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Move exceptions.py to utils/exceptions.py #6296

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I didn't notice the path while reviewing the PR yesterday :(

@mariosasko mariosasko changed the title Move exceptions to utils/exceptions.py Move exceptions.py to utils/exceptions.py Oct 11, 2023
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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) 0.006695 / 0.011353 (-0.004658) 0.004321 / 0.011008 (-0.006687) 0.084558 / 0.038508 (0.046050) 0.076290 / 0.023109 (0.053181) 0.312331 / 0.275898 (0.036433) 0.349854 / 0.323480 (0.026374) 0.004267 / 0.007986 (-0.003719) 0.003595 / 0.004328 (-0.000733) 0.065077 / 0.004250 (0.060826) 0.057461 / 0.037052 (0.020409) 0.314989 / 0.258489 (0.056500) 0.364767 / 0.293841 (0.070926) 0.031726 / 0.128546 (-0.096820) 0.008674 / 0.075646 (-0.066972) 0.288282 / 0.419271 (-0.130990) 0.052845 / 0.043533 (0.009312) 0.317501 / 0.255139 (0.062362) 0.333241 / 0.283200 (0.050041) 0.026412 / 0.141683 (-0.115271) 1.475648 / 1.452155 (0.023493) 1.551656 / 1.492716 (0.058939)

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) 0.276512 / 0.018006 (0.258506) 0.576350 / 0.000490 (0.575861) 0.009518 / 0.000200 (0.009318) 0.000280 / 0.000054 (0.000226)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.029332 / 0.037411 (-0.008079) 0.082904 / 0.014526 (0.068379) 0.102516 / 0.176557 (-0.074041) 0.159355 / 0.737135 (-0.577780) 0.104112 / 0.296338 (-0.192226)

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) 0.379144 / 0.215209 (0.163935) 3.785283 / 2.077655 (1.707629) 1.833753 / 1.504120 (0.329633) 1.667906 / 1.541195 (0.126711) 1.751551 / 1.468490 (0.283061) 0.480998 / 4.584777 (-4.103779) 3.533433 / 3.745712 (-0.212279) 3.343363 / 5.269862 (-1.926498) 2.094169 / 4.565676 (-2.471508) 0.056613 / 0.424275 (-0.367662) 0.007410 / 0.007607 (-0.000197) 0.455077 / 0.226044 (0.229033) 4.541380 / 2.268929 (2.272452) 2.269151 / 55.444624 (-53.175473) 1.955663 / 6.876477 (-4.920814) 2.227663 / 2.142072 (0.085591) 0.580597 / 4.805227 (-4.224630) 0.135034 / 6.500664 (-6.365630) 0.062091 / 0.075469 (-0.013378)

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) 1.276295 / 1.841788 (-0.565492) 20.072827 / 8.074308 (11.998519) 14.296462 / 10.191392 (4.105070) 0.164936 / 0.680424 (-0.515488) 0.018415 / 0.534201 (-0.515786) 0.390894 / 0.579283 (-0.188389) 0.415515 / 0.434364 (-0.018849) 0.462798 / 0.540337 (-0.077540) 0.650099 / 1.386936 (-0.736837)
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) 0.007218 / 0.011353 (-0.004135) 0.004246 / 0.011008 (-0.006763) 0.065818 / 0.038508 (0.027310) 0.087315 / 0.023109 (0.064206) 0.406449 / 0.275898 (0.130551) 0.442008 / 0.323480 (0.118528) 0.005752 / 0.007986 (-0.002233) 0.003624 / 0.004328 (-0.000704) 0.065349 / 0.004250 (0.061099) 0.062423 / 0.037052 (0.025371) 0.410099 / 0.258489 (0.151610) 0.448929 / 0.293841 (0.155088) 0.032498 / 0.128546 (-0.096048) 0.008877 / 0.075646 (-0.066770) 0.071611 / 0.419271 (-0.347661) 0.048038 / 0.043533 (0.004506) 0.407957 / 0.255139 (0.152818) 0.424045 / 0.283200 (0.140846) 0.025222 / 0.141683 (-0.116461) 1.496191 / 1.452155 (0.044037) 1.580765 / 1.492716 (0.088048)

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) 0.274798 / 0.018006 (0.256792) 0.581410 / 0.000490 (0.580920) 0.007302 / 0.000200 (0.007102) 0.000160 / 0.000054 (0.000106)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.034068 / 0.037411 (-0.003343) 0.096116 / 0.014526 (0.081590) 0.110234 / 0.176557 (-0.066323) 0.163246 / 0.737135 (-0.573889) 0.110250 / 0.296338 (-0.186089)

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) 0.442381 / 0.215209 (0.227172) 4.427061 / 2.077655 (2.349406) 2.361013 / 1.504120 (0.856893) 2.185048 / 1.541195 (0.643853) 2.312544 / 1.468490 (0.844054) 0.498347 / 4.584777 (-4.086430) 3.640839 / 3.745712 (-0.104873) 3.353405 / 5.269862 (-1.916457) 2.082038 / 4.565676 (-2.483638) 0.058786 / 0.424275 (-0.365489) 0.007403 / 0.007607 (-0.000205) 0.517894 / 0.226044 (0.291850) 5.184257 / 2.268929 (2.915329) 2.838467 / 55.444624 (-52.606157) 2.511116 / 6.876477 (-4.365361) 2.757816 / 2.142072 (0.615743) 0.644050 / 4.805227 (-4.161177) 0.136446 / 6.500664 (-6.364218) 0.062219 / 0.075469 (-0.013250)

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) 1.350916 / 1.841788 (-0.490872) 20.549280 / 8.074308 (12.474972) 14.697569 / 10.191392 (4.506177) 0.149818 / 0.680424 (-0.530606) 0.020187 / 0.534201 (-0.514014) 0.396008 / 0.579283 (-0.183275) 0.427535 / 0.434364 (-0.006829) 0.484544 / 0.540337 (-0.055794) 0.687076 / 1.386936 (-0.699860)

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I put the exceptions module at the root of the project in purpose because it is part of the public API.

Note that having the exceptions/errors module at the root is a common pattern followed in many open-source libraries, like numpy, pandas, pyarrow, requests...

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I'd rather be consistent with huggingface_hub and have this module in utils/ with the exceptions exposed in utils/__init__.py ...

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Maybe we could ask huggingface_hub to align with the rest of open-source libraries and expose the errors/exceptions at the root of the library...

In [11]: requests.ConnectionError
Out[11]: requests.exceptions.ConnectionError

In [12]: pandas.errors.ClosedFileError
Out[12]: pandas.errors.ClosedFileError

In [13]: numpy.AxisError  # defined in numpy/exceptions.py
Out[13]: numpy.AxisError

In [14]: pyarrow.ArrowKeyError  # defined in pyarrow/error.pxi
Out[14]: pyarrow.lib.ArrowKeyError

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Ok, I'll close this PR.

Maybe we could ask huggingface_hub to align with the rest of open-source libraries and expose the errors/exceptions at the root of the library...

cc @Wauplin

It would be nice to have an HF style guide to ensure consistency across our libraries 🙂.

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Wauplin commented Oct 17, 2023

I can expose exceptions at root level yes.

About having guidelines and consistency, let's try to do our best but it's not really in the essence of HF to formalize stuff in libraries 😒

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Wauplin commented Sep 2, 2024

Better late than never, we now have all the exceptions defined in huggingface_hub.errors! See huggingface/huggingface_hub#2069.

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Thanks for taking care, @Wauplin. I am closing this PR.

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