0:\r\n remove_field_names.append(FieldName.FEAT_STATIC_CAT)\r\n\r\n # a bit like torchvision.transforms.Compose\r\n return Chain(\r\n # step 1: remove static/dynamic fields if not specified\r\n [RemoveFields(field_names=remove_field_names)]\r\n # step 2: convert the data to NumPy (potentially not needed)\r\n + (\r\n [\r\n AsNumpyArray(\r\n field=FieldName.FEAT_STATIC_CAT,\r\n expected_ndim=1,\r\n dtype=int,\r\n )\r\n ]\r\n if config.num_static_categorical_features > 0\r\n else []\r\n )\r\n + (\r\n [\r\n AsNumpyArray(\r\n field=FieldName.FEAT_STATIC_REAL,\r\n expected_ndim=1,\r\n )\r\n ]\r\n if config.num_static_real_features > 0\r\n else []\r\n )\r\n + [\r\n AsNumpyArray(\r\n field=FieldName.TARGET,\r\n # we expect an extra dim for the multivariate case:\r\n expected_ndim=1 if config.input_size == 1 else 2,\r\n ),\r\n # step 3: handle the NaN's by filling in the target with zero\r\n # and return the mask (which is in the observed values)\r\n # true for observed values, false for nan's\r\n # the decoder uses this mask (no loss is incurred for unobserved values)\r\n # see loss_weights inside the xxxForPrediction model\r\n AddObservedValuesIndicator(\r\n target_field=FieldName.TARGET,\r\n output_field=FieldName.OBSERVED_VALUES,\r\n ),\r\n # step 4: add temporal features based on freq of the dataset\r\n # month of year in the case when freq=\"M\"\r\n # these serve as positional encodings\r\n AddTimeFeatures(\r\n start_field=FieldName.START,\r\n target_field=FieldName.TARGET,\r\n output_field=FieldName.FEAT_TIME,\r\n time_features=time_features_from_frequency_str(freq),\r\n pred_length=config.prediction_length,\r\n ),\r\n # step 5: add another temporal feature (just a single number)\r\n # tells the model where in the life the value of the time series is\r\n # sort of running counter\r\n AddAgeFeature(\r\n target_field=FieldName.TARGET,\r\n output_field=FieldName.FEAT_AGE,\r\n pred_length=config.prediction_length,\r\n log_scale=True,\r\n ),\r\n # step 6: vertically stack all the temporal features into the key FEAT_TIME\r\n VstackFeatures(\r\n output_field=FieldName.FEAT_TIME,\r\n input_fields=[FieldName.FEAT_TIME, FieldName.FEAT_AGE]\r\n + (\r\n [FieldName.FEAT_DYNAMIC_REAL]\r\n if config.num_dynamic_real_features > 0\r\n else []\r\n ),\r\n ),\r\n # step 7: rename to match HuggingFace names\r\n RenameFields(\r\n mapping={\r\n FieldName.FEAT_STATIC_CAT: \"static_categorical_features\",\r\n FieldName.FEAT_STATIC_REAL: \"static_real_features\",\r\n FieldName.FEAT_TIME: \"time_features\",\r\n FieldName.TARGET: \"values\",\r\n FieldName.OBSERVED_VALUES: \"observed_mask\",\r\n }\r\n ),\r\n ]\r\n )\r\ndef create_instance_splitter(\r\n config: PretrainedConfig,\r\n mode: str,\r\n train_sampler: Optional[InstanceSampler] = None,\r\n validation_sampler: Optional[InstanceSampler] = None,\r\n) -> Transformation:\r\n assert mode in [\"train\", \"validation\", \"test\"]\r\n\r\n instance_sampler = {\r\n \"train\": train_sampler\r\n or ExpectedNumInstanceSampler(\r\n num_instances=1.0, min_future=config.prediction_length\r\n ),\r\n \"validation\": validation_sampler\r\n or ValidationSplitSampler(min_future=config.prediction_length),\r\n \"test\": TestSplitSampler(),\r\n }[mode]\r\n\r\n return InstanceSplitter(\r\n target_field=\"values\",\r\n is_pad_field=FieldName.IS_PAD,\r\n start_field=FieldName.START,\r\n forecast_start_field=FieldName.FORECAST_START,\r\n instance_sampler=instance_sampler,\r\n past_length=config.context_length + max(config.lags_sequence),\r\n future_length=config.prediction_length,\r\n time_series_fields=[\"time_features\", \"observed_mask\"],\r\n )\r\n\r\ndef create_train_dataloader(\r\n config: PretrainedConfig,\r\n freq,\r\n data,\r\n batch_size: int,\r\n num_batches_per_epoch: int,\r\n shuffle_buffer_length: Optional[int] = None,\r\n cache_data: bool = True,\r\n **kwargs,\r\n) -> Iterable:\r\n PREDICTION_INPUT_NAMES = [\r\n \"past_time_features\",\r\n \"past_values\",\r\n \"past_observed_mask\",\r\n \"future_time_features\",\r\n ]\r\n if config.num_static_categorical_features > 0:\r\n PREDICTION_INPUT_NAMES.append(\"static_categorical_features\")\r\n\r\n if config.num_static_real_features > 0:\r\n PREDICTION_INPUT_NAMES.append(\"static_real_features\")\r\n\r\n TRAINING_INPUT_NAMES = PREDICTION_INPUT_NAMES + [\r\n \"future_values\",\r\n \"future_observed_mask\",\r\n ]\r\n\r\n transformation = create_transformation(freq, config)\r\n transformed_data = transformation.apply(data, is_train=True)\r\n if cache_data:\r\n transformed_data = Cached(transformed_data)\r\n\r\n # we initialize a Training instance\r\n instance_splitter = create_instance_splitter(config, \"train\")\r\n\r\n # the instance splitter will sample a window of\r\n # context length + lags + prediction length (from the 366 possible transformed time series)\r\n # randomly from within the target time series and return an iterator.\r\n stream = Cyclic(transformed_data).stream()\r\n training_instances = instance_splitter.apply(stream)\r\n \r\n return as_stacked_batches(\r\n training_instances,\r\n batch_size=batch_size,\r\n shuffle_buffer_length=shuffle_buffer_length,\r\n field_names=TRAINING_INPUT_NAMES,\r\n output_type=torch.tensor,\r\n num_batches_per_epoch=num_batches_per_epoch,\r\n )\r\n\r\ndef create_backtest_dataloader(\r\n config: PretrainedConfig,\r\n freq,\r\n data,\r\n batch_size: int,\r\n **kwargs,\r\n):\r\n PREDICTION_INPUT_NAMES = [\r\n \"past_time_features\",\r\n \"past_values\",\r\n \"past_observed_mask\",\r\n \"future_time_features\",\r\n ]\r\n if config.num_static_categorical_features > 0:\r\n PREDICTION_INPUT_NAMES.append(\"static_categorical_features\")\r\n\r\n if config.num_static_real_features > 0:\r\n PREDICTION_INPUT_NAMES.append(\"static_real_features\")\r\n\r\n transformation = create_transformation(freq, config)\r\n transformed_data = transformation.apply(data)\r\n\r\n # We create a Validation Instance splitter which will sample the very last\r\n # context window seen during training only for the encoder.\r\n instance_sampler = create_instance_splitter(config, \"validation\")\r\n\r\n # we apply the transformations in train mode\r\n testing_instances = instance_sampler.apply(transformed_data, is_train=True)\r\n \r\n return as_stacked_batches(\r\n testing_instances,\r\n batch_size=batch_size,\r\n output_type=torch.tensor,\r\n field_names=PREDICTION_INPUT_NAMES,\r\n )\r\n\r\ndef create_test_dataloader(\r\n config: PretrainedConfig,\r\n freq,\r\n data,\r\n batch_size: int,\r\n **kwargs,\r\n):\r\n PREDICTION_INPUT_NAMES = [\r\n \"past_time_features\",\r\n \"past_values\",\r\n \"past_observed_mask\",\r\n \"future_time_features\",\r\n ]\r\n if config.num_static_categorical_features > 0:\r\n PREDICTION_INPUT_NAMES.append(\"static_categorical_features\")\r\n\r\n if config.num_static_real_features > 0:\r\n PREDICTION_INPUT_NAMES.append(\"static_real_features\")\r\n\r\n transformation = create_transformation(freq, config)\r\n transformed_data = transformation.apply(data, is_train=False)\r\n\r\n # We create a test Instance splitter to sample the very last\r\n # context window from the dataset provided.\r\n instance_sampler = create_instance_splitter(config, \"test\")\r\n\r\n # We apply the transformations in test mode\r\n testing_instances = instance_sampler.apply(transformed_data, is_train=False)\r\n \r\n return as_stacked_batches(\r\n testing_instances,\r\n batch_size=batch_size,\r\n output_type=torch.tensor,\r\n field_names=PREDICTION_INPUT_NAMES,\r\n )\r\n\r\n```\r\n", "As a reminder, the code is taken from the following [github code](https://huggingface.co/blog/time-series-transformers). It works as a Jupyter Notebook but not as a python script launched via SLURM.\r\n\r\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28913). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Force pushed since tests were not running." ]

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[ "This the error : \"/opt/omniai/software/Miniconda/lib/python3.8/site-packages/transformers/generation/utils.py in _validate_model_class(self)\r\n 1063 if generate_compatible_classes:\r\n 1064 exception_message += f\" Please use one of the following classes instead: {generate_compatible_classes}\"\r\n-> 1065 raise TypeError(exception_message)\r\n 1066 \r\n 1067 def _validate_model_kwargs(self, model_kwargs: Dict[str, Any]):\r\n\r\nTypeError: The current model class (ALGORITHMIAT5) is not compatible with `.generate()`, as it doesn't have a language model head.", "The \"google/flan-t5-small\" model is used.", "Hey 🤗 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://discuss.huggingface.co/) instead? I'm sure the community will be of help!\r\n\r\nFor you example, I would recommend you to properly define your class with\r\n```python\r\ndef generate():\r\n self.model.generate()\r\n```\r\nor something like that! ", "Thanks Arthur. Will do\r\n\r\nOn Wed, Feb 7, 2024 at 9:31 PM Arthur ***@***.***> wrote:\r\n\r\n> Hey 🤗 thanks for opening an issue! We try to keep the github issues for\r\n> bugs/feature requests.\r\n> Could you ask your question on the forum <https://discuss.huggingface.co/>\r\n> instead? I'm sure the community will be of help!\r\n>\r\n> For you example, I would recommend you to properly define your class with\r\n>\r\n> def generate():\r\n> self.model.generate()\r\n>\r\n> or something like that!\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/transformers/issues/28911#issuecomment-1933269590>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/BF7NDUZQYEKMMNOLWYCSPGLYSQ2H7AVCNFSM6AAAAABC6GU3ZCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSMZTGI3DSNJZGA>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> ***@***.***>\r\n>\r\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28910). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "In transformers the input ids cannot be negative 😉 ", "However, I have experienced the negative input ids for the batch-generating text", "Can you share a reproducer? 🤗 " ]

[ { "id": 2648621985, "node_id": "MDU6TGFiZWwyNjQ4NjIxOTg1", "url": "https://api.github.com/repos/huggingface/transformers/labels/Feature%20request", "name": "Feature request", "color": "FBCA04", "default": false, "description": "Request for a new feature" } ]

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[ "to understand more about this try checking these 2 checkpoints\r\n```python\r\n# checkpoint that doesn't have a custom pipeline\r\nmodel_without_custom_pipeline = AutoModelForImageClassification.from_pretrained(\"not-lain/MyRepo\", trust_remote_code=True, revision=\"dba8d15072d743b6cb4a707246f801699897fb72\")\r\nmodel_without_custom_pipeline.push_to_hub(\"model_without_custom_pipeline_repo\")\r\n```\r\n\r\n```python\r\n# checkpoint that has a custom pipeline\r\nmodel_with_custom_pipeline = AutoModelForImageClassification.from_pretrained(\"not-lain/MyRepo\", trust_remote_code=True, revision=\"4b57ca965d5070af9975666e2a0f584241991597\")\r\nmodel_with_custom_pipeline.push_to_hub(\"model_with_custom_pipeline_repo\")\r\n```\r\nand try checking the difference in configuration between them", "cc @Rocketknight1 if you can have a look!", "@Rocketknight1 I'm going to give you a helping hand. you can trace [`custom_object_save`](https://github.com/huggingface/transformers/blob/main/src/transformers/dynamic_module_utils.py#L503) function and check in which files it got called.\r\nI'll try to help out tomorrow, but thought I post this in public to let you know where you can start" ]

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[ "Hi @fColangelo 👋 \r\n\r\nThat happens because `top_k` sets the scores of all but the `top_k` most likely tokens to `-inf` (50 in your case). If you want the model logits, and not the post-processed scores before sampling, have a look at this feature that is about to get merged: https://github.com/huggingface/transformers/pull/28667", "Hi and thanks for the answer! I noticed this since I was trying to calculate a perplexity for an answer. I guess my code was incorrect, since the probability values for the selected tokens should not be -inf. I will check, confirm and close, ty!", "It was in fact a bug in my indexing. Thanks for the info!" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28905). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Yes, I thought changing cache number will fix all, but unfortunately no. There are some inter-dependencies prevent to get the desired torch/torchvision/torchaudio versions.\r\n\r\nI don't have the time to look at this in depth - it took me quite time in the last two days. Let's unblock other PRs in a quick way and I will check later 🙏 " ]

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[ "cc @muellerzr ", "@kwon0408 please update your transformers version, this was fixed in the next patch that was released ~1 week ago" ]

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[ "Hi @Junyoungpark \r\nDue to the fact the returned attention might not be correct (e.g.: https://github.com/Dao-AILab/flash-attention/blob/61a777247900f6c2a37376f3ffd7134385fdc95c/flash_attn/flash_attn_interface.py#L668) unfortunately we had to force-disable `output_attention` to `False` for all FA-2 models. We can consider to enable it once we have gurantees on FA-2 side that the returned output attentions are correct", "Hi @younesbelkada.\r\n\r\nThanks for the detailed reply. I understand the current situation. Thanks 👍 ", "Thank you @Junyoungpark !" ]

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[ "Got and error when tried to pass embeddings \r\n\r\noutput = encoder(embeddings)\r\n", "Hi @manoja328 \r\nThanks for the issue! This is I think expected as this class is not meant to be used as a standalone class, `head_mask` is automatically created as a list of `None` here: https://github.com/huggingface/transformers/blob/1c31b7aa3bb4e7ef24c77596d2a76f45a770159f/src/transformers/modeling_tf_utils.py#L1219\r\nbut I agree we could make it more userfriendly, one way could be to initialize `head_mask` in the encoder to be `[None for _ in range(len(self.layers))]` to preserve `head_mask[i]` and keep a consistency across expected arguments. ", "Yup, that should be sufficient. I encountered this while I was trying to compute attribution of logits with respect the input embeddings using jacobians. So there first i computed embeddings for the sequence then if I pass the embeddings to the encoder to get the logits, which is when I encountered this error.\r\n" ]

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[ "Hey! Thanks for opening the PR, we try to leave things such as custom loss computation to the user (not providing the labels should do the trick) 🤗 " ]

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[ "Hey! Thanks for opening this issue. Would you like to dive in this and open a PR for a fix? It might be a known bug + overflowing tokens are not supported on all slow tokenizer. The fast is probably right behaviour", "I don't know what is the correct behaviour. You can get the overflowing tokens from both tokenizers. It's just that the returned data structure needs to be more consistent. I prefer the fast tokenizers behaviour, but the BatchEncoding returns None for the overflowing_tokens and is inconsistent with the advertised API in reference help. \r\nI can try to fix this late in March, but I would appreciate your decision on which direction the API should go since I'm not an expert on transformers API." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28899). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The failing will disappear after the unpin torch PR is merged", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28898). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "cc @Narsil and @ArthurZucker for visibility \r\n\r\nGoing to merge to keep CI green\r\n\r\n", "Just FMI who will take over the PR again? ", "> Just FMI who will take over the PR again?\r\n\r\nI assume it would be @Narsil ...?" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28897). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@MKhalusova thanks, I addressed your comments I think", "@ArthurZucker please take a look. the guide LGTM", "I hope feedback from the community is welcome. Nice work! 🤗", "@ArthurZucker can you merge this if it's ok? ", "@merveenoyan I can merge. Thanks for adding this! " ]

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[ "I personally don't think this is a bug but rather a correct behavior because this error is only triggered when the param `num_beams` is set, which means the user either opt `beam search` or `beam sample`. Both the two sampling methods only keep `num_beams` candidate during searching so they really can not return more than `num_beams` generations. \r\n\r\n(N.B. beam sample != sample multiple times)\r\n\r\nIf the user wants to get multiple generations from random sampling, the `num_beams` param should be removed. ", "@JohnnieDavidov 👋 @Saibo-creator wrote the correct answer :)" ]

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