Rename LargeList.dtype to LargeList.feature
State: closed | Author: albertvillanova (MEMBER)

Rename `LargeList.dtype` to `LargeList.feature`.

Note that `dtype` is usually used for NumPy data types ("int64", "float32",...): see `Value.dtype`.

However, `LargeList` attribute (like `Sequence.feature`) expects a `FeatureType` instead.

With this renaming:
- we avoid confusion about the expected type and
- we also align `LargeList` with `Sequence`.

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7106). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

<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) | 0.005598 / 0.011353 (-0.005755) | 0.004327 / 0.011008 (-0.006681) | 0.063961 / 0.038508 (0.025453) | 0.031039 / 0.023109 (0.007930) | 0.245586 / 0.275898 (-0.030312) | 0.273765 / 0.323480 (-0.049715) | 0.003463 / 0.007986 (-0.004523) | 0.002871 / 0.004328 (-0.001457) | 0.049169 / 0.004250 (0.044918) | 0.049342 / 0.037052 (0.012290) | 0.259255 / 0.258489 (0.000766) | 0.295688 / 0.293841 (0.001847) | 0.029527 / 0.128546 (-0.099019) | 0.012507 / 0.075646 (-0.063139) | 0.209420 / 0.419271 (-0.209851) | 0.036666 / 0.043533 (-0.006866) | 0.272031 / 0.255139 (0.016892) | 0.272585 / 0.283200 (-0.010614) | 0.020004 / 0.141683 (-0.121679) | 1.158605 / 1.452155 (-0.293550) | 1.230930 / 1.492716 (-0.261787) |

### 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) | 0.109196 / 0.018006 (0.091189) | 0.377759 / 0.000490 (0.377270) | 0.000222 / 0.000200 (0.000022) | 0.000043 / 0.000054 (-0.000011) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018961 / 0.037411 (-0.018450) | 0.063189 / 0.014526 (0.048663) | 0.075253 / 0.176557 (-0.101303) | 0.122912 / 0.737135 (-0.614223) | 0.077961 / 0.296338 (-0.218378) |

### 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) | 0.278425 / 0.215209 (0.063216) | 2.748336 / 2.077655 (0.670681) | 1.468410 / 1.504120 (-0.035710) | 1.347859 / 1.541195 (-0.193336) | 1.389175 / 1.468490 (-0.079315) | 0.742833 / 4.584777 (-3.841943) | 2.358930 / 3.745712 (-1.386782) | 3.062720 / 5.269862 (-2.207141) | 1.912264 / 4.565676 (-2.653412) | 0.079263 / 0.424275 (-0.345012) | 0.005212 / 0.007607 (-0.002396) | 0.332482 / 0.226044 (0.106438) | 3.287045 / 2.268929 (1.018116) | 1.827862 / 55.444624 (-53.616762) | 1.525087 / 6.876477 (-5.351390) | 1.581742 / 2.142072 (-0.560330) | 0.791737 / 4.805227 (-4.013490) | 0.135774 / 6.500664 (-6.364890) | 0.043700 / 0.075469 (-0.031769) |

### 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) | 0.982104 / 1.841788 (-0.859683) | 12.227639 / 8.074308 (4.153331) | 9.492719 / 10.191392 (-0.698673) | 0.144792 / 0.680424 (-0.535632) | 0.014844 / 0.534201 (-0.519357) | 0.304919 / 0.579283 (-0.274364) | 0.262955 / 0.434364 (-0.171409) | 0.339517 / 0.540337 (-0.200821) | 0.430929 / 1.386936 (-0.956007) |

</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) | 0.005982 / 0.011353 (-0.005371) | 0.004199 / 0.011008 (-0.006809) | 0.050674 / 0.038508 (0.012166) | 0.032713 / 0.023109 (0.009604) | 0.270071 / 0.275898 (-0.005827) | 0.300469 / 0.323480 (-0.023011) | 0.005159 / 0.007986 (-0.002826) | 0.002961 / 0.004328 (-0.001368) | 0.048403 / 0.004250 (0.044152) | 0.042024 / 0.037052 (0.004971) | 0.288927 / 0.258489 (0.030438) | 0.321412 / 0.293841 (0.027571) | 0.032436 / 0.128546 (-0.096110) | 0.012472 / 0.075646 (-0.063175) | 0.060527 / 0.419271 (-0.358744) | 0.034222 / 0.043533 (-0.009311) | 0.276259 / 0.255139 (0.021120) | 0.293168 / 0.283200 (0.009969) | 0.019245 / 0.141683 (-0.122438) | 1.180766 / 1.452155 (-0.271388) | 1.220269 / 1.492716 (-0.272447) |

### 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) | 0.110082 / 0.018006 (0.092076) | 0.364221 / 0.000490 (0.363731) | 0.000221 / 0.000200 (0.000021) | 0.000044 / 0.000054 (-0.000011) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.022923 / 0.037411 (-0.014488) | 0.078022 / 0.014526 (0.063496) | 0.089543 / 0.176557 (-0.087013) | 0.129855 / 0.737135 (-0.607280) | 0.090891 / 0.296338 (-0.205448) |

### 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) | 0.304169 / 0.215209 (0.088960) | 2.969772 / 2.077655 (0.892117) | 1.582647 / 1.504120 (0.078527) | 1.464446 / 1.541195 (-0.076749) | 1.485422 / 1.468490 (0.016932) | 0.720105 / 4.584777 (-3.864672) | 0.966730 / 3.745712 (-2.778982) | 3.017549 / 5.269862 (-2.252313) | 1.924574 / 4.565676 (-2.641103) | 0.079938 / 0.424275 (-0.344337) | 0.005684 / 0.007607 (-0.001923) | 0.364093 / 0.226044 (0.138048) | 3.569470 / 2.268929 (1.300541) | 1.956535 / 55.444624 (-53.488089) | 1.669432 / 6.876477 (-5.207045) | 1.687596 / 2.142072 (-0.454476) | 0.802725 / 4.805227 (-4.002502) | 0.132874 / 6.500664 (-6.367790) | 0.041403 / 0.075469 (-0.034067) |

### 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) | 1.033317 / 1.841788 (-0.808471) | 12.590652 / 8.074308 (4.516344) | 10.618609 / 10.191392 (0.427217) | 0.131833 / 0.680424 (-0.548591) | 0.015675 / 0.534201 (-0.518526) | 0.300804 / 0.579283 (-0.278479) | 0.127253 / 0.434364 (-0.307111) | 0.342559 / 0.540337 (-0.197779) | 0.464302 / 1.386936 (-0.922634) |

</details>
</details>

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