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add serialization benchmark & forking pickler
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#!/usr/bin/env python3 | ||
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import numpy as np | ||
import argparse | ||
import pyarrow as pa | ||
from tabulate import tabulate | ||
import operator | ||
from tensorpack.utils import logger | ||
from tensorpack.utils.serialize import ( | ||
MsgpackSerializer, | ||
PyarrowSerializer, | ||
PickleSerializer, | ||
ForkingPickler, | ||
) | ||
from tensorpack.utils.timer import Timer | ||
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def benchmark_serializer(dumps, loads, data, num): | ||
buf = dumps(data) | ||
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enc_timer = Timer() | ||
dec_timer = Timer() | ||
enc_timer.pause() | ||
dec_timer.pause() | ||
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for k in range(num): | ||
enc_timer.resume() | ||
buf = dumps(data) | ||
enc_timer.pause() | ||
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dec_timer.resume() | ||
loads(buf) | ||
dec_timer.pause() | ||
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dumps_time = enc_timer.seconds() / num | ||
loads_time = dec_timer.seconds() / num | ||
return dumps_time, loads_time | ||
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def display_results(name, results): | ||
logger.info("Encoding benchmark for {}:".format(name)) | ||
data = sorted([(x, y[0]) for x, y in results], key=operator.itemgetter(1)) | ||
print(tabulate(data, floatfmt='.5f')) | ||
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logger.info("Decoding benchmark for {}:".format(name)) | ||
data = sorted([(x, y[1]) for x, y in results], key=operator.itemgetter(1)) | ||
print(tabulate(data, floatfmt='.5f')) | ||
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def benchmark_all(name, serializers, data, num=30): | ||
logger.info("Benchmarking {} ...".format(name)) | ||
results = [] | ||
for serializer_name, dumps, loads in serializers: | ||
results.append((serializer_name, benchmark_serializer(dumps, loads, data, num=num))) | ||
display_results(name, results) | ||
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def fake_json_data(): | ||
return { | ||
'words': """ | ||
Lorem ipsum dolor sit amet, consectetur adipiscing | ||
elit. Mauris adipiscing adipiscing placerat. | ||
Vestibulum augue augue, | ||
pellentesque quis sollicitudin id, adipiscing. | ||
""" * 100, | ||
'list': list(range(100)) * 500, | ||
'dict': dict((str(i), 'a') for i in range(50000)), | ||
'dict2': dict((i, 'a') for i in range(50000)), | ||
'int': 3000, | ||
'float': 100.123456 | ||
} | ||
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if __name__ == '__main__': | ||
parser = argparse.ArgumentParser() | ||
parser.add_argument("task") | ||
args = parser.parse_args() | ||
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serializers = [ | ||
("msgpack", MsgpackSerializer.dumps, MsgpackSerializer.loads), | ||
("pyarrow-buf", PyarrowSerializer.dumps, PyarrowSerializer.loads), | ||
("pyarrow-bytes", PyarrowSerializer.dumps_bytes, PyarrowSerializer.loads), | ||
("pickle", PickleSerializer.dumps, PickleSerializer.loads), | ||
("forking-pickle", ForkingPickler.dumps, ForkingPickler.loads), | ||
] | ||
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if args.task == "numpy": | ||
numpy_data = [np.random.rand(64, 224, 224, 3).astype("float32"), np.random.rand(64).astype('int32')] | ||
benchmark_all("numpy data", serializers, numpy_data) | ||
elif args.task == "json": | ||
benchmark_all("json data", serializers, fake_json_data(), num=50) | ||
elif args.task == "torch": | ||
import torch | ||
from pyarrow.lib import _default_serialization_context | ||
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pa.register_torch_serialization_handlers(_default_serialization_context) | ||
torch_data = [torch.rand(64, 224, 224, 3), torch.rand(64).to(dtype=torch.int32)] | ||
benchmark_all("torch data", serializers[1:], torch_data) |