Automatic Conversion for uint16/uint32 to Compatible PyTorch Dtypes

[mohalisad]

This PR addresses an issue encountered when utilizing uint16 or uint32 datatypes with datasets, followed by attempting to convert these datasets into PyTorch-compatible formats. Currently, doing so results in a TypeError due to incompatible datatype conversion, as illustrated by the following example:

from datasets import Dataset, Sequence, Value, Features

def gen():
    for i in range(100):
        yield {'seq': list(range(i, i + 20))}

ds = Dataset.from_generator(gen, features=Features({'seq': Sequence(feature=Value(dtype='uint16'), length=-1)}))

ds.set_format('torch')

print(ds[0])

This code snippet triggers the following error due to the inability to convert numpy.uint16 arrays to a PyTorch-supported format:

TypeError: can't convert np.ndarray of type numpy.uint16. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint8, and bool.

This PR introduces an automatic mechanism to convert np.uint16 and np.uint32 datatypes to np.int64 for seamless compatibility with PyTorch formats, simplifying workflows and improving developer experience by eliminating the need for manual conversion handling.

[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.

[lhoestq]

Good catch ! Thanks for the fix :)

[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)	0.004995 / 0.011353 (-0.006357)	0.003230 / 0.011008 (-0.007779)	0.062836 / 0.038508 (0.024328)	0.026684 / 0.023109 (0.003575)	0.249286 / 0.275898 (-0.026612)	0.272936 / 0.323480 (-0.050544)	0.003952 / 0.007986 (-0.004033)	0.002708 / 0.004328 (-0.001620)	0.055346 / 0.004250 (0.051095)	0.040023 / 0.037052 (0.002971)	0.263350 / 0.258489 (0.004860)	0.294727 / 0.293841 (0.000886)	0.027280 / 0.128546 (-0.101266)	0.010273 / 0.075646 (-0.065373)	0.206035 / 0.419271 (-0.213236)	0.035715 / 0.043533 (-0.007818)	0.255474 / 0.255139 (0.000335)	0.273960 / 0.283200 (-0.009240)	0.018563 / 0.141683 (-0.123120)	1.140013 / 1.452155 (-0.312142)	1.188655 / 1.492716 (-0.304062)

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)	0.091895 / 0.018006 (0.073888)	0.284621 / 0.000490 (0.284131)	0.000213 / 0.000200 (0.000013)	0.000043 / 0.000054 (-0.000011)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.018610 / 0.037411 (-0.018801)	0.061554 / 0.014526 (0.047028)	0.072454 / 0.176557 (-0.104103)	0.120283 / 0.737135 (-0.616853)	0.073744 / 0.296338 (-0.222595)

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)	0.288850 / 0.215209 (0.073641)	2.836761 / 2.077655 (0.759107)	1.533407 / 1.504120 (0.029287)	1.409394 / 1.541195 (-0.131801)	1.421667 / 1.468490 (-0.046823)	0.566183 / 4.584777 (-4.018594)	2.390670 / 3.745712 (-1.355043)	2.732031 / 5.269862 (-2.537831)	1.730886 / 4.565676 (-2.834791)	0.064280 / 0.424275 (-0.359995)	0.004959 / 0.007607 (-0.002648)	0.342664 / 0.226044 (0.116619)	3.398969 / 2.268929 (1.130040)	1.887354 / 55.444624 (-53.557270)	1.572955 / 6.876477 (-5.303522)	1.596179 / 2.142072 (-0.545894)	0.645844 / 4.805227 (-4.159383)	0.118050 / 6.500664 (-6.382614)	0.042158 / 0.075469 (-0.033311)

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)	0.959170 / 1.841788 (-0.882617)	11.276491 / 8.074308 (3.202183)	9.471198 / 10.191392 (-0.720194)	0.128346 / 0.680424 (-0.552078)	0.013851 / 0.534201 (-0.520350)	0.286125 / 0.579283 (-0.293158)	0.266915 / 0.434364 (-0.167449)	0.332811 / 0.540337 (-0.207526)	0.444780 / 1.386936 (-0.942156)

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)	0.005665 / 0.011353 (-0.005687)	0.003267 / 0.011008 (-0.007741)	0.050238 / 0.038508 (0.011730)	0.032882 / 0.023109 (0.009773)	0.269320 / 0.275898 (-0.006578)	0.293140 / 0.323480 (-0.030340)	0.004127 / 0.007986 (-0.003858)	0.002728 / 0.004328 (-0.001601)	0.049360 / 0.004250 (0.045109)	0.043764 / 0.037052 (0.006712)	0.291211 / 0.258489 (0.032722)	0.319745 / 0.293841 (0.025904)	0.029371 / 0.128546 (-0.099175)	0.010212 / 0.075646 (-0.065434)	0.059064 / 0.419271 (-0.360207)	0.051148 / 0.043533 (0.007615)	0.276698 / 0.255139 (0.021559)	0.292329 / 0.283200 (0.009129)	0.018349 / 0.141683 (-0.123334)	1.150816 / 1.452155 (-0.301338)	1.184292 / 1.492716 (-0.308425)

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)	0.091646 / 0.018006 (0.073640)	0.301737 / 0.000490 (0.301247)	0.000214 / 0.000200 (0.000014)	0.000051 / 0.000054 (-0.000004)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.021529 / 0.037411 (-0.015883)	0.075596 / 0.014526 (0.061070)	0.087912 / 0.176557 (-0.088645)	0.125240 / 0.737135 (-0.611895)	0.088035 / 0.296338 (-0.208303)

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)	0.305097 / 0.215209 (0.089888)	2.979612 / 2.077655 (0.901957)	1.647009 / 1.504120 (0.142889)	1.520251 / 1.541195 (-0.020944)	1.513361 / 1.468490 (0.044870)	0.571733 / 4.584777 (-4.013044)	2.415587 / 3.745712 (-1.330125)	2.615983 / 5.269862 (-2.653879)	1.732637 / 4.565676 (-2.833039)	0.062830 / 0.424275 (-0.361445)	0.004972 / 0.007607 (-0.002635)	0.348559 / 0.226044 (0.122515)	3.450567 / 2.268929 (1.181639)	1.970743 / 55.444624 (-53.473882)	1.702232 / 6.876477 (-5.174245)	1.799592 / 2.142072 (-0.342480)	0.649477 / 4.805227 (-4.155751)	0.115940 / 6.500664 (-6.384724)	0.040364 / 0.075469 (-0.035105)

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)	1.000014 / 1.841788 (-0.841773)	11.937886 / 8.074308 (3.863578)	10.169478 / 10.191392 (-0.021914)	0.153359 / 0.680424 (-0.527064)	0.015205 / 0.534201 (-0.518996)	0.287812 / 0.579283 (-0.291471)	0.278688 / 0.434364 (-0.155676)	0.322831 / 0.540337 (-0.217507)	0.425631 / 1.386936 (-0.961305)