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Fix ArrayXD YAML conversion #6168

Merged
merged 4 commits into from
Dec 12, 2023
Merged

Fix ArrayXD YAML conversion #6168

merged 4 commits into from
Dec 12, 2023

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mariosasko
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Replace the shape tuple with a list in the ArrayXD YAML conversion.

Fix #6112

@HuggingFaceDocBuilderDev

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint.

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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) 0.009350 / 0.011353 (-0.002003) 0.005658 / 0.011008 (-0.005350) 0.123173 / 0.038508 (0.084664) 0.096354 / 0.023109 (0.073244) 0.464398 / 0.275898 (0.188500) 0.544455 / 0.323480 (0.220975) 0.007337 / 0.007986 (-0.000648) 0.004424 / 0.004328 (0.000096) 0.089715 / 0.004250 (0.085465) 0.072462 / 0.037052 (0.035410) 0.460601 / 0.258489 (0.202112) 0.544384 / 0.293841 (0.250543) 0.052994 / 0.128546 (-0.075552) 0.014459 / 0.075646 (-0.061187) 0.464368 / 0.419271 (0.045096) 0.072889 / 0.043533 (0.029356) 0.471387 / 0.255139 (0.216248) 0.560982 / 0.283200 (0.277783) 0.041398 / 0.141683 (-0.100285) 1.964688 / 1.452155 (0.512533) 2.240727 / 1.492716 (0.748011)

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) 0.308524 / 0.018006 (0.290518) 0.669306 / 0.000490 (0.668816) 0.006644 / 0.000200 (0.006444) 0.000108 / 0.000054 (0.000053)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.037395 / 0.037411 (-0.000016) 0.111303 / 0.014526 (0.096777) 0.158988 / 0.176557 (-0.017569) 0.236155 / 0.737135 (-0.500980) 0.134775 / 0.296338 (-0.161564)

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) 0.648830 / 0.215209 (0.433621) 6.614794 / 2.077655 (4.537139) 2.867526 / 1.504120 (1.363407) 2.472967 / 1.541195 (0.931772) 2.488419 / 1.468490 (1.019929) 0.915785 / 4.584777 (-3.668992) 6.010754 / 3.745712 (2.265042) 5.468873 / 5.269862 (0.199011) 3.446535 / 4.565676 (-1.119141) 0.118592 / 0.424275 (-0.305684) 0.012005 / 0.007607 (0.004398) 0.808467 / 0.226044 (0.582423) 8.152122 / 2.268929 (5.883193) 3.751282 / 55.444624 (-51.693342) 3.009569 / 6.876477 (-3.866908) 3.282613 / 2.142072 (1.140540) 1.152727 / 4.805227 (-3.652500) 0.240224 / 6.500664 (-6.260440) 0.097871 / 0.075469 (0.022402)

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) 1.824944 / 1.841788 (-0.016843) 27.840842 / 8.074308 (19.766533) 24.368669 / 10.191392 (14.177277) 0.260621 / 0.680424 (-0.419803) 0.033730 / 0.534201 (-0.500471) 0.552494 / 0.579283 (-0.026789) 0.666921 / 0.434364 (0.232557) 0.648812 / 0.540337 (0.108475) 0.912602 / 1.386936 (-0.474334)
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) 0.011688 / 0.011353 (0.000335) 0.005794 / 0.011008 (-0.005215) 0.093466 / 0.038508 (0.054958) 0.102583 / 0.023109 (0.079474) 0.593572 / 0.275898 (0.317674) 0.614351 / 0.323480 (0.290871) 0.007006 / 0.007986 (-0.000980) 0.005557 / 0.004328 (0.001229) 0.087779 / 0.004250 (0.083529) 0.072639 / 0.037052 (0.035586) 0.577464 / 0.258489 (0.318975) 0.628240 / 0.293841 (0.334399) 0.053876 / 0.128546 (-0.074670) 0.015383 / 0.075646 (-0.060263) 0.110633 / 0.419271 (-0.308639) 0.067467 / 0.043533 (0.023934) 0.613457 / 0.255139 (0.358318) 0.604939 / 0.283200 (0.321739) 0.041738 / 0.141683 (-0.099945) 1.967167 / 1.452155 (0.515012) 2.121009 / 1.492716 (0.628293)

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) 0.449937 / 0.018006 (0.431930) 0.694410 / 0.000490 (0.693921) 0.064051 / 0.000200 (0.063851) 0.000810 / 0.000054 (0.000756)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.045138 / 0.037411 (0.007727) 0.116831 / 0.014526 (0.102306) 0.131906 / 0.176557 (-0.044651) 0.202421 / 0.737135 (-0.534714) 0.132568 / 0.296338 (-0.163770)

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) 0.698046 / 0.215209 (0.482837) 7.112591 / 2.077655 (5.034936) 3.332679 / 1.504120 (1.828559) 2.946384 / 1.541195 (1.405189) 3.074484 / 1.468490 (1.605994) 0.970917 / 4.584777 (-3.613859) 6.143506 / 3.745712 (2.397794) 5.572496 / 5.269862 (0.302634) 3.602673 / 4.565676 (-0.963004) 0.115068 / 0.424275 (-0.309207) 0.009971 / 0.007607 (0.002364) 0.891090 / 0.226044 (0.665046) 8.761788 / 2.268929 (6.492859) 4.362685 / 55.444624 (-51.081939) 3.612893 / 6.876477 (-3.263583) 3.797948 / 2.142072 (1.655876) 1.202890 / 4.805227 (-3.602337) 0.238120 / 6.500664 (-6.262544) 0.095612 / 0.075469 (0.020143)

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) 1.958880 / 1.841788 (0.117092) 28.216454 / 8.074308 (20.142146) 25.361424 / 10.191392 (15.170032) 0.308203 / 0.680424 (-0.372221) 0.032903 / 0.534201 (-0.501298) 0.539714 / 0.579283 (-0.039569) 0.688278 / 0.434364 (0.253914) 0.644818 / 0.540337 (0.104481) 0.905694 / 1.386936 (-0.481242)

@lhoestq
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lhoestq commented Aug 29, 2023

Maybe convert all the tuples by default instead of hardcoding a logic specific to ArrayXD ?

@wissamBylaw
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@mariosasko Have you been able to fix this issue ? we're having quite a rough time updating our dataset lately

@mariosasko
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@lhoestq Does it look good now?

Comment on lines +1784 to +1794
def to_yaml_types(obj: dict) -> dict:
if isinstance(obj, dict):
return {k: to_yaml_types(v) for k, v in obj.items()}
elif isinstance(obj, list):
return [to_yaml_types(v) for v in obj]
elif isinstance(obj, tuple):
return to_yaml_types(list(obj))
else:
return obj

return to_yaml_types(to_yaml_inner(yaml_data)["struct"])
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is this needed ?

Suggested change
def to_yaml_types(obj: dict) -> dict:
if isinstance(obj, dict):
return {k: to_yaml_types(v) for k, v in obj.items()}
elif isinstance(obj, list):
return [to_yaml_types(v) for v in obj]
elif isinstance(obj, tuple):
return to_yaml_types(list(obj))
else:
return obj
return to_yaml_types(to_yaml_inner(yaml_data)["struct"])
return to_yaml_inner(yaml_data)["struct"]

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Yes, to_yaml_inner doesn't traverse the leaves (attributes) of the complex types (Image, ArrayXD, etc.)

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LGTM !

@mariosasko mariosasko merged commit 53b8c2d into main Dec 12, 2023
12 checks passed
@mariosasko mariosasko deleted the fix-6112 branch December 12, 2023 15:00
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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) 0.005519 / 0.011353 (-0.005834) 0.003482 / 0.011008 (-0.007527) 0.064904 / 0.038508 (0.026396) 0.052399 / 0.023109 (0.029289) 0.247238 / 0.275898 (-0.028660) 0.273426 / 0.323480 (-0.050054) 0.003102 / 0.007986 (-0.004884) 0.003420 / 0.004328 (-0.000908) 0.048029 / 0.004250 (0.043779) 0.039378 / 0.037052 (0.002326) 0.253809 / 0.258489 (-0.004680) 0.287483 / 0.293841 (-0.006358) 0.028096 / 0.128546 (-0.100450) 0.010806 / 0.075646 (-0.064841) 0.207799 / 0.419271 (-0.211472) 0.035861 / 0.043533 (-0.007672) 0.251912 / 0.255139 (-0.003227) 0.278877 / 0.283200 (-0.004323) 0.019498 / 0.141683 (-0.122185) 1.104916 / 1.452155 (-0.347238) 1.157376 / 1.492716 (-0.335340)

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) 0.093051 / 0.018006 (0.075045) 0.303331 / 0.000490 (0.302841) 0.000209 / 0.000200 (0.000009) 0.000044 / 0.000054 (-0.000011)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.018052 / 0.037411 (-0.019359) 0.060597 / 0.014526 (0.046071) 0.074033 / 0.176557 (-0.102524) 0.120966 / 0.737135 (-0.616169) 0.075012 / 0.296338 (-0.221326)

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) 0.283922 / 0.215209 (0.068713) 2.805445 / 2.077655 (0.727791) 1.498292 / 1.504120 (-0.005828) 1.371686 / 1.541195 (-0.169509) 1.402074 / 1.468490 (-0.066416) 0.567231 / 4.584777 (-4.017546) 2.393291 / 3.745712 (-1.352422) 2.800329 / 5.269862 (-2.469533) 1.789197 / 4.565676 (-2.776479) 0.063620 / 0.424275 (-0.360655) 0.005008 / 0.007607 (-0.002600) 0.338929 / 0.226044 (0.112884) 3.292122 / 2.268929 (1.023193) 1.813313 / 55.444624 (-53.631311) 1.557122 / 6.876477 (-5.319354) 1.576395 / 2.142072 (-0.565677) 0.666714 / 4.805227 (-4.138513) 0.118253 / 6.500664 (-6.382411) 0.042633 / 0.075469 (-0.032836)

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) 0.950678 / 1.841788 (-0.891110) 11.589806 / 8.074308 (3.515498) 10.436701 / 10.191392 (0.245309) 0.141048 / 0.680424 (-0.539376) 0.014766 / 0.534201 (-0.519435) 0.298359 / 0.579283 (-0.280924) 0.268850 / 0.434364 (-0.165514) 0.340242 / 0.540337 (-0.200095) 0.451447 / 1.386936 (-0.935489)
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) 0.005405 / 0.011353 (-0.005948) 0.003545 / 0.011008 (-0.007464) 0.048959 / 0.038508 (0.010451) 0.056565 / 0.023109 (0.033455) 0.274289 / 0.275898 (-0.001609) 0.296565 / 0.323480 (-0.026915) 0.004790 / 0.007986 (-0.003196) 0.002772 / 0.004328 (-0.001557) 0.048605 / 0.004250 (0.044354) 0.040676 / 0.037052 (0.003624) 0.279949 / 0.258489 (0.021460) 0.312816 / 0.293841 (0.018976) 0.029605 / 0.128546 (-0.098941) 0.010799 / 0.075646 (-0.064848) 0.056941 / 0.419271 (-0.362331) 0.034518 / 0.043533 (-0.009014) 0.277193 / 0.255139 (0.022054) 0.292334 / 0.283200 (0.009134) 0.018836 / 0.141683 (-0.122847) 1.145228 / 1.452155 (-0.306927) 1.198958 / 1.492716 (-0.293758)

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) 0.093618 / 0.018006 (0.075612) 0.303687 / 0.000490 (0.303197) 0.000234 / 0.000200 (0.000034) 0.000044 / 0.000054 (-0.000010)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.021347 / 0.037411 (-0.016065) 0.067811 / 0.014526 (0.053286) 0.080631 / 0.176557 (-0.095926) 0.119289 / 0.737135 (-0.617846) 0.082085 / 0.296338 (-0.214254)

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) 0.293374 / 0.215209 (0.078165) 2.864516 / 2.077655 (0.786861) 1.611042 / 1.504120 (0.106922) 1.466124 / 1.541195 (-0.075071) 1.480509 / 1.468490 (0.012019) 0.569463 / 4.584777 (-4.015314) 2.448181 / 3.745712 (-1.297531) 2.841732 / 5.269862 (-2.428130) 1.754458 / 4.565676 (-2.811219) 0.063771 / 0.424275 (-0.360505) 0.004976 / 0.007607 (-0.002631) 0.346094 / 0.226044 (0.120050) 3.440090 / 2.268929 (1.171162) 1.961862 / 55.444624 (-53.482763) 1.675780 / 6.876477 (-5.200697) 1.679676 / 2.142072 (-0.462396) 0.641063 / 4.805227 (-4.164164) 0.116268 / 6.500664 (-6.384396) 0.041767 / 0.075469 (-0.033702)

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) 0.980286 / 1.841788 (-0.861502) 12.055227 / 8.074308 (3.980919) 10.685417 / 10.191392 (0.494025) 0.140842 / 0.680424 (-0.539582) 0.015413 / 0.534201 (-0.518788) 0.286939 / 0.579283 (-0.292344) 0.278796 / 0.434364 (-0.155568) 0.326740 / 0.540337 (-0.213597) 0.574516 / 1.386936 (-0.812421)

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yaml error using push_to_hub with generated README.md
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