Add concurrent loading of shards to datasets.load_from_disk #6464

kkoutini · 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 Slow dataloading with big datasets issue persists #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).

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

lhoestq · 2023-12-06T13:49:39Z

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

kkoutini · 2023-12-06T14:40:57Z

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.

kkoutini · 2023-12-06T15:31:56Z

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

lhoestq

That's a nice speed up !

I just have one comment:

lhoestq · 2023-12-06T15:46:21Z

src/datasets/arrow_reader.py

        for f_dict in files:
            pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)
            pa_tables.append(pa_table)


I think you can remove this, and also remove the pa_tables = [] a few lines above this

Suggested change

for f_dict in files:

pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)

pa_tables.append(pa_table)

oh! Thanks I missed that :)

HuggingFaceDocBuilderDev · 2023-12-06T15:54:01Z

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 · 2024-01-25T10:55:57Z

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

kkoutini · 2024-01-25T12:43:21Z

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 :)

gblazex · 2024-01-25T16:12:38Z

great work guys! letting you know here too

github-actions · 2024-01-26T15:17:42Z

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)	0.005268 / 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)

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)	0.005609 / 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)

kkoutini marked this pull request as ready for review December 1, 2023 18:31

kkoutini mentioned this pull request Dec 4, 2023

Slow dataloading with big datasets issue persists #2252

Closed

kkoutini closed this Dec 6, 2023

kkoutini force-pushed the multi-threaded-disk-load branch from 0f39880 to 2d31e43 Compare December 6, 2023 14:50

kkoutini added 2 commits December 6, 2023 16:13

add threadmap to load_from_disk huggingface#2252

555c8a3

Add threadmap to arrow_reader.read_files huggingface#2252

15e6206

kkoutini reopened this Dec 6, 2023

lhoestq reviewed Dec 6, 2023

View reviewed changes

kkoutini and others added 5 commits December 6, 2023 17:45

remove old way of loading files

2a4b9f3

Merge branch 'huggingface:main' into multi-threaded-disk-load

8776ece

Merge branch 'huggingface:main' into multi-threaded-disk-load

c02271e

Merge branch 'huggingface:main' into multi-threaded-disk-load

068a1fe

sort imports

6e8ef61

kkoutini requested a review from lhoestq January 25, 2024 09:10

Merge branch 'main' into multi-threaded-disk-load

c0262aa

lhoestq merged commit 65434e4 into huggingface:main Jan 26, 2024
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unography mentioned this pull request Nov 8, 2024

Concurrent loading in load_from_disk - num_proc as a param #7286

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Add concurrent loading of shards to datasets.load_from_disk #6464

Add concurrent loading of shards to datasets.load_from_disk #6464

kkoutini commented Dec 1, 2023 •

edited

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lhoestq commented Dec 6, 2023 •

edited

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kkoutini commented Dec 6, 2023

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lhoestq Dec 6, 2023 •

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kkoutini Dec 6, 2023

HuggingFaceDocBuilderDev commented Dec 6, 2023

lhoestq commented Jan 25, 2024

kkoutini commented Jan 25, 2024

gblazex commented Jan 25, 2024

github-actions bot commented Jan 26, 2024

Benchmark: benchmark_array_xd.json

Benchmark: benchmark_getitem_100B.json

Benchmark: benchmark_indices_mapping.json

Benchmark: benchmark_iterating.json

Benchmark: benchmark_map_filter.json

Benchmark: benchmark_array_xd.json

Benchmark: benchmark_getitem_100B.json

Benchmark: benchmark_indices_mapping.json

Benchmark: benchmark_iterating.json

Benchmark: benchmark_map_filter.json

	for f_dict in files:
	pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)
	pa_tables.append(pa_table)

Add concurrent loading of shards to datasets.load_from_disk #6464

Add concurrent loading of shards to datasets.load_from_disk #6464

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kkoutini commented Dec 1, 2023 • edited Loading

Tests on luster file system (on a shared partial node):

Run 1

Run 2

Run 3

lhoestq commented Dec 6, 2023 • edited Loading

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lhoestq commented Jan 25, 2024

kkoutini commented Jan 25, 2024

gblazex commented Jan 25, 2024

github-actions bot commented Jan 26, 2024

Benchmark: benchmark_array_xd.json

Benchmark: benchmark_getitem_100B.json

Benchmark: benchmark_indices_mapping.json

Benchmark: benchmark_iterating.json

Benchmark: benchmark_map_filter.json

Benchmark: benchmark_array_xd.json

Benchmark: benchmark_getitem_100B.json

Benchmark: benchmark_indices_mapping.json

Benchmark: benchmark_iterating.json

Benchmark: benchmark_map_filter.json

kkoutini commented Dec 1, 2023 •

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lhoestq commented Dec 6, 2023 •

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