Revert "pass the dataset features to the IterableDataset.from_generator"
State: closed | Author: lhoestq (MEMBER)

This reverts commit b91070b9c09673e2e148eec458036ab6a62ac042 (temporarily)

It hurts iterable dataset performance a lot (e.g. x4 slower because it encodes+decodes images unnecessarily). I think we need to fix this before re-adding it

cc @mariosasko @Hubert-Bonisseur 

Comments: _The documentation is not available anymore as the PR was closed or merged._

<details>
<summary>Show benchmarks</summary>

PyArrow==6.0.0

<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) | 0.008442 / 0.011353 (-0.002911) | 0.004567 / 0.011008 (-0.006441) | 0.100688 / 0.038508 (0.062180) | 0.029568 / 0.023109 (0.006459) | 0.306993 / 0.275898 (0.031095) | 0.362626 / 0.323480 (0.039146) | 0.006983 / 0.007986 (-0.001002) | 0.003424 / 0.004328 (-0.000905) | 0.079050 / 0.004250 (0.074799) | 0.036087 / 0.037052 (-0.000966) | 0.318205 / 0.258489 (0.059716) | 0.353882 / 0.293841 (0.060041) | 0.033091 / 0.128546 (-0.095455) | 0.011468 / 0.075646 (-0.064178) | 0.321125 / 0.419271 (-0.098146) | 0.040645 / 0.043533 (-0.002888) | 0.309827 / 0.255139 (0.054688) | 0.344848 / 0.283200 (0.061648) | 0.087100 / 0.141683 (-0.054583) | 1.465123 / 1.452155 (0.012968) | 1.499457 / 1.492716 (0.006741) |

### 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) | 0.171619 / 0.018006 (0.153613) | 0.410198 / 0.000490 (0.409709) | 0.002391 / 0.000200 (0.002191) | 0.000075 / 0.000054 (0.000021) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.022913 / 0.037411 (-0.014499) | 0.097275 / 0.014526 (0.082749) | 0.103902 / 0.176557 (-0.072655) | 0.148855 / 0.737135 (-0.588281) | 0.107247 / 0.296338 (-0.189092) |

### 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) | 0.413139 / 0.215209 (0.197930) | 4.131760 / 2.077655 (2.054105) | 1.854491 / 1.504120 (0.350371) | 1.625524 / 1.541195 (0.084329) | 1.666665 / 1.468490 (0.198175) | 0.687105 / 4.584777 (-3.897672) | 3.327124 / 3.745712 (-0.418588) | 1.830820 / 5.269862 (-3.439042) | 1.147930 / 4.565676 (-3.417746) | 0.081586 / 0.424275 (-0.342689) | 0.012422 / 0.007607 (0.004815) | 0.523723 / 0.226044 (0.297678) | 5.246977 / 2.268929 (2.978049) | 2.288350 / 55.444624 (-53.156275) | 1.933740 / 6.876477 (-4.942737) | 1.954356 / 2.142072 (-0.187716) | 0.804434 / 4.805227 (-4.000793) | 0.147621 / 6.500664 (-6.353043) | 0.064835 / 0.075469 (-0.010634) |

### 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) | 1.244841 / 1.841788 (-0.596947) | 13.758465 / 8.074308 (5.684157) | 13.984576 / 10.191392 (3.793184) | 0.144860 / 0.680424 (-0.535564) | 0.028616 / 0.534201 (-0.505584) | 0.401928 / 0.579283 (-0.177355) | 0.415294 / 0.434364 (-0.019069) | 0.476483 / 0.540337 (-0.063854) | 0.569257 / 1.386936 (-0.817679) |

</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) | 0.006556 / 0.011353 (-0.004797) | 0.004502 / 0.011008 (-0.006507) | 0.074828 / 0.038508 (0.036319) | 0.027537 / 0.023109 (0.004427) | 0.339961 / 0.275898 (0.064063) | 0.372491 / 0.323480 (0.049011) | 0.005010 / 0.007986 (-0.002976) | 0.004624 / 0.004328 (0.000295) | 0.074459 / 0.004250 (0.070208) | 0.037539 / 0.037052 (0.000486) | 0.341031 / 0.258489 (0.082542) | 0.383397 / 0.293841 (0.089556) | 0.031706 / 0.128546 (-0.096840) | 0.011542 / 0.075646 (-0.064104) | 0.084882 / 0.419271 (-0.334389) | 0.041860 / 0.043533 (-0.001673) | 0.338699 / 0.255139 (0.083560) | 0.365666 / 0.283200 (0.082467) | 0.088966 / 0.141683 (-0.052717) | 1.502493 / 1.452155 (0.050339) | 1.570746 / 1.492716 (0.078030) |

### 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) | 0.217547 / 0.018006 (0.199541) | 0.392407 / 0.000490 (0.391918) | 0.000388 / 0.000200 (0.000188) | 0.000058 / 0.000054 (0.000004) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.024571 / 0.037411 (-0.012840) | 0.099259 / 0.014526 (0.084734) | 0.107850 / 0.176557 (-0.068707) | 0.157686 / 0.737135 (-0.579449) | 0.109761 / 0.296338 (-0.186578) |

### 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) | 0.434791 / 0.215209 (0.219582) | 4.323099 / 2.077655 (2.245444) | 2.063610 / 1.504120 (0.559490) | 1.866136 / 1.541195 (0.324941) | 1.910185 / 1.468490 (0.441695) | 0.696584 / 4.584777 (-3.888193) | 3.398017 / 3.745712 (-0.347695) | 1.848473 / 5.269862 (-3.421388) | 1.168238 / 4.565676 (-3.397438) | 0.083222 / 0.424275 (-0.341053) | 0.012332 / 0.007607 (0.004725) | 0.538953 / 0.226044 (0.312909) | 5.421273 / 2.268929 (3.152344) | 2.499877 / 55.444624 (-52.944747) | 2.161853 / 6.876477 (-4.714624) | 2.183941 / 2.142072 (0.041868) | 0.803916 / 4.805227 (-4.001311) | 0.150266 / 6.500664 (-6.350398) | 0.067399 / 0.075469 (-0.008070) |

### 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) | 1.280479 / 1.841788 (-0.561309) | 13.728074 / 8.074308 (5.653766) | 12.946098 / 10.191392 (2.754706) | 0.128459 / 0.680424 (-0.551965) | 0.016567 / 0.534201 (-0.517634) | 0.374461 / 0.579283 (-0.204822) | 0.386973 / 0.434364 (-0.047391) | 0.459754 / 0.540337 (-0.080583) | 0.543870 / 1.386936 (-0.843066) |

</details>
</details>

![](https://cml.dev/watermark.png#595b3d47e1fc579f5db1cbc376f756edf32904dd "CML watermark")


Instead of reverting the change, maybe we can use the same conversion in `to_iterable_dataset` as in `ArrowBasedBuilder._as_streaming_dataset` to avoid decoding images twice?

True, let me take a look

Closing in favor of https://github.com/huggingface/datasets/pull/5655