Datasets.map is severely broken

### Describe the bug

Regardless of how many cores I used, I have 16 or 32 threads, map slows down to a crawl at around 80% done, lingers maybe until 97% extremely slowly and NEVER finishes the job. It just hangs.

After watching this for 27 hours I control-C out of it. Until the end one process appears to be doing something, but it never ends.

I saw some comments about fast tokenizers using Rust and all and tried different variations. NOTHING works.



### Steps to reproduce the bug

Running it without breaking the dataset into parts results in the same behavior. The loop was an attempt to see if this was a RAM issue. 

for idx in range(100):
    dataset = load_dataset("togethercomputer/RedPajama-Data-1T-Sample", cache_dir=cache_dir, split=f'train[{idx}%:{idx+1}%]')
    dataset = dataset.map(partial(tokenize_fn, tokenizer), batched=False, num_proc=1, remove_columns=["text", "meta"])
    dataset.save_to_disk(training_args.cache_dir + f"/training_data_{idx}")


### Expected behavior

I expect map to run at more or less the same speed it starts with and FINISH its processing. 

### Environment info

Python 3.8, same with 3.10 makes no difference.
Ubuntu 20.04, 

Hi! Instead of processing a single example at a time, you should use the batched `map` for the best performance (with `num_proc=1`) - the fast tokenizers can process a batch's samples in parallel in that scenario.

E.g., the following code in Colab takes an hour to complete:
```python
# !pip install datasets transformers
from datasets import load_dataset
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
dataset = dataset.map(lambda ex: tokenizer(ex["text"]), batched=True, remove_columns=["text", "meta"])
```

Batched is far worse. A single batch of 1000 took hours and that was only 1%


On Thu, Oct 19, 2023, 2:26 PM Mario Šaško ***@***.***> wrote:

> Hi! You should use the batched map for the best performance (with
> num_proc=1) - the fast tokenizers can process a batch's samples in
> parallel.
>
> E.g., the following code in Colab takes an hour to complete:
>
> # !pip install datasets transformersfrom datasets import load_datasetfrom transformers import AutoTokenizertokenizer = AutoTokenizer.from_pretrained("bert-base-cased")dataset = dataset.map(lambda ex: tokenizer(ex["text"]), batched=True, remove_columns=["text", "meta"])
>
> —
> Reply to this email directly, view it on GitHub
> <https://github.com/huggingface/datasets/issues/6319#issuecomment-1771503757>,
> or unsubscribe
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> .
> You are receiving this because you authored the thread.Message ID:
> ***@***.***>
>


Can you please provide a self-contained reproducer?

Which specific version of datasets are you using?

What is the architecture of your colab setup? Ram? Cores? OS?


On Thu, Oct 19, 2023, 2:27 PM pensive introvert ***@***.***>
wrote:

> Batched is far worse. A single batch of 1000 took hours and that was only
> 1%
>
>
> On Thu, Oct 19, 2023, 2:26 PM Mario Šaško ***@***.***>
> wrote:
>
>> Hi! You should use the batched map for the best performance (with
>> num_proc=1) - the fast tokenizers can process a batch's samples in
>> parallel.
>>
>> E.g., the following code in Colab takes an hour to complete:
>>
>> # !pip install datasets transformersfrom datasets import load_datasetfrom transformers import AutoTokenizertokenizer = AutoTokenizer.from_pretrained("bert-base-cased")dataset = dataset.map(lambda ex: tokenizer(ex["text"]), batched=True, remove_columns=["text", "meta"])
>>
>> —
>> Reply to this email directly, view it on GitHub
>> <https://github.com/huggingface/datasets/issues/6319#issuecomment-1771503757>,
>> or unsubscribe
>> <https://github.com/notifications/unsubscribe-auth/ABDD3ZJHPSRVDEXFNMXR2N3YAFWFZAVCNFSM6AAAAAA6HDKPSCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTONZRGUYDGNZVG4>
>> .
>> You are receiving this because you authored the thread.Message ID:
>> ***@***.***>
>>
>


from functools import partial
import transformers
from datasets import load_dataset, concatenate_datasets, load_from_disk

model_name_or_path="/opt/data/data/daryl149/llama-2-7b-chat-hf"
output_dir="/opt/data/data/LongLoRA/checkpoints"
cache_dir="/opt/data/data/LongLoRA/cache"
model_max_length=16384

IGNORE_INDEX = -100
DEFAULT_PAD_TOKEN = "[PAD]"
DEFAULT_EOS_TOKEN = "
"
DEFAULT_BOS_TOKEN = "
"
DEFAULT_UNK_TOKEN = "<unk>"


tokenizer = transformers.LlamaTokenizerFast.from_pretrained(
        model_name_or_path,
        cache_dir=cache_dir,
        model_max_length=model_max_length,
        padding_side="right",
        use_fast=True,
        #use_fast=False
)

special_tokens_dict = dict()
if tokenizer.pad_token is None:
    special_tokens_dict["pad_token"] = DEFAULT_PAD_TOKEN
if tokenizer.eos_token is None:
    special_tokens_dict["eos_token"] = DEFAULT_EOS_TOKEN
if tokenizer.bos_token is None:
    special_tokens_dict["bos_token"] = DEFAULT_BOS_TOKEN
if tokenizer.unk_token is None:
    special_tokens_dict["unk_token"] = DEFAULT_UNK_TOKEN

tokenizer.add_special_tokens(special_tokens_dict)

def tokenize_fn(tokenizer, example):
    context_length = tokenizer.model_max_length
    outputs = tokenizer(
        tokenizer.eos_token.join(example["text"]),
        #truncation=False,
        truncation=True,
        return_tensors="pt",
        #return_tensors="np",
        pad_to_multiple_of=context_length,
        padding=True,
    )
    return {"input_ids": outputs["input_ids"].view(-1, context_length)}

for idx in range(100):
    dataset = load_dataset("togethercomputer/RedPajama-Data-1T-Sample",
cache_dir=cache_dir, split=f'train[{idx}%:{idx+1}%]')
    dataset = dataset.map(partial(tokenize_fn, tokenizer), batched=False,
num_proc=16, remove_columns=["text", "meta"])
    dataset.save_to_disk(training_args.cache_dir + f"/training_data_{idx}")


On Thu, Oct 19, 2023 at 2:30 PM Mario Šaško ***@***.***>
wrote:

> Can you please provide a self-contained reproducer?
>
> —
> Reply to this email directly, view it on GitHub
> <https://github.com/huggingface/datasets/issues/6319#issuecomment-1771509229>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/ABDD3ZNBZ3BE7Q4EQZZK6MLYAFWURAVCNFSM6AAAAAA6HDKPSCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTONZRGUYDSMRSHE>
> .
> You are receiving this because you authored the thread.Message ID:
> ***@***.***>
>


I changed the tokenizer to one without "Fast suffix, and something changed.
The fraction, although still slowed a lot at 80% was able to get over the
finish line of 100%

I have to do more testng, see if the whole set can be processed



On Thu, Oct 19, 2023 at 3:03 PM pensive introvert <
***@***.***> wrote:

> from functools import partial
> import transformers
> from datasets import load_dataset, concatenate_datasets, load_from_disk
>
> model_name_or_path="/opt/data/data/daryl149/llama-2-7b-chat-hf"
> output_dir="/opt/data/data/LongLoRA/checkpoints"
> cache_dir="/opt/data/data/LongLoRA/cache"
> model_max_length=16384
>
> IGNORE_INDEX = -100
> DEFAULT_PAD_TOKEN = "[PAD]"
> DEFAULT_EOS_TOKEN = "
"
> DEFAULT_BOS_TOKEN = "
"
> DEFAULT_UNK_TOKEN = "<unk>"
>
>
> tokenizer = transformers.LlamaTokenizerFast.from_pretrained(
>         model_name_or_path,
>         cache_dir=cache_dir,
>         model_max_length=model_max_length,
>         padding_side="right",
>         use_fast=True,
>         #use_fast=False
> )
>
> special_tokens_dict = dict()
> if tokenizer.pad_token is None:
>     special_tokens_dict["pad_token"] = DEFAULT_PAD_TOKEN
> if tokenizer.eos_token is None:
>     special_tokens_dict["eos_token"] = DEFAULT_EOS_TOKEN
> if tokenizer.bos_token is None:
>     special_tokens_dict["bos_token"] = DEFAULT_BOS_TOKEN
> if tokenizer.unk_token is None:
>     special_tokens_dict["unk_token"] = DEFAULT_UNK_TOKEN
>
> tokenizer.add_special_tokens(special_tokens_dict)
>
> def tokenize_fn(tokenizer, example):
>     context_length = tokenizer.model_max_length
>     outputs = tokenizer(
>         tokenizer.eos_token.join(example["text"]),
>         #truncation=False,
>         truncation=True,
>         return_tensors="pt",
>         #return_tensors="np",
>         pad_to_multiple_of=context_length,
>         padding=True,
>     )
>     return {"input_ids": outputs["input_ids"].view(-1, context_length)}
>
> for idx in range(100):
>     dataset = load_dataset("togethercomputer/RedPajama-Data-1T-Sample",
> cache_dir=cache_dir, split=f'train[{idx}%:{idx+1}%]')
>     dataset = dataset.map(partial(tokenize_fn, tokenizer), batched=False,
> num_proc=16, remove_columns=["text", "meta"])
>     dataset.save_to_disk(training_args.cache_dir + f"/training_data_{idx}")
>
>
> On Thu, Oct 19, 2023 at 2:30 PM Mario Šaško ***@***.***>
> wrote:
>
>> Can you please provide a self-contained reproducer?
>>
>> —
>> Reply to this email directly, view it on GitHub
>> <https://github.com/huggingface/datasets/issues/6319#issuecomment-1771509229>,
>> or unsubscribe
>> <https://github.com/notifications/unsubscribe-auth/ABDD3ZNBZ3BE7Q4EQZZK6MLYAFWURAVCNFSM6AAAAAA6HDKPSCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTONZRGUYDSMRSHE>
>> .
>> You are receiving this because you authored the thread.Message ID:
>> ***@***.***>
>>
>


So, using LlamaTokenizerFast was the problem. Changing it to LlamaTokenizer
fixed things,

On Thu, Oct 19, 2023 at 4:04 PM pensive introvert <
***@***.***> wrote:

> I changed the tokenizer to one without "Fast suffix, and something
> changed. The fraction, although still slowed a lot at 80% was able to get
> over the finish line of 100%
>
> I have to do more testng, see if the whole set can be processed
>
>
>
> On Thu, Oct 19, 2023 at 3:03 PM pensive introvert <
> ***@***.***> wrote:
>
>> from functools import partial
>> import transformers
>> from datasets import load_dataset, concatenate_datasets, load_from_disk
>>
>> model_name_or_path="/opt/data/data/daryl149/llama-2-7b-chat-hf"
>> output_dir="/opt/data/data/LongLoRA/checkpoints"
>> cache_dir="/opt/data/data/LongLoRA/cache"
>> model_max_length=16384
>>
>> IGNORE_INDEX = -100
>> DEFAULT_PAD_TOKEN = "[PAD]"
>> DEFAULT_EOS_TOKEN = "
"
>> DEFAULT_BOS_TOKEN = "
"
>> DEFAULT_UNK_TOKEN = "<unk>"
>>
>>
>> tokenizer = transformers.LlamaTokenizerFast.from_pretrained(
>>         model_name_or_path,
>>         cache_dir=cache_dir,
>>         model_max_length=model_max_length,
>>         padding_side="right",
>>         use_fast=True,
>>         #use_fast=False
>> )
>>
>> special_tokens_dict = dict()
>> if tokenizer.pad_token is None:
>>     special_tokens_dict["pad_token"] = DEFAULT_PAD_TOKEN
>> if tokenizer.eos_token is None:
>>     special_tokens_dict["eos_token"] = DEFAULT_EOS_TOKEN
>> if tokenizer.bos_token is None:
>>     special_tokens_dict["bos_token"] = DEFAULT_BOS_TOKEN
>> if tokenizer.unk_token is None:
>>     special_tokens_dict["unk_token"] = DEFAULT_UNK_TOKEN
>>
>> tokenizer.add_special_tokens(special_tokens_dict)
>>
>> def tokenize_fn(tokenizer, example):
>>     context_length = tokenizer.model_max_length
>>     outputs = tokenizer(
>>         tokenizer.eos_token.join(example["text"]),
>>         #truncation=False,
>>         truncation=True,
>>         return_tensors="pt",
>>         #return_tensors="np",
>>         pad_to_multiple_of=context_length,
>>         padding=True,
>>     )
>>     return {"input_ids": outputs["input_ids"].view(-1, context_length)}
>>
>> for idx in range(100):
>>     dataset = load_dataset("togethercomputer/RedPajama-Data-1T-Sample",
>> cache_dir=cache_dir, split=f'train[{idx}%:{idx+1}%]')
>>     dataset = dataset.map(partial(tokenize_fn, tokenizer), batched=False,
>> num_proc=16, remove_columns=["text", "meta"])
>>     dataset.save_to_disk(training_args.cache_dir +
>> f"/training_data_{idx}")
>>
>>
>> On Thu, Oct 19, 2023 at 2:30 PM Mario Šaško ***@***.***>
>> wrote:
>>
>>> Can you please provide a self-contained reproducer?
>>>
>>> —
>>> Reply to this email directly, view it on GitHub
>>> <https://github.com/huggingface/datasets/issues/6319#issuecomment-1771509229>,
>>> or unsubscribe
>>> <https://github.com/notifications/unsubscribe-auth/ABDD3ZNBZ3BE7Q4EQZZK6MLYAFWURAVCNFSM6AAAAAA6HDKPSCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTONZRGUYDSMRSHE>
>>> .
>>> You are receiving this because you authored the thread.Message ID:
>>> ***@***.***>
>>>
>>


Indeed, the tokenizer is super slow. Perhaps @ArthurZucker knows the reason why.

([This](https://colab.research.google.com/drive/1VgeurX-4Fl2X6aBQTwh_X4kuQKZ6K9L1?usp=sharing) simplified Colab can be used to reproduce the behavior)

same issue here
sample to reproduce: https://github.com/philschmid/document-ai-transformers/blob/main/training/donut_sroie.ipynb
with following map line
https://github.com/philschmid/document-ai-transformers/blob/main/training/donut_sroie.ipynb

If I directly iterate over the dataset and call the mapping method, it is very fast
```py
for sample in dataset:
    def preprocess_documents_for_donut(sample):
```

if i removed `.convert('RGB')` It can run to completion without getting stuck. I suspect it has something to do with the Image.

If I use batch, it's even slower.

@ewfian 

> If I directly iterate over the dataset and call the mapping method, it is very fast

`Dataset.map` must also convert the images into bytes to write them to an Arrow file (the write itself takes some time, too). 

You can make the `map` faster by manually converting the images into an "arrow-compatible" representation. Otherwise,  the Pillow defaults are used when saving an image, which seems particularly slow for the notebook's case.

```python
def preprocess_documents_for_donut(sample):
    text = json.loads(sample["text"])
    d_doc = task_start_token + json2token(text) + eos_token
    image = sample["image"].convert('RGB')
    # convert image to bytes
    buffer = io.BytesIO()
    image.save(buffer, format="PNG", compress_level=1)
    return {"image": {"bytes": buffer.getvalue()}, "text": d_doc}

proc_dataset = dataset.map(preprocess_documents_for_donut, writer_batch_size=50)
```

The problem I had was to do with map using fork and copying locks from the
parent process in acquired state. I ended up changing the context to use
forkserver instead.


On Wed, Nov 29, 2023, 10:04 PM Mario Šaško ***@***.***> wrote:

> @ewfian <https://github.com/ewfian>
>
> If I directly iterate over the dataset and call the mapping method, it is
> very fast
>
> Dataset.map must also convert the images into bytes to write them to an
> Arrow file (the write itself takes some time, too).
>
> You can make the map faster by manually converting the images into an
> "arrow-compatible" representation. Otherwise, the Pillow defaults are used
> when saving an image, which seems particularly slow for the notebook's case.
>
> def preprocess_documents_for_donut(sample):
>     text = json.loads(sample["text"])
>     d_doc = task_start_token + json2token(text) + eos_token
>     image = sample["image"].convert('RGB')
>     # convert image to bytes
>     buffer = io.BytesIO()
>     image.save(buffer, format="PNG", compress_level=1)
>     return {"image": {"bytes": buffer.getvalue()}, "text": d_doc}
> proc_dataset = dataset.map(preprocess_documents_for_donut, writer_batch_size=50)
>
> —
> Reply to this email directly, view it on GitHub
> <https://github.com/huggingface/datasets/issues/6319#issuecomment-1833033973>,
> or unsubscribe
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> .
> You are receiving this because you authored the thread.Message ID:
> ***@***.***>
>