add split argument to Generator
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The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7015). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
2024-07-09T08:07:48Z
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CONTRIBUTOR
@albertvillanova thanks for the review, please take a look
2024-07-10T06:17:40Z
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CONTRIBUTOR
@albertvillanova please take a look
2024-07-11T07:44:44Z
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MEMBER
Thank you again! Your PR is merged.
2024-07-26T09:32:11Z
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<details>
<summary>Show benchmarks</summary>

PyArrow==8.0.0

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.005267 / 0.011353 (-0.006086) | 0.003711 / 0.011008 (-0.007297) | 0.062288 / 0.038508 (0.023780) | 0.031357 / 0.023109 (0.008248) | 0.233592 / 0.275898 (-0.042306) | 0.257722 / 0.323480 (-0.065758) | 0.003124 / 0.007986 (-0.004861) | 0.003335 / 0.004328 (-0.000994) | 0.048594 / 0.004250 (0.044344) | 0.043853 / 0.037052 (0.006801) | 0.248589 / 0.258489 (-0.009900) | 0.278474 / 0.293841 (-0.015367) | 0.029573 / 0.128546 (-0.098973) | 0.011779 / 0.075646 (-0.063868) | 0.204989 / 0.419271 (-0.214282) | 0.035734 / 0.043533 (-0.007799) | 0.240064 / 0.255139 (-0.015075) | 0.263105 / 0.283200 (-0.020094) | 0.018764 / 0.141683 (-0.122919) | 1.115705 / 1.452155 (-0.336449) | 1.175457 / 1.492716 (-0.317260) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.092664 / 0.018006 (0.074657) | 0.297893 / 0.000490 (0.297403) | 0.000217 / 0.000200 (0.000017) | 0.000047 / 0.000054 (-0.000007) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.019056 / 0.037411 (-0.018355) | 0.062472 / 0.014526 (0.047946) | 0.073462 / 0.176557 (-0.103094) | 0.119723 / 0.737135 (-0.617412) | 0.074420 / 0.296338 (-0.221919) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.283131 / 0.215209 (0.067922) | 2.776694 / 2.077655 (0.699039) | 1.455586 / 1.504120 (-0.048534) | 1.323902 / 1.541195 (-0.217293) | 1.333169 / 1.468490 (-0.135321) | 0.723921 / 4.584777 (-3.860856) | 2.385842 / 3.745712 (-1.359870) | 2.926843 / 5.269862 (-2.343018) | 1.896773 / 4.565676 (-2.668903) | 0.079754 / 0.424275 (-0.344521) | 0.005188 / 0.007607 (-0.002419) | 0.342466 / 0.226044 (0.116421) | 3.404204 / 2.268929 (1.135275) | 1.856575 / 55.444624 (-53.588049) | 1.554507 / 6.876477 (-5.321970) | 1.564065 / 2.142072 (-0.578007) | 0.810363 / 4.805227 (-3.994864) | 0.135537 / 6.500664 (-6.365127) | 0.041987 / 0.075469 (-0.033482) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.962288 / 1.841788 (-0.879500) | 11.310837 / 8.074308 (3.236529) | 9.630034 / 10.191392 (-0.561358) | 0.131108 / 0.680424 (-0.549316) | 0.015225 / 0.534201 (-0.518976) | 0.304211 / 0.579283 (-0.275072) | 0.272707 / 0.434364 (-0.161657) | 0.341550 / 0.540337 (-0.198787) | 0.444528 / 1.386936 (-0.942408) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.005665 / 0.011353 (-0.005688) | 0.003916 / 0.011008 (-0.007092) | 0.049946 / 0.038508 (0.011438) | 0.031760 / 0.023109 (0.008651) | 0.273826 / 0.275898 (-0.002072) | 0.300193 / 0.323480 (-0.023287) | 0.004350 / 0.007986 (-0.003635) | 0.002749 / 0.004328 (-0.001579) | 0.048451 / 0.004250 (0.044201) | 0.039798 / 0.037052 (0.002746) | 0.284570 / 0.258489 (0.026081) | 0.318855 / 0.293841 (0.025014) | 0.032724 / 0.128546 (-0.095822) | 0.012103 / 0.075646 (-0.063543) | 0.059857 / 0.419271 (-0.359414) | 0.034185 / 0.043533 (-0.009348) | 0.276079 / 0.255139 (0.020940) | 0.294070 / 0.283200 (0.010871) | 0.018168 / 0.141683 (-0.123515) | 1.149681 / 1.452155 (-0.302473) | 1.191349 / 1.492716 (-0.301367) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.092676 / 0.018006 (0.074669) | 0.304971 / 0.000490 (0.304481) | 0.000203 / 0.000200 (0.000003) | 0.000050 / 0.000054 (-0.000004) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023110 / 0.037411 (-0.014301) | 0.079117 / 0.014526 (0.064591) | 0.087457 / 0.176557 (-0.089099) | 0.128295 / 0.737135 (-0.608840) | 0.089747 / 0.296338 (-0.206592) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.305158 / 0.215209 (0.089949) | 2.992277 / 2.077655 (0.914623) | 1.595369 / 1.504120 (0.091249) | 1.462955 / 1.541195 (-0.078240) | 1.476269 / 1.468490 (0.007779) | 0.731652 / 4.584777 (-3.853124) | 0.961053 / 3.745712 (-2.784659) | 2.800259 / 5.269862 (-2.469602) | 1.881249 / 4.565676 (-2.684428) | 0.079503 / 0.424275 (-0.344772) | 0.005252 / 0.007607 (-0.002355) | 0.354921 / 0.226044 (0.128877) | 3.495272 / 2.268929 (1.226343) | 1.956419 / 55.444624 (-53.488205) | 1.654941 / 6.876477 (-5.221536) | 1.782506 / 2.142072 (-0.359567) | 0.816487 / 4.805227 (-3.988741) | 0.135870 / 6.500664 (-6.364794) | 0.041114 / 0.075469 (-0.034355) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.050346 / 1.841788 (-0.791442) | 12.510129 / 8.074308 (4.435821) | 10.524835 / 10.191392 (0.333443) | 0.152388 / 0.680424 (-0.528036) | 0.016073 / 0.534201 (-0.518128) | 0.301956 / 0.579283 (-0.277327) | 0.126871 / 0.434364 (-0.307493) | 0.339554 / 0.540337 (-0.200783) | 0.435873 / 1.386936 (-0.951064) |

</details>
</details>

![](https://cml.dev/watermark.png#ead089d949febdce415d79ef3802e188316c0b26 "CML watermark")

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## Actual

When creating a multi-split dataset using generators like 

```python
datasets.DatasetDict({
  "val": datasets.Dataset.from_generator(
      generator=generator_val,
      features=features
  ),
  "test": datasets.Dataset.from_generator(
      generator=generator_test,
      features=features,
  )
})
```

It displays (for both test and val)

```
Generating train split
```


## Expected

I would like to be able to improve this behavior by doing

```python
datasets.DatasetDict({
  "val": datasets.Dataset.from_generator(
      generator=generator_val,
      features=features,
      split="val"
  ),
  "test": datasets.Dataset.from_generator(
      generator=generator_test,
      features=features,
      split="test"
  )
})
```


It would display

```
Generating val split
```

and 

```
Generating test split
```

## Proposal

Current PR is adding an explicit `split` argument and replace the implicit "train" split in the following classes/function : 
* Generator
* from_generator
* AbstractDatasetInputStream
* GeneratorDatasetInputStream

Please share your feedbacks