未验证 提交 2094a584 编写于 作者: Z Zhang Ting 提交者: GitHub

implement autotune python API (#42299)

上级 cf780097
......@@ -15,12 +15,14 @@
from __future__ import print_function
import unittest
import numpy as np
import tempfile
import warnings
import json
import paddle
import paddle.nn as nn
from paddle.io import Dataset, DataLoader, BatchSampler, SequenceSampler
from paddle.fluid.reader import set_autotune_config
import sys
import os
class RandomDataset(Dataset):
......@@ -51,12 +53,21 @@ class TestAutoTune(unittest.TestCase):
self.dataset = RandomDataset(10)
def test_dataloader_use_autotune(self):
set_autotune_config(True, 1)
paddle.incubate.autotune.set_config(
config={"dataloader": {
"enable": True,
"tuning_steps": 1,
}})
loader = DataLoader(
self.dataset, batch_size=self.batch_size, num_workers=0)
def test_dataloader_disable_autotune(self):
set_autotune_config(False)
config = {"dataloader": {"enable": False, "tuning_steps": 1}}
tfile = tempfile.NamedTemporaryFile(mode="w+", delete=False)
json.dump(config, tfile)
tfile.close()
paddle.incubate.autotune.set_config(tfile.name)
os.remove(tfile.name)
loader = DataLoader(
self.dataset, batch_size=self.batch_size, num_workers=2)
if (sys.platform == 'darwin' or sys.platform == 'win32'):
......@@ -65,12 +76,28 @@ class TestAutoTune(unittest.TestCase):
self.assertEqual(loader.num_workers, 2)
def test_distributer_batch_sampler_autotune(self):
set_autotune_config(True, 1)
paddle.incubate.autotune.set_config(
config={"dataloader": {
"enable": True,
"tuning_steps": 1,
}})
batch_sampler = paddle.io.DistributedBatchSampler(
self.dataset, batch_size=self.batch_size)
loader = DataLoader(
self.dataset, batch_sampler=batch_sampler, num_workers=2)
class TestAutoTuneAPI(unittest.TestCase):
def test_set_config_warnings(self):
with warnings.catch_warnings(record=True) as w:
config = {"kernel": {"enable": 1, "tuning_range": True}}
tfile = tempfile.NamedTemporaryFile(mode="w+", delete=False)
json.dump(config, tfile)
tfile.close()
paddle.incubate.autotune.set_config(tfile.name)
os.remove(tfile.name)
self.assertTrue(len(w) == 2)
if __name__ == '__main__':
unittest.main()
......@@ -16,6 +16,10 @@ import paddle
import unittest
import numpy
import paddle.nn.functional as F
import tempfile
import warnings
import json
import os
class SimpleNet(paddle.nn.Layer):
......@@ -41,10 +45,18 @@ class SimpleNet(paddle.nn.Layer):
class LayoutAutoTune(unittest.TestCase):
def use_autoune(self):
if paddle.is_compiled_with_cuda():
paddle.fluid.core.enable_layout_autotune()
paddle.incubate.autotune.set_config(
config={"layout": {
"enable": True
}})
return paddle.fluid.core.use_layout_autotune()
else:
paddle.fluid.core.disable_layout_autotune()
config = {"layout": {"enable": False}}
tfile = tempfile.NamedTemporaryFile(mode="w+", delete=False)
json.dump(config, tfile)
tfile.close()
paddle.incubate.autotune.set_config(tfile.name)
os.remove(tfile.name)
return paddle.fluid.core.use_layout_autotune()
def train(self, data_format):
......@@ -103,7 +115,6 @@ class LayoutAutoTune(unittest.TestCase):
def test_flatten_op_transposer(self):
if not self.use_autoune():
return
paddle.fluid.core.enable_layout_autotune()
conv = paddle.nn.Conv2D(3, 8, (3, 3))
flatten = paddle.nn.Flatten(start_axis=1, stop_axis=2)
data = paddle.rand([1, 3, 16, 14])
......@@ -119,5 +130,20 @@ class LayoutAutoTune(unittest.TestCase):
self.assertEqual(out.shape, [1, 112, 12])
class TestAutoTuneAPI(unittest.TestCase):
def test_set_config_warnings(self):
with warnings.catch_warnings(record=True) as w:
config = {"layout": {"enable": 1}}
# On linux, we can open the file again to read the content
# without closing the file, but on windows system, there is
# no permission to open it again without closing it.
tfile = tempfile.NamedTemporaryFile(mode="w+", delete=False)
json.dump(config, tfile)
tfile.close()
paddle.incubate.autotune.set_config(tfile.name)
os.remove(tfile.name)
self.assertTrue(len(w) == 1)
if __name__ == '__main__':
unittest.main()
......@@ -15,6 +15,10 @@
import paddle
import unittest
import numpy as np
import tempfile
import warnings
import json
import os
class SimpleNet(paddle.nn.Layer):
......@@ -73,10 +77,13 @@ class TestAutoTune(unittest.TestCase):
return expected_res
def test_autotune(self):
paddle.fluid.core.disable_autotune()
paddle.incubate.autotune.set_config(
config={"kernel": {
"enable": False
}})
self.assertEqual(self.get_flags("FLAGS_use_autotune"), False)
paddle.fluid.core.enable_autotune()
paddle.incubate.autotune.set_config(config={"kernel": {"enable": True}})
self.assertEqual(self.get_flags("FLAGS_use_autotune"), True)
def check_status(self, expected_res):
......@@ -93,10 +100,16 @@ class TestDygraphAutoTuneStatus(TestAutoTune):
def run_program(self, enable_autotune):
self.set_flags(enable_autotune)
if enable_autotune:
paddle.fluid.core.enable_autotune()
paddle.incubate.autotune.set_config(
config={"kernel": {
"enable": True,
"tuning_range": [1, 2]
}})
else:
paddle.fluid.core.disable_autotune()
paddle.fluid.core.set_autotune_range(1, 2)
paddle.incubate.autotune.set_config(
config={"kernel": {
"enable": False
}})
x_var = paddle.uniform((1, 1, 8, 8), dtype='float32', min=-1., max=1.)
net = SimpleNet()
for i in range(3):
......@@ -141,10 +154,18 @@ class TestStaticAutoTuneStatus(TestAutoTune):
self.set_flags(enable_autotune)
if enable_autotune:
paddle.fluid.core.enable_autotune()
config = {"kernel": {"enable": True, "tuning_range": [1, 2]}}
tfile = tempfile.NamedTemporaryFile(mode="w+", delete=False)
json.dump(config, tfile)
tfile.close()
paddle.incubate.autotune.set_config(tfile.name)
os.remove(tfile.name)
else:
paddle.fluid.core.disable_autotune()
paddle.fluid.core.set_autotune_range(1, 2)
paddle.incubate.autotune.set_config(
config={"kernel": {
"enable": False,
"tuning_range": [1, 2]
}})
for i in range(3):
exe.run(program=main_program, feed={'X': x}, fetch_list=[loss])
......@@ -166,5 +187,22 @@ class TestStaticAutoTuneStatus(TestAutoTune):
self.func_disable_autotune()
class TestAutoTuneAPI(unittest.TestCase):
def test_set_config_warnings(self):
with warnings.catch_warnings(record=True) as w:
config = {"kernel": {"enable": 1, "tuning_range": 1}}
tfile = tempfile.NamedTemporaryFile(mode="w+", delete=False)
json.dump(config, tfile)
tfile.close()
paddle.incubate.autotune.set_config(tfile.name)
os.remove(tfile.name)
self.assertTrue(len(w) == 2)
def test_set_config_attr(self):
paddle.incubate.autotune.set_config(config=None)
self.assertEqual(
paddle.get_flags("FLAGS_use_autotune")["FLAGS_use_autotune"], True)
if __name__ == '__main__':
unittest.main()
......@@ -29,6 +29,7 @@ from .tensor import segment_max
from .tensor import segment_min
from .passes import fuse_resnet_unit_pass
import paddle.incubate.autograd
import paddle.incubate.autotune
from . import nn #noqa: F401
......
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
import json
import warnings
from paddle.fluid import core
__all__ = ['set_config']
def set_config(config=None):
r"""
Set the configuration for kernel, layout and dataloader auto-tuning.
1. kernel: When it is enabled, exhaustive search method will be used to select
and cache the best algorithm for the operator in the tuning iteration. Tuning
parameters are as follows:
- enable(bool): Whether to enable kernel tuning.
- tuning_range(list): Start and end iteration for auto-tuning. Default: [1, 10].
2. layout: When it is enabled, the best data layout such as NCHW or NHWC will be
determined based on the device and data type. When the origin layout setting is
not best, layout transformation will be automaticly performed to improve model
performance. Layout auto-tuning only supports dygraph mode currently. Tuning
parameters are as follows:
- enable(bool): Whether to enable layout tuning.
3. dataloader: When it is enabled, the best num_workers will be selected to replace
the origin dataloader setting. Tuning parameters are as follows:
- enable(bool): Whether to enable dataloader tuning.
Args:
config (dict|str|None, optional): Configuration for auto-tuning. If it is a
dictionary, the key is the tuning type, and the value is a dictionary
of the corresponding tuning parameters. If it is a string, the path of
a json file will be specified and the tuning configuration will be set
by the the json file. Default: None, auto-tuning for kernel, layout and
dataloader will be enabled.
Examples:
.. code-block:: python
:name: auto-tuning
import paddle
import json
# config is a dict.
config = {
"kernel": {
"enable": True,
"tuning_range": [1, 5],
},
"layout": {
"enable": True,
},
"dataloader": {
"enable": True,
}
}
paddle.incubate.autotune.set_config(config)
# config is the path of json file.
config_json = json.dumps(config)
with open('config.json', 'w') as json_file:
json_file.write(config_json)
paddle.incubate.autotune.set_config('config.json')
"""
if config is None:
core.enable_autotune()
core.enable_layout_autotune()
paddle.fluid.reader.set_autotune_config(use_autotune=True)
return
config_dict = {}
if isinstance(config, dict):
config_dict = config
elif isinstance(config, str):
try:
with open(config, 'r') as filehandle:
config_dict = json.load(filehandle)
except Exception as e:
print('Load config error: {}'.format(e))
warnings.warn("Use default configuration for auto-tuning.")
if "kernel" in config_dict:
kernel_config = config_dict["kernel"]
if "enable" in kernel_config:
if isinstance(kernel_config['enable'], bool):
if kernel_config['enable']:
core.enable_autotune()
else:
core.disable_autotune()
else:
warnings.warn(
"The auto-tuning configuration of the kernel is incorrect."
"The `enable` should be bool. Use default parameter instead."
)
if "tuning_range" in kernel_config:
if isinstance(kernel_config['tuning_range'], list):
tuning_range = kernel_config['tuning_range']
assert len(tuning_range) == 2
core.set_autotune_range(tuning_range[0], tuning_range[1])
else:
warnings.warn(
"The auto-tuning configuration of the kernel is incorrect."
"The `tuning_range` should be list. Use default parameter instead."
)
if "layout" in config_dict:
layout_config = config_dict["layout"]
if "enable" in layout_config:
if isinstance(layout_config['enable'], bool):
if layout_config['enable']:
core.enable_layout_autotune()
else:
core.disable_layout_autotune()
else:
warnings.warn(
"The auto-tuning configuration of the layout is incorrect."
"The `enable` should be bool. Use default parameter instead."
)
if "dataloader" in config_dict:
dataloader_config = config_dict["dataloader"]
use_autoune = False
if "enable" in dataloader_config:
if isinstance(dataloader_config['enable'], bool):
use_autoune = dataloader_config['enable']
else:
warnings.warn(
"The auto-tuning configuration of the dataloader is incorrect."
"The `enable` should be bool. Use default parameter instead."
)
if "tuning_steps" in dataloader_config:
if isinstance(dataloader_config['tuning_steps'], int):
paddle.fluid.reader.set_autotune_config(
use_autoune, dataloader_config['tuning_steps'])
else:
warnings.warn(
"The auto-tuning configuration of the dataloader is incorrect."
"The `tuning_steps` should be int. Use default parameter instead."
)
paddle.fluid.reader.set_autotune_config(use_autoune)
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