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[ "Hi !\r\nAs far as I know not all faiss indexes can be computed in parallel and then merged. \r\nFor example [here](https://github.com/facebookresearch/faiss/wiki/Special-operations-on-indexes#splitting-and-merging-indexes) is is mentioned that only IndexIVF indexes can be merged.\r\nMoreover faiss already works using multithreading to parallelize the workload over your different CPU cores. You can find more info [here](https://github.com/facebookresearch/faiss/wiki/Threads-and-asynchronous-calls#internal-threading)\r\nSo I feel like the gains we would get by implementing a parallel `add_faiss_index` would not be that important, but let me know what you think.\r\n", "Actually, you are right. I also had the same idea. I am trying this in the context of end-ton-end retrieval training in RAG. So far I have parallelized the embedding re-computation within the training loop by using datasets shards. \r\n\r\nThen I was thinking of can I calculate the indexes for each shard and combined them with **concatenate** before I save.", "@lhoestq As you mentioned faiss is already using multiprocessing. I tried to do the add_index with faiss for a dataset object inside a RAY actor and the process became very slow... if fact it takes so much time. It is because a ray actor comes with a single CPU core unless we assign it more. I also tried assigning more cores but still running add_index in the main process is very fast. " ]

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[ "Hi !\r\n\r\nTo fix 1, an you try to run this code ?\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"squad\", download_mode=\"force_redownload\")\r\n```\r\nMaybe the file your downloaded was corrupted, in this case redownloading this way should fix your issue 1.\r\n\r\nRegarding your 2nd point, you're right that loading the raw json this way doesn't give you a dataset with the column \"context\", \"question\" and \"answers\". Indeed the squad format is a very nested format so you have to preprocess the data. You can do it this way:\r\n```python\r\ndef process_squad(examples):\r\n \"\"\"\r\n Process a dataset in the squad format with columns \"title\" and \"paragraphs\"\r\n to return the dataset with columns \"context\", \"question\" and \"answers\".\r\n \"\"\"\r\n out = {\"context\": [], \"question\": [], \"answers\":[]} \r\n for paragraphs in examples[\"paragraphs\"]: \r\n for paragraph in paragraphs: \r\n for qa in paragraph[\"qas\"]: \r\n answers = [{\"answer_start\": answer[\"answer_start\"], \"text\": answer[\"text\"].strip()} for answer in qa[\"answers\"]] \r\n out[\"context\"].append(paragraph[\"context\"].strip()) \r\n out[\"question\"].append(qa[\"question\"].strip()) \r\n out[\"answers\"].append(answers) \r\n return out\r\n\r\ndatasets = load_dataset(extension, data_files=data_files, field=\"data\")\r\ncolumn_names = datasets[\"train\"].column_names\r\n\r\nif set(column_names) == {\"title\", \"paragraphs\"}:\r\n datasets = datasets.map(process_squad, batched=True, remove_columns=column_names)\r\n```\r\n\r\nHope that helps :)", "Thks for quickly answering！\r\n### 1 I try the first way,but seems not work \r\n```\r\nTraceback (most recent call last):\r\n File \"examples/question-answering/run_qa.py\", line 503, in <module>\r\n main()\r\n File \"examples/question-answering/run_qa.py\", line 218, in main\r\n datasets = load_dataset(data_args.dataset_name, download_mode=\"force_redownload\")\r\n File \"/home2/zhenggo1/anaconda3/envs/lpot/lib/python3.7/site-packages/datasets/load.py\", line 746, in load_dataset\r\n use_auth_token=use_auth_token,\r\n File \"/home2/zhenggo1/anaconda3/envs/lpot/lib/python3.7/site-packages/datasets/builder.py\", line 573, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/home2/zhenggo1/anaconda3/envs/lpot/lib/python3.7/site-packages/datasets/builder.py\", line 633, in _download_and_prepare\r\n self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n File \"/home2/zhenggo1/anaconda3/envs/lpot/lib/python3.7/site-packages/datasets/utils/info_utils.py\", line 39, in verify_checksums\r\n raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json']\r\n```\r\n### 2 I try the second way,and run the examples/question-answering/run_qa.py,it lead to another bug orz..\r\n```\r\nTraceback (most recent call last):\r\n File \"examples/question-answering/run_qa.py\", line 523, in <module>\r\n main()\r\n File \"examples/question-answering/run_qa.py\", line 379, in main\r\n load_from_cache_file=not data_args.overwrite_cache,\r\n File \"/home2/zhenggo1/anaconda3/envs/lpot/lib/python3.7/site-packages/datasets/arrow_dataset.py\", line 1120, in map\r\n update_data = does_function_return_dict(test_inputs, test_indices)\r\n File \"/home2/zhenggo1/anaconda3/envs/lpot/lib/python3.7/site-packages/datasets/arrow_dataset.py\", line 1091, in does_function_return_dict\r\n function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n File \"examples/question-answering/run_qa.py\", line 339, in prepare_train_features\r\n if len(answers[\"answer_start\"]) == 0:\r\nTypeError: list indices must be integers or slices, not str\r\n```\r\n## may be the function prepare_train_features in run_qa.py need to fix,I think is that the prep\r\n```python\r\nfor i, offsets in enumerate(offset_mapping):\r\n # We will label impossible answers with the index of the CLS token.\r\n input_ids = tokenized_examples[\"input_ids\"][i]\r\n cls_index = input_ids.index(tokenizer.cls_token_id)\r\n\r\n # Grab the sequence corresponding to that example (to know what is the context and what is the question).\r\n sequence_ids = tokenized_examples.sequence_ids(i)\r\n\r\n # One example can give several spans, this is the index of the example containing this span of text.\r\n sample_index = sample_mapping[i]\r\n answers = examples[answer_column_name][sample_index]\r\n print(examples,answers)\r\n # If no answers are given, set the cls_index as answer.\r\n if len(answers[\"answer_start\"]) == 0:\r\n tokenized_examples[\"start_positions\"].append(cls_index)\r\n tokenized_examples[\"end_positions\"].append(cls_index)\r\n else:\r\n # Start/end character index of the answer in the text.\r\n start_char = answers[\"answer_start\"][0]\r\n end_char = start_char + len(answers[\"text\"][0])\r\n\r\n # Start token index of the current span in the text.\r\n token_start_index = 0\r\n while sequence_ids[token_start_index] != (1 if pad_on_right else 0):\r\n token_start_index += 1\r\n\r\n # End token index of the current span in the text.\r\n token_end_index = len(input_ids) - 1\r\n while sequence_ids[token_end_index] != (1 if pad_on_right else 0):\r\n token_end_index -= 1\r\n\r\n # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):\r\n tokenized_examples[\"start_positions\"].append(cls_index)\r\n tokenized_examples[\"end_positions\"].append(cls_index)\r\n else:\r\n # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n # Note: we could go after the last offset if the answer is the last word (edge case).\r\n while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:\r\n token_start_index += 1\r\n tokenized_examples[\"start_positions\"].append(token_start_index - 1)\r\n while offsets[token_end_index][1] >= end_char:\r\n token_end_index -= 1\r\n tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n\r\n return tokenized_examples\r\n``` ", "## I have fixed it, @lhoestq \r\n### the first section change as you said and add [\"id\"]\r\n```python\r\ndef process_squad(examples):\r\n \"\"\"\r\n Process a dataset in the squad format with columns \"title\" and \"paragraphs\"\r\n to return the dataset with columns \"context\", \"question\" and \"answers\".\r\n \"\"\"\r\n # print(examples)\r\n out = {\"context\": [], \"question\": [], \"answers\":[],\"id\":[]} \r\n for paragraphs in examples[\"paragraphs\"]: \r\n for paragraph in paragraphs: \r\n for qa in paragraph[\"qas\"]: \r\n answers = [{\"answer_start\": answer[\"answer_start\"], \"text\": answer[\"text\"].strip()} for answer in qa[\"answers\"]] \r\n out[\"context\"].append(paragraph[\"context\"].strip()) \r\n out[\"question\"].append(qa[\"question\"].strip()) \r\n out[\"answers\"].append(answers) \r\n out[\"id\"].append(qa[\"id\"]) \r\n return out\r\ncolumn_names = datasets[\"train\"].column_names if training_args.do_train else datasets[\"validation\"].column_names\r\n# print(datasets[\"train\"].column_names)\r\nif set(column_names) == {\"title\", \"paragraphs\"}:\r\n datasets = datasets.map(process_squad, batched=True, remove_columns=column_names)\r\n# Preprocessing the datasets.\r\n# Preprocessing is slighlty different for training and evaluation.\r\nif training_args.do_train:\r\n column_names = datasets[\"train\"].column_names\r\nelse:\r\n column_names = datasets[\"validation\"].column_names\r\n# print(column_names)\r\nquestion_column_name = \"question\" if \"question\" in column_names else column_names[0]\r\ncontext_column_name = \"context\" if \"context\" in column_names else column_names[1]\r\nanswer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\n```\r\n### the second section\r\n```python\r\ndef prepare_train_features(examples):\r\n # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results\r\n # in one example possible giving several features when a context is long, each of those features having a\r\n # context that overlaps a bit the context of the previous feature.\r\n tokenized_examples = tokenizer(\r\n examples[question_column_name if pad_on_right else context_column_name],\r\n examples[context_column_name if pad_on_right else question_column_name],\r\n truncation=\"only_second\" if pad_on_right else \"only_first\",\r\n max_length=data_args.max_seq_length,\r\n stride=data_args.doc_stride,\r\n return_overflowing_tokens=True,\r\n return_offsets_mapping=True,\r\n padding=\"max_length\" if data_args.pad_to_max_length else False,\r\n )\r\n\r\n # Since one example might give us several features if it has a long context, we need a map from a feature to\r\n # its corresponding example. This key gives us just that.\r\n sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n # The offset mappings will give us a map from token to character position in the original context. This will\r\n # help us compute the start_positions and end_positions.\r\n offset_mapping = tokenized_examples.pop(\"offset_mapping\")\r\n\r\n # Let's label those examples!\r\n tokenized_examples[\"start_positions\"] = []\r\n tokenized_examples[\"end_positions\"] = []\r\n\r\n for i, offsets in enumerate(offset_mapping):\r\n # We will label impossible answers with the index of the CLS token.\r\n input_ids = tokenized_examples[\"input_ids\"][i]\r\n cls_index = input_ids.index(tokenizer.cls_token_id)\r\n\r\n # Grab the sequence corresponding to that example (to know what is the context and what is the question).\r\n sequence_ids = tokenized_examples.sequence_ids(i)\r\n\r\n # One example can give several spans, this is the index of the example containing this span of text.\r\n sample_index = sample_mapping[i]\r\n answers = examples[answer_column_name][sample_index]\r\n # print(examples,answers,offset_mapping,tokenized_examples)\r\n # If no answers are given, set the cls_index as answer.\r\n if len(answers) == 0:#len(answers[\"answer_start\"]) == 0:\r\n tokenized_examples[\"start_positions\"].append(cls_index)\r\n tokenized_examples[\"end_positions\"].append(cls_index)\r\n else:\r\n # Start/end character index of the answer in the text.\r\n start_char = answers[0][\"answer_start\"]\r\n end_char = start_char + len(answers[0][\"text\"])\r\n\r\n # Start token index of the current span in the text.\r\n token_start_index = 0\r\n while sequence_ids[token_start_index] != (1 if pad_on_right else 0):\r\n token_start_index += 1\r\n\r\n # End token index of the current span in the text.\r\n token_end_index = len(input_ids) - 1\r\n while sequence_ids[token_end_index] != (1 if pad_on_right else 0):\r\n token_end_index -= 1\r\n\r\n # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):\r\n tokenized_examples[\"start_positions\"].append(cls_index)\r\n tokenized_examples[\"end_positions\"].append(cls_index)\r\n else:\r\n # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n # Note: we could go after the last offset if the answer is the last word (edge case).\r\n while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:\r\n token_start_index += 1\r\n tokenized_examples[\"start_positions\"].append(token_start_index - 1)\r\n while offsets[token_end_index][1] >= end_char:\r\n token_end_index -= 1\r\n tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n return tokenized_examples\r\n```", "I'm glad you managed to fix run_qa.py for your case :)\r\n\r\nRegarding the checksum error, I'm not able to reproduce on my side.\r\nThis errors says that the downloaded file doesn't match the expected file.\r\n\r\nCould you try running this and let me know if you get the same output as me ?\r\n```python\r\nfrom datasets.utils.info_utils import get_size_checksum_dict\r\nfrom datasets import cached_path\r\n\r\nget_size_checksum_dict(cached_path(\"https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json\"))\r\n# {'num_bytes': 30288272, 'checksum': '3527663986b8295af4f7fcdff1ba1ff3f72d07d61a20f487cb238a6ef92fd955'}\r\n```", "I run the code,and it show below:\r\n```\r\n>>> from datasets.utils.info_utils import get_size_checksum_dict\r\n>>> from datasets import cached_path\r\n>>> get_size_checksum_dict(cached_path(\"https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json\"))\r\nDownloading: 30.3MB [04:13, 120kB/s]\r\n{'num_bytes': 30288272, 'checksum': '3527663986b8295af4f7fcdff1ba1ff3f72d07d61a20f487cb238a6ef92fd955'}\r\n```", "Alright ! So in this case redownloading the file with `download_mode=\"force_redownload\"` should fix it. Can you try using `download_mode=\"force_redownload\"` again ?\r\n\r\nNot sure why it didn't work for you the first time though :/" ]

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[ "Hi ! The labels -1 correspond to the examples without gold labels in the original snli dataset.\r\nFeel free to remove these examples if you don't need them by using\r\n```python\r\ndata = data.filter(lambda x: x[\"label\"] != -1)\r\n```" ]

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[ "@lhoestq Re-added the arg. The ConnectionError in CI seems unrelated to this PR (the same test fails on master as well).", "Thanks !\r\nI'm re-running the CI, maybe this was an issue with circleCI", "Looks all good now, merged :)" ]

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[ "@lhoestq Added a test. If you can come up with a better stateful callable, I'm all ears 😄. ", "Sorry I said earlier that it was good to have it inside the loop, my mistake !", "@lhoestq Okay, did some refactoring and now the \"cache\" part comes before the for loop. Thanks for the guidance.\r\n\r\nThink this is ready for the final review." ]

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[ "Hi,\r\n\r\ntry `skiprows` instead. This part is not properly documented in the docs it seems.\r\n\r\n@lhoestq I'll fix this as part of a bigger PR that fixes typos in the docs." ]

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[ "Never mind, I ran it again and it worked this time. Strange." ]

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[ "You can pass the same `experiment_id` for all the metrics of the same group, and use another `experiment_id` for the other groups.\r\nMaybe we can add an environment variable that sets the default value for `experiment_id` ? What do you think ?", "Ah, you're absolutely correct, @lhoestq - it's exactly the equivalent of the shared secret. Thank you!" ]

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[ "Hi\r\nnot sure how change the lable after creation, but this is an issue not dataset request. thanks ", "Hi ! Currently you have to use `map` . You can see an example of how to do it in this comment: https://github.com/huggingface/datasets/issues/853#issuecomment-727872188\r\n\r\nIn the future we'll add support for a more native way of adding a new column ;)" ]

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[ "I never said the calls were hanging indefinitely, what we need is quite different - in the firewalled env with a network, there should be no network calls or they should fail instantly.\r\n\r\nTo make this work I suppose on top of this PR we need:\r\n1. `DATASETS_OFFLINE` env var to force set timeout to 0 globally (or to 0.0001 if 0 has a special meaning of no timeout)\r\n2. `DATASETS_OFFLINE` should guard against failing network calls and not fail the program if it has all the data it needs locally.\r\n\r\nBottom line - if the logic wants to check online if the local file matches online dataset name, let it go wild, but it should fail instantly, recover and use the local file - if one is specified explicitly or cache if there is one. And only if neither was found only then assert.\r\n\r\nI hope this makes sense and is doable.\r\n\r\nI have started on the same approach for transformers https://github.com/huggingface/transformers/pull/10407\r\n\r\nThank you, @lhoestq ", "Yes that was the first step to add DATASETS_OFFLINE :)\r\n\r\nWith this PR, if a request times out (which couldn't happen before because no time out was set), it falls back on the local files with no error.\r\n\r\nAs you said, setting the timeout to something like 1e-16 makes the requests fail instantly, which is one step forward. One last thing left is to disable request retries and everything will be instant !", "Ah, fantastic. Thank you for elucidating that this PR is part of a bigger master plan! ", "Merging this one, then I'll open a new PR for the `DATASETS_OFFLINE` env var :)" ]

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[ "@mariosasko This is kinda cool! " ]

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[ "Hi @gchhablani :)\r\nThanks for proposing your help !\r\n\r\nI'll be doing a refactor of some parts related to filtering in the scope of https://github.com/huggingface/datasets/issues/1877\r\nSo I would first wait for this refactor to be done before working on the filtering. In particular because I plan to make things simpler to manipulate.\r\n\r\nYour feedback on this refactor would also be appreciated since it also aims at making the core code more accessible (basically my goal is that no one's ever \"having troubles getting started\" ^^)\r\n\r\nThis will be available in a few days, I will be able to give you more details at that time if you don't mind waiting a bit !", "Sure! I don't mind waiting. I'll check the refactor and try to understand what you're trying to do :)" ]

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[ "These warnings are showed when there's a call to `.map` to say to the user that a dataset is reloaded from the cache instead of being recomputed.\r\nThey are warnings since we want to make sure the users know that it's not recomputed.", "Thank you for explaining the intention, @lhoestq \r\n\r\n1. Could it be then made more human-friendly? Currently the hex gibberish tells me nothing of what's really going on. e.g. the following is instructive, IMHO:\r\n\r\n```\r\nWARNING: wmt16/ro-en/train dataset was loaded from cache instead of being recomputed\r\nWARNING: wmt16/ro-en/validation dataset was loaded from cache instead of being recomputed\r\nWARNING: wmt16/ro-en/test dataset was loaded from cache instead of being recomputed\r\n```\r\nnote that it removes the not so useful hex info and tells the user instead which split it's referring to - but probably no harm in keeping the path if it helps the debug. But the key is that now the warning is telling me what it is it's warning me about.\r\n```\r\nWarning:Loading cache path\r\n```\r\non the other hand isn't telling what it is warning about.\r\n\r\nAnd I still suggest this is INFO level, otherwise you need to turn all 'using cache' statements to WARNING to be consistent. The user is most likely well aware the cache is used for models, etc. So this feels very similar.\r\n\r\n2. Should there be a way for a user to void warranty by having a flag - `I know I'm expecting the cached version to load if it's available - please do not warn me about it=True`\r\n\r\nTo explain the need: Warnings are a problem, they constantly take attention away because they could be the harbinger of a problem. Therefore I prefer not to have any warnings in the log, and if I get any I usually try to deal with those so that my log is clean. \r\n\r\nIt's less of an issue for somebody doing long runs. It's a huge issue for someone who does a new run every few minutes and on the lookout for any potential problems which is what I have been doing a lot of integrating DeepSpeed and other things. And since there are already problems to deal with during the integration it's nice to have a clean log to start with. \r\n\r\nI hope my need is not unreasonable and I was able to explain it adequately. \r\n\r\nThank you." ]

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[ "@lhoestq question about public API: `keep_in_memory` or just `in_memory`?", "For consistence I'd say `keep_in_memory`, but no strong opinion.", "@lhoestq done!" ]

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[ "sorry my mistake, datasets were overwritten closing now, thanks a lot" ]

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[ "I updated your suggestions. Thank you very much for your support. @lhoestq ", "> Thanks for changing to ClassLabel :)\r\n> This is all good now !\r\n> \r\n> However I can see changes in other files than the ones for interpress_news_category_tr_lite, can you please fix that ?\r\n> To do so you can create another branch and another PR to only include the interpress_news_category_tr_lite files.\r\n> \r\n> Maybe this happened because of a git rebase ? Once you've already pushed your code, please use git merge instead of rebase in order to avoid this.\r\n\r\nThanks for the feedback.\r\nNew PR https://github.com/huggingface/datasets/pull/1967" ]

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[ "Thanks @lhoestq. I was trying to follow @thomwolf suggestion about integrating that script but as `from_json` method...\r\n> Note that I don't think this is necessary a breaking change, we can still keep the old scripts around\r\n\r\nDo you think there is a better way of doing it?\r\n\r\nI was trying to implement more or less the same logic as in the script, but I confess I assumed the target was in-memory only...", "Basically, I was trying to reimplement `Json(datasets.ArrowBasedBuilder)._generate_tables`, and no writing to arrow file (I assumed only in-memory usage). I started with the first \"else\" clause... \r\n\r\nI was planning to remove my `_cast_table_to_info_features` and use `paj.read_json(parse_options=...)` instead (like in the script).", "@lhoestq I am wondering why `keep_in_memory` has no effect for JSON...", "What's the issue exactly ? Apparently it's correctly passed to as_dataset so I don't find the issue", "Nevermind @lhoestq, I found where the problem was in my code... I push!", "<s>merging master into this branch should fix the CI issue :)</s>\r\n\r\nOops I didn't refresh the page sorry ^^'\r\n\r\nLooks all good !", "Good job ! I think we can merge after the last changes regarding the error message and the docstring above :)", "@lhoestq Done! And I have also added some tests for the `field` parameter.", "Let me add some more tests for dict of lists JSON file, please.", "@lhoestq done! ;)", "We can merge. Additional work will be done in another PR. ;)" ]

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[ "Hi !\r\n\r\nThe cache at `~/.cache/huggingface/metrics` stores the users data for metrics computations (hence the arrow files).\r\n\r\nHowever python modules (i.e. dataset scripts, metric scripts) are stored in `~/.cache/huggingface/modules/datasets_modules`.\r\n\r\nIn particular the metrics are cached in `~/.cache/huggingface/modules/datasets_modules/metrics/`\r\n\r\nFeel free to take a look at your cache and let me know if you find any issue that would help explaining why you had an issue with `rouge` with no connection. I'm doing some tests on my side to try to reproduce the issue you have\r\n", "Thank you for clarifying that the metrics files are to be found elsewhere, @lhoestq \r\n\r\n> The cache at ~/.cache/huggingface/metrics stores the users data for metrics computations (hence the arrow files).\r\n\r\ncould it be renamed to reflect that? otherwise it misleadingly suggests that it's the metrics. Perhaps `~/.cache/huggingface/metrics-user-data`?\r\n\r\nAnd there are so many `.lock` files w/o corresponding files under `~/.cache/huggingface/metrics/`. Why are they there? \r\n\r\nfor example after I wipe out the dir completely and do one training I end up with:\r\n```\r\n~/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow.lock\r\n```\r\nwhat is that lock file locking when nothing is running?", "The lock files come from an issue with filelock (see comment in the code [here](https://github.com/benediktschmitt/py-filelock/blob/master/filelock.py#L394-L398)). Basically on unix there're always .lock files left behind. I haven't dove into this issue", "are you sure you need an external lock file? if it's a single purpose locking in the same scope you can lock the caller `__file__` instead, e.g. here is how one can `flock` the script file itself to ensure atomic printing:\r\n\r\n```\r\nimport fcntl\r\ndef printflock(*msgs):\r\n \"\"\" print in multiprocess env so that the outputs from different processes don't get interleaved \"\"\"\r\n with open(__file__, \"r\") as fh:\r\n fcntl.flock(fh, fcntl.LOCK_EX)\r\n try:\r\n print(*msgs)\r\n finally:\r\n fcntl.flock(fh, fcntl.LOCK_UN)\r\n```\r\n", "OK, this issue is not about caching but some internal conflict/race condition it seems, I have just run into it on my normal env:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py\", line 356, in _finalize\r\n self.data = Dataset(**reader.read_files([{\"filename\": f} for f in file_paths]))\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/arrow_reader.py\", line 236, in read_files\r\n pa_table = self._read_files(files, in_memory=in_memory)\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/arrow_reader.py\", line 171, in _read_files\r\n pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/arrow_reader.py\", line 322, in read_table\r\n stream = stream_from(filename)\r\n File \"pyarrow/io.pxi\", line 782, in pyarrow.lib.memory_map\r\n File \"pyarrow/io.pxi\", line 743, in pyarrow.lib.MemoryMappedFile._open\r\n File \"pyarrow/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 97, in pyarrow.lib.check_status\r\nFileNotFoundError: [Errno 2] Failed to open local file '/home/stas/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow'. Detail: [errno 2] No such file or directory\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"examples/seq2seq/run_seq2seq.py\", line 655, in <module>\r\n main()\r\n File \"examples/seq2seq/run_seq2seq.py\", line 619, in main\r\n test_results = trainer.predict(\r\n File \"/mnt/nvme1/code/huggingface/transformers-master/src/transformers/trainer_seq2seq.py\", line 121, in predict\r\n return super().predict(test_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)\r\n File \"/mnt/nvme1/code/huggingface/transformers-master/src/transformers/trainer.py\", line 1706, in predict\r\n output = self.prediction_loop(\r\n File \"/mnt/nvme1/code/huggingface/transformers-master/src/transformers/trainer.py\", line 1813, in prediction_loop\r\n metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))\r\n File \"examples/seq2seq/run_seq2seq.py\", line 556, in compute_metrics\r\n result = metric.compute(predictions=decoded_preds, references=decoded_labels)\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py\", line 388, in compute\r\n self._finalize()\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py\", line 358, in _finalize\r\n raise ValueError(\r\nValueError: Error in finalize: another metric instance is already using the local cache file. Please specify an experiment_id to avoid colision between distributed metric instances.\r\n```\r\n\r\nI'm just running `run_seq2seq.py` under DeepSpeed:\r\n\r\n```\r\nexport BS=16; rm -r output_dir; PYTHONPATH=src USE_TF=0 CUDA_VISIBLE_DEVICES=0,1 deepspeed --num_gpus=2 examples/seq2seq/run_seq2seq.py --model_name_or_path t5-small --output_dir output_dir --adam_eps 1e-06 --do_eval --do_train --do_predict --evaluation_strategy=steps --label_smoothing 0.1 --learning_rate 3e-5 --logging_first_step --logging_steps 1000 --max_source_length 128 --max_target_length 128 --num_train_epochs 1 --overwrite_output_dir --per_device_eval_batch_size $BS --per_device_train_batch_size $BS --predict_with_generate --eval_steps 25000 --sortish_sampler --task translation_en_to_ro --val_max_target_length 128 --warmup_steps 500 --max_train_samples 100 --max_val_samples 100 --max_test_samples 100 --dataset_name wmt16 --dataset_config ro-en --source_prefix \"translate English to Romanian: \" --deepspeed examples/tests/deepspeed/ds_config.json\r\n```\r\n\r\nIt finished the evaluation OK and crashed on the prediction part of the Trainer. But the eval / predict parts no longer run under Deepspeed, it's just plain ddp.\r\n\r\nIs this some kind of race condition? It happens intermittently - there is nothing else running at the same time.\r\n\r\nBut if 2 independent instances of the same script were to run at the same time it's clear to see that this problem would happen. Perhaps it'd help to create a unique hash which is shared between all processes in the group and use that as the default experiment id?\r\n", "When you're using metrics in a distributed setup, there are two cases:\r\n1. you're doing two completely different experiments (two evaluations) and the 2 metrics jobs have nothing to do with each other\r\n2. you're doing one experiment (one evaluation) but use multiple processes to feed the data to the metric.\r\n\r\nIn case 1. you just need to provide two different `experiment_id` so that the metrics don't collide.\r\nIn case 2. they must have the same experiment_id (or use the default one), but in this case you also need to provide the `num_processes` and `process_id`\r\n\r\nIf understand correctly you're in situation 2.\r\n\r\nIf so, you make sure that you instantiate the metrics with both the right `num_processes` and `process_id` parameters ?\r\n\r\nIf they're not set, then the cache files of the two metrics collide it can cause issues. For example if one metric finishes before the other, then the cache file is deleted and the other metric gets a FileNotFoundError\r\nThere's more information in the [documentation](https://huggingface.co/docs/datasets/loading_metrics.html#distributed-setups) if you want\r\n\r\nHope that helps !", "Thank you for explaining that in a great way, @lhoestq \r\n\r\nSo the bottom line is that the `transformers` examples are broken since they don't do any of that. At least `run_seq2seq.py` just does `metric = load_metric(metric_name)`\r\n\r\nWhat test would you recommend to reliably reproduce this bug in `examples/seq2seq/run_seq2seq.py`?", "To give more context, we are just using the metrics for the `comput_metric` function and nothing else. Is there something else we can use that just applies the function to the full arrays of predictions and labels? Because that's all we need, all the gathering has already been done because the datasets Metric multiprocessing relies on file storage and thus does not work in a multi-node distributed setup (whereas the Trainer does).\r\n\r\nOtherwise, we'll have to switch to something else to compute the metrics :-(", "OK, it definitely leads to a race condition in how it's used right now. Here is how you can reproduce it - by injecting a random sleep time different for each process before the locks are acquired. \r\n```\r\n--- a/src/datasets/metric.py\r\n+++ b/src/datasets/metric.py\r\n@@ -348,6 +348,16 @@ class Metric(MetricInfoMixin):\r\n\r\n elif self.process_id == 0:\r\n # Let's acquire a lock on each node files to be sure they are finished writing\r\n+\r\n+ import time\r\n+ import random\r\n+ import os\r\n+ pid = os.getpid()\r\n+ random.seed(pid)\r\n+ secs = random.randint(1, 15)\r\n+ time.sleep(secs)\r\n+ print(f\"sleeping {secs}\")\r\n+\r\n file_paths, filelocks = self._get_all_cache_files()\r\n\r\n # Read the predictions and references\r\n@@ -385,7 +395,10 @@ class Metric(MetricInfoMixin):\r\n\r\n if predictions is not None:\r\n self.add_batch(predictions=predictions, references=references)\r\n+ print(\"FINALIZE START\")\r\n+\r\n self._finalize()\r\n+ print(\"FINALIZE END\")\r\n\r\n self.cache_file_name = None\r\n self.filelock = None\r\n```\r\n\r\nthen run with 2 procs: `python -m torch.distributed.launch --nproc_per_node=2`\r\n```\r\nexport BS=16; rm -r output_dir; PYTHONPATH=src USE_TF=0 CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 examples/seq2seq/run_seq2seq.py --model_name_or_path t5-small --output_dir output_dir --adam_eps 1e-06 --do_eval --do_train --do_predict --evaluation_strategy=steps --label_smoothing 0.1 --learning_rate 3e-5 --logging_first_step --logging_steps 1000 --max_source_length 128 --max_target_length 128 --num_train_epochs 1 --overwrite_output_dir --per_device_eval_batch_size $BS --per_device_train_batch_size $BS --predict_with_generate --eval_steps 25000 --sortish_sampler --task translation_en_to_ro --val_max_target_length 128 --warmup_steps 500 --max_train_samples 10 --max_val_samples 10 --max_test_samples 10 --dataset_name wmt16 --dataset_config ro-en --source_prefix \"translate English to Romanian: \"\r\n```\r\n\r\n```\r\n***** Running Evaluation *****\r\n Num examples = 10\r\n Batch size = 16\r\n 0%| | 0/1 [00:00<?, ?it/s]FINALIZE START\r\nFINALIZE START\r\nsleeping 11\r\nFINALIZE END\r\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:11<00:00, 11.06s/it]\r\nsleeping 11\r\nTraceback (most recent call last):\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py\", line 368, in _finalize\r\n self.data = Dataset(**reader.read_files([{\"filename\": f} for f in file_paths]))\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/arrow_reader.py\", line 236, in read_files\r\n pa_table = self._read_files(files, in_memory=in_memory)\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/arrow_reader.py\", line 171, in _read_files\r\n pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/arrow_reader.py\", line 322, in read_table\r\n stream = stream_from(filename)\r\n File \"pyarrow/io.pxi\", line 782, in pyarrow.lib.memory_map\r\n File \"pyarrow/io.pxi\", line 743, in pyarrow.lib.MemoryMappedFile._open\r\n File \"pyarrow/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 97, in pyarrow.lib.check_status\r\nFileNotFoundError: [Errno 2] Failed to open local file '/home/stas/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow'. Detail: [errno 2] No such file or directory\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"examples/seq2seq/run_seq2seq.py\", line 645, in <module>\r\n main()\r\n File \"examples/seq2seq/run_seq2seq.py\", line 601, in main\r\n metrics = trainer.evaluate(\r\n File \"/mnt/nvme1/code/huggingface/transformers-mp-pp/src/transformers/trainer_seq2seq.py\", line 74, in evaluate\r\n return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)\r\n File \"/mnt/nvme1/code/huggingface/transformers-mp-pp/src/transformers/trainer.py\", line 1703, in evaluate\r\n output = self.prediction_loop(\r\n File \"/mnt/nvme1/code/huggingface/transformers-mp-pp/src/transformers/trainer.py\", line 1876, in prediction_loop\r\n metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))\r\n File \"examples/seq2seq/run_seq2seq.py\", line 556, in compute_metrics\r\n result = metric.compute(predictions=decoded_preds, references=decoded_labels)\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py\", line 402, in compute\r\n self._finalize()\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py\", line 370, in _finalize\r\n raise ValueError(\r\nValueError: Error in finalize: another metric instance is already using the local cache file. Please specify an experiment_id to avoid colision between distributed metric instances.\r\n```", "I tried to adjust `run_seq2seq.py` and trainer to use the suggested dist env:\r\n```\r\n import torch.distributed as dist\r\n metric = load_metric(metric_name, num_process=dist.get_world_size(), process_id=dist.get_rank())\r\n```\r\nand in `trainer.py` added to call just for rank 0:\r\n```\r\n if self.is_world_process_zero() and self.compute_metrics is not None and preds is not None and label_ids is not None:\r\n metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))\r\n```\r\nand then the process hangs in a deadlock. \r\n\r\nHere is the tb:\r\n```\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/filelock.py\", line 275 in acquire\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py\", line 306 in _check_all_processes_locks\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py\", line 501 in _init_writer\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py\", line 440 in add_batch\r\n File \"/mnt/nvme1/code/huggingface/datasets-master/src/datasets/metric.py\", line 397 in compute\r\n File \"examples/seq2seq/run_seq2seq.py\", line 558 in compute_metrics\r\n File \"/mnt/nvme1/code/huggingface/transformers-mp-pp/src/transformers/trainer.py\", line 1876 in prediction_loop\r\n File \"/mnt/nvme1/code/huggingface/transformers-mp-pp/src/transformers/trainer.py\", line 1703 in evaluate\r\n File \"/mnt/nvme1/code/huggingface/transformers-mp-pp/src/transformers/trainer_seq2seq.py\", line 74 in evaluate\r\n File \"examples/seq2seq/run_seq2seq.py\", line 603 in main\r\n File \"examples/seq2seq/run_seq2seq.py\", line 651 in <module>\r\n```\r\n\r\nBut this sounds right, since in the above diff I set up a distributed metric and only called one process - so it's blocking on waiting for other processes to do the same.\r\n\r\nSo one working solution is to leave:\r\n\r\n```\r\n metric = load_metric(metric_name)\r\n```\r\nalone, and only call `compute_metrics` from rank 0\r\n```\r\n if self.is_world_process_zero() and self.compute_metrics is not None and preds is not None and label_ids is not None:\r\n metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))\r\n```\r\n\r\nso we now no longer use the distributed env as far as `datasets` is concerned, it's just a single process.\r\n\r\nAre there any repercussions/side-effects to this proposed change in Trainer? If it always gathers all inputs on rank 0 then this is how it should have been done in first place - i.e. only run for rank 0. It appears that currently it was re-calculating the metrics on all processes on the same data just to throw the results away other than for rank 0. Unless I missed something.\r\n", "But no, since \r\n`\r\n metric = load_metric(metric_name)\r\n`\r\nis called for each process, the race condition is still there. So still getting:\r\n\r\n```\r\nValueError: Error in finalize: another metric instance is already using the local cache file. Please specify an experiment_id to avoid colision between distributed metric instances.\r\n```\r\n\r\ni.e. the only way to fix this is to `load_metric` only for rank 0, but this requires huge changes in the code and all end users' code.\r\n", "OK, here is a workaround that works. The onus here is absolutely on the user:\r\n\r\n```\r\ndiff --git a/examples/seq2seq/run_seq2seq.py b/examples/seq2seq/run_seq2seq.py\r\nindex 2a060dac5..c82fd83ea 100755\r\n--- a/examples/seq2seq/run_seq2seq.py\r\n+++ b/examples/seq2seq/run_seq2seq.py\r\n@@ -520,7 +520,11 @@ def main():\r\n\r\n # Metric\r\n metric_name = \"rouge\" if data_args.task.startswith(\"summarization\") else \"sacrebleu\"\r\n- metric = load_metric(metric_name)\r\n+ import torch.distributed as dist\r\n+ if dist.is_initialized():\r\n+ metric = load_metric(metric_name, num_process=dist.get_world_size(), process_id=dist.get_rank())\r\n+ else:\r\n+ metric = load_metric(metric_name)\r\n\r\n def postprocess_text(preds, labels):\r\n preds = [pred.strip() for pred in preds]\r\n@@ -548,12 +552,17 @@ def main():\r\n # Some simple post-processing\r\n decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)\r\n\r\n+ kwargs = dict(predictions=decoded_preds, references=decoded_labels)\r\n+ if metric_name == \"rouge\":\r\n+ kwargs.update(use_stemmer=True)\r\n+ result = metric.compute(**kwargs) # must call for all processes\r\n+ if result is None: # only process with rank-0 will return metrics, others None\r\n+ return {}\r\n+\r\n if metric_name == \"rouge\":\r\n- result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)\r\n # Extract a few results from ROUGE\r\n result = {key: value.mid.fmeasure * 100 for key, value in result.items()}\r\n else:\r\n- result = metric.compute(predictions=decoded_preds, references=decoded_labels)\r\n result = {\"bleu\": result[\"score\"]}\r\n\r\n prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]\r\n```\r\n\r\nThis is not user-friendly to say the least. And it's still wasteful as we don't need other processes to do anything.\r\n\r\nBut it solves the current race condition.\r\n\r\nClearly this calls for a design discussion as it's the responsibility of the Trainer to handle this and not user's. Perhaps in the `transformers` land?", "I don't see how this could be the responsibility of `Trainer`, who hasn't the faintest idea of what a `datasets.Metric` is. The trainer takes a function `compute_metrics` that goes from predictions + labels to metric results, there is nothing there. That computation is done on all processes \r\n\r\nThe fact a `datasets.Metric` object cannot be used as a simple compute function in a multi-process environment is, in my opinion, a bug in `datasets`. Especially since, as I mentioned before, the multiprocessing part of `datasets.Metric` has a deep flaw since it can't work in a multinode environment. So you actually need to do the job of gather predictions and labels yourself.\r\n\r\nThe changes you are proposing Stas are making the code less readable and also concatenate all the predictions and labels `number_of_processes` times I believe, which is not going to make the metric computation any faster.\r\n\r\n", "Right, to clarify, I meant it'd be good to have it sorted on the library side and not requiring the user to figure it out. This is too complex and error-prone and if not coded correctly the bug will be intermittent which is even worse.\r\n\r\nOh I guess I wasn't clear in my message - in no way I'm proposing that we use this workaround code - I was just showing what I had to do to make it work.\r\n\r\nWe are on the same page.\r\n\r\n> The changes you are proposing Stas are making the code less readable and also concatenate all the predictions and labels number_of_processes times I believe, which is not going to make the metric computation any faster.\r\n\r\nAnd yes, this is another problem that my workaround introduces. Thank you for pointing it out, @sgugger \r\n", "> The fact a datasets.Metric object cannot be used as a simple compute function in a multi-process environment is, in my opinion, a bug in datasets\r\n\r\nYes totally, this use case is supposed to be supported by `datasets`. And in this case there shouldn't be any collision between the metrics. I'm looking into it :)\r\nMy guess is that at one point the metric isn't using the right file name. It's supposed to use one with a unique uuid in order to avoid the collisions.", "I just opened #1966 to fix this :)\r\n@stas00 if have a chance feel free to try it !", "Thank you, @lhoestq - I will experiment and report back. \r\n\r\nedit: It works! Thank you" ]

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[ "Thanks for reporting ! I'm taking a look", "Index training was missing, I fixed it here: https://github.com/huggingface/datasets/commit/f5986c46323583989f6ed1dabaf267854424a521\r\n\r\nCan you try again please ?", "Works great 👍 I just put a minor comment on the commit, I think you meant to pass the `train_size` from the one obtained from the config.\r\n\r\nThanks for a quick response!" ]

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[ "Thanks for the report !\r\n\r\nCurrently we don't have a way to let the user easily disable this behavior.\r\nHowever I agree that we should support stateful processing functions, ideally by removing `does_function_return_dict`.\r\n\r\nWe needed this function in order to know whether the `map` functions needs to write data or not. if `does_function_return_dict` returns False then we don't write anything.\r\n\r\nInstead of checking the output of the processing function outside of the for loop that iterates through the dataset to process it, we can check the output of the first processed example and at that point decide if we need to write data or not.\r\n\r\nTherefore it's definitely possible to fix this unwanted behavior, any contribution going into this direction is welcome :)", "Thanks @mariosasko for the PR!" ]

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[ "Thanks for reporting and for all the details and suggestions.\r\n\r\nI'm totally in favor of having a HF_DATASETS_OFFLINE env variable to disable manually all the connection checks, remove retries etc.\r\n\r\nMoreover you may know that the use case that you are mentioning is already supported from `datasets` 1.3.0, i.e. you already can:\r\n- first load datasets and metrics from an instance with internet connection\r\n- then be able to reload datasets and metrics from another instance without connection (as long as the filesystem is shared)\r\n\r\nThis is already implemented, but currently it only works if the requests return a `ConnectionError` (or any error actually). Not sure why it would hang instead of returning an error.\r\n\r\nMaybe this is just a issue with the timeout value being not set or too high ?\r\nIs there a way I can have access to one of the instances on which there's this issue (we can discuss this offline) ?\r\n", "I'm on master, so using all the available bells and whistles already.\r\n\r\nIf you look at the common issues - it for example tries to look up files if they appear in `_PACKAGED_DATASETS_MODULES` which it shouldn't do.\r\n\r\n--------------\r\n\r\nYes, there is a nuance to it. As I mentioned it's firewalled - that is it has a network but making any calls outside - it just hangs in:\r\n\r\n```\r\nsin_addr=inet_addr(\"xx.xx.xx.xx\")}, [28->16]) = 0\r\nclose(5) = 0\r\nsocket(AF_INET, SOCK_STREAM|SOCK_CLOEXEC, IPPROTO_TCP) = 5\r\nconnect(5, {sa_family=AF_INET, sin_port=htons(3128), sin_addr=inet_addr(\"yy.yy.yy.yy\")}, 16^C) = ? ERESTARTSYS (To be restarted if SA_RESTART is set)\r\n```\r\nuntil it times out.\r\n\r\nThat's why we need to be able to tell the software that there is no network to rely on even if there is one (good for testing too).\r\n\r\nSo what I'm thinking is that this is a simple matter of pre-ambling any network call wrappers with:\r\n\r\n```\r\nif HF_DATASETS_OFFLINE:\r\n assert \"Attempting to make a network call under Offline mode\"\r\n```\r\n\r\nand then fixing up if there is anything else to fix to make it work.\r\n\r\n--------------\r\n\r\nOtherwise I think the only other problem I encountered is that we need to find a way to pre-cache metrics, for some reason it's not caching it and wanting to fetch it from online.\r\n\r\nWhich is extra strange since it already has those files in the `datasets` repo itself that is on the filesystem.\r\n\r\nThe workaround I had to do is to copy `rouge/rouge.py` (with the parent folder) from the datasets repo to the current dir - and then it proceeded.", "Ok understand better the hanging issue.\r\nI guess catching connection errors is not enough, we should also avoid all the hangings.\r\nCurrently the offline mode tests are only done by simulating an instant connection fail that returns an error, let's have another connection mock that hangs instead.\r\n\r\nI'll also take a look at why you had to do this for `rouge`.\r\n", "FWIW, I think instant failure on the behalf of a network call is the simplest solution to correctly represent the environment and having the caller to sort it out is the next thing to do, since here it is the case of having no functional network, it's just that the software doesn't know this is the case, because there is some network. So we just need to help it to bail out instantly rather than hang waiting for it to time out. And afterwards everything else you said.", "Update on this: \r\n\r\nI managed to create a mock environment for tests that makes the connections hang until timeout.\r\nI managed to reproduce the issue you're having in this environment.\r\n\r\nI'll update the offline test cases to also test the robustness to connection hangings, and make sure we set proper timeouts where it's needed in the code. This should cover the _automatic_ section you mentioned.", "Fabulous! I'm glad you were able to reproduce the issues, @lhoestq!", "I lost access to the firewalled setup, but I emulated it with:\r\n\r\n```\r\nsudo ufw enable\r\nsudo ufw default deny outgoing\r\n```\r\n(thanks @mfuntowicz)\r\n\r\nI was able to test `HF_DATASETS_OFFLINE=1` and it worked great - i.e. didn't try to reach out with it and used the cached files instead.\r\n\r\nThank you!" ]

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[ "Facing the same issue for [Squad](https://huggingface.co/datasets/viewer/?dataset=squad) and [TriviaQA](https://huggingface.co/datasets/viewer/?dataset=trivia_qa) datasets as well.", "We just fixed the issue, thanks for reporting !" ]

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[ "Thanks ! could you maybe add a few tests in test_arrow_dataset.py to make sure from_pandas works as expected with categorical types ?\r\n\r\nIn particular I'm pretty sure that if you now try to `cast` the dataset to the same features at its current features, it will break instead of just being a no-op.\r\nThis is because `features.type` returns an arrow int64 type for the classlabel column instead of the arrow dictionary type that you have in the arrow table. There are two issues in this case:\r\n- it will try to replace the arrow type from dictionary to int64 instead of being a no-op\r\n- it will crash because pyarrow is not able to cast a dictionary to int64 (even if it's actually possible do cast the column by hand by accessing the sub-array of the dictionary array containing the indices/integers)\r\n\r\nIt would be awesome to fix this case ! Ideally the arrow `pa_type` of classlabel ([here](https://github.com/huggingface/datasets/blob/7072e1becd69d421d863374b825e3da4c6551798/src/datasets/features.py#L558)) should be an arrow dictionary type. This should fix the issue. Then we can start working on backward compatibility.\r\n\r\nLet me know if you have questions or if I can help.\r\nIn particular if there is some glue-ing to do I can take care of that if you want ;)\r\n\r\n--------------\r\n\r\nAlso just a few information regarding the functions you mentioned\r\n\r\n`int2str` and `str2int` are used by users to transforms the labels if they want to. Here sine ClassLabel is instantiated without the class names, they would crash. I was about to make a PR to disallow the creation of an empty ClassLabel feature type.\r\nTherefore can you provide class_names= when creating the ClassLabel ?\r\n\r\n`encode_example` is mostly used with a dataset builder (e.g. squad.py) so it's not used when using .from_pandas.\r\n\r\n\r\n", "Got it - that's super helpful, I was trying to figure out what would break!\r\n\r\nI think there are two issues we're discussing here:\r\n\r\n1. modifying the pa_type of ClassLabel: totally agree with you on that one if that's OK from a back-compat perspective. (i.e. are users of `datasets` not supposed to access or use the .pa_type attribute of ClassLabel?)\r\n2. creating a ClassLabel requires information that's not present on the pa.DictionaryType object: I think the crux of the problem is that at this line (https://github.com/huggingface/datasets/pull/1936/files#diff-54081ede051fd0a7ef65748c481cc06f90209f01bb89968747089d13a2ca052bR933) - you only have access to the `pa_type`, which is `DictionaryType[int8, string]`. I've unpacked it and looked at all of the available methods, and I don't believe that any of the actual values (\"names\") are present - those are stored on the `pyarrow.DictArray.dictionary` attribute (i.e. as data, not on the pyarrow.DataType) - so in order to actually be able to instantiate the ClassLabel with the names= parameter, we need to pass in more information to this method.\r\n\r\nWe *could* mostly accomplish this by modifying https://github.com/huggingface/datasets/pull/1936/files#diff-54081ede051fd0a7ef65748c481cc06f90209f01bb89968747089d13a2ca052bR909 to accept a pyarrow Table in addition to the type, and it's not too difficult to do, but it feels a little bit off to me:\r\n\r\n- It feels a bit off that a \"schema\" definition will change depending on what data gets added to the dataset. In particular, if someone adds rows or concatenates two datasets, the ClassLabel \"names\" will also need to change, right? I think maybe we're getting around this because a Dataset is immutable (I think?) and so any new dataset is freshly constructed, but for example - I think this check wouldn't work for `ClassLabel`s if we were to compare the `Dataset.features` instead of the underlying pyarrow type https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_dataset.py#L2664\r\n- To that end I wonder if ClassLabel should actually just be the \"type\" akin to Category, and the \"names\" should be considered \"data\" and not part of the \"type\"? Similar to how pyarrow maintains two data objects - the array of indices and the array of string values.\r\n\r\nWith that in mind, I'm wondering if you *should* allow an empty ClassLabel (and`int2str`, etc. can be updated to have more descriptive error messages if labels aren't provided or inferred), and if the underlying data is a pa.DictionaryType, then the names can be inferred and applied at these points in the code:\r\n- https://github.com/huggingface/datasets/blob/96578adface7e4bc1f3e8bafbac920d72ca1ca60/src/datasets/arrow_dataset.py#L274\r\n- https://github.com/huggingface/datasets/blob/96578adface7e4bc1f3e8bafbac920d72ca1ca60/src/datasets/arrow_dataset.py#L686\r\n- https://github.com/huggingface/datasets/blob/96578adface7e4bc1f3e8bafbac920d72ca1ca60/src/datasets/arrow_dataset.py#L673\r\n\r\nI think perhaps the mismatch here is when the data is stored on disk as an int there should be a convenient way of saying \"this is a dictionary and here are some explicitly provided labels\", whereas when it's stored as a string, we'd ideally like to say \"this is a Category and please condense the representation and automatically infer the labels\".\r\n\r\nSorry for the long comment! Hopefully my thoughts make sense - thanks for taking the time to discuss!", "Yes that makes sense. I completely forgot that the label names of an arrow Dictionary type were not stored in the type but in the DictionaryArray.\r\n\r\nThis is made me realize that it's actually pretty unpractical and I feel that handling this can add unnecessary complexity in the handling of dtypes.\r\nMore specifically:\r\n- it's not possible to create a DictionaryArray from a call to pyarrow.array with python objects, which is the function we use to convert python objects to pyarrow objects (or we would need to convert the python objects to pandas categorical series beforehand but it doesn't work for nested types)\r\n- casting nested types containing Dictionary types would require a lot of array manipulations since it's not compatible with pyarrow.array.cast\r\n\r\nI feel like the original feature request (support of pandas Categorical) should be addressable without adding so much complexity to the library.\r\n\r\nIf we admit that we don't want to deal with arrow Dictionary type, maybe we can simply convert the pandas categorical series to an int64 series and set the feature type to the right ClassLabel in `from_pandas`. We can have the reverse operation in `to_pandas`. This way we don't need to support the arrow DictionaryType and so we can keep simple/accessible code for conversion from python to arrow and also for type casting. Let me know what you think.\r\n\r\nIn the future depending on the usage of the ClassLabel types with pandas/pyarrow we might reconsider this but for now I believe this simple solution is enough.", "I like that idea! Let me try working up a PR for this", "OK! I just whipped up the `from_pandas()` portion of this PR, and it works, though I'm not *super* familiar with the available APIs so I'm not sure if there's a more \"vectorized\" way of doing all of these updates - so happy to get some feedback and iterate!\r\n\r\nApologies for multiple commits - I realized how to solve a few different problems right after I gave up and pushed with the intent to ask for help :-)\r\n\r\nI wanted to get some guidance on how to handle the reverse direction: I think there are two main areas to look at, `.to_pandas()` and also `.set_format('pandas')` and then pulling out a dataframe like so: `dataset[:]`. Is there a single place where I can handle both of these cases at once or do these need to be handled independently?", "Thanks ! This is awesome :) \r\nCould you also add a test ? There is already `test_to_pandas` in test_arrow_dataset.py\r\nFeel free to complete this test to make sure it works for Categorical :)\r\n\r\nTo make it work with the \"pandas\" formating (when you do `set_format(\"pandas\")` and then query `dataset[0]`, `dataset[:]`, etc.), you can take a look and the `PandasFormatter` in formatting.py\r\nIt takes a pyarrow table as input of its formatting methods (one method for rows, one for columns and one for batches) and returns a pandas DataFrame (or a Series for the method for formatting a column). You can cast to Categorical in each one of the formatter methods and it should work directly when you use a pandas-formatted dataset.\r\n\r\nThis formatter can then also be used in `to_pandas` (currently it does `pa_table.to_pandas()` but `PandasFormatter().format_batch(pa_table)` can be used instead)." ]

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[ "@patrickvonplaten \r\nI removed the mp3 files, dummy_data is much smaller now!" ]

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[ "Dataset added in release [1.5.0](https://github.com/huggingface/datasets/releases/tag/1.5.0), I think I can close this." ]

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[ "Hi, it seems I am somewhat stuck here. The failed test `ci/circleci: run_dataset_script_tests_pyarrow_1_WIN` seems to be caused by some broken connection (`ConnectionResetError: [WinError 10054] An existing connection was forcibly closed by the remote host`). Any help on this is appreciated. \r\n\r\nEdit: Seems to be resolved now.", "I guess the `dummy_data.zip` is too large. I can reduce the languages that are contained there, but when testing it, it obviously throws an error, as not all files can be found. I guess I can either i) change the default value regarding which languages are loaded or ii) let the `_generate_examples` silently skip any language for which it cannot find files. Both solutions are not really pretty - is there another way around this?", "Thanks for the review and the constructive comments :) ! I tried to address them, and reduced the number of lines in the dummy data to 1 to reduce its size. " ]

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[ "Hi @JieyuZhao ! Have you had a chance to add the different configurations ?\r\nThanks again for your help on this !", "> Hi @JieyuZhao ! Have you had a chance to add the different configurations ?\r\n> Thanks again for your help on this !\r\n\r\nHi @lhoestq Yes, I've updated the code. Now the configuration will have dev/test splits.", "> Cool thanks !\r\n> This looks perfect this way.\r\n> \r\n> Now we just need to update the dataset_infos.json (it contains the metadata of the dataset) and add dummy data to be able to test this script automatically.\r\n> \r\n> To update the dataset_infos.json you just need delete the current one at `./datasets/wino_biais/dataset_infos.json`, and then run this command:\r\n> \r\n> ```\r\n> datasets-cli test ./datasets/wino_biais --save_infos --all_configs --ignore_verifications\r\n> ```\r\n> \r\n> To add the dummy data there's also a tool to add them automatically.\r\n> First delete the folder at `./datasets/wino_biais/dummy` and then run\r\n> \r\n> ```\r\n> datasets-cli dummy_data ./datasets/wino_biais --auto_generate --match_text_files \"*conll\" --n_lines 15\r\n> ```\r\n> \r\n> Let me know if you have questions :)\r\n> Also don't forget to run `make style` to format the code properly.\r\n\r\nThanks for the instruction! I've updated the metadata and the dummy data and also do the formatting. Please let me know if more is needed. :)" ]

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[ "@lhoestq Thanks for the quick review.", "I merged master to this branch to re-run the CI before merging :)" ]

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[ "Hi @lhoestq ,\r\n\r\nI am running into an issue now when trying to run RAG. Running exactly as described [here](https://huggingface.co/facebook/rag-token-nq?fbclid=IwAR3bTfhls5U_t9DqsX2Vzb7NhtRHxJxfQ-uwFT7VuCPMZUM2AdAlKF_qkI8#usage) I get the error below. Wondering if it's related to this.\r\n\r\nRunning Transformers 4.3.2 with datasets installed from source from `master` branch.\r\n\r\n```bash\r\n(venv) sergey_mkrtchyan datasets (master) $ python\r\nPython 3.8.6 (v3.8.6:db455296be, Sep 23 2020, 13:31:39)\r\n[Clang 6.0 (clang-600.0.57)] on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration\r\n>>> tokenizer = RagTokenizer.from_pretrained(\"facebook/rag-token-nq\")\r\n>>> retriever = RagRetriever.from_pretrained(\"facebook/rag-token-nq\", index_name=\"exact\", use_dummy_dataset=True)\r\nUsing custom data configuration dummy.psgs_w100.nq.no_index-dummy=True,with_index=False\r\nReusing dataset wiki_dpr (/Users/sergey_mkrtchyan/.cache/huggingface/datasets/wiki_dpr/dummy.psgs_w100.nq.no_index-dummy=True,with_index=False/0.0.0/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb)\r\nUsing custom data configuration dummy.psgs_w100.nq.exact-50b6cda57ff32ab4\r\nReusing dataset wiki_dpr (/Users/sergey_mkrtchyan/.cache/huggingface/datasets/wiki_dpr/dummy.psgs_w100.nq.exact-50b6cda57ff32ab4/0.0.0/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb)\r\n 0%| | 0/10 [00:00<?, ?it/s]\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py\", line 425, in from_pretrained\r\n return cls(\r\n File \"/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py\", line 387, in __init__\r\n self.init_retrieval()\r\n File \"/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py\", line 458, in init_retrieval\r\n self.index.init_index()\r\n File \"/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py\", line 284, in init_index\r\n self.dataset = load_dataset(\r\n File \"/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/load.py\", line 750, in load_dataset\r\n ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n File \"/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/builder.py\", line 734, in as_dataset\r\n datasets = utils.map_nested(\r\n File \"/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/utils/py_utils.py\", line 195, in map_nested\r\n return function(data_struct)\r\n File \"/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/builder.py\", line 769, in _build_single_dataset\r\n post_processed = self._post_process(ds, resources_paths)\r\n File \"/Users/sergey_mkrtchyan/.cache/huggingface/modules/datasets_modules/datasets/wiki_dpr/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb/wiki_dpr.py\", line 205, in _post_process\r\n dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n File \"/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/arrow_dataset.py\", line 2516, in add_faiss_index\r\n super().add_faiss_index(\r\n File \"/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/search.py\", line 416, in add_faiss_index\r\n faiss_index.add_vectors(self, column=column, train_size=train_size, faiss_verbose=faiss_verbose)\r\n File \"/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/search.py\", line 281, in add_vectors\r\n self.faiss_index.add(vecs)\r\n File \"/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/faiss/__init__.py\", line 104, in replacement_add\r\n self.add_c(n, swig_ptr(x))\r\n File \"/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/faiss/swigfaiss.py\", line 3263, in add\r\n return _swigfaiss.IndexHNSW_add(self, n, x)\r\nRuntimeError: Error in virtual void faiss::IndexHNSW::add(faiss::Index::idx_t, const float *) at /Users/runner/work/faiss-wheels/faiss-wheels/faiss/faiss/IndexHNSW.cpp:356: Error: 'is_trained' failed\r\n>>>\r\n```\r\n\r\nThe error message is hinting that it could be related to this, but I might be wrong. Any ideas?\r\n\r\n\r\nEdit: Can confirm it's working fine with datasets==1.2.0\r\n\r\nDouble Edit: Did some further digging. The issue is related to this commit: 8c5220307c33f00e01c3bf7b8. I opened a separate issue #1941 for proper tracking." ]

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[ "Hi !\r\nI guess you can add a dataset without the fields that must be kept anonymous, and then update those when the anonymity period is over.\r\nYou can also make the PR from an anonymous org.\r\nPinging @yjernite just to make sure it's ok", "Hello,\r\nI would prefer to do the reverse: adding a link to an anonymous paper without the people names/institution in the PR. Would it be conceivable ?\r\nCheers\r\n", "Sure, I think it's ok on our side", "Yup, sounds good!" ]

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[ "Hi @JieyuZhao !\r\n\r\nYou can edit the dataset card of wino_bias to update the URL via a Pull Request. This would be really appreciated :)\r\n\r\nThe dataset card is the README.md file you can find at https://github.com/huggingface/datasets/tree/master/datasets/wino_bias\r\nAlso the homepage url is also mentioned in the wino_bias.py so feel free to update it there as well.\r\n\r\nYou can create a Pull Request directly from the github interface by editing the files you want and submit a PR, or from a local clone of the repository.\r\n\r\nThanks for noticing !" ]

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[ "@lhoestq - apologies for the multiple PRs, my previous one (#1905) got mangled due to some merge conflicts that I had trouble resolving so I just cherry-picked my changes onto a fresh branch here." ]

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[ "So I was curious why the issue reported at #1919 wasn't caught in [this test](https://github.com/huggingface/datasets/blob/248104c4bdb2e01c036b7578867199191fbff181/tests/test_arrow_dataset.py#L209), so I did some digging.\r\nI tried to save to a temporary directory (just like in the test), like this:\r\n```python\r\nwith tempfile.TemporaryDirectory() as requested_tempdir:\r\n squad.save_to_disk(requested_tempdir) # no error\r\n```\r\nand it executes succesfuly without problems.\r\nSo why does it work, but this doesn't?\r\n```python\r\nsquad.save_to_disk(\"./squad\") # error\r\n```\r\nIt's because `save_to_disk` also creates a temporary directory (let's call it `tempdir`), and since `tempdir` and `requested_tempdir` share the same parents, the `Path.joinpath` method [(here)](https://github.com/huggingface/datasets/blob/248104c4bdb2e01c036b7578867199191fbff181/src/datasets/arrow_dataset.py#L469) will keep `requested_tempdir` as it is and the *train* directory will be created under `requested_tempdir` and hence no errors will arise.\r\n\r\nBut in the second case (where we are saving to a local dir), the *train* directory is created under *squad* which in turn is created under `tempdir`, not under `.` (current dir).\r\n\r\nSo, all of this probably doesn't help solving the issue but it might help creating a better test, and it also makes me wonder why are we saving to a temporary dir in `save_to_disk` anyway? I mean, won't it be removed with all its contents upon execution completion? what's the point then? ", "CLosing in favor of #1923" ]

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[ "Hi thanks for reporting and for proposing a fix :)\r\n\r\nI just merged a fix, feel free to try it from the master branch !", "Closing since this has been fixed by #1923" ]

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[ "upgraded to php 3.9.2 and it works!" ]

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[ "Hmmm this one broke master. I'm fixing it.\r\n\r\nMaybe because your branch was outdated ?", "Sorry @lhoestq, I forgot to update the imports... :/", "It's fine, the CI should have caught this tbh. Not sure why it did't fail" ]

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[ "Thanks for reporting ! This is a bug. For now feel free to set `ignore_verifications=False` in `load_dataset`.\r\nI'm working on a fix", "I just merged a fix :)\r\n\r\nWe'll do a patch release soon. In the meantime feel free to try it from the master branch\r\nThanks again for reporting !", "Closing since this has been fixed by #1925" ]

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[ "Just so I understand how it can be used in practice, do you have an example showing how to load a text dataset with this option?", "Sure ! Here is an example:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"text\", keep_linebreaks=True, data_files=...)\r\n```\r\n\r\nI'll update the documentation to explain this", "Perfect!" ]

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[ "So much better - thank you for doing that, @lhoestq!", "Also fixed the `uncorpus` urls for wmt19 ru-en and zh-en for https://github.com/huggingface/datasets/issues/1893", "Thanks!\r\nCan this be merged sooner? \r\nI manually update it and it works well." ]

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[ "@thomwolf @lhoestq can you guys please take a look and recommend some solution.", "am suspicious of this thing? what's the purpose of this? pickling and unplickling\r\n`self = pickle.loads(pickle.dumps(self))`\r\n\r\n```\r\n def save_to_disk(self, dataset_path: str, fs=None):\r\n \"\"\"\r\n Saves a dataset to a dataset directory, or in a filesystem using either :class:`datasets.filesystem.S3FileSystem` or any implementation of ``fsspec.spec.AbstractFileSystem``.\r\n\r\n Args:\r\n dataset_path (``str``): path (e.g. ``dataset/train``) or remote uri (e.g. ``s3://my-bucket/dataset/train``) of the dataset directory where the dataset will be saved to\r\n fs (Optional[:class:`datasets.filesystem.S3FileSystem`,``fsspec.spec.AbstractFileSystem``], `optional`, defaults ``None``): instance of :class:`datasets.filesystem.S3FileSystem` or ``fsspec.spec.AbstractFileSystem`` used to download the files from remote filesystem.\r\n \"\"\"\r\n assert (\r\n not self.list_indexes()\r\n ), \"please remove all the indexes using `dataset.drop_index` before saving a dataset\"\r\n self = pickle.loads(pickle.dumps(self))\r\n ```", "It's been 24 hours and sadly it's still running. With not a single byte written", "Tried finding the root cause but was unsuccessful.\r\nI am using lazy tokenization with `dataset.set_transform()`, it works like a charm with almost same performance as pre-compute.", "Hi ! This very probably comes from the hack you used.\r\n\r\nThe pickling line was added an a sanity check because save_to_disk uses the same assumptions as pickling for a dataset object. The main assumption is that memory mapped pyarrow tables must be reloadable from the disk. In your case it's not possible since you altered the pyarrow table.\r\nI would suggest you to rebuild a valid Dataset object from your new pyarrow table. To do so you must first save your new table to a file, and then make a new Dataset object from that arrow file.\r\n\r\nYou can save the raw arrow table (without all the `datasets.Datasets` metadata) by calling `map` with `cache_file_name=\"path/to/outut.arrow\"` and `function=None`. Having `function=None` makes the `map` write your dataset on disk with no data transformation.\r\n\r\nOnce you have your new arrow file, load it with `datasets.Dataset.from_file` to have a brand new Dataset object :)\r\n\r\nIn the future we'll have a better support for the fast filtering method from pyarrow so you don't have to do this very unpractical workaround. Since it breaks somes assumptions regarding the core behavior of Dataset objects, this is very discouraged.", "Thanks, @lhoestq for your response. Will try your solution and let you know." ]

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[ "It looks like this PR now includes changes to many other files than the ones for CoNLLpp.\r\n\r\nTo fix that feel free to create another branch and another PR.\r\n\r\nThis was probably caused by a git rebase. You can avoid this issue by using git merge if you've already pushed your branch." ]

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[ "Hi ! :)\r\n\r\nThis looks like the same issue as https://github.com/huggingface/datasets/issues/1856 \r\nBasically google drive has quota issues that makes it inconvenient for downloading files.\r\n\r\nIf the quota of a file is exceeded, you have to wait 24h for the quota to reset (which is painful).\r\n\r\nThe error says that the checksum of the downloaded file doesn't match because google drive returns a text file with the \"Quota Exceeded\" error instead of the actual data file.", "Thanks @lhoestq! Yes, it seems back to normal after a couple of days." ]

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[ "We already have a ClassLabel type that does this kind of mapping between the label ids (integers) and actual label values (strings).\r\n\r\nI wonder if actually we should use the DictionaryType from Arrow and the Categorical type from pandas for the `datasets` ClassLabel feature type.\r\nCurrently ClassLabel corresponds to `pa.int64()` in pyarrow and `dtype('int64')` in pandas (so the label names are lost during conversions).\r\n\r\nWhat do you think ?", "Now that I've heard you explain ClassLabel, that makes a lot of sense! While DictionaryType for Arrow (I think) can have arbitrarily typed keys, so it won't cover all potential cases, pandas' Category is *probably* the most common use for that pyarrow type, and ClassLabel should match that perfectly?\r\n\r\nOther thoughts:\r\n\r\n- changing the resulting patype on ClassLabel might be backward-incompatible? I'm not totally sure if users of the `datasets` library tend to directly access the `patype` attribute (I don't think we really do, but we haven't been using it for very long yet).\r\n- would ClassLabel's dtype change to `dict[int64, string]`? It seems like in practice a ClassLabel (when not explicitly specified) would be constructed from the DictionaryType branch of `generate_from_arrow_type`, so it's not totally clear to me that anyone ever actually accesses/uses that dtype?\r\n- I don't quite know how `.int2str` and `.str2int` are used in practice - would those be kept? Perhaps the implementation might actually be substantially smaller if we can just delegate to pyarrow's dict methods?\r\n\r\nAnother idea that just occurred to me: add a branch in here to generate a ClassLabel if the dict key is int64 and the values are string: https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L932 , and then don't touch anything else.\r\n\r\nIn practice, I don't think this would be backward-incompatible in a way anyone would care about since the current behavior just throws an exception, and this way, we could support *reading* a pandas Categorical into a `Dataset` as a ClassLabel. I *think* from there, while it would require some custom glue it wouldn't be too hard to convert the ClassLabel into a pandas Category if we want to go back - I think this would improve on the current behavior without risking changing the behavior of ClassLabel in a backward-incompat way.\r\n\r\nThoughts? I'm not sure if this is overly cautious. Whichever approach you think is better, I'd be happy to take it on!\r\n", "I think we can first keep the int64 precision but with an arrow Dictionary for ClassLabel, and focus on the connection with arrow and pandas.\r\n\r\nIn this scope, I really like the idea of checking for the dictionary type:\r\n\r\n> Another idea that just occurred to me: add a branch in here to generate a ClassLabel if the dict key is int64 and the values are string: https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L932 , and then don't touch anything else.\r\n\r\nThis looks like a great start.\r\n\r\nThen as you said we'd have to add the conversion from classlabel to the correct arrow dictionary type. Arrow is already able to convert from arrow Dictionary to pandas Categorical so it should be enough.\r\n\r\nI can see two things that we must take case of to make this change backward compatible:\r\n- first we must still be able to load an arrow file with arrow int64 dtype and `datasets` ClassLabel type without crashing. This can be fixed by casting the arrow int64 array to an arrow Dictionary array on-the-fly when loading the table in the ArrowReader.\r\n- then we still have to return integers when accessing examples from a ClassLabel column. Currently it would return the strings values since it's based on the pandas behavior for converting from pandas to python/numpy. To do so we just have to adapt the python/numpy extractors in formatting.py (it takes care of converting an arrow table to a dictionary of python objects by doing arrow table -> pandas dataframe -> python dictionary)\r\n\r\nAny help on this matter is very much welcome :)" ]

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[ "Also - I took a stab at updating the docs, but I'm not sure how to actually check the outputs to see if it's formatted properly." ]

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[ "Thanks!" ]

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[ "@patrickvonplaten could you please review and help me close this PR?", "@lhoestq Thank you so much for your comments and for patiently reviewing the code. Have _hopefully_ included all the suggested changes. Let me know if any more changes are required.\r\n\r\nSorry the code had lots of silly errors from my side!:' Will be more careful from next time! :)\r\n\r\n\r\n" ]

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[ "OK! Thank you for the review - I will follow up with a separate PR for the comments here (https://github.com/huggingface/datasets/pull/1900#discussion_r578319725)!" ]

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[ "Thanks for reporting. This looks like a very bad issue. I'm looking into it", "I just merged a fix, we'll do a patch release soon. Thanks again for reporting, and sorry for the inconvenience.\r\nIn the meantime you can load `ALT` using `datasets` from the master branch", "Thanks!!! works perfectly in the bleading edge master version", "Closed by #1899" ]

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[ "Thanks for reporting !\r\n\r\nYou're right, `string_to_arrow` should be able to take `\"timestamp[ns]\"` as input and return the right pyarrow timestamp type.\r\nFeel free to suggest a fix for `string_to_arrow` and open a PR if you want to contribute ! This would be very appreciated :)\r\n\r\nTo give you more context:\r\n\r\nAs you may know we define the features types of a dataset using the `Features` object in combination with feature types like `Value`. For example\r\n```python\r\nfeatures = Features({\r\n \"age\": Value(\"int32\")\r\n})\r\n```\r\nHowever under the hood we are actually using pyarrow to store the data, and so we have a mapping between the feature types of `datasets` and the types of pyarrow.\r\n\r\nFor example, the `Value` feature types are created from a pyarrow type with `Value(str(pa_type))`.\r\nHowever it looks like the conversion back to a pyarrow type doesn't work with `\"timestamp[ns]\"`.\r\nThis is the `string_to_arrow` function you highlighted that does this conversion, so we should fix that.\r\n\r\n", "Thanks for the clarification @lhoestq !\r\n\r\nThis may be a little bit of a stupid question, but I wanted to clarify one more thing before I took a stab at this:\r\n\r\nWhen the features get inferred, I believe they already have a pyarrow schema (https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_dataset.py#L234).\r\n\r\nWe then convert it to a string (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L778) only to convert it back into the arrow type (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L143, and https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L35). Is there a reason for this round-trip?\r\n\r\nI'll open a PR later to add `timestamp` support to `string_to_arrow`, but I'd be curious to understand since it feels like there may be some opportunities to simplify!", "The objective in terms of design is to make it easy to create Features in a pythonic way. So for example we use a string to define a Value type.\r\nThat's why when inferring the Features from an arrow schema we have to find the right string definitions for Value types. I guess we could also have a constructor `Value.from_arrow_type` to avoid recreating the arrow type, but this could create silent errors if the pyarrow type doesn't have a valid mapping with the string definition. The \"round-trip\" is used to enforce that the ground truth is the string definition, not the pyarrow type, and also as a sanity check.\r\n\r\nLet me know if that makes sense ", "OK I think I understand now:\r\n\r\nFeatures are datasets' internal representation of a schema type, distinct from pyarrow's schema.\r\nValue() corresponds to pyarrow's \"primitive\" types (e.g. `int` or `string`, but not things like `list` or `dict`).\r\n`get_nested_type()` (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L698) and `generate_from_arrow_type()` (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L778) *should* be inverses of each other, and similarly, for the primitive values, `string_to_arrow()` and `Value.__call__` (https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L146) should be inverses of each other?\r\n\r\nThanks for taking the time to answer - I just wanted to make sure I understood before opening a PR so I'm not disrupting anything about how the codebase is expected to work!", "Yes you're totally right :)" ]

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[ "Hi sam !\r\nIndeed we can expect the performances to be very close since both MMapIndexedDataset and the `datasets` implem use memory mapping. With memory mapping what determines the I/O performance is the speed of your hard drive/SSD.\r\n\r\nIn terms of performance we're pretty close to the optimal speed for reading text, even though I found recently that we could still slightly improve speed for big datasets (see [here](https://github.com/huggingface/datasets/issues/1803)).\r\n\r\nIn terms of number of examples and example sizes, the only limit is the available disk space you have.\r\n\r\nI haven't used `psrecord` yet but it seems to be a very interesting tool for benchmarking. Currently for benchmarks we only have github actions to avoid regressions in terms of speed. But it would be cool to have benchmarks with comparisons with other dataset tools ! This would be useful to many people", "Also I would be interested to know what data types `MMapIndexedDataset` supports. Is there some documentation somewhere ?", "no docs haha, it's written to support integer numpy arrays.\r\n\r\nYou can build one in fairseq with, roughly:\r\n```bash\r\n\r\nwget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.zip\r\nunzip wikitext-103-raw-v1.zip\r\nexport dd=$HOME/fairseq-py/wikitext-103-raw\r\n\r\nexport mm_dir=$HOME/mmap_wikitext2\r\nmkdir -p gpt2_bpe\r\nwget -O gpt2_bpe/encoder.json https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json\r\nwget -O gpt2_bpe/vocab.bpe https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe\r\nwget -O gpt2_bpe/dict.txt https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt\r\nfor SPLIT in train valid; do \\\r\n python -m examples.roberta.multiprocessing_bpe_encoder \\\r\n --encoder-json gpt2_bpe/encoder.json \\\r\n --vocab-bpe gpt2_bpe/vocab.bpe \\\r\n --inputs /scratch/stories_small/${SPLIT}.txt \\\r\n --outputs /scratch/stories_small/${SPLIT}.bpe \\\r\n --keep-empty \\\r\n --workers 60; \\\r\ndone\r\n\r\nmkdir -p $mm_dir\r\nfairseq-preprocess \\\r\n --only-source \\\r\n --srcdict gpt2_bpe/dict.txt \\\r\n --trainpref $dd/wiki.train.bpe \\\r\n --validpref $dd/wiki.valid.bpe \\\r\n --destdir $mm_dir \\\r\n --workers 60 \\\r\n --dataset-impl mmap\r\n```\r\n\r\nI'm noticing in my benchmarking that it's much smaller on disk than arrow (200mb vs 900mb), and that both incur significant cost by increasing the number of data loader workers. \r\nThis somewhat old [post](https://ray-project.github.io/2017/10/15/fast-python-serialization-with-ray-and-arrow.html) suggests there are some gains to be had from using `pyarrow.serialize(array).tobuffer()`. I haven't yet figured out how much of this stuff `pa.Table` does under the hood.\r\n\r\nThe `MMapIndexedDataset` bottlenecks we are working on improving (by using arrow) are:\r\n1) `MMapIndexedDataset`'s index, which stores offsets, basically gets read in its entirety by each dataloading process.\r\n2) we have separate, identical, `MMapIndexedDatasets` on each dataloading worker, so there's redundancy there; we wonder if there is a way that arrow can somehow dedupe these in shared memory.\r\n\r\nIt will take me a few hours to get `MMapIndexedDataset` benchmarks out of `fairseq`/onto a branch in this repo, but I'm happy to invest the time if you're interested in collaborating on some performance hacking." ]

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[ "This was also mentioned in https://github.com/huggingface/datasets/issues/488 \r\n\r\nThe bucket where is data was stored seems to be unavailable now. Maybe we can change the URL to the ones in https://conferences.unite.un.org/uncorpus/en/downloadoverview ?", "Closing since this has been fixed by #1912" ]

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[ "Yes that would be awesome. Not only the download speeds are awful, but also some files are missing.\r\nWe list all the URLs in the datasets/wmt19/wmt_utils.py so we can make a script to download them all and host on S3.\r\nAlso I think most of the materials are under the CC BY-NC-SA 3.0 license (must double check) so it should be possible to redistribute the data with no issues.\r\n\r\ncc @patrickvonplaten who knows more about the wmt scripts", "Yeah, the scripts are pretty ugly! A big refactor would make sense here...and I also remember that the datasets were veeery slow to download", "I'm downloading them.\r\nI'm starting with the ones hosted on http://data.statmt.org which are the slowest ones", "@lhoestq better to use our new git-based system than just raw S3, no? (that way we have built-in CDN etc.)", "Closing since the urls were changed to mirror urls in #1912 ", "Hi there! What about mirroring other datasets like [CCAligned](http://www.statmt.org/cc-aligned/) as well? All of them are really slow to download..." ]

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