Issue: [WebDataset] Add `.pth` support for torch tensors
State: closed
Author: lhoestq

In this PR I add support for `.pth` but with `weights_only=True` to disallow the use of pickle

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6920). 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) | 0.005643 / 0.011353 (-0.005710) | 0.003810 / 0.011008 (-0.007198) | 0.065896 / 0.038508 (0.027388) | 0.031692 / 0.023109 (0.008583) | 0.258297 / 0.275898 (-0.017601) | 0.294555 / 0.323480 (-0.028925) | 0.004403 / 0.007986 (-0.003583) | 0.002857 / 0.004328 (-0.001472) | 0.049848 / 0.004250 (0.045597) | 0.049719 / 0.037052 (0.012666) | 0.266393 / 0.258489 (0.007904) | 0.306214 / 0.293841 (0.012373) | 0.028283 / 0.128546 (-0.100264) | 0.010450 / 0.075646 (-0.065196) | 0.203064 / 0.419271 (-0.216208) | 0.036535 / 0.043533 (-0.006998) | 0.247839 / 0.255139 (-0.007300) | 0.270538 / 0.283200 (-0.012661) | 0.018748 / 0.141683 (-0.122935) | 1.117478 / 1.452155 (-0.334677) | 1.162575 / 1.492716 (-0.330141) |

### 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) | 0.101074 / 0.018006 (0.083068) | 0.304321 / 0.000490 (0.303831) | 0.000270 / 0.000200 (0.000070) | 0.000045 / 0.000054 (-0.000009) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.019036 / 0.037411 (-0.018376) | 0.064496 / 0.014526 (0.049970) | 0.076848 / 0.176557 (-0.099709) | 0.122979 / 0.737135 (-0.614156) | 0.078008 / 0.296338 (-0.218330) |

### 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) | 0.287009 / 0.215209 (0.071800) | 2.839084 / 2.077655 (0.761429) | 1.495977 / 1.504120 (-0.008143) | 1.379147 / 1.541195 (-0.162047) | 1.413170 / 1.468490 (-0.055320) | 0.616408 / 4.584777 (-3.968369) | 2.419183 / 3.745712 (-1.326529) | 2.905720 / 5.269862 (-2.364142) | 1.801634 / 4.565676 (-2.764043) | 0.064034 / 0.424275 (-0.360241) | 0.005098 / 0.007607 (-0.002509) | 0.341732 / 0.226044 (0.115688) | 3.365262 / 2.268929 (1.096334) | 1.844335 / 55.444624 (-53.600289) | 1.561450 / 6.876477 (-5.315027) | 1.646254 / 2.142072 (-0.495819) | 0.654993 / 4.805227 (-4.150234) | 0.119837 / 6.500664 (-6.380827) | 0.043375 / 0.075469 (-0.032094) |

### 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) | 1.000352 / 1.841788 (-0.841435) | 12.765122 / 8.074308 (4.690813) | 9.818879 / 10.191392 (-0.372513) | 0.133986 / 0.680424 (-0.546438) | 0.014065 / 0.534201 (-0.520136) | 0.295859 / 0.579283 (-0.283424) | 0.268497 / 0.434364 (-0.165867) | 0.330909 / 0.540337 (-0.209429) | 0.449218 / 1.386936 (-0.937718) |

</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) | 0.005646 / 0.011353 (-0.005707) | 0.003926 / 0.011008 (-0.007082) | 0.050437 / 0.038508 (0.011929) | 0.031828 / 0.023109 (0.008719) | 0.268218 / 0.275898 (-0.007680) | 0.292987 / 0.323480 (-0.030493) | 0.004353 / 0.007986 (-0.003633) | 0.002933 / 0.004328 (-0.001395) | 0.050357 / 0.004250 (0.046107) | 0.042988 / 0.037052 (0.005935) | 0.281627 / 0.258489 (0.023138) | 0.305664 / 0.293841 (0.011824) | 0.030162 / 0.128546 (-0.098385) | 0.010856 / 0.075646 (-0.064790) | 0.059528 / 0.419271 (-0.359744) | 0.033800 / 0.043533 (-0.009733) | 0.268200 / 0.255139 (0.013061) | 0.284982 / 0.283200 (0.001782) | 0.019105 / 0.141683 (-0.122578) | 1.171714 / 1.452155 (-0.280441) | 1.205690 / 1.492716 (-0.287026) |

### 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) | 0.100979 / 0.018006 (0.082973) | 0.314691 / 0.000490 (0.314201) | 0.000217 / 0.000200 (0.000017) | 0.000050 / 0.000054 (-0.000005) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023816 / 0.037411 (-0.013596) | 0.081749 / 0.014526 (0.067223) | 0.090118 / 0.176557 (-0.086438) | 0.131615 / 0.737135 (-0.605520) | 0.091821 / 0.296338 (-0.204517) |

### 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) | 0.301222 / 0.215209 (0.086013) | 2.835310 / 2.077655 (0.757655) | 1.562396 / 1.504120 (0.058276) | 1.432365 / 1.541195 (-0.108830) | 1.468358 / 1.468490 (-0.000132) | 0.561300 / 4.584777 (-4.023477) | 0.962294 / 3.745712 (-2.783419) | 2.799705 / 5.269862 (-2.470157) | 1.803035 / 4.565676 (-2.762642) | 0.064104 / 0.424275 (-0.360171) | 0.005480 / 0.007607 (-0.002127) | 0.342519 / 0.226044 (0.116475) | 3.406286 / 2.268929 (1.137357) | 1.966962 / 55.444624 (-53.477663) | 1.654664 / 6.876477 (-5.221813) | 1.829303 / 2.142072 (-0.312769) | 0.650932 / 4.805227 (-4.154295) | 0.119211 / 6.500664 (-6.381453) | 0.043739 / 0.075469 (-0.031730) |

### 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) | 1.006657 / 1.841788 (-0.835130) | 12.915348 / 8.074308 (4.841040) | 10.808156 / 10.191392 (0.616764) | 0.132664 / 0.680424 (-0.547760) | 0.015574 / 0.534201 (-0.518627) | 0.284525 / 0.579283 (-0.294758) | 0.122322 / 0.434364 (-0.312042) | 0.326826 / 0.540337 (-0.213511) | 0.416593 / 1.386936 (-0.970343) |

</details>
</details>

![](https://cml.dev/watermark.png#15ffefe5be194790a50af88ae1236a51b0ac95e6 "CML watermark")