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PR_kwDODunzps5UoJ91

Drop Python 3.7 support

closed

CONTRIBUTOR

`hfh` and `transformers` have dropped Python 3.7 support, so we should do the same :). (Based on the stats, it seems less than 10% of the users use `datasets` with Python 3.7)

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PR_kwDODunzps5UjN2h

Misc improvements

closed

CONTRIBUTOR

Contains the following improvements: * fixes a "share dataset" link in README and modifies the "hosting" part in the disclaimer section * updates `Makefile` to also run the style checks on `utils` and `setup.py` * deletes a test for GH-hosted datasets (no longer supported) * deletes `convert_dataset.sh` (outdated) * aligns `utils/release.py` with `transformers` (the current version is outdated)

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I_kwDODunzps5qfKb-

interleave_datasets & DataCollatorForLanguageModeling having a conflict ?

open

NONE

### Describe the bug Hi everyone :) I have two local & custom datasets (1 "sentence" per line) which I split along the 95/5 lines for pre-training a Bert model. I use a modified version of `run_mlm.py` in order to be able to make use of `interleave_dataset`: - `tokenize()` runs fine - `group_text()` runs fine Everytime, on step 19, I get ```pytb File "env/lib/python3.9/site-packages/transformers/data/data_collator.py", line 779, in torch_mask_tokens inputs[indices_random] = random_words[indices_random] RuntimeError: Index put requires the source and destination dtypes match, got Float for the destination and Long for the source. ``` I tried: - training without interleave on dataset 1, it runs - training without interleave on dataset 2, it runs - training without `.to_iterable_dataset()`, it hangs then crash - training without group_text() and padding to max_length seemed to fix the issue, but who knows if this was just because it was an issue that would come much later in terms of steps. I might have coded something wrong, but I don't get what ### Steps to reproduce the bug I have this function: ```py def build_dataset(path: str, percent: str): dataset = load_dataset( "text", data_files={"train": [path]}, split=f"train[{percent}]" ) dataset = dataset.map( lambda examples: tokenize(examples["text"]), batched=True, num_proc=num_proc, ) dataset = dataset.map( group_texts, batched=True, num_proc=num_proc, desc=f"Grouping texts in chunks of {tokenizer.max_seq_length}", remove_columns=["text"] ) print(len(dataset)) return dataset.to_iterable_dataset() ``` I hardcoded group_text: ```py def group_texts(examples): # Concatenate all texts. concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, and if the total_length < max_seq_length we exclude this batch and return an empty dict. # We could add padding if the model supported it instead of this drop, you can customize this part to your needs. total_length = (total_length // 512) * 512 # Split by chunks of max_len. result = { k: [t[i: i + 512] for i in range(0, total_length, 512)] for k, t in concatenated_examples.items() } # result = {k: [el for el in elements if el] for k, elements in result.items()} return result ``` And then I build datasets using the following code: ```py train1 = build_dataset("d1.txt", ":95%") train2 = build_dataset("d2.txt", ":95%") dev1 = build_dataset("d1.txt", "95%:") dev2 = build_dataset("d2.txt", "95%:") ``` and finally I run ```py train_dataset = interleave_datasets( [train1, train2], probabilities=[0.8, 0.2], seed=42 ) eval_dataset = interleave_datasets( [dev1, dev2], probabilities=[0.8, 0.2], seed=42 ) ``` Then I run the training part which remains mostly untouched: > CUDA_VISIBLE_DEVICES=1 python custom_dataset.py --model_type bert --per_device_train_batch_size 32 --do_train --output_dir /var/mlm/training-bert/model --max_seq_length 512 --save_steps 10000 --save_total_limit 3 --auto_find_batch_size --logging_dir ./logs-bert --learning_rate 0.0001 --do_train --num_train_epochs 25 --warmup_steps 10000 --max_step 45000 --fp16 ### Expected behavior The model should then train normally, but fails every time at the same step (19). printing the variables at `inputs[indices_random] = random_words[indices_random]` shows a magnificient empty tensor (, 32) [if I remember well] ### Environment info transformers[torch] 4.30.2 Ubuntu A100 0 CUDA 12 Driver Version: 525.116.04

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PR_kwDODunzps5UhP-Z

Add KLUE-MRC metrics

closed

NONE

## Metrics for KLUE-MRC (Korean Language Understanding Evaluation — Machine Reading Comprehension) Adding metrics for [KLUE-MRC](https://huggingface.co/datasets/klue). KLUE-MRC is very similar to SQuAD 2.0 but has a slightly different format which is why I added metrics for KLUE-MRC. Specifically, in the case of [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness), it leverages the scoring script of SQuAD to evaluate SQuAD 2.0 and KorQuAD. But the script isn't suitable for KLUE-MRC because KLUE-MRC is a bit different from SQuAD 2.0. And this is why I added the scoring script for KLUE-MRC. - [x] All tests passed - [x] Added a metric card (referred the metric card of SQuAD 2.0) - [x] Compatibility test with [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) passed ### References - [KLUE: Korean Language Understanding Evaluation](https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/file/98dce83da57b0395e163467c9dae521b-Paper-round2.pdf) - [KLUE on Hugging Face Datasets](https://huggingface.co/datasets/klue) - #2416

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PR_kwDODunzps5UVMMh

Align `column_names` type check with type hint in `sort`

closed

CONTRIBUTOR

Fix #5998

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PR_kwDODunzps5UU_FB

Pin `joblib` to avoid `joblibspark` test failures

closed

CONTRIBUTOR

`joblibspark` doesn't support the latest `joblib` release. See https://github.com/huggingface/datasets/actions/runs/5401870932/jobs/9812337078 for the errors

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I_kwDODunzps5qNOV5

Getting a 409 error while loading xglue dataset

closed

NONE

### Describe the bug Unable to load xglue dataset ### Steps to reproduce the bug ```python import datasets dataset = datasets.load_dataset("xglue", "ntg") ``` > ConnectionError: Couldn't reach https://xglue.blob.core.windows.net/xglue/xglue_full_dataset.tar.gz (error 409) ### Expected behavior Expected the dataset to load ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-5.15.107+-x86_64-with-glibc2.31 - Python version: 3.10.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 9.0.0 - Pandas version: 1.5.3

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I_kwDODunzps5qNC_a

The current implementation has a potential bug in the sort method

closed

NONE

### Describe the bug In the sort method，here's a piece of code ```python # column_names: Union[str, Sequence_[str]] # Check proper format of and for duplicates in column_names if not isinstance(column_names, list): column_names = [column_names] ``` I get an error when I pass in a tuple based on the column_names type annotation, it will raise an errror.As in the example below, while the type annotation implies that a tuple can be passed. ```python from datasets import load_dataset dataset = load_dataset('glue', 'ax')['test'] dataset.sort(column_names=('premise', 'hypothesis')) # Raise ValueError: Column '('premise', 'hypothesis')' not found in the dataset. ``` Of course, after I modified the tuple into a list, everything worked fine Change the code to the following so there will be no problem ```python # Check proper format of and for duplicates in column_names if not isinstance(column_names, list): if isinstance(column_names, str): column_names = [column_names] else: column_names = list(column_names) ``` ### Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('glue', 'ax')['test'] dataset.sort(column_names=('premise', 'hypothesis')) # Raise ValueError: Column '('premise', 'hypothesis')' not found in the dataset. ``` ### Expected behavior Passing tuple into column_names should be equivalent to passing list ### Environment info - `datasets` version: 2.13.0 - Platform: macOS-13.1-arm64-arm-64bit - Python version: 3.10.11 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.2

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I_kwDODunzps5qMMvi

extend the map function so it can wrap around long text that does not fit in the context window

open

NONE

### Feature request I understand `dataset` provides a [`map`](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L2849) function. This function in turn takes in a callable that is used to tokenize the text on which a model is trained. Frequently this text will not fit within a models's context window. In this case it would be useful to wrap around the text into multiple rows with each row fitting the model's context window. I tried to do it using this code as example which in turn I have borrowed from [here](https://stackoverflow.com/a/76343993/147530): ``` data = data.map(lambda samples: tokenizer(samples["text"], max_length=tokenizer.model_max_length, truncation=True, stride=4, return_overflowing_tokens=True), batched=True) ``` but running the code gives me this error: ``` File "/llm/fine-tune.py", line 117, in <module> data = data.map(lambda samples: tokenizer(samples["text"], max_length=tokenizer.model_max_length, truncation=True, stride=4, return_overflowing_tokens=True), batched=True) File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 580, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 545, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3087, in map for rank, done, content in Dataset._map_single(**dataset_kwargs): File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3480, in _map_single writer.write_batch(batch) File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_writer.py", line 556, in write_batch pa_table = pa.Table.from_arrays(arrays, schema=schema) File "pyarrow/table.pxi", line 3798, in pyarrow.lib.Table.from_arrays File "pyarrow/table.pxi", line 2962, in pyarrow.lib.Table.validate File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Column 1 named input_ids expected length 394 but got length 447 ``` The lambda function I have provided is correctly chopping up long text so it wraps around (and because of this 394 samples become 447 after wrap around) but the dataset `map` function does not like it. ### Motivation please see above ### Your contribution I'm afraid I don't have much knowledge to help

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PR_kwDODunzps5UKP0i

Deprecate `use_auth_token` in favor of `token`

closed

CONTRIBUTOR

... to be consistent with `transformers` and `huggingface_hub`.

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PR_kwDODunzps5UCvYJ

Support returning dataframe in map transform

closed

CONTRIBUTOR

Allow returning Pandas DataFrames in `map` transforms. (Plus, raise an error in the non-batched mode if a returned PyArrow table/Pandas DataFrame has more than one row)

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PR_kwDODunzps5UB1cA

Fix select_columns columns order

closed

MEMBER

Fix the order of the columns in dataset.features when the order changes with `dataset.select_columns()`. I also fixed the same issue for `dataset.flatten()` Close https://github.com/huggingface/datasets/issues/5993

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I_kwDODunzps5p5W3j

ValueError: Table schema does not match schema used to create file

closed

NONE

### Describe the bug Saving a dataset as parquet fails with a `ValueError: Table schema does not match schema used to create file` if the dataset was obtained out of a `.select_columns()` call with columns selected out of order. ### Steps to reproduce the bug ```python import datasets dataset = datasets.Dataset.from_dict( { "x1": [1, 2, 3], "x2": [10, 11, 12], } ) ds = dataset.select_columns(["x2", "x1"]) ds.to_parquet("demo.parquet") ``` ```shell >>> ValueError: Table schema does not match schema used to create file: table: x2: int64 x1: int64 -- schema metadata -- huggingface: '{"info": {"features": {"x2": {"dtype": "int64", "_type": "V' + 53 vs. file: x1: int64 x2: int64 -- schema metadata -- huggingface: '{"info": {"features": {"x1": {"dtype": "int64", "_type": "V' + 53 ``` --- I think this is because after the `.select_columns()` call with out of order columns, the output dataset features' schema ends up being out of sync with the schema of the arrow table backing it. ```python ds.features.arrow_schema >>> x1: int64 x2: int64 -- schema metadata -- huggingface: '{"info": {"features": {"x1": {"dtype": "int64", "_type": "V' + 53 ds.data.schema >>> x2: int64 x1: int64 -- schema metadata -- huggingface: '{"info": {"features": {"x2": {"dtype": "int64", "_type": "V' + 53 ``` So when we call `.to_parquet()`, the call behind the scenes to `datasets.io.parquet.ParquetDatasetWriter(...).write()` which initialises the backend `pyarrow.parquet.ParquetWriter` with `schema = self.dataset.features.arrow_schema` triggers `pyarrow` on write when [it checks](https://github.com/apache/arrow/blob/11b140a734a516e436adaddaeb35d23f30dcce44/python/pyarrow/parquet/core.py#L1086-L1090) that the `ParquetWriter` schema matches the schema of the table being written 🙌 https://github.com/huggingface/datasets/blob/6ed837325cb539a5deb99129e5ad181d0269e050/src/datasets/io/parquet.py#L139-L141 ### Expected behavior The dataset gets successfully saved as parquet. *In the same way as it does if saving it as csv: ```python import datasets dataset = datasets.Dataset.from_dict( { "x1": [1, 2, 3], "x2": [10, 11, 12], } ) ds = dataset.select_columns(["x2", "x1"]) ds.to_csv("demo.csv") ``` ### Environment info `python==3.11` `datasets==2.13.1`

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PR_kwDODunzps5UAk3C

speedup

closed

CONTRIBUTOR

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I_kwDODunzps5pxA7G

`map` with any joblib backend

open

MEMBER

We recently enabled the (experimental) parallel backend switch for data download and extraction but not for `map` yet. Right now we're using our `iflatmap_unordered` implementation for multiprocessing that uses a shared Queue to gather progress updates from the subprocesses and show a progress bar in the main process. If a Queue implementation that would work on any joblib backend by leveraging the filesystem that is shared among workers, we can have `iflatmap_unordered` for joblib and therefore a `map` with any joblib backend with a progress bar ! Note that the Queue doesn't need to be that optimized though since we can choose a small frequency for progress updates (like 1 update per second).

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I_kwDODunzps5pvyNL

Set a rule on the config and split names

open

CONTRIBUTOR

> should we actually allow characters like spaces? maybe it's better to add validation for whitespace symbols and directly in datasets and raise https://github.com/huggingface/datasets-server/issues/853

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I_kwDODunzps5pscRk

ConnectionError: Couldn't reach dataset_infos.json

closed

NONE

### Describe the bug I'm trying to load codeparrot/codeparrot-clean-train, but get the following error: ConnectionError: Couldn't reach https://huggingface.co/datasets/codeparrot/codeparrot-clean-train/resolve/main/dataset_infos.json (ConnectionError(ProtocolError('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer')))) ### Steps to reproduce the bug train_data = load_dataset('codeparrot/codeparrot-clean-train', split='train') ### Expected behavior download the dataset ### Environment info centos7

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I_kwDODunzps5prpBl

Why max_shard_size is not supported in load_dataset and passed to download_and_prepare

closed

CONTRIBUTOR

### Describe the bug https://github.com/huggingface/datasets/blob/a8a797cc92e860c8d0df71e0aa826f4d2690713e/src/datasets/load.py#L1809 What I can to is break the `load_dataset` and use `load_datset_builder` + `download_and_prepare` instead. ### Steps to reproduce the bug https://github.com/huggingface/datasets/blob/a8a797cc92e860c8d0df71e0aa826f4d2690713e/src/datasets/load.py#L1809 ### Expected behavior Users can define the max shard size. ### Environment info datasets==2.13.1

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https://github.com/huggingface/datasets/pull/5986

PR_kwDODunzps5TygOZ

Make IterableDataset.from_spark more efficient

closed

CONTRIBUTOR

Moved the code from using collect() to using toLocalIterator, which allows for prefetching partitions that will be selected next, thus allowing for better performance when iterating.

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I_kwDODunzps5pmEo-

Cannot reuse tokenizer object for dataset map

closed

NONE

### Describe the bug Related to https://github.com/huggingface/transformers/issues/24441. Not sure if this is a tokenizer issue or caching issue, so filing in both. Passing the tokenizer to the dataset map function causes the tokenizer to be fingerprinted weirdly. After calling the tokenizer with arguments like padding and truncation the tokenizer object changes interanally, even though the hash remains the same. But dumps is able to detect that internal change which causes the tokenizer object's fingerprint to change. ### Steps to reproduce the bug ```python from transformers import AutoTokenizer from datasets.utils.py_utils import dumps # Huggingface datasets t = AutoTokenizer.from_pretrained('bert-base-uncased') t.save_pretrained("tok1") th1 = hash(dumps(t)) text = "This is an example text" ttext = t(text, max_length=512, padding="max_length", truncation=True) t.save_pretrained("tok2") th2 = hash(dumps(t)) assert th1 == th2 # Assertion Error ``` But if you use just the hash of the object without dumps, the hashes don't change ```python from transformers import AutoTokenizer from datasets.utils.py_utils import dumps # Huggingface datasets t = AutoTokenizer.from_pretrained('bert-base-uncased') th1 = hash(t) # Just hash no dumps text = "This is an example text" ttext = t(text, max_length=512, padding="max_length", truncation=True) th2 = hash(t) # Just hash no dumps assert th1 == th2 # This is OK ``` This causes situations such as the following 1. Create a text file like this `yes "This is an example text" | head -n 10000 > lines.txt` ```python from transformers import AutoTokenizer import datasets class TokenizeMapper(object): """Mapper for tokenizer. This is needed because the caching mechanism of HuggingFace does not work on lambdas. Each time a new lambda will be created by a new process which will lead to a different hash. This way we can have a universal mapper object in init and reuse it with the same hash for each process. """ def __init__(self, tokenizer): """Initialize the tokenizer.""" self.tokenizer = tokenizer def __call__(self, examples, **kwargs): """Run the mapper.""" texts = examples["text"] tt = self.tokenizer(texts, max_length=256, padding="max_length", truncation=True) batch_outputs = { "input_ids": tt.input_ids, "attention_mask": tt.attention_mask, } return batch_outputs t = AutoTokenizer.from_pretrained('bert-base-uncased') mapper = TokenizeMapper(t) ds = datasets.load_dataset("text", data_files="lines.txt") mds1 = ds.map( mapper, batched=False, remove_columns=["text"], ).with_format("torch") mds2 = ds.map( mapper, batched=False, remove_columns=["text"], ).with_format("torch") ``` The second call to map should reuse the cached processed dataset from mds1, but it instead it redoes the tokenization because of the behavior of dumps. ### Expected behavior We should be able to initialize a tokenizer. And reusing it should let us reuse the same map computation for the same dataset. The second call to map should reuse the cached processed dataset from mds1, but it instead it redoes the tokenization because of the behavior of dumps. ### Environment info - `datasets` version: 2.13.0 - Platform: Linux-6.1.31_1-x86_64-with-glibc2.36 - Python version: 3.9.16 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.2

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I_kwDODunzps5pmAkC

AutoSharding IterableDataset's when num_workers > 1

open

NONE

### Feature request Minimal Example ``` import torch from datasets import IterableDataset d = IterableDataset.from_file(<file_name>) dl = torch.utils.data.dataloader.DataLoader(d,num_workers=3) for sample in dl: print(sample) ``` Warning: Too many dataloader workers: 2 (max is dataset.n_shards=1). Stopping 1 dataloader workers. To parallelize data loading, we give each process some shards (or data sources) to process. Therefore it's unnecessary to have a number of workers greater than dataset.n_shards=1. To enable more parallelism, please split the dataset in more files than 1. Expected Behavior: Dataset is sharded each cpu uses subset (contiguously - so you can do checkpoint loading/saving) ### Motivation I have a lot of unused cpu's and would like to be able to shard iterable datasets with pytorch's dataloader when num_workers > 1. This is for a very large single file. I am aware that we can use the `split_dataset_by_node` to ensure that each node (for distributed) gets different shards, but we should extend it so that this also continues for multiple workers. ### Your contribution If someone points me to what needs to change, I can create a PR.

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PR_kwDODunzps5TtDdy

replaced PathLike as a variable for save_to_disk for dataset_path wit…

closed

NONE

…h str like that of load_from_disk

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I_kwDODunzps5phSRw

404 on Datasets Documentation Page

closed

NONE

### Describe the bug Getting a 404 from the Hugging Face Datasets docs page: https://huggingface.co/docs/datasets/index ### Steps to reproduce the bug 1. Go to URL https://huggingface.co/docs/datasets/index 2. Notice 404 not found ### Expected behavior URL should either show docs or redirect to new location ### Environment info hugginface.co

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I_kwDODunzps5phMnH

Only two cores are getting used in sagemaker with pytorch 3.10 kernel

closed

NONE

### Describe the bug When using the newer pytorch 3.10 kernel, only 2 cores are being used by huggingface filter and map functions. The Pytorch 3.9 kernel would use as many cores as specified in the num_proc field. We have solved this in our own code by placing the following snippet in the code that is called inside subprocesses: ```os.sched_setaffinity(0, {i for i in range(1000)})``` The problem, as near as we can tell, us that once upon a time, cpu affinity was set using a bitmask ("0xfffff" and the like), and affinity recently changed to a list of processors rather than to using the mask. As such, only processors 1 and 17 are shown to be working in htop.  When running functions via `map`, the above resetting of affinity works to spread across the cores. When using `filter`, however, only two cores are active. ### Steps to reproduce the bug Repro steps: 1. Create an aws sagemaker instance 2. use the pytorch 3_10 kernel 3. Load a dataset 4. run a filter operation 5. watch as only 2 cores are used when num_proc > 2 6. run a map operation 7. watch as only 2 cores are used when num_proc > 2 8. run a map operation with processor affinity reset inside the function called via map 9. Watch as all cores run ### Expected behavior All specified cores are used via the num_proc argument. ### Environment info AWS sagemaker with the following init script run in the terminal after instance creation: conda init bash bash conda activate pytorch_p310 pip install Wand PyPDF pytesseract datasets seqeval pdfplumber transformers pymupdf sentencepiece timm donut-python accelerate optimum xgboost python -m pip install 'git+https://github.com/facebookresearch/detectron2.git' sudo yum -y install htop sudo yum -y update sudo yum -y install wget libstdc++ autoconf automake libtool autoconf-archive pkg-config gcc gcc-c++ make libjpeg-devel libpng-devel libtiff-devel zlib-devel

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I_kwDODunzps5pg_Zl

Viewing dataset card returns “502 Bad Gateway”

closed

NONE

The url is: https://huggingface.co/datasets/Confirm-Labs/pile_ngrams_trigrams I am able to successfully view the “Files and versions” tab: [Confirm-Labs/pile_ngrams_trigrams at main](https://huggingface.co/datasets/Confirm-Labs/pile_ngrams_trigrams/tree/main) Any help would be appreciated! Thanks! I hope this is the right place to report an issue like this.

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PR_kwDODunzps5TrxS_

set dev version

closed

MEMBER

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PR_kwDODunzps5Tru2_

Release: 2.13.1

closed

MEMBER

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https://github.com/huggingface/datasets/pull/5976

PR_kwDODunzps5TmAFp

Avoid stuck map operation when subprocesses crashes

closed

CONTRIBUTOR

I've been using Dataset.map() with `num_proc=os.cpu_count()` to leverage multicore processing for my datasets, but from time to time I get stuck processes waiting forever. Apparently, when one of the subprocesses is abruptly killed (OOM killer, segfault, SIGKILL, etc), the main process keeps waiting for the async task sent to that child process to finish. It seems to be easy to reproduce the issue with the following script: ``` import os from datasets import Dataset, Features, Value def do_stuck(item): os.kill(os.getpid(), 9) data = { "col1": list(range(5)), "col2": list(range(5)), } ds = Dataset.from_dict( data, features=Features({ "col1": Value("int64"), "col2": Value("int64"), }), ) print(ds.map(do_stuck, num_proc=4)) ``` This is an old behavior in Python, which apparently was fixed a few years ago in `concurrent.futures.ProcessPoolExecutor` ([ref](https://bugs.python.org/issue9205)), but not in `multiprocessing.pool.Pool` / `multiprocess.pool.Pool`, which is used by `Dataset.map` ([ref](https://bugs.python.org/issue22393)). This PR is an idea to try to detect when a child process gets killed, and raises a `RuntimeError` warning the dataset.map() caller. EDIT: Related proposal for future improvement: https://github.com/huggingface/datasets/discussions/5977

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I_kwDODunzps5pZa3v

Streaming Dataset behind Proxy - FileNotFoundError

closed

NONE

### Describe the bug When trying to stream a dataset i get the following error after a few minutes of waiting. ``` FileNotFoundError: https://huggingface.co/datasets/facebook/voxpopuli/resolve/main/data/n_files.json If the repo is private or gated, make sure to log in with `huggingface-cli login`. ``` I have already set the proxy environment variables. Downloading a Dataset without streaming works as expected. Still i suspect that this is connected to being behind a proxy. Is there a way to set the proxy for streaming datasets? Possibly a keyword argument that gets passed to ffspec? ### Steps to reproduce the bug This is the code i use. ``` import os os.environ['http_proxy'] = "http://example.com:xxxx" os.environ['https_proxy'] = "http://example.com:xxxx" from datasets import load_dataset ds = load_dataset("facebook/voxpopuli", name="de", streaming=True) ``` ### Expected behavior I would expect the streaming functionality to use the set proxy settings. ### Environment info - `datasets` version: 2.13.0 - Platform: Linux-5.15.0-73-generic-x86_64-with-glibc2.35 - Python version: 3.10.11 - Huggingface_hub version: 0.15.1 - PyArrow version: 11.0.0 - Pandas version: 2.0.2

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PR_kwDODunzps5TkXCb

Deprecate `errors` param in favor of `encoding_errors` in text builder

closed

CONTRIBUTOR

For consistency with the JSON builder and Pandas

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https://github.com/huggingface/datasets/pull/5972

PR_kwDODunzps5TkE7K

Filter unsupported extensions

closed

MEMBER

I used a regex to filter the data files based on their extension for packaged builders. I tried and a regex is 10x faster that using `in` to check if the extension is in the list of supported extensions. Supersedes https://github.com/huggingface/datasets/pull/5850 Close https://github.com/huggingface/datasets/issues/5849 I also did a small change to favor the parquet module in case of a draw in the extension counter.

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I_kwDODunzps5pUxlD

Docs: make "repository structure" easier to find

open

CONTRIBUTOR

The page https://huggingface.co/docs/datasets/repository_structure explains how to create a simple repository structure without a dataset script. It's the simplest way to create a dataset and should be easier to find, particularly on the docs' first pages.

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I_kwDODunzps5pQy30

description disappearing from Info when Uploading a Dataset Created with `from_dict`

open

NONE

### Describe the bug When uploading a dataset created locally using `from_dict` with a specified `description` field. It appears before upload, but is missing after upload and re-download. ### Steps to reproduce the bug I think the most relevant pattern in the code might be the following lines: ``` description_json_str = json.dumps( { "dataset_id": dataset.spec.dataset_id, "env_name": dataset.spec.env_spec.id, "action_space": serialize_space(dataset.spec.action_space), "observation_space": serialize_space(dataset.spec.observation_space), } ) hugging_face_dataset = Dataset.from_dict( episodes_dict, info=DatasetInfo(description=description_json_str) ) ``` Which comes from this function https://github.com/balisujohn/minarai/blob/8e023727f0a8488c4451651d9f7a79b981412c40/minari/integrations/hugging_face.py#L39 To replicate, clone this branch of my Minari fork https://github.com/balisujohn/minarai/tree/dev-huggingface then run ``` python3.8 -m venv env source env/bin/activate python3 -m pip install -e . python3 -m pip install pytest ``` The change the hugging face repo path in the test called `test_hugging_face_push_and_pull_dataset` in `tests/integrations/test_hugging_face.py` to one you have permissions to write to. Then run: ``` pytest tests/integrations/test_hugging_face.py::test_hugging_face_push_and_pull_dataset ``` ### Expected behavior DATASET INFO BEFORE UPLOADING DatasetInfo(description='{"dataset_id": "dummy-combo-test-v0", "env_name": "DummyComboEnv-v0", "action_space": "{\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, {\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [4.0], \\"high\\": [5.0]}]}", "observation_space": "{\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, {\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, {\\"type\\": \\"Dict\\", \\"subspaces\\": {\\"component_1\\": {\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [-1.0], \\"high\\": [1.0]}, \\"component_2\\": {\\"type\\": \\"Dict\\", \\"subspaces\\": {\\"subcomponent_1\\": {\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, \\"subcomponent_2\\": {\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [4.0], \\"high\\": [5.0]}, {\\"type\\": \\"Discrete\\", \\"dtype\\": \\"int64\\", \\"start\\": 0, \\"n\\": 10}]}}}}}]}]}"}', citation='', homepage='', license='', features={'observations': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'component_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'component_2': {'subcomponent_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'subcomponent_2': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Value(dtype='int64', id=None)}}}}}, 'actions': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None)}, 'rewards': Value(dtype='int64', id=None), 'truncations': Value(dtype='bool', id=None), 'terminations': Value(dtype='bool', id=None), 'episode_ids': Value(dtype='int64', id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name=None, config_name=None, version=None, splits=None, download_checksums=None, download_size=None, post_processing_size=None, dataset_size=None, size_in_bytes=None) ... DATASET INFO AFTER UPLOADING AND DOWNLOADING DatasetInfo(description='', citation='', homepage='', license='', features={'observations': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'component_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'component_2': {'subcomponent_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'subcomponent_2': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Value(dtype='int64', id=None)}}}}}, 'actions': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None)}, 'rewards': Value(dtype='int64', id=None), 'truncations': Value(dtype='bool', id=None), 'terminations': Value(dtype='bool', id=None), 'episode_ids': Value(dtype='int64', id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name=None, config_name=None, version=None, splits={'train': SplitInfo(name='train', num_bytes=4846, num_examples=60, shard_lengths=None, dataset_name='parquet')}, download_checksums={'https://huggingface.co/datasets/balisujohn/minari_test/resolve/8217b614ff9ba5edc1a30c7df430e92a46f65363/data/train-00000-of-00001-7c5900b93b35745e.parquet': {'num_bytes': 9052, 'checksum': None}}, download_size=9052, post_processing_size=None, dataset_size=4846, size_in_bytes=13898) ... ### Environment info - `datasets` version: 2.13.0 - Platform: Linux-5.15.0-75-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.2

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https://github.com/huggingface/datasets/pull/5969

PR_kwDODunzps5Tcgq4

Add `encoding` and `errors` params to JSON loader

closed

CONTRIBUTOR

"Requested" in https://discuss.huggingface.co/t/utf-16-for-datasets/43828/3. `pd.read_json` also has these parameters, so it makes sense to be consistent.

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I_kwDODunzps5pN53R

Common Voice datasets still need `use_auth_token=True`

closed

MEMBER

### Describe the bug We don't need to pass `use_auth_token=True` anymore to download gated datasets or models, so the following should work if correctly logged in. ```py from datasets import load_dataset load_dataset("mozilla-foundation/common_voice_6_1", "tr", split="train+validation") ``` However it throws an error - probably because something weird is hardcoded into the dataset loading script. ### Steps to reproduce the bug 1.) ``` huggingface-cli login ``` 2.) Make sure that you have accepted the license here: https://huggingface.co/datasets/mozilla-foundation/common_voice_6_1 3.) Run: ```py from datasets import load_dataset load_dataset("mozilla-foundation/common_voice_6_1", "tr", split="train+validation") ``` 4.) You'll get: ``` File ~/hf/lib/python3.10/site-packages/datasets/builder.py:963, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs) 961 split_dict = SplitDict(dataset_name=self.name) 962 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 963 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 965 # Checksums verification 966 if verification_mode == VerificationMode.ALL_CHECKS and dl_manager.record_checksums: File ~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_6_1/f4d7854c466f5bd4908988dbd39044ec4fc634d89e0515ab0c51715c0127ffe3/common_voice_6_1.py:150, in CommonVoice._split_generators(self, dl_manager) 148 hf_auth_token = dl_manager.download_config.use_auth_token 149 if hf_auth_token is None: --> 150 raise ConnectionError( 151 "Please set use_auth_token=True or use_auth_token='<TOKEN>' to download this dataset" 152 ) 154 bundle_url_template = STATS["bundleURLTemplate"] 155 bundle_version = bundle_url_template.split("/")[0] ConnectionError: Please set use_auth_token=True or use_auth_token='<TOKEN>' to download this dataset ``` ### Expected behavior One should not have to pass `use_auth_token=True`. Also see discussion here: https://github.com/huggingface/blog/pull/1243#discussion_r1235131150 ### Environment info ``` - `datasets` version: 2.13.0 - Platform: Linux-6.2.0-76060200-generic-x86_64-with-glibc2.35 - Python version: 3.10.6 - Huggingface_hub version: 0.16.0.dev0 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 ```

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I_kwDODunzps5pI2H4

Config name / split name lost after map with multiproc

open

CONTRIBUTOR

### Describe the bug Performing a `.map` method on a dataset loses it's config name / split name only if run with multiproc ### Steps to reproduce the bug ```python from datasets import Audio, load_dataset from transformers import AutoFeatureExtractor import numpy as np # load dummy dataset libri = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean") # make train / test splits libri = libri["validation"].train_test_split(seed=42, shuffle=True, test_size=0.1) # example feature extractor model_id = "ntu-spml/distilhubert" feature_extractor = AutoFeatureExtractor.from_pretrained(model_id, do_normalize=True, return_attention_mask=True) sampling_rate = feature_extractor.sampling_rate libri = libri.cast_column("audio", Audio(sampling_rate=sampling_rate)) max_duration = 30.0 def preprocess_function(examples): audio_arrays = [x["array"] for x in examples["audio"]] inputs = feature_extractor( audio_arrays, sampling_rate=feature_extractor.sampling_rate, max_length=int(feature_extractor.sampling_rate * max_duration), truncation=True, return_attention_mask=True, ) return inputs # single proc map libri_encoded = libri.map( preprocess_function, remove_columns=["audio", "file"], batched=True, num_proc=1 ) print(10 * "=" ,"Single processing", 10 * "=") print("Config name before: ", libri["train"].config_name, " Split name before: ", libri["train"].split) print("Config name after: ", libri_encoded["train"].config_name, " Split name after: ", libri_encoded["train"].split) # multi proc map libri_encoded = libri.map( preprocess_function, remove_columns=["audio", "file"], batched=True, num_proc=2 ) print(10 * "=" ,"Multi processing", 10 * "=") print("Config name before: ", libri["train"].config_name, " Split name before: ", libri["train"].split) print("Config name after: ", libri_encoded["train"].config_name, " Split name after: ", libri_encoded["train"].split) ``` **Print Output:** ``` ========== Single processing ========== Config name before: clean Split name before: validation Config name after: clean Split name after: validation ========== Multi processing ========== Config name before: clean Split name before: validation Config name after: None Split name after: None ``` => we can see that the config/split names are lost in the multiprocessing setting ### Expected behavior Should retain both config / split names in the multiproc setting ### Environment info - `datasets` version: 2.13.1.dev0 - Platform: Linux-5.15.0-67-generic-x86_64-with-glibc2.35 - Python version: 3.10.6 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.2

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PR_kwDODunzps5TXBLP

Fix JSON generation in benchmarks CI

closed

CONTRIBUTOR

Related to changes made in https://github.com/iterative/dvc/pull/9475

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I_kwDODunzps5pHyQc

"Couldn't cast array of type" in complex datasets

closed

NONE

### Describe the bug When doing a map of a dataset with complex types, sometimes `datasets` is unable to interpret the valid schema of a returned datasets.map() function. This often comes from conflicting types, like when both empty lists and filled lists are competing for the same field value. This is prone to happen in batch mapping, when the mapper returns a sequence of null/empty values and other batches are non-null. A workaround is to manually cast the new batch to a pyarrow table (like implemented in this [workaround](https://github.com/piercefreeman/lassen/pull/3)) but it feels like this ideally should be solved at the core library level. Note that the reproduction case only throws this error if the first datapoint has the empty list. If it is processed later, datasets already detects its representation as list-type and therefore allows the empty list to be provided. ### Steps to reproduce the bug A trivial reproduction case: ```python from typing import Iterator, Any import pandas as pd from datasets import Dataset def batch_to_examples(batch: dict[str, list[Any]]) -> Iterator[dict[str, Any]]: for i in range(next(iter(lengths))): yield {feature: values[i] for feature, values in batch.items()} def examples_to_batch(examples) -> dict[str, list[Any]]: batch = {} for example in examples: for feature, value in example.items(): if feature not in batch: batch[feature] = [] batch[feature].append(value) return batch def batch_process(examples, explicit_schema: bool): new_examples = [] for example in batch_to_examples(examples): new_examples.append(dict(texts=example["raw_text"].split())) return examples_to_batch(new_examples) df = pd.DataFrame( [ {"raw_text": ""}, {"raw_text": "This is a test"}, {"raw_text": "This is another test"}, ] ) dataset = Dataset.from_pandas(df) # datasets won't be able to typehint a dataset that starts with an empty example. with pytest.raises(TypeError, match="Couldn't cast array of type"): dataset = dataset.map( batch_process, batched=True, batch_size=1, num_proc=1, remove_columns=dataset.column_names, ) ``` This results in crashes like: ```bash File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 1819, in wrapper return func(array, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 2109, in cast_array_to_feature return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 1819, in wrapper return func(array, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 1998, in array_cast raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}") TypeError: Couldn't cast array of type string to null ``` ### Expected behavior The code should successfully map and create a new dataset without error. ### Environment info Mac OSX, Linux

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PR_kwDODunzps5TVweZ

Always return list in `list_datasets`

closed

CONTRIBUTOR

Fix #5925 Plus, deprecate `list_datasets`/`inspect_dataset` in favor of `huggingface_hub.list_datasets`/"git clone workflow" (downloads data files)

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I_kwDODunzps5pEc25

Got an error _pickle.PicklingError use Dataset.from_spark.

closed

NONE

python 3.9.2 Got an error _pickle.PicklingError use Dataset.from_spark. Did the dataset import load data from spark dataframe using multi-node Spark cluster df = spark.read.parquet(args.input_data).repartition(50) ds = Dataset.from_spark(df, keep_in_memory=True, cache_dir="/pnc-data/data/nuplan/t5_spark/cache_data") ds.save_to_disk(args.output_data) Error : _pickle.PicklingError: Could not serialize object: RuntimeError: It appears that you are attempting to reference SparkContext from a broadcast variable, action, or transforma tion. SparkContext can only be used on the driver, not in code that it run on workers. For more information, see SPARK-5063. 23/06/16 21:17:20 WARN ExecutorPodsWatchSnapshotSource: Kubernetes client has been closed (this is expected if the application is shutting down.) _Originally posted by @yanzia12138 in https://github.com/huggingface/datasets/issues/5701#issuecomment-1594674306_ W Traceback (most recent call last): File "/home/work/main.py", line 100, in <module> run(args) File "/home/work/main.py", line 80, in run ds = Dataset.from_spark(df1, keep_in_memory=True, File "/home/work/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 1281, in from_spark return SparkDatasetReader( File "/home/work/.local/lib/python3.9/site-packages/datasets/io/spark.py", line 53, in read self.builder.download_and_prepare( File "/home/work/.local/lib/python3.9/site-packages/datasets/builder.py", line 909, in download_and_prepare self._download_and_prepare( File "/home/work/.local/lib/python3.9/site-packages/datasets/builder.py", line 1004, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/work/.local/lib/python3.9/site-packages/datasets/packaged_modules/spark/spark.py", line 254, in _prepare_split self._validate_cache_dir() File "/home/work/.local/lib/python3.9/site-packages/datasets/packaged_modules/spark/spark.py", line 122, in _validate_cache_dir self._spark.sparkContext.parallelize(range(1), 1).mapPartitions(create_cache_and_write_probe).collect() File "/home/work/.local/lib/python3.9/site-packages/pyspark/rdd.py", line 950, in collect sock_info = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd()) File "/home/work/.local/lib/python3.9/site-packages/pyspark/rdd.py", line 2951, in _jrdd wrapped_func = _wrap_function(self.ctx, self.func, self._prev_jrdd_deserializer, File "/home/work/.local/lib/python3.9/site-packages/pyspark/rdd.py", line 2830, in _wrap_function pickled_command, broadcast_vars, env, includes = _prepare_for_python_RDD(sc, command) File "/home/work/.local/lib/python3.9/site-packages/pyspark/rdd.py", line 2816, in _prepare_for_python_RDD pickled_command = ser.dumps(command) File "/home/work/.local/lib/python3.9/site-packages/pyspark/serializers.py", line 447, in dumps raise pickle.PicklingError(msg) _pickle.PicklingError: Could not serialize object: RuntimeError: It appears that you are attempting to reference SparkContext from a broadcast variable, action, or transformation. S parkContext can only be used on the driver, not in code that it run on workers. For more information, see SPARK-5063. 23/06/19 13:51:21 WARN ExecutorPodsWatchSnapshotSource: Kubernetes client has been closed (this is expected if the application is shutting down.)

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I_kwDODunzps5o_7p6

Issue with train_test_split maintaining the same underlying PyArrow Table

open

NONE

### Describe the bug I've been using the train_test_split method in the datasets module to split my HuggingFace Dataset into separate training, validation, and testing subsets. However, I've noticed an issue where the split datasets appear to maintain the same underlying PyArrow Table. ### Steps to reproduce the bug 1. Load any dataset ```dataset = load_dataset("lhoestq/demo1")``` 2. Try the next code: ```python from datasets import Dataset, DatasetDict train_size = 0.6 split_train = dataset["train"].train_test_split( train_size=train_size, ) separate_dataset_dict = DatasetDict({ "train": split_train["train"], "test": split_train["test"], }) ``` 3. The next code ```print(separate_dataset_dict)``` when printing the dataset it gives the indication that they have 3 and 2 rows respectively. 4. But the next code: ```python print(len(separate_dataset_dict["train"].data['id'])) print(len(separate_dataset_dict["test"].data['id'])) ``` Indicates that both tables still have 5 rows. ### Expected behavior However, I've noticed that train_test_split["train"].data, test_val_split["train"].data, and test_val_split["test"].data are identical, suggesting that they all point to the same underlying PyArrow Table. This means that the split datasets are not independent, as I expected. I believe this is a bug in the train_test_split implementation, as I would expect this function to return datasets with separate underlying PyArrow Tables. Could you please help me understand if this is expected behavior, or if there's a workaround to create truly independent split datasets? I would appreciate any assistance with this issue. Thank you. ### Environment info I tried in Colab: - `datasets` version: 2.13.0 - Platform: Windows-10-10.0.22621-SP0 - Python version: 3.10.11 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.1 and my PC: - `datasets` version: 2.13.0 - Platform: Linux-5.15.107+-x86_64-with-glibc2.31 - Python version: 3.10.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 9.0.0 - Pandas version: 1.5.3

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I_kwDODunzps5o0Pa3

IterableDataset: split by node and map may preprocess samples that will be skipped anyway

open

NONE

There are two ways an iterable dataset can be split by node: 1. if the number of shards is a factor of number of GPUs: in that case the shards are evenly distributed per GPU 2. otherwise, each GPU iterate on the data and at the end keeps 1 sample out of n(GPUs) - skipping the others. In case 2. it's therefore possible to have the same examples passed to `prepare_dataset` for each GPU. This doesn't sound optimized though, because it runs the preprocessing on samples that won't be used in the end. Could you open a new issue so that we can discuss about this and find a solution ? _Originally posted by @lhoestq in https://github.com/huggingface/datasets/issues/5360#issuecomment-1592729051_

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I_kwDODunzps5ov8ID

read metric glue.py from local file

closed

NONE

### Describe the bug Currently, The server is off-line. I am using the glue metric from the local file downloaded from the hub. I download / cached datasets using `load_dataset('glue','sst2', cache_dir='/xxx')` to cache them and then in the off-line mode, I use `load_dataset('xxx/glue.py','sst2', cache_dir='/xxx')`. I can successfully reuse cached datasets. My problem is about the load_metric. When I run `load_dataset('xxx/glue_metric.py','sst2',cache_dir='/xxx')` , it returns ` File "xx/lib64/python3.9/site-packages/datasets/utils/deprecation_utils.py", line 46, in wrapper return deprecated_function(*args, **kwargs) File "xx//lib64/python3.9/site-packages/datasets/load.py", line 1392, in load_metric metric = metric_cls( TypeError: 'NoneType' object is not callable` Thanks in advance for help! ### Steps to reproduce the bug N/A ### Expected behavior N/A ### Environment info `datasets == 2.12.0`

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PR_kwDODunzps5TA3__

set dev version

closed

MEMBER

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PR_kwDODunzps5TA1EB

Release: 2.13.0

closed

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PR_kwDODunzps5S_1o2

Fix ArrowExamplesIterable.shard_data_sources

closed

MEMBER

ArrowExamplesIterable.shard_data_sources was outdated I also fixed a warning message by not using format_type= in with_format()

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I_kwDODunzps5otw39

Strange bug in loading local JSON files, using load_dataset

closed

NONE

### Describe the bug I am using 'load_dataset 'loads a JSON file, but I found a strange bug: an error will be reported when the length of the JSON file exceeds 160000 (uncertain exact number). I have checked the data through the following code and there are no issues. So I cannot determine the true reason for this error. The data is a list containing a dictionary. As follows: [ {'input': 'someting...', 'target': 'someting...', 'type': 'someting...', 'history': ['someting...', ...]}, ... ] ### Steps to reproduce the bug ``` import json from datasets import load_dataset path = "target.json" temp_path = "temp.json" with open(path, "r") as f: data = json.load(f) print(f"\n-------the JSON file length is: {len(data)}-------\n") with open(temp_path, "w") as f: json.dump(data[:160000], f) dataset = load_dataset("json", data_files=temp_path) print("\n-------This works when the JSON file length is 160000-------\n") with open(temp_path, "w") as f: json.dump(data[160000:], f) dataset = load_dataset("json", data_files=temp_path) print("\n-------This works and eliminates data issues-------\n") with open(temp_path, "w") as f: json.dump(data[:170000], f) dataset = load_dataset("json", data_files=temp_path) ``` ### Expected behavior ``` -------the JSON file length is: 173049------- Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-acf3c7f418c5f4b4/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4... Downloading data files: 100%|███████████████████| 1/1 [00:00<00:00, 3328.81it/s] Extracting data files: 100%|█████████████████████| 1/1 [00:00<00:00, 639.47it/s] Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-acf3c7f418c5f4b4/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4. Subsequent calls will reuse this data. 100%|████████████████████████████████████████████| 1/1 [00:00<00:00, 265.85it/s] -------This works when the JSON file length is 160000------- Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-a42f04b263ceea6a/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4... Downloading data files: 100%|███████████████████| 1/1 [00:00<00:00, 2038.05it/s] Extracting data files: 100%|█████████████████████| 1/1 [00:00<00:00, 794.83it/s] Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-a42f04b263ceea6a/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4. Subsequent calls will reuse this data. 100%|████████████████████████████████████████████| 1/1 [00:00<00:00, 681.00it/s] -------This works and eliminates data issues------- Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-63f391c89599c7b0/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4... Downloading data files: 100%|███████████████████| 1/1 [00:00<00:00, 3682.44it/s] Extracting data files: 100%|█████████████████████| 1/1 [00:00<00:00, 788.70it/s] Generating train split: 0 examples [00:00, ? examples/s]Failed to read file '/home/lakala/hjc/code/pycode/glm/temp.json' with error <class 'pyarrow.lib.ArrowInvalid'>: cannot mix list and non-list, non-null values Traceback (most recent call last): File "/home/lakala/conda/envs/glm/lib/python3.8/site-packages/datasets/builder.py", line 1858, in _prepare_split_single for _, table in generator: File "/home/lakala/conda/envs/glm/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py", line 146, in _generate_tables raise ValueError(f"Not able to read records in the JSON file at {file}.") from None ValueError: Not able to read records in the JSON file at /home/lakala/hjc/code/pycode/glm/temp.json. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/lakala/hjc/code/pycode/glm/test.py", line 22, in <module> dataset = load_dataset("json", data_files=temp_path) File "/home/lakala/conda/envs/glm/lib/python3.8/site-packages/datasets/load.py", line 1797, in load_dataset builder_instance.download_and_prepare( File "/home/lakala/conda/envs/glm/lib/python3.8/site-packages/datasets/builder.py", line 890, in download_and_prepare self._download_and_prepare( File "/home/lakala/conda/envs/glm/lib/python3.8/site-packages/datasets/builder.py", line 985, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/lakala/conda/envs/glm/lib/python3.8/site-packages/datasets/builder.py", line 1746, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/lakala/conda/envs/glm/lib/python3.8/site-packages/datasets/builder.py", line 1891, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Environment info ``` Ubuntu==22.04 python==3.8 pytorch-transformers==1.2.0 transformers== 4.27.1 datasets==2.12.0 numpy==1.24.3 pandas==1.5.3 ```

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PR_kwDODunzps5S-hSP

Better filenotfound for gated

closed

MEMBER

close https://github.com/huggingface/datasets/issues/5953 <img width="1292" alt="image" src="https://github.com/huggingface/datasets/assets/42851186/270fe5bc-1739-4878-b7bc-ab6d35336d4d">

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I_kwDODunzps5osmBL

Bad error message when trying to download gated dataset

closed

MEMBER

### Describe the bug When I attempt to download a model from the Hub that is gated without being logged in, I get a nice error message. E.g.: E.g. ```sh Repository Not Found for url: https://huggingface.co/api/models/DeepFloyd/IF-I-XL-v1.0. Please make sure you specified the correct `repo_id` and `repo_type`. If you are trying to access a private or gated repo, make sure you are authenticated. Invalid username or password.. Will try to load from local cache. ``` If I do the same for a gated dataset on the Hub, I'm not gated a nice error message IMO: ```sh File ~/hf/lib/python3.10/site-packages/fsspec/implementations/http.py:430, in HTTPFileSystem._info(self, url, **kwargs) 427 except Exception as exc: 428 if policy == "get": 429 # If get failed, then raise a FileNotFoundError --> 430 raise FileNotFoundError(url) from exc 431 logger.debug(str(exc)) 433 return {"name": url, "size": None, **info, "type": "file"} FileNotFoundError: https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0/resolve/main/n_shards.json ``` ### Steps to reproduce the bug ``` huggingface-cli logout ``` and then: ```py from datasets import load_dataset, Audio # English stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "en", split="test", streaming=True) stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000)) en_sample = next(iter(stream_data))["audio"]["array"] # Swahili stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "sw", split="test", streaming=True) stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000)) sw_sample = next(iter(stream_data))["audio"]["array"] ``` ### Expected behavior Better error message ### Environment info Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.12.0 - Platform: Linux-6.2.0-76060200-generic-x86_64-with-glibc2.35 - Python version: 3.10.6 - Huggingface_hub version: 0.16.0.dev0 - PyArrow version: 11.0.0 - Pandas version: 1.5.3

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PR_kwDODunzps5S-OIh

Add Arrow builder docs

closed

MEMBER

following https://github.com/huggingface/datasets/pull/5944

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I_kwDODunzps5or_sa

What is the Right way to use discofuse dataset??

closed

NONE

[Click here for Dataset link](https://huggingface.co/datasets/discofuse/viewer/discofuse-wikipedia/train?row=6) **Below is the following way, as per my understanding , Is it correct :question: :question:** The **columns/features from `DiscoFuse dataset`** that will be the **input to the `encoder` and `decoder`** are: [Click here for Dataset link](https://huggingface.co/datasets/discofuse/viewer/discofuse-wikipedia/train?row=6) 1. **coherent_first_sentence** 2. **coherent_second_sentence** 3. **incoherent_first_sentence** 4. **incoherent_second_sentence** [Click here for Dataset link](https://huggingface.co/datasets/discofuse/viewer/discofuse-wikipedia/train?row=6) The **`encoder` will take these four columns as input and encode them into a sequence of hidden states. The `decoder` will then take these hidden states as input and decode them into a new sentence that fuses the two original sentences together.** The **discourse type, connective_string, has_coref_type_pronoun, and has_coref_type_nominal columns will not be used as input to the encoder or decoder.** These columns are used to provide additional information about the dataset, but they are not necessary for the task of sentence fusion. Please correct me if I am wrong; otherwise, if this understanding is right, how shall I implement this task practically?

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I_kwDODunzps5onjH6

Support for data with instance-wise dictionary as features

open

NONE

### Feature request I notice that when loading data instances with feature type of python dictionary, the dictionary keys would be broadcast so that every instance has the same set of keys. Please see an example in the Motivation section. It is possible to avoid this behavior, i.e., load dictionary features as it is and do not broadcast the keys among instances? Please note that these dictionaries would have to be processed dynamically at each training iteration into strings (and tokenized). ### Motivation I am trying to load a dataset from a json file. Each instance of the dataset has a feature that is a dictionary but its keys depend on the instance. Every two instances may have different keys. For example, imagine a dataset that contains a set of math expressions from a bunch of mutually redundant expressions: ``` { "index": 0, "feature": { "2 * x + y >= 3": ["2 * x + y >= 3", "4 * x + 2 * y >= 6"], ... } }, ... { "index": 9999, "feature": { "x >= 6": ["x >= 6", "x >= 0", "x >= -1"], ... } }, ... ``` When directly loading the dataset using `data = load_dataset("json", data_files=file_paths, split='train')`, each instance would have all the keys from other instances and None as values. That is, instance of index 0 becomes: ``` { "index": 0, "feature": { "2 * x + y >= 3": ["2 * x + y >= 3", "4 * x + 2 * y >= 6"], ... "x >= 6": None, # keys from other instances ... } }, ``` This is not desirable. Moreover, issue would be raised if I attempt to combine two such datasets using `data = concatenate_datasets(multi_datasets)`, perhaps because their dictionary features contain different keys. A solution I can think of is to store the dictionary features as a long string, and evaluate it later. Please kindly suggest any other solution using existing methods of datasets. ### Your contribution N/A

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PR_kwDODunzps5S4oPC

Replace metadata utils with `huggingface_hub`'s RepoCard API

closed

CONTRIBUTOR

Use `huggingface_hub`'s RepoCard API instead of `DatasetMetadata` for modifying the card's YAML, and deprecate `datasets.utils.metadata` and `datasets.utils.readme`. After removing these modules, we can also delete `datasets.utils.resources` since the moon landing repo now stores its own version of these resources for the metadata UI. PS: this change requires bumping `huggingface_hub` to 0.13.0 (Transformers requires 0.14.0, so should be ok)

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PR_kwDODunzps5S4dUt

Fix sequence of array support for most dtype

closed

CONTRIBUTOR

Fixes #5936 Also, a related fix to #5927

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I_kwDODunzps5okWYU

Return the audio filename when decoding fails due to corrupt files

open

NONE

### Feature request Return the audio filename when the audio decoding fails. Although currently there are some checks for mp3 and opus formats with the library version there are still cases when the audio decoding could fail, eg. Corrupt file. ### Motivation When you try to load an object file dataset and the decoding fails you can't know which file is corrupt ``` raise LibsndfileError(err, prefix="Error opening {0!r}: ".format(self.name)) soundfile.LibsndfileError: Error opening <_io.BytesIO object at 0x7f5ab7e38290>: Format not recognised. ``` ### Your contribution Make a PR to Add exceptions for LIbsndfileError to return the audio filename or path when soundfile decoding fails.

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I_kwDODunzps5oj35l

IndexError Not Solving -> IndexError: Invalid key: ?? is out of bounds for size 0 or ??

open

NONE

### Describe the bug in <cell line: 1>:1 │ │ │ │ /usr/local/lib/python3.10/dist-packages/transformers/trainer.py:1537 in train │ │ │ │ 1534 │ │ inner_training_loop = find_executable_batch_size( │ │ 1535 │ │ │ self._inner_training_loop, self._train_batch_size, args.auto_find_batch_size │ │ 1536 │ │ ) │ │ ❱ 1537 │ │ return inner_training_loop( │ │ 1538 │ │ │ args=args, │ │ 1539 │ │ │ resume_from_checkpoint=resume_from_checkpoint, │ │ 1540 │ │ │ trial=trial, │ │ │ │ /usr/local/lib/python3.10/dist-packages/transformers/trainer.py:1789 in _inner_training_loop │ │ │ │ 1786 │ │ │ │ rng_to_sync = True │ │ 1787 │ │ │ │ │ 1788 │ │ │ step = -1 │ │ ❱ 1789 │ │ │ for step, inputs in enumerate(epoch_iterator): │ │ 1790 │ │ │ │ total_batched_samples += 1 │ │ 1791 │ │ │ │ if rng_to_sync: │ │ 1792 │ │ │ │ │ self._load_rng_state(resume_from_checkpoint) │ │ │ │ /usr/local/lib/python3.10/dist-packages/accelerate/data_loader.py:377 in __iter__ │ │ │ │ 374 │ │ dataloader_iter = super().__iter__() │ │ 375 │ │ # We iterate one batch ahead to check when we are at the end │ │ 376 │ │ try: │ │ ❱ 377 │ │ │ current_batch = next(dataloader_iter) │ │ 378 │ │ except StopIteration: │ │ 379 │ │ │ yield │ │ 380 │ │ │ │ /usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:633 in __next__ │ │ │ │ 630 │ │ │ if self._sampler_iter is None: │ │ 631 │ │ │ │ # TODO(https://github.com/pytorch/pytorch/issues/76750) │ │ 632 │ │ │ │ self._reset() # type: ignore[call-arg] │ │ ❱ 633 │ │ │ data = self._next_data() │ │ 634 │ │ │ self._num_yielded += 1 │ │ 635 │ │ │ if self._dataset_kind == _DatasetKind.Iterable and \ │ │ 636 │ │ │ │ │ self._IterableDataset_len_called is not None and \ │ │ │ │ /usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:677 in _next_data │ │ │ │ 674 │ │ │ 675 │ def _next_data(self): │ │ 676 │ │ index = self._next_index() # may raise StopIteration │ │ ❱ 677 │ │ data = self._dataset_fetcher.fetch(index) # may raise StopIteration │ │ 678 │ │ if self._pin_memory: │ │ 679 │ │ │ data = _utils.pin_memory.pin_memory(data, self._pin_memory_device) │ │ 680 │ │ return data │ │ │ │ /usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py:49 in fetch │ │ │ │ 46 │ def fetch(self, possibly_batched_index): │ │ 47 │ │ if self.auto_collation: │ │ 48 │ │ │ if hasattr(self.dataset, "__getitems__") and self.dataset.__getitems__: │ │ ❱ 49 │ │ │ │ data = self.dataset.__getitems__(possibly_batched_index) │ │ 50 │ │ │ else: │ │ 51 │ │ │ │ data = [self.dataset[idx] for idx in possibly_batched_index] │ │ 52 │ │ else: │ │ │ │ /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:2782 in __getitems__ │ │ │ │ 2779 │ │ │ 2780 │ def __getitems__(self, keys: List) -> List: │ │ 2781 │ │ """Can be used to get a batch using a list of integers indices.""" │ │ ❱ 2782 │ │ batch = self.__getitem__(keys) │ │ 2783 │ │ n_examples = len(batch[next(iter(batch))]) │ │ 2784 │ │ return [{col: array[i] for col, array in batch.items()} for i in range(n_example │ │ 2785 │ │ │ │ /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:2778 in __getitem__ │ │ │ │ 2775 │ │ │ 2776 │ def __getitem__(self, key): # noqa: F811 │ │ 2777 │ │ """Can be used to index columns (by string names) or rows (by integer index or i │ │ ❱ 2778 │ │ return self._getitem(key) │ │ 2779 │ │ │ 2780 │ def __getitems__(self, keys: List) -> List: │ │ 2781 │ │ """Can be used to get a batch using a list of integers indices.""" │ │ │ │ /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:2762 in _getitem │ │ │ │ 2759 │ │ format_kwargs = kwargs["format_kwargs"] if "format_kwargs" in kwargs else self._ │ │ 2760 │ │ format_kwargs = format_kwargs if format_kwargs is not None else {} │ │ 2761 │ │ formatter = get_formatter(format_type, features=self._info.features, **format_kw │ │ ❱ 2762 │ │ pa_subtable = query_table(self._data, key, indices=self._indices if self._indice │ │ 2763 │ │ formatted_output = format_table( │ │ 2764 │ │ │ pa_subtable, key, formatter=formatter, format_columns=format_columns, output │ │ 2765 │ │ ) │ │ │ │ /usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py:578 in query_table │ │ │ │ 575 │ │ _check_valid_column_key(key, table.column_names) │ │ 576 │ else: │ │ 577 │ │ size = indices.num_rows if indices is not None else table.num_rows │ │ ❱ 578 │ │ _check_valid_index_key(key, size) │ │ 579 │ # Query the main table │ │ 580 │ if indices is None: │ │ 581 │ │ pa_subtable = _query_table(table, key) │ │ │ │ /usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py:531 in │ │ _check_valid_index_key │ │ │ │ 528 │ │ │ _check_valid_index_key(min(key), size=size) │ │ 529 │ elif isinstance(key, Iterable): │ │ 530 │ │ if len(key) > 0: │ │ ❱ 531 │ │ │ _check_valid_index_key(int(max(key)), size=size) │ │ 532 │ │ │ _check_valid_index_key(int(min(key)), size=size) │ │ 533 │ else: │ │ 534 │ │ _raise_bad_key_type(key) │ │ │ │ /usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py:521 in │ │ _check_valid_index_key │ │ │ │ 518 def _check_valid_index_key(key: Union[int, slice, range, Iterable], size: int) -> None: │ │ 519 │ if isinstance(key, int): │ │ 520 │ │ if (key < 0 and key + size < 0) or (key >= size): │ │ ❱ 521 │ │ │ raise IndexError(f"Invalid key: {key} is out of bounds for size {size}") │ │ 522 │ │ return │ │ 523 │ elif isinstance(key, slice): │ │ 524 │ │ pass ### Steps to reproduce the bug `` import json import os from pprint import pprint import bitsandbytes as bnb import pandas as pd import torch import torch.nn as nn import transformers from datasets import Dataset,load_dataset from peft import ( LoraConfig, PeftConfig, PeftModel, get_peft_model, prepare_model_for_kbit_training ) from transformers import ( AutoConfig, AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, ) os.environ["CUDA_VISIBLE_DEVICES"] = "0" def print_trainable_parameters(model): """ Prints the number of trainable parameters in the model. """ trainable_params = 0 all_param = 0 for _, param in model.named_parameters(): all_param += param.numel() if param.requires_grad: trainable_params += param.numel() print( f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}" ) MODEL_NAME = "tiiuae/falcon-7b" bnb_config = BitsAndBytesConfig( load_in_4bit = True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, ) model = AutoModelForCausalLM.from_pretrained( MODEL_NAME, device_map = "auto", trust_remote_code = True, quantization_config = bnb_config ) tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) tokenizer.pad_token = tokenizer.eos_token model.gradient_checkpointing_enable() model = prepare_model_for_kbit_training(model) config = LoraConfig( r = 16, lora_alpha = 32, target_modules = ["query_key_value"], lora_dropout = 0.05, bias = "none", task_type = "CASUAL_LM" ) model = get_peft_model(model,config) print_trainable_parameters(model) def generate_prompt(data_point): return f""" <human>: {data_point["question"]} <assistant>: {data_point["answer"]} """.strip() def generate_and_tokenize_prompt(data_point): full_prompt = generate_prompt(data_point) tokenized_full_prompt = tokenizer(full_prompt, padding = True, truncation = True,return_tensors = None) return dict({ "input_ids" : tokenized_full_prompt["input_ids"], "attention_mask" : tokenized_full_prompt["attention_mask"] }) data = data["train"].shuffle().map(generate_and_tokenize_prompt, batched = False) OUTPUT_DIR = "experiments" trainings_args = transformers.TrainingArguments( per_device_train_batch_size = 1, gradient_accumulation_steps = 4, num_train_epochs = 1, learning_rate = 2e-4, fp16 = True, save_total_limit = 3, logging_steps = 1, output_dir = OUTPUT_DIR, max_steps = 80, optim = "paged_adamw_8bit", lr_scheduler_type = "cosine", warmup_ratio = 0.05, #remove_unused_columns=True ) trainer = transformers.Trainer( model = model, train_dataset = data, args = trainings_args, data_collator = transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False), ) model.config.use_cache = False trainer.train() IndexError: Invalid key: 32 is out of bounds for size 0 DataSet Format is like : [{"question": "How can I create an account?", "answer": "To create an account, click on the 'Sign Up' button on the top right corner of our website and follow the instructions to complete the registration process."}, .... ] ### Expected behavior - ### Environment info !pip install -q pip !pip install -q bitsandbytes==0.39.0 !pip install -q torch==2.0.1 !pip install -q git+https://github.com/huggingface/transformers.git !pip install -q git+https://github.com/huggingface/peft.git !pip install -q git+https://github.com/huggingface/accelerate.git !pip install -q datasets !pip install -q loralib==0.1.1 !pip install -q einops==0.6.1 import json import os from pprint import pprint import bitsandbytes as bnb import pandas as pd import torch import torch.nn as nn import transformers from datasets import Dataset,load_dataset from peft import ( LoraConfig, PeftConfig, PeftModel, get_peft_model, prepare_model_for_kbit_training ) from transformers import ( AutoConfig, AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, ) os.environ["CUDA_VISIBLE_DEVICES"] = "0"

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I_kwDODunzps5ojTTh

Failing to upload dataset to the hub

closed

NONE

### Describe the bug Trying to upload a dataset of hundreds of thousands of audio samples (the total volume is not very large, 60 gb) to the hub with push_to_hub, it doesn't work. From time to time one piece of the data (parquet) gets pushed and then I get RemoteDisconnected even though my internet is stable. Please help. I'm trying to upload the dataset for almost a week. Thanks ### Steps to reproduce the bug not relevant ### Expected behavior Be able to upload thedataset ### Environment info python: 3.9

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PR_kwDODunzps5Sx7O4

Arrow dataset builder to be able to load and stream Arrow datasets

closed

CONTRIBUTOR

This adds a Arrow dataset builder to be able to load and stream from already preprocessed Arrow files. It's related to https://github.com/huggingface/datasets/issues/3035

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PR_kwDODunzps5Su-V4

Pass datasets-cli additional args as kwargs to DatasetBuilder in `run_beam.py`

open

NONE

Hi, Following this <https://discuss.huggingface.co/t/how-to-preprocess-a-wikipedia-dataset-using-dataflowrunner/41991/3>, here is a simple PR to pass any additional args to datasets-cli as kwargs in the DatasetBuilder in `run_beam.py`. I also took the liberty to add missing setup steps to the `beam.mdx` docs in order to help everyone. @lhoestq

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I_kwDODunzps5oavCx

Load Data Sets Too Slow In Train Seq2seq Model

closed

NONE

### Describe the bug step 'Generating train split' in load_dataset is too slow：  ### Steps to reproduce the bug Data： own data，16K16B Mono wav Oficial Script:[ run_speech_recognition_seq2seq.py](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py) Add Code： if data_args.data_path is not None: print(data_args.data_path) raw_datasets = load_dataset("audiofolder", data_dir=data_args.data_path, cache_dir=model_args.cache_dir) raw_datasets = raw_datasets.cast_column("audio", Audio(sampling_rate=16000)) raw_datasets = raw_datasets["train"].train_test_split(test_size=0.005, shuffle=True) （change cache_dir to other path ，ex:/DATA/cache） ### Expected behavior load data fast,at least 1000+ `Generating train split: 387875 examples [32:24:45, 1154.83 examples/s]` ### Environment info - `transformers` version: 4.28.0.dev0 - Platform: Linux-5.4.0-149-generic-x86_64-with-debian-bullseye-sid - Python version: 3.7.16 - Huggingface_hub version: 0.13.2 - PyTorch version (GPU?): 1.13.1+cu116 (True) - Tensorflow version (GPU?): not installed (NA) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in>

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I_kwDODunzps5pwwpe

Pushing a large dataset on the hub consistently hangs

open

NONE

### Describe the bug Once I have locally built a large dataset that I want to push to hub, I use the recommended approach of .push_to_hub to get the dataset on the hub, and after pushing a few shards, it consistently hangs. This has happened over 40 times over the past week, and despite my best efforts to try and catch this happening and kill a process and restart, it seems to be extremely time wasting -- so I came to you to report this and to seek help. I already tried installing hf_transfer, but it doesn't support Byte file uploads so I uninstalled it. ### Reproduction ```python import multiprocessing as mp import pathlib from math import ceil import datasets import numpy as np from tqdm.auto import tqdm from tali.data.data import select_subtitles_between_timestamps from tali.utils import load_json tali_dataset_dir = "/data/" if __name__ == "__main__": full_dataset = datasets.load_dataset( "Antreas/TALI", num_proc=mp.cpu_count(), cache_dir=tali_dataset_dir ) def data_generator(set_name, percentage: float = 1.0): dataset = full_dataset[set_name] for item in tqdm(dataset): video_list = item["youtube_content_video"] video_list = np.random.choice( video_list, int(ceil(len(video_list) * percentage)) ) if len(video_list) == 0: continue captions = item["youtube_subtitle_text"] captions = select_subtitles_between_timestamps( subtitle_dict=load_json( captions.replace( "/data/", tali_dataset_dir, ) ), starting_timestamp=0, ending_timestamp=100000000, ) for video_path in video_list: temp_path = video_path.replace("/data/", tali_dataset_dir) video_path_actual: pathlib.Path = pathlib.Path(temp_path) if video_path_actual.exists(): item["youtube_content_video"] = open(video_path_actual, "rb").read() item["youtube_subtitle_text"] = captions yield item train_generator = lambda: data_generator("train", percentage=0.1) val_generator = lambda: data_generator("val") test_generator = lambda: data_generator("test") train_data = datasets.Dataset.from_generator( train_generator, num_proc=mp.cpu_count(), writer_batch_size=5000, cache_dir=tali_dataset_dir, ) val_data = datasets.Dataset.from_generator( val_generator, writer_batch_size=5000, num_proc=mp.cpu_count(), cache_dir=tali_dataset_dir, ) test_data = datasets.Dataset.from_generator( test_generator, writer_batch_size=5000, num_proc=mp.cpu_count(), cache_dir=tali_dataset_dir, ) dataset = datasets.DatasetDict( { "train": train_data, "val": val_data, "test": test_data, } ) succesful_competion = False while not succesful_competion: try: dataset.push_to_hub(repo_id="Antreas/TALI-small", max_shard_size="5GB") succesful_competion = True except Exception as e: print(e) ``` ### Logs ```shell Pushing dataset shards to the dataset hub: 33%|██████████████████████████████████████▎ | 7/21 [24:33<49:06, 210.45s/it] Error while uploading 'data/val-00007-of-00021-6b216a984af1a4c8.parquet' to the Hub. Pushing split train to the Hub. Resuming upload of the dataset shards. Pushing dataset shards to the dataset hub: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 46/46 [42:10<00:00, 55.01s/it] Pushing split val to the Hub. Resuming upload of the dataset shards. Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:01<00:00, 1.55ba/s] Upload 1 LFS files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:23<00:00, 23.51s/it] Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.39ba/s] Upload 1 LFS files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:30<00:00, 30.19s/it] Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.28ba/s] Upload 1 LFS files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:24<00:00, 24.08s/it] Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.42ba/s] Upload 1 LFS files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:23<00:00, 23.97s/it] Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.49ba/s] Creating parquet from Arrow format: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:02<00:00, 1.54ba/s^ Upload 1 LFS files: 0%| | 0/1 [04:42<?, ?it/s] Pushing dataset shards to the dataset hub: 52%|████████████████████████████████████████████████████████████▏ | 11/21 [17:23<15:48, 94.82s/it] That's where it got stuck ``` ### System info ```shell - huggingface_hub version: 0.15.1 - Platform: Linux-5.4.0-147-generic-x86_64-with-glibc2.35 - Python version: 3.10.11 - Running in iPython ?: No - Running in notebook ?: No - Running in Google Colab ?: No - Token path ?: /root/.cache/huggingface/token - Has saved token ?: True - Who am I ?: Antreas - Configured git credential helpers: store - FastAI: N/A - Tensorflow: N/A - Torch: 2.1.0.dev20230606+cu121 - Jinja2: 3.1.2 - Graphviz: N/A - Pydot: N/A - Pillow: 9.5.0 - hf_transfer: N/A - gradio: N/A - numpy: 1.24.3 - ENDPOINT: https://huggingface.co - HUGGINGFACE_HUB_CACHE: /root/.cache/huggingface/hub - HUGGINGFACE_ASSETS_CACHE: /root/.cache/huggingface/assets - HF_TOKEN_PATH: /root/.cache/huggingface/token - HF_HUB_OFFLINE: False - HF_HUB_DISABLE_TELEMETRY: False - HF_HUB_DISABLE_PROGRESS_BARS: None - HF_HUB_DISABLE_SYMLINKS_WARNING: False - HF_HUB_DISABLE_EXPERIMENTAL_WARNING: False - HF_HUB_DISABLE_IMPLICIT_TOKEN: False - HF_HUB_ENABLE_HF_TRANSFER: False ```

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I_kwDODunzps5oTjUr

.

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NONE

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PR_kwDODunzps5SmbkI

Make get_from_cache use custom temp filename that is locked

closed

MEMBER

This PR ensures that the temporary filename created is the same as the one that is locked, while writing to the cache. This PR stops using `tempfile` to generate the temporary filename. Additionally, the behavior now is aligned for both `resume_download` `True` and `False`. Refactor temp_file_manager so that it uses the filename that is locked: - Use: `cache_path + ".incomplete"`, when the locked one is `cache_path + ".lock"` Before it was using `tempfile` inside `cache_dir`, which was not locked: although very improbable name collision (8 random characters), this was not impossible when huge number of multiple processes. Maybe related to "Stale file handle" issues caused by `tempfile`: - [ ] https://huggingface.co/datasets/tapaco/discussions/4 - [ ] https://huggingface.co/datasets/xcsr/discussions/1 - [ ] https://huggingface.co/datasets/covost2/discussions/3 ``` Error code: ConfigNamesError Exception: OSError Message: [Errno 116] Stale file handle Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 61, in compute_config_names_response for config in sorted(get_dataset_config_names(path=dataset, use_auth_token=use_auth_token)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 323, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1219, in dataset_module_factory raise e1 from None File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1188, in dataset_module_factory return HubDatasetModuleFactoryWithScript( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 907, in get_module dataset_readme_path = self.download_dataset_readme_file() File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 896, in download_dataset_readme_file return cached_path( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 183, in cached_path output_path = get_from_cache( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 611, in get_from_cache http_get( File "/usr/local/lib/python3.9/tempfile.py", line 496, in __exit__ result = self.file.__exit__(exc, value, tb) OSError: [Errno 116] Stale file handle ``` - the stale file handle error can be raised when `tempfile` tries to close (when exiting its context manager) a filename that has been already closed by other process - note that `tempfile` filenames are randomly generated but not locked in our code CC: @severo

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PR_kwDODunzps5SmLIs

Avoid parallel redownload in cache

closed

MEMBER

Avoid parallel redownload in cache by retrying inside the lock if path exists.

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I_kwDODunzps5oNtbE

Sequence of array not supported for most dtype

closed

CONTRIBUTOR

### Describe the bug Create a dataset composed of sequence of array fails for most dtypes (see code below). ### Steps to reproduce the bug ```python from datasets import Sequence, Array2D, Features, Dataset import numpy as np for dtype in [ "bool", # ok "int8", # failed "int16", # failed "int32", # failed "int64", # ok "uint8", # failed "uint16", # failed "uint32", # failed "uint64", # failed "float16", # failed "float32", # failed "float64", # ok ]: features = Features({"foo": Sequence(Array2D(dtype=dtype, shape=(2, 2)))}) sequence = [ [[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0], [7.0, 8.0]], ] array = np.array(sequence, dtype=dtype) try: dataset = Dataset.from_dict({"foo": [array]}, features=features) except Exception as e: print(f"Failed for dtype={dtype}") ``` Traceback for `dtype="int8"`: ``` Traceback (most recent call last): File "/home/qgallouedec/datasets/a.py", line 29, in <module> raise e File "/home/qgallouedec/datasets/a.py", line 26, in <module> dataset = Dataset.from_dict({"foo": [array]}, features=features) File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 899, in from_dict pa_table = InMemoryTable.from_pydict(mapping=mapping) File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 799, in from_pydict return cls(pa.Table.from_pydict(*args, **kwargs)) File "pyarrow/table.pxi", line 3725, in pyarrow.lib.Table.from_pydict File "pyarrow/table.pxi", line 5254, in pyarrow.lib._from_pydict File "pyarrow/array.pxi", line 350, in pyarrow.lib.asarray File "pyarrow/array.pxi", line 236, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/arrow_writer.py", line 204, in __arrow_array__ out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type) File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 1833, in wrapper return func(array, *args, **kwargs) File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 2091, in cast_array_to_feature casted_values = _c(array.values, feature.feature) File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 1833, in wrapper return func(array, *args, **kwargs) File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 2139, in cast_array_to_feature return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 1833, in wrapper return func(array, *args, **kwargs) File "/home/qgallouedec/env/lib/python3.10/site-packages/datasets/table.py", line 1967, in array_cast return pa_type.wrap_array(array) File "pyarrow/types.pxi", line 879, in pyarrow.lib.BaseExtensionType.wrap_array TypeError: Incompatible storage type for extension<arrow.py_extension_type<Array2DExtensionType>>: expected list<item: list<item: int8>>, got list<item: list<item: int64>> ``` ### Expected behavior Not to fail. ### Environment info - Python 3.10.6 - datasets: master branch - Numpy: 1.23.4

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https://github.com/huggingface/datasets/pull/5935

PR_kwDODunzps5Sh9Mg

Better row group size in push_to_hub

closed

MEMBER

This is a very simple change that improves `to_parquet` to use a more reasonable row group size for image and audio datasets. This is especially useful for `push_to_hub` and will provide a better experience with the dataset viewer on HF

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PR_kwDODunzps5ShUxQ

Modify levels of some logging messages

closed

CONTRIBUTOR

Some warning messages didn't quite sound like warnings so I modified their logging levels to info.

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https://github.com/huggingface/datasets/pull/5933

PR_kwDODunzps5Sfi5J

Fix `to_numpy` when None values in the sequence

closed

CONTRIBUTOR

Closes #5927 I've realized that the error was overlooked during testing due to the presence of only one None value in the sequence. Unfortunately, it was the only case where the function works as expected. When the sequence contained more than one None value, the function failed. Consequently, I've updated the tests to include sequences with multiple None values.

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PR_kwDODunzps5Sbrzo

[doc build] Use secrets

closed

CONTRIBUTOR

Companion pr to https://github.com/huggingface/doc-builder/pull/379

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https://github.com/huggingface/datasets/issues/5931

I_kwDODunzps5oCNMQ

`datasets.map` not reusing cached copy by default

closed

CONTRIBUTOR

### Describe the bug When I load the dataset from local directory, it's cached copy is picked up after first time. However, for `map` operation, the operation is applied again and cached copy is not picked up. Is there any way to pick cached copy instead of processing it again? The only solution I could think of was to use `save_to_disk` after my last transform and then use that in my DataLoader pipeline. Are there any other solutions for the same? One more thing, my dataset is occupying 6GB storage memory after I use `map`, is there any way I can reduce that memory usage? ### Steps to reproduce the bug ``` # make sure that dataset decodes audio with correct sampling rate dataset_sampling_rate = next(iter(self.raw_datasets.values())).features["audio"].sampling_rate if dataset_sampling_rate != self.feature_extractor.sampling_rate: self.raw_datasets = self.raw_datasets.cast_column( "audio", datasets.features.Audio(sampling_rate=self.feature_extractor.sampling_rate) ) vectorized_datasets = self.raw_datasets.map( self.prepare_dataset, remove_columns=next(iter(self.raw_datasets.values())).column_names, num_proc=self.num_workers, desc="preprocess datasets", ) # filter data that is longer than max_input_length self.vectorized_datasets = vectorized_datasets.filter( self.is_audio_in_length_range, num_proc=self.num_workers, input_columns=["input_length"], ) def prepare_dataset(self, batch): # load audio sample = batch["audio"] inputs = self.feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"]) batch["input_values"] = inputs.input_values[0] batch["input_length"] = len(batch["input_values"]) batch["labels"] = self.tokenizer(batch["target_text"]).input_ids return batch ``` ### Expected behavior `map` to use cached copy and if possible an alternative technique to reduce memory usage after using `map` ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-3.10.0-1160.71.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.8.16 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.2

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