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@ZachNagengast ZachNagengast commented Oct 19, 2023

With this pr #6309, it is formatting the entire base path into regex, which results in the undesired formatting error doesn't match the pattern because of the line in glob_pattern_to_regex: .replace("//", "/"):

  • Input: hf://datasets/...
  • Output: hf:/datasets/...

This fix will only convert the split_pattern to regex and keep the base_path unchanged.

cc @albertvillanova hopefully this still works with your implementation

@ZachNagengast ZachNagengast changed the title Fix regex get_data_files formatting for url base paths Fix regex get_data_files formatting for base paths Oct 19, 2023
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Thanks for the proposed fix, @ZachNagengast.

EDIT:

The reason why I used the the glob_pattern_to_regex in the entire pattern is because otherwise I got an error for Windows local paths: a base_path like 'C:\\Users\\runneradmin... made the function string_to_dict raise re.error: incomplete escape \U at position 2

See: https://github.com/huggingface/datasets/actions/runs/6544904352/job/17772361643

We should include a test that includes the case you mention, and find a solution that works for all cases.

That issue was fixed once we pass the base_path as POSIX.

Maybe we could add a test that fails in the case you mention.

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HuggingFaceDocBuilderDev commented Oct 20, 2023

The documentation is not available anymore as the PR was closed or merged.

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ZachNagengast commented Oct 20, 2023

The reason why I used the the glob_pattern_to_regex in the entire pattern is because otherwise I got an error for Windows local paths: a base_path like 'C:\Users\runneradmin... made the function string_to_dict raise re.error: incomplete escape \U at position 2

What is the expected inputs and outputs for the windows base_path

That issue was fixed once we pass the base_path as POSIX.

I'm not sure what you meant by that, are there still changes needed?

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lhoestq commented Oct 23, 2023

We took the liberty of continuing this PR to include it in today's patch release :)
I hope you don't mind

splits: Set[str] = {
string_to_dict(xbasename(p), glob_pattern_to_regex(xbasename(split_pattern)))["split"]
for p in data_files
}
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@albertvillanova albertvillanova Oct 23, 2023

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If you are matching just in the basename, then what is the point of having 2 kinds of patterns?

  • ALL_SPLIT_PATTERNS: data/{split}-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*
  • ALL_DEFAULT_PATTERNS: **/*[{sep}/]{keyword}[{sep}/]**

Maybe I'm missing something, but why do we need the former? I would naively say the latter contains the former.

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Only ALL_SPLIT_PATTERNS are parsed to infer custom split names.

While the second only detects train/valid/test

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OK, and what is the point of the directory data/ in ALL_SPLIT_PATTERNS if we only match the basename?

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This is for old push_to_hub to work: they push custom splits using this pattern in the data directory.
New push_to_hub have some YAML to specify the pattern to use, so get_data_patterns isn't called

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OK, all clear now. Thanks.

@lhoestq lhoestq merged commit 02ecc84 into huggingface:main Oct 23, 2023
lhoestq added a commit that referenced this pull request Oct 23, 2023
* Fix regex from formatting url base_path

* Test test_get_data_patterns from Hub

* simply match basename instead

* more tests

* minor

* remove comment

---------

Co-authored-by: Albert Villanova del Moral <[email protected]>
Co-authored-by: Quentin Lhoest <[email protected]>
Co-authored-by: Quentin Lhoest <[email protected]>
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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) 0.007109 / 0.011353 (-0.004244) 0.004209 / 0.011008 (-0.006799) 0.097401 / 0.038508 (0.058892) 0.079532 / 0.023109 (0.056423) 0.341300 / 0.275898 (0.065402) 0.402165 / 0.323480 (0.078685) 0.005838 / 0.007986 (-0.002148) 0.003310 / 0.004328 (-0.001018) 0.072804 / 0.004250 (0.068553) 0.059418 / 0.037052 (0.022366) 0.339277 / 0.258489 (0.080788) 0.418495 / 0.293841 (0.124654) 0.035975 / 0.128546 (-0.092571) 0.008101 / 0.075646 (-0.067546) 0.339236 / 0.419271 (-0.080035) 0.059326 / 0.043533 (0.015794) 0.326880 / 0.255139 (0.071741) 0.393614 / 0.283200 (0.110414) 0.025830 / 0.141683 (-0.115852) 1.657726 / 1.452155 (0.205571) 1.817250 / 1.492716 (0.324534)

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) 0.256015 / 0.018006 (0.238008) 0.482447 / 0.000490 (0.481957) 0.012166 / 0.000200 (0.011966) 0.000343 / 0.000054 (0.000288)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.029898 / 0.037411 (-0.007514) 0.088218 / 0.014526 (0.073692) 0.102353 / 0.176557 (-0.074203) 0.165863 / 0.737135 (-0.571272) 0.100342 / 0.296338 (-0.195996)

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) 0.429362 / 0.215209 (0.214153) 4.147327 / 2.077655 (2.069672) 2.014653 / 1.504120 (0.510533) 1.824394 / 1.541195 (0.283199) 1.936408 / 1.468490 (0.467917) 0.542960 / 4.584777 (-4.041817) 3.917215 / 3.745712 (0.171503) 3.714825 / 5.269862 (-1.555036) 2.180279 / 4.565676 (-2.385398) 0.057808 / 0.424275 (-0.366467) 0.008426 / 0.007607 (0.000819) 0.472372 / 0.226044 (0.246327) 4.879656 / 2.268929 (2.610728) 2.602729 / 55.444624 (-52.841896) 2.142593 / 6.876477 (-4.733884) 2.206070 / 2.142072 (0.063997) 0.635591 / 4.805227 (-4.169636) 0.140928 / 6.500664 (-6.359736) 0.065119 / 0.075469 (-0.010350)

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) 1.455909 / 1.841788 (-0.385879) 20.803592 / 8.074308 (12.729284) 14.788713 / 10.191392 (4.597321) 0.170546 / 0.680424 (-0.509878) 0.021189 / 0.534201 (-0.513012) 0.432368 / 0.579283 (-0.146915) 0.444664 / 0.434364 (0.010300) 0.517744 / 0.540337 (-0.022593) 0.699265 / 1.386936 (-0.687671)
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) 0.007592 / 0.011353 (-0.003760) 0.004045 / 0.011008 (-0.006964) 0.073434 / 0.038508 (0.034926) 0.076962 / 0.023109 (0.053853) 0.468873 / 0.275898 (0.192975) 0.479968 / 0.323480 (0.156488) 0.006270 / 0.007986 (-0.001716) 0.003652 / 0.004328 (-0.000677) 0.069893 / 0.004250 (0.065643) 0.061902 / 0.037052 (0.024850) 0.443379 / 0.258489 (0.184890) 0.492627 / 0.293841 (0.198786) 0.035967 / 0.128546 (-0.092579) 0.009276 / 0.075646 (-0.066370) 0.083060 / 0.419271 (-0.336212) 0.050870 / 0.043533 (0.007337) 0.438246 / 0.255139 (0.183107) 0.472074 / 0.283200 (0.188874) 0.023724 / 0.141683 (-0.117959) 1.677178 / 1.452155 (0.225023) 1.732273 / 1.492716 (0.239557)

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) 0.244693 / 0.018006 (0.226687) 0.470067 / 0.000490 (0.469577) 0.005574 / 0.000200 (0.005374) 0.000105 / 0.000054 (0.000051)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.036242 / 0.037411 (-0.001169) 0.099166 / 0.014526 (0.084641) 0.116785 / 0.176557 (-0.059772) 0.174986 / 0.737135 (-0.562149) 0.118130 / 0.296338 (-0.178209)

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) 0.475907 / 0.215209 (0.260698) 4.708125 / 2.077655 (2.630470) 2.600855 / 1.504120 (1.096735) 2.446498 / 1.541195 (0.905303) 2.538786 / 1.468490 (1.070296) 0.566787 / 4.584777 (-4.017990) 4.066187 / 3.745712 (0.320475) 3.743632 / 5.269862 (-1.526229) 2.337737 / 4.565676 (-2.227939) 0.068402 / 0.424275 (-0.355873) 0.008674 / 0.007607 (0.001067) 0.593428 / 0.226044 (0.367384) 5.840687 / 2.268929 (3.571759) 3.194937 / 55.444624 (-52.249688) 2.899033 / 6.876477 (-3.977444) 2.977870 / 2.142072 (0.835797) 0.683673 / 4.805227 (-4.121554) 0.154933 / 6.500664 (-6.345731) 0.071619 / 0.075469 (-0.003850)

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) 1.501895 / 1.841788 (-0.339893) 21.709792 / 8.074308 (13.635484) 15.679556 / 10.191392 (5.488164) 0.188028 / 0.680424 (-0.492396) 0.022555 / 0.534201 (-0.511646) 0.439840 / 0.579283 (-0.139443) 0.452140 / 0.434364 (0.017776) 0.526421 / 0.540337 (-0.013916) 0.731692 / 1.386936 (-0.655244)

albertvillanova added a commit that referenced this pull request Oct 24, 2023
* Fix regex from formatting url base_path

* Test test_get_data_patterns from Hub

* simply match basename instead

* more tests

* minor

* remove comment

---------

Co-authored-by: Albert Villanova del Moral <[email protected]>
Co-authored-by: Quentin Lhoest <[email protected]>
Co-authored-by: Quentin Lhoest <[email protected]>
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4 participants