Update features.py to avoid bfloat16 unsupported error
State: closed | Author: skaulintel (CONTRIBUTOR)

Fixes https://github.com/huggingface/datasets/issues/6566

Let me know if there's any tests I need to clear.

Comments: I think not all torch tensors should be converted to float, what if it's a tensor of integers for example ?
Maybe you can check for the tensor dtype before converting

@lhoestq Please could this be merged? 🙏

<details>
<summary>Show benchmarks</summary>

PyArrow==8.0.0

<details>
<summary>Show updated benchmarks!</summary>

### 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) | 0.005552 / 0.011353 (-0.005801) | 0.003707 / 0.011008 (-0.007301) | 0.063794 / 0.038508 (0.025286) | 0.031897 / 0.023109 (0.008788) | 0.263086 / 0.275898 (-0.012812) | 0.281184 / 0.323480 (-0.042296) | 0.003183 / 0.007986 (-0.004802) | 0.002681 / 0.004328 (-0.001648) | 0.050259 / 0.004250 (0.046009) | 0.048395 / 0.037052 (0.011342) | 0.266925 / 0.258489 (0.008436) | 0.298146 / 0.293841 (0.004305) | 0.027995 / 0.128546 (-0.100551) | 0.010689 / 0.075646 (-0.064957) | 0.204956 / 0.419271 (-0.214316) | 0.036453 / 0.043533 (-0.007080) | 0.255406 / 0.255139 (0.000267) | 0.271388 / 0.283200 (-0.011811) | 0.019748 / 0.141683 (-0.121935) | 1.103926 / 1.452155 (-0.348228) | 1.167250 / 1.492716 (-0.325466) |

### 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) | 0.100483 / 0.018006 (0.082477) | 0.307331 / 0.000490 (0.306841) | 0.000216 / 0.000200 (0.000016) | 0.000043 / 0.000054 (-0.000011) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018918 / 0.037411 (-0.018493) | 0.062569 / 0.014526 (0.048044) | 0.074935 / 0.176557 (-0.101621) | 0.122590 / 0.737135 (-0.614545) | 0.076475 / 0.296338 (-0.219864) |

### 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) | 0.279001 / 0.215209 (0.063792) | 2.771630 / 2.077655 (0.693975) | 1.439666 / 1.504120 (-0.064454) | 1.303422 / 1.541195 (-0.237773) | 1.355670 / 1.468490 (-0.112820) | 0.576264 / 4.584777 (-4.008513) | 2.394868 / 3.745712 (-1.350844) | 2.941487 / 5.269862 (-2.328375) | 1.808733 / 4.565676 (-2.756943) | 0.063691 / 0.424275 (-0.360584) | 0.005399 / 0.007607 (-0.002208) | 0.335610 / 0.226044 (0.109566) | 3.295903 / 2.268929 (1.026974) | 1.771836 / 55.444624 (-53.672788) | 1.511246 / 6.876477 (-5.365231) | 1.535926 / 2.142072 (-0.606147) | 0.649020 / 4.805227 (-4.156207) | 0.119754 / 6.500664 (-6.380910) | 0.043319 / 0.075469 (-0.032150) |

### 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) | 0.967275 / 1.841788 (-0.874513) | 12.358482 / 8.074308 (4.284174) | 9.933324 / 10.191392 (-0.258068) | 0.133565 / 0.680424 (-0.546859) | 0.015650 / 0.534201 (-0.518551) | 0.286978 / 0.579283 (-0.292305) | 0.262912 / 0.434364 (-0.171451) | 0.330335 / 0.540337 (-0.210002) | 0.427671 / 1.386936 (-0.959265) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### 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) | 0.005660 / 0.011353 (-0.005693) | 0.003908 / 0.011008 (-0.007101) | 0.051874 / 0.038508 (0.013366) | 0.033141 / 0.023109 (0.010032) | 0.270512 / 0.275898 (-0.005386) | 0.296790 / 0.323480 (-0.026690) | 0.004335 / 0.007986 (-0.003651) | 0.002842 / 0.004328 (-0.001487) | 0.078264 / 0.004250 (0.074014) | 0.044436 / 0.037052 (0.007384) | 0.283230 / 0.258489 (0.024741) | 0.318026 / 0.293841 (0.024185) | 0.031459 / 0.128546 (-0.097087) | 0.010710 / 0.075646 (-0.064937) | 0.058152 / 0.419271 (-0.361119) | 0.034021 / 0.043533 (-0.009512) | 0.269956 / 0.255139 (0.014817) | 0.288783 / 0.283200 (0.005583) | 0.019246 / 0.141683 (-0.122436) | 1.127264 / 1.452155 (-0.324891) | 1.169777 / 1.492716 (-0.322939) |

### 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) | 0.101523 / 0.018006 (0.083516) | 0.315120 / 0.000490 (0.314630) | 0.000218 / 0.000200 (0.000018) | 0.000053 / 0.000054 (-0.000001) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023078 / 0.037411 (-0.014333) | 0.080021 / 0.014526 (0.065495) | 0.089574 / 0.176557 (-0.086982) | 0.131258 / 0.737135 (-0.605877) | 0.090604 / 0.296338 (-0.205734) |

### 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) | 0.302197 / 0.215209 (0.086988) | 2.980071 / 2.077655 (0.902416) | 1.585480 / 1.504120 (0.081360) | 1.462904 / 1.541195 (-0.078291) | 1.501102 / 1.468490 (0.032612) | 0.580342 / 4.584777 (-4.004435) | 0.972118 / 3.745712 (-2.773594) | 2.930530 / 5.269862 (-2.339331) | 1.824132 / 4.565676 (-2.741545) | 0.064711 / 0.424275 (-0.359564) | 0.005084 / 0.007607 (-0.002523) | 0.352693 / 0.226044 (0.126649) | 3.522775 / 2.268929 (1.253847) | 1.965063 / 55.444624 (-53.479561) | 1.679250 / 6.876477 (-5.197226) | 1.711691 / 2.142072 (-0.430382) | 0.663719 / 4.805227 (-4.141509) | 0.119858 / 6.500664 (-6.380806) | 0.041744 / 0.075469 (-0.033725) |

### 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) | 1.017970 / 1.841788 (-0.823817) | 12.898917 / 8.074308 (4.824609) | 10.244728 / 10.191392 (0.053336) | 0.133860 / 0.680424 (-0.546564) | 0.016044 / 0.534201 (-0.518157) | 0.287543 / 0.579283 (-0.291740) | 0.126418 / 0.434364 (-0.307946) | 0.394970 / 0.540337 (-0.145368) | 0.420455 / 1.386936 (-0.966481) |

</details>
</details>

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