Title: Add concurrent loading of shards to datasets.load_from_disk 
Author: kkoutini (CONTRIBUTOR)
State: closed
Created: 2023-12-01T13:13:53Z

In some file systems (like luster), memory mapping arrow files takes time. This can be accelerated by performing the mmap in parallel on processes or threads.

- Threads seem to be faster than processes when gathering the list of tables from the workers (see https://github.com/huggingface/datasets/issues/2252).
- I'm not sure if using threads would respect the `IN_MEMORY_MAX_SIZE` config.
- I'm not sure if we need to expose num_proc from `BaseReader.read` to `DatasetBuilder.as_dataset`. Since ` DatasetBuilder.as_dataset` is used in many places beside `load_dataset`.

### Tests on luster file system (on a shared partial node):
Loading 1231 shards of ~2GBs.
The files were pre-loaded in another process before the script runs (couldn't get a fresh node).
```python
import logging
from time import perf_counter

import datasets
logger = datasets.logging.get_logger(__name__)
datasets.logging.set_verbosity_info()
logging.basicConfig(level=logging.DEBUG, format="%(message)s")

class catchtime:
    # context to measure loading time: https://stackoverflow.com/questions/33987060/python-context-manager-that-measures-time
    def __init__(self, debug_print="Time", logger=logger):
        self.debug_print = debug_print
        self.logger = logger

    def __enter__(self):
        self.start = perf_counter()
        return self

    def __exit__(self, type, value, traceback):
        self.time = perf_counter() - self.start
        readout = f"{self.debug_print}: {self.time:.3f} seconds"
        self.logger.info(readout)

dataset_path=""

# warmup
with catchtime("Loading in parallel", logger=logger):
   ds = datasets.load_from_disk(dataset_path,num_proc=16)

# num_proc=16
with catchtime("Loading in parallel", logger=logger):
   ds = datasets.load_from_disk(dataset_path,num_proc=16)
# num_proc=32
with catchtime("Loading in parallel", logger=logger):
   ds = datasets.load_from_disk(dataset_path,num_proc=32)
# num_proc=1
with catchtime("Loading in conseq", logger=logger):
   ds = datasets.load_from_disk(dataset_path,num_proc=1)

```

#### Run 1
```
open file: .../dataset_dict.json
Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:28<00:00, 13.96shards/s]
Loading in parallel: 88.690 seconds
open file: .../dataset_dict.json
Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:48<00:00, 11.31shards/s]
Loading in parallel: 109.339 seconds
open file: .../dataset_dict.json
Loading the dataset from disk using 32 threads: 100%|██████████| 1231/1231 [01:06<00:00, 18.56shards/s]
Loading in parallel: 66.931 seconds
open file: .../dataset_dict.json
Loading the dataset from disk: 100%|██████████| 1231/1231 [05:09<00:00,  3.98shards/s]
Loading in conseq: 309.792 seconds
```
#### Run 2

```
open file: .../dataset_dict.json
Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:38<00:00, 12.53shards/s]
Loading in parallel: 98.831 seconds
open file: .../dataset_dict.json
Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [02:01<00:00, 10.16shards/s]
Loading in parallel: 121.669 seconds
open file: .../dataset_dict.json
Loading the dataset from disk using 32 threads: 100%|██████████| 1231/1231 [01:07<00:00, 18.18shards/s]
Loading in parallel: 68.192 seconds
open file: .../dataset_dict.json
Loading the dataset from disk: 100%|██████████| 1231/1231 [05:19<00:00,  3.86shards/s]
Loading in conseq: 319.759 seconds
```
#### Run 3
```
open file: .../dataset_dict.json
Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:36<00:00, 12.74shards/s]
Loading in parallel: 96.936 seconds
open file: .../dataset_dict.json
Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [02:00<00:00, 10.24shards/s]
Loading in parallel: 120.761 seconds
open file: .../dataset_dict.json
Loading the dataset from disk using 32 threads: 100%|██████████| 1231/1231 [01:08<00:00, 18.04shards/s]
Loading in parallel: 68.666 seconds
open file: .../dataset_dict.json
Loading the dataset from disk: 100%|██████████| 1231/1231 [05:35<00:00,  3.67shards/s]
Loading in conseq: 335.777 seconds
```


fix #2252

Comments:
If we use multithreading no need to ask for `num_proc`. And maybe we the same numbers of threads as tqdm by default (IIRC it's `max(32, cpu_count() + 4)`) - you can even use `tqdm.contrib.concurrent.thread_map` directly to simplify the code

Also you can ignore the `IN_MEMORY_MAX_SIZE` config for this. This parameter is kinda legacy.

Have you been able to run the benchmark on a fresh node ? The speed up doesn't seem that big in your first report

I got some fresh nodes with the 32 threads I'm loading the dataset with around 315 seconds (without any preloading). Sequentially, it used to take around 1865 seconds. 
Ok I'll roll back the changes and switch to `tqdm.contrib.concurrent.thread_map` without the `num_proc` parameter. 

I switched to `tqdm.contrib.concurrent.thread_map` the code looks much simpler now.

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6464). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Thanks for the update ! Btw you should tell Jack Morris that you added this :) see https://x.com/jxmnop/status/1749812573984461145?s=20 

The CI fail is unrelated to this PR - I'm trying to fix it on `main` right now

> Thanks for the update ! Btw you should tell Jack Morris that you added this :) see https://x.com/jxmnop/status/1749812573984461145?s=20
> 
> The CI fail is unrelated to this PR - I'm trying to fix it on `main` right now

Thank you! I'll let him know :)

great work guys! letting you know here too

<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) | 0.005268 / 0.011353 (-0.006085) | 0.003520 / 0.011008 (-0.007488) | 0.063247 / 0.038508 (0.024739) | 0.032337 / 0.023109 (0.009228) | 0.243251 / 0.275898 (-0.032647) | 0.265816 / 0.323480 (-0.057664) | 0.002960 / 0.007986 (-0.005025) | 0.002733 / 0.004328 (-0.001595) | 0.048965 / 0.004250 (0.044715) | 0.044341 / 0.037052 (0.007289) | 0.260352 / 0.258489 (0.001863) | 0.288546 / 0.293841 (-0.005295) | 0.027903 / 0.128546 (-0.100643) | 0.010897 / 0.075646 (-0.064749) | 0.210852 / 0.419271 (-0.208419) | 0.036302 / 0.043533 (-0.007231) | 0.247440 / 0.255139 (-0.007699) | 0.263024 / 0.283200 (-0.020176) | 0.017732 / 0.141683 (-0.123951) | 1.144206 / 1.452155 (-0.307949) | 1.206135 / 1.492716 (-0.286581) |

### 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) | 0.098404 / 0.018006 (0.080398) | 0.310268 / 0.000490 (0.309778) | 0.000231 / 0.000200 (0.000031) | 0.000044 / 0.000054 (-0.000010) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018342 / 0.037411 (-0.019070) | 0.060620 / 0.014526 (0.046094) | 0.074248 / 0.176557 (-0.102308) | 0.121025 / 0.737135 (-0.616110) | 0.075331 / 0.296338 (-0.221008) |

### 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) | 0.293721 / 0.215209 (0.078512) | 2.854259 / 2.077655 (0.776605) | 1.520735 / 1.504120 (0.016615) | 1.393490 / 1.541195 (-0.147705) | 1.494905 / 1.468490 (0.026415) | 0.573812 / 4.584777 (-4.010965) | 2.418383 / 3.745712 (-1.327329) | 2.803916 / 5.269862 (-2.465945) | 1.741646 / 4.565676 (-2.824030) | 0.063341 / 0.424275 (-0.360934) | 0.004950 / 0.007607 (-0.002658) | 0.341758 / 0.226044 (0.115714) | 3.392918 / 2.268929 (1.123989) | 1.867037 / 55.444624 (-53.577587) | 1.571381 / 6.876477 (-5.305096) | 1.582883 / 2.142072 (-0.559190) | 0.663660 / 4.805227 (-4.141567) | 0.119587 / 6.500664 (-6.381077) | 0.042071 / 0.075469 (-0.033398) |

### 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) | 0.940976 / 1.841788 (-0.900811) | 11.841958 / 8.074308 (3.767650) | 10.510954 / 10.191392 (0.319562) | 0.131927 / 0.680424 (-0.548497) | 0.015373 / 0.534201 (-0.518828) | 0.294245 / 0.579283 (-0.285038) | 0.269355 / 0.434364 (-0.165009) | 0.330173 / 0.540337 (-0.210165) | 0.436809 / 1.386936 (-0.950127) |

</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) | 0.005609 / 0.011353 (-0.005744) | 0.003800 / 0.011008 (-0.007208) | 0.055693 / 0.038508 (0.017185) | 0.032606 / 0.023109 (0.009497) | 0.302372 / 0.275898 (0.026474) | 0.370530 / 0.323480 (0.047050) | 0.004291 / 0.007986 (-0.003694) | 0.002783 / 0.004328 (-0.001546) | 0.049351 / 0.004250 (0.045101) | 0.048186 / 0.037052 (0.011133) | 0.290022 / 0.258489 (0.031533) | 0.323358 / 0.293841 (0.029517) | 0.053929 / 0.128546 (-0.074617) | 0.011251 / 0.075646 (-0.064395) | 0.058885 / 0.419271 (-0.360387) | 0.033833 / 0.043533 (-0.009699) | 0.283546 / 0.255139 (0.028407) | 0.292416 / 0.283200 (0.009216) | 0.017682 / 0.141683 (-0.124001) | 1.141791 / 1.452155 (-0.310364) | 1.202540 / 1.492716 (-0.290177) |

### 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) | 0.101240 / 0.018006 (0.083233) | 0.313274 / 0.000490 (0.312784) | 0.000255 / 0.000200 (0.000055) | 0.000054 / 0.000054 (-0.000000) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023144 / 0.037411 (-0.014268) | 0.078418 / 0.014526 (0.063892) | 0.089716 / 0.176557 (-0.086840) | 0.129065 / 0.737135 (-0.608070) | 0.090976 / 0.296338 (-0.205362) |

### 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) | 0.294585 / 0.215209 (0.079376) | 2.921350 / 2.077655 (0.843695) | 1.600977 / 1.504120 (0.096857) | 1.483218 / 1.541195 (-0.057977) | 1.533599 / 1.468490 (0.065109) | 0.580064 / 4.584777 (-4.004712) | 2.463501 / 3.745712 (-1.282211) | 2.905853 / 5.269862 (-2.364009) | 1.799701 / 4.565676 (-2.765975) | 0.065057 / 0.424275 (-0.359218) | 0.005080 / 0.007607 (-0.002527) | 0.352292 / 0.226044 (0.126248) | 3.429664 / 2.268929 (1.160735) | 1.970752 / 55.444624 (-53.473872) | 1.697151 / 6.876477 (-5.179326) | 1.751678 / 2.142072 (-0.390394) | 0.679264 / 4.805227 (-4.125963) | 0.118197 / 6.500664 (-6.382467) | 0.041834 / 0.075469 (-0.033635) |

### 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) | 0.985756 / 1.841788 (-0.856032) | 13.335160 / 8.074308 (5.260852) | 11.524807 / 10.191392 (1.333415) | 0.134892 / 0.680424 (-0.545532) | 0.016855 / 0.534201 (-0.517346) | 0.294599 / 0.579283 (-0.284685) | 0.285988 / 0.434364 (-0.148376) | 0.331423 / 0.540337 (-0.208914) | 0.418765 / 1.386936 (-0.968171) |

</details>
</details>

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