Datasets' cache not re-used

## Describe the bug
For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory.

## Steps to reproduce the bug
Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example.
```python
from datasets import load_dataset
from transformers import AutoTokenizer

raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1")
# tokenizer = AutoTokenizer.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("roberta-base")
text_column_name = "text"
column_names = raw_datasets["train"].column_names

def tokenize_function(examples):
    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)

tokenized_datasets = raw_datasets.map(
    tokenize_function,
    batched=True,
    remove_columns=column_names,
    load_from_cache_file=True,
    desc="Running tokenizer on every text in dataset",
)
```

## Expected results
No tokenization would be required after the 1st run. Everything should be loaded from the cache.

## Actual results
Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache.

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Ubuntu 18.04.6 LTS
- Python version: 3.6.9
- PyArrow version: 6.0.1



I think this is because the tokenizer is stateful and because the order in which the splits are processed is not deterministic. Because of that, the hash of the tokenizer may change for certain splits, which causes issues with caching.

To fix this we can try making the order of the splits deterministic for map.


Actually this is not because of the order of the splits, but most likely because the tokenizer used to process the second split is in a state that has been modified by the first split.

Therefore after reloading the first split from the cache, then the second split can't be reloaded since the tokenizer hasn't seen the first split (and therefore is considered a different tokenizer).

This is a bit trickier to fix, we can explore fixing this next week maybe

Sorry didn't have the bandwidth to take care of this yet - will re-assign when I'm diving into it again !

I had this issue with `run_speech_recognition_ctc.py` for wa2vec2.0 fine-tuning. I made a small change and the hash for the function (which includes tokenisation) is now the same before and after pre-porocessing. With the hash being the same, the caching works as intended.

Before:
```
    def prepare_dataset(batch):
        # load audio
        sample = batch[audio_column_name]

        inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
        batch["input_values"] = inputs.input_values[0]
        batch["input_length"] = len(batch["input_values"])

        # encode targets
        additional_kwargs = {}
        if phoneme_language is not None:
            additional_kwargs["phonemizer_lang"] = phoneme_language

        batch["labels"] = tokenizer(batch["target_text"], **additional_kwargs).input_ids

        return batch

    with training_args.main_process_first(desc="dataset map preprocessing"):
        vectorized_datasets = raw_datasets.map(
            prepare_dataset,
            remove_columns=next(iter(raw_datasets.values())).column_names,
            num_proc=num_workers,
            desc="preprocess datasets",
        )
```
After:
```
    def prepare_dataset(batch, feature_extractor, tokenizer):
        # load audio
        sample = batch[audio_column_name]

        inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
        batch["input_values"] = inputs.input_values[0]
        batch["input_length"] = len(batch["input_values"])

        # encode targets
        additional_kwargs = {}
        if phoneme_language is not None:
            additional_kwargs["phonemizer_lang"] = phoneme_language

        batch["labels"] = tokenizer(batch["target_text"], **additional_kwargs).input_ids

        return batch

    pd = lambda batch: prepare_dataset(batch, feature_extractor, tokenizer)

    with training_args.main_process_first(desc="dataset map preprocessing"):
        vectorized_datasets = raw_datasets.map(
            pd,
            remove_columns=next(iter(raw_datasets.values())).column_names,
            num_proc=num_workers,
            desc="preprocess datasets",
        )
```

Not sure why the second one would work and not the first one - they're basically the same with respect to hashing. In both cases the function is hashed recursively, and therefore the feature_extractor and the tokenizer are hashed the same way.

With which tokenizer or feature extractor are you experiencing this behavior ?

Do you also experience this ?
> Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache.

Thanks ! Hopefully this can be useful to others, and also to better understand and improve hashing/caching 

`tokenizer.save_pretrained(training_args.output_dir)` produces a different tokenizer hash when loaded on restart of the script. When I was debugging before I was terminating the script prior to this command, then rerunning. 

I compared the tokenizer items on the first and second runs, there are two different items:
1st:
```
('_additional_special_tokens', [AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True)])

...

('tokens_trie', <transformers.tokenization_utils.Trie object at 0x7f4d6d0ddb38>)
```

2nd:
```
('_additional_special_tokens', [AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("
", rstrip=False, lstrip=False, single_word=False, normalized=True)])

...

('tokens_trie', <transformers.tokenization_utils.Trie object at 0x7efc23dcce80>)
```

 On every run of this the special tokens are being added on, and the hash is different on the `tokens_trie`. The increase in the special tokens category could be cleaned, but not sure about the hash for the `tokens_trie`. What might work is that the call for the tokenizer encoding can be translated into a function that strips any unnecessary information out, but that's a guess.


Thanks for investigating ! Does that mean that `save_pretrained`() produces non-deterministic tokenizers on disk ? Or is it `from_pretrained()` which is not deterministic given the same files on disk ?

I think one way to fix this would be to make save/from_pretrained deterministic, or make the pickling of `transformers.tokenization_utils.Trie` objects deterministic (this could be implemented in `transformers`, but maybe let's discuss in an issue in `transformers` before opening a PR)

Late to the party but everything should be deterministic (afaik at least).

But `Trie` is a simple class object, so afaik it's hash function is linked to its `id(self)` so basically where it's stored in memory, so super highly non deterministic. Could that be the issue ?

> But Trie is a simple class object, so afaik it's hash function is linked to its id(self) so basically where it's stored in memory, so super highly non deterministic. Could that be the issue ?

We're computing the hash of the pickle dump of the class so it should be fine, as long as the pickle dump is deterministic

I've ported wav2vec2.0 fine-tuning into Optimum-Graphcore which is where I found the issue. The majority of the script was copied from the Transformers version to keep it similar, [here is the tokenizer loading section from the source](https://github.com/huggingface/transformers/blob/f0982682bd6fd0b438dda79ec45f3a8fac83a985/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py#L531).

In the last comment I have two loaded tokenizers, one from run 'N' of the script and one from 'N+1'. I think what's happening is that when you add special tokens (e.g. PAD and UNK) another AddedToken object is appended when tokenizer is saved regardless of whether special tokens are there already. 

If there is a AddedTokens cleanup at load/save this could solve the issue, but then is Trie going to cause hash to be different? I'm not sure. 

Which Python version are you using ?

The trie is basically a big dict of dics, so deterministic nature depends on python version:
https://stackoverflow.com/questions/2053021/is-the-order-of-a-python-dictionary-guaranteed-over-iterations

Maybe the investigation is actually not finding the right culprit though (the memory id is changed, but `datasets` is not using that to compare, so maybe we need to be looking within  `datasets` so see where the comparison fails)

Similar issue found on `BartTokenizer`. You can bypass the bug by loading a fresh new tokenizer everytime.

```
    dataset = dataset.map(lambda x: tokenize_func(x, BartTokenizer.from_pretrained(xxx)),
                          num_proc=num_proc, desc='Tokenize')
```

Linking in https://github.com/huggingface/datasets/issues/6179#issuecomment-1701244673 with an explanation.

I got the same problem while using Wav2Vec2CTCTokenizer in a distributed experiment (many processes), and found that the problem was localized in the serialization (pickle dump) of the field `tokenizer.tokens_trie._tokens` (just a python set). I focussed into the set serialization and found it is not deterministic:

```
from datasets.fingerprint import Hasher
from pickle import dumps,loads

# used just once to get a serialized literal
#print(dumps(set("abc")))
serialized = b'\x80\x04\x95\x11\x00\x00\x00\x00\x00\x00\x00\x8f\x94(\x8c\x01a\x94\x8c\x01c\x94\x8c\x01b\x94\x90.'

myset = loads(serialized)
print(f'{myset=} {Hasher.hash(myset)}')
print(serialized == dumps(myset))
```

Every time you run the python script (different processes) you get a random result. @lhoestq does it make any sense?

OK, I assume python's set is just a hash table implementation that uses internally the hash() function. The problem is that python's hash() is not deterministic. I believe that setting the environment variable PYTHONHASHSEED to a fixed value, you can force it to be deterministic. I tried it (file `set_pickle_dump.py`):

```
#!/usr/bin/python3

from datasets.fingerprint import Hasher
from pickle import dumps,loads

# used just once to get a serialized literal (with environment variable PYTHONHASHSEED set to 42)
#print(dumps(set("abc")))
serialized = b'\x80\x04\x95\x11\x00\x00\x00\x00\x00\x00\x00\x8f\x94(\x8c\x01b\x94\x8c\x01c\x94\x8c\x01a\x94\x90.'

myset = loads(serialized)
print(f'{myset=} {Hasher.hash(myset)}')
print(serialized == dumps(myset))
```

and now every run (`PYTHONHASHSEED=42 ./set_pickle_dump.py`) gets tthe same result. I tried then to test it with the tokenizer (file `test_tokenizer.py`):

```
#!/usr/bin/python3
from transformers import AutoTokenizer
from datasets.fingerprint import Hasher

tokenizer = AutoTokenizer.from_pretrained('model')
print(f'{type(tokenizer)=}')
print(f'{Hasher.hash(tokenizer)=}')
```

executed as `PYTHONHASHSEED=42 ./test_tokenizer.py` and now the tokenizer fingerprint is allways the same!


Thanks for reporting. I opened a PR here to propose a fix: https://github.com/huggingface/datasets/pull/6318 and doesn't require setting `PYTHONHASHSEED`

Can you try to install `datasets` from this branch and tell me if it fixes the issue ?

I patched (*) the file `datasets/utils/py_utils.py` and cache is working propperly now. Thanks!

(*): I am running my experiments inside a docker container that depends on `huggingface/transformers-pytorch-gpu:latest`, so pattched the file instead of rebuilding the container from scratch

Fixed by #6318.

The OP issue hasn't been fixed, re-opening

I think the Trie()._tokens of PreTrainedTokenizer need to be a sorted set So that the results of `hash_bytes(dumps(tokenizer))` are consistent every time

I believe the issue may be linked to [tokenization_utils.py#L507](https://github.com/huggingface/transformers/blob/main/src/transformers/tokenization_utils.py#L507)，specifically in the line where self.tokens_trie.add(token.content) is called. The function _update_trie appears to modify an unordered set. Consequently, this line:
`value = hash_bytes(dumps(tokenizer.tokens_trie._tokens))`
can lead to inconsistencies when rerunning the code.

This, in turn, results in inconsistent outputs for both `hash_bytes(dumps(function))`  at  [arrow_dataset.py#L3053](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L3053) and
`hasher.update(transform_args[key])` at  [fingerprint.py#L323](https://github.com/huggingface/datasets/blob/main/src/datasets/fingerprint.py#L323)

```
dataset_kwargs = {
    "shard": raw_datasets,
    "function": tokenize_function,
}
transform = format_transform_for_fingerprint(Dataset._map_single)
kwargs_for_fingerprint = format_kwargs_for_fingerprint(Dataset._map_single, (), dataset_kwargs)
kwargs_for_fingerprint["fingerprint_name"] = "new_fingerprint"
new_fingerprint = update_fingerprint(raw_datasets._fingerprint, transform, kwargs_for_fingerprint)
```


Alternatively, does the "dumps" function require separate processing for the set?

We did a fix that does sorting whenever we hash sets. The fix is available on `main` if you want to try it out. We'll do a new release soon :)

Is there a documentation chapter that discusses in which cases you should expect your dataset preprocessing to be cached. Including do's and don'ts for the preprocessing functions? I think Datasets team does amazing job at tacking this issue on their side, but it would be great to have some guidelines on the user side as well.

In our current project we have two cases (text-to-text classification and summarization) and in one of them the cache is sometimes reused when it's not supposed to be reused while in the other it's never used at all 😅

You can find some docs here :) 
https://huggingface.co/docs/datasets/about_cache