Better error handling in dataset_module_factory

[Wauplin]

cc @cakiki who reported it on slack (private link)

This PR updates how errors are handled in dataset_module_factory when the dataset_info cannot be accessed:

1. Use multiple except ... as e instead of using isinstance(e, ...)
2. Always raise DatasetNotFoundError with from e so that the initial error is explicitly logged in the stacktrace.
3. Differentiate RepoNotFoundError / GatedRepoError / RevisionNotFoundError cases

[HuggingFaceDocBuilderDev]

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

[albertvillanova]

Thanks. Much clearer now.

[albertvillanova]

Just a test needs being fixed because of the change in the error messages:

FAILED tests/test_load.py::LoadTest::test_load_dataset_from_hub - AssertionError: "at revision '0.0.0'" not found in "Dataset '_dummy' doesn't exist on the Hub or cannot be accessed."

[Wauplin]

Test should be fixed by ef8f7ce (tested locally). Let's see what CI says 🤞

[lhoestq]

thanks !

[lhoestq]

maybe mention that the user may have to login ?

[Wauplin]

addressed in 62350f5

[albertvillanova]

Thanks.

[github-actions]

Show benchmarks

PyArrow==8.0.0

Show updated benchmarks!

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)	0.005678 / 0.011353 (-0.005675)	0.004119 / 0.011008 (-0.006889)	0.063901 / 0.038508 (0.025393)	0.032071 / 0.023109 (0.008961)	0.243182 / 0.275898 (-0.032716)	0.280709 / 0.323480 (-0.042770)	0.004195 / 0.007986 (-0.003791)	0.002810 / 0.004328 (-0.001518)	0.048722 / 0.004250 (0.044472)	0.049381 / 0.037052 (0.012328)	0.257816 / 0.258489 (-0.000673)	0.288460 / 0.293841 (-0.005381)	0.028518 / 0.128546 (-0.100029)	0.010775 / 0.075646 (-0.064871)	0.203149 / 0.419271 (-0.216122)	0.038792 / 0.043533 (-0.004741)	0.248502 / 0.255139 (-0.006637)	0.268251 / 0.283200 (-0.014949)	0.019536 / 0.141683 (-0.122147)	1.133935 / 1.452155 (-0.318220)	1.182855 / 1.492716 (-0.309862)

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)	0.097531 / 0.018006 (0.079525)	0.303612 / 0.000490 (0.303122)	0.000222 / 0.000200 (0.000022)	0.000044 / 0.000054 (-0.000011)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.019670 / 0.037411 (-0.017741)	0.063439 / 0.014526 (0.048913)	0.075119 / 0.176557 (-0.101438)	0.122419 / 0.737135 (-0.614717)	0.076965 / 0.296338 (-0.219374)

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)	0.286780 / 0.215209 (0.071571)	2.811860 / 2.077655 (0.734206)	1.485165 / 1.504120 (-0.018954)	1.373296 / 1.541195 (-0.167898)	1.412700 / 1.468490 (-0.055790)	0.566442 / 4.584777 (-4.018335)	2.382616 / 3.745712 (-1.363096)	2.677214 / 5.269862 (-2.592647)	1.760073 / 4.565676 (-2.805603)	0.062673 / 0.424275 (-0.361602)	0.005050 / 0.007607 (-0.002557)	0.341701 / 0.226044 (0.115657)	3.321182 / 2.268929 (1.052253)	1.811715 / 55.444624 (-53.632909)	1.554986 / 6.876477 (-5.321491)	1.727448 / 2.142072 (-0.414624)	0.642193 / 4.805227 (-4.163034)	0.117878 / 6.500664 (-6.382786)	0.042814 / 0.075469 (-0.032655)

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)	0.985894 / 1.841788 (-0.855894)	12.195975 / 8.074308 (4.121667)	9.890180 / 10.191392 (-0.301212)	0.142638 / 0.680424 (-0.537786)	0.015207 / 0.534201 (-0.518994)	0.283140 / 0.579283 (-0.296143)	0.266016 / 0.434364 (-0.168348)	0.325518 / 0.540337 (-0.214820)	0.418994 / 1.386936 (-0.967942)

PyArrow==latest

Show updated benchmarks!

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)	0.005978 / 0.011353 (-0.005374)	0.003915 / 0.011008 (-0.007093)	0.051592 / 0.038508 (0.013084)	0.033338 / 0.023109 (0.010229)	0.267925 / 0.275898 (-0.007973)	0.296011 / 0.323480 (-0.027469)	0.004503 / 0.007986 (-0.003483)	0.002854 / 0.004328 (-0.001475)	0.049958 / 0.004250 (0.045707)	0.041708 / 0.037052 (0.004656)	0.287185 / 0.258489 (0.028696)	0.322715 / 0.293841 (0.028874)	0.030088 / 0.128546 (-0.098458)	0.010709 / 0.075646 (-0.064938)	0.059736 / 0.419271 (-0.359536)	0.034294 / 0.043533 (-0.009239)	0.264316 / 0.255139 (0.009177)	0.285471 / 0.283200 (0.002272)	0.019197 / 0.141683 (-0.122486)	1.135571 / 1.452155 (-0.316583)	1.190019 / 1.492716 (-0.302698)

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)	0.099251 / 0.018006 (0.081245)	0.305357 / 0.000490 (0.304867)	0.000215 / 0.000200 (0.000015)	0.000045 / 0.000054 (-0.000010)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.023206 / 0.037411 (-0.014205)	0.077835 / 0.014526 (0.063310)	0.090242 / 0.176557 (-0.086315)	0.131208 / 0.737135 (-0.605928)	0.091726 / 0.296338 (-0.204612)

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)	0.292487 / 0.215209 (0.077278)	2.837044 / 2.077655 (0.759389)	1.553155 / 1.504120 (0.049035)	1.433645 / 1.541195 (-0.107550)	1.476702 / 1.468490 (0.008212)	0.561926 / 4.584777 (-4.022851)	0.954630 / 3.745712 (-2.791082)	2.752286 / 5.269862 (-2.517575)	1.782746 / 4.565676 (-2.782931)	0.062984 / 0.424275 (-0.361291)	0.005056 / 0.007607 (-0.002551)	0.341700 / 0.226044 (0.115656)	3.343726 / 2.268929 (1.074798)	1.953390 / 55.444624 (-53.491234)	1.616989 / 6.876477 (-5.259488)	1.785104 / 2.142072 (-0.356969)	0.643465 / 4.805227 (-4.161763)	0.115905 / 6.500664 (-6.384759)	0.041678 / 0.075469 (-0.033791)

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)	1.000237 / 1.841788 (-0.841550)	12.633517 / 8.074308 (4.559208)	10.553485 / 10.191392 (0.362092)	0.143188 / 0.680424 (-0.537236)	0.016020 / 0.534201 (-0.518181)	0.286739 / 0.579283 (-0.292544)	0.128488 / 0.434364 (-0.305876)	0.321932 / 0.540337 (-0.218405)	0.418635 / 1.386936 (-0.968301)