Pickle support for `torch.Generator` objects
Opened by mariosasko (closed)

Fix for https://discuss.huggingface.co/t/caching-a-dataset-processed-with-randomness/65616

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6502). 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) | 0.005472 / 0.011353 (-0.005881) | 0.003715 / 0.011008 (-0.007293) | 0.063257 / 0.038508 (0.024749) | 0.060683 / 0.023109 (0.037574) | 0.250885 / 0.275898 (-0.025013) | 0.271685 / 0.323480 (-0.051795) | 0.003051 / 0.007986 (-0.004934) | 0.002799 / 0.004328 (-0.001530) | 0.049113 / 0.004250 (0.044863) | 0.038965 / 0.037052 (0.001912) | 0.252688 / 0.258489 (-0.005801) | 0.282536 / 0.293841 (-0.011305) | 0.028722 / 0.128546 (-0.099824) | 0.010586 / 0.075646 (-0.065060) | 0.205145 / 0.419271 (-0.214127) | 0.036996 / 0.043533 (-0.006537) | 0.248874 / 0.255139 (-0.006265) | 0.266148 / 0.283200 (-0.017051) | 0.018540 / 0.141683 (-0.123143) | 1.120216 / 1.452155 (-0.331938) | 1.191072 / 1.492716 (-0.301644) |

### 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) | 0.095721 / 0.018006 (0.077714) | 0.313401 / 0.000490 (0.312911) | 0.000234 / 0.000200 (0.000034) | 0.000053 / 0.000054 (-0.000002) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018604 / 0.037411 (-0.018807) | 0.061571 / 0.014526 (0.047045) | 0.075343 / 0.176557 (-0.101213) | 0.121272 / 0.737135 (-0.615864) | 0.076448 / 0.296338 (-0.219890) |

### 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) | 0.286885 / 0.215209 (0.071676) | 2.809100 / 2.077655 (0.731445) | 1.485365 / 1.504120 (-0.018755) | 1.367672 / 1.541195 (-0.173523) | 1.423570 / 1.468490 (-0.044920) | 0.571063 / 4.584777 (-4.013714) | 2.385248 / 3.745712 (-1.360464) | 2.855251 / 5.269862 (-2.414610) | 1.799371 / 4.565676 (-2.766306) | 0.063491 / 0.424275 (-0.360784) | 0.004942 / 0.007607 (-0.002665) | 0.346181 / 0.226044 (0.120137) | 3.388123 / 2.268929 (1.119195) | 1.819093 / 55.444624 (-53.625532) | 1.552998 / 6.876477 (-5.323479) | 1.627930 / 2.142072 (-0.514143) | 0.653438 / 4.805227 (-4.151789) | 0.123831 / 6.500664 (-6.376833) | 0.043340 / 0.075469 (-0.032129) |

### 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) | 0.952167 / 1.841788 (-0.889621) | 12.149515 / 8.074308 (4.075207) | 10.665085 / 10.191392 (0.473693) | 0.127768 / 0.680424 (-0.552656) | 0.014022 / 0.534201 (-0.520179) | 0.285959 / 0.579283 (-0.293324) | 0.269727 / 0.434364 (-0.164637) | 0.336646 / 0.540337 (-0.203692) | 0.442932 / 1.386936 (-0.944005) |

</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) | 0.005351 / 0.011353 (-0.006002) | 0.003561 / 0.011008 (-0.007448) | 0.048890 / 0.038508 (0.010382) | 0.054093 / 0.023109 (0.030984) | 0.274397 / 0.275898 (-0.001501) | 0.296980 / 0.323480 (-0.026500) | 0.004126 / 0.007986 (-0.003860) | 0.002751 / 0.004328 (-0.001578) | 0.049131 / 0.004250 (0.044880) | 0.040769 / 0.037052 (0.003716) | 0.279147 / 0.258489 (0.020658) | 0.302014 / 0.293841 (0.008173) | 0.029847 / 0.128546 (-0.098699) | 0.010710 / 0.075646 (-0.064936) | 0.057626 / 0.419271 (-0.361645) | 0.032801 / 0.043533 (-0.010732) | 0.272698 / 0.255139 (0.017559) | 0.289238 / 0.283200 (0.006039) | 0.017876 / 0.141683 (-0.123807) | 1.152059 / 1.452155 (-0.300096) | 1.212289 / 1.492716 (-0.280427) |

### 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) | 0.092914 / 0.018006 (0.074908) | 0.303092 / 0.000490 (0.302603) | 0.000214 / 0.000200 (0.000014) | 0.000058 / 0.000054 (0.000004) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.022074 / 0.037411 (-0.015337) | 0.070109 / 0.014526 (0.055583) | 0.083360 / 0.176557 (-0.093196) | 0.122445 / 0.737135 (-0.614690) | 0.083625 / 0.296338 (-0.212714) |

### 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) | 0.282788 / 0.215209 (0.067579) | 2.789229 / 2.077655 (0.711574) | 1.571077 / 1.504120 (0.066957) | 1.452627 / 1.541195 (-0.088567) | 1.493176 / 1.468490 (0.024686) | 0.556892 / 4.584777 (-4.027885) | 2.442771 / 3.745712 (-1.302941) | 2.826316 / 5.269862 (-2.443545) | 1.758276 / 4.565676 (-2.807401) | 0.063039 / 0.424275 (-0.361236) | 0.004928 / 0.007607 (-0.002679) | 0.338247 / 0.226044 (0.112202) | 3.346344 / 2.268929 (1.077416) | 1.952520 / 55.444624 (-53.492104) | 1.664520 / 6.876477 (-5.211956) | 1.701528 / 2.142072 (-0.440544) | 0.634746 / 4.805227 (-4.170481) | 0.116879 / 6.500664 (-6.383786) | 0.040990 / 0.075469 (-0.034479) |

### 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) | 0.969521 / 1.841788 (-0.872267) | 12.431395 / 8.074308 (4.357087) | 10.907503 / 10.191392 (0.716111) | 0.131028 / 0.680424 (-0.549396) | 0.015239 / 0.534201 (-0.518962) | 0.290793 / 0.579283 (-0.288490) | 0.275072 / 0.434364 (-0.159292) | 0.331036 / 0.540337 (-0.209301) | 0.567858 / 1.386936 (-0.819078) |

</details>
</details>

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