wehub-resource-sync
337 lines
11 KiB
Python
Executable File
337 lines
11 KiB
Python
Executable File
# 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 os
|
|
import sys
|
|
import tempfile
|
|
import unittest
|
|
|
|
import numpy as np
|
|
|
|
|
|
class TestCustomCPUPlugin(unittest.TestCase):
|
|
def setUp(self):
|
|
# compile so and set to current path
|
|
cur_dir = os.path.dirname(os.path.abspath(__file__))
|
|
self.temp_dir = tempfile.TemporaryDirectory()
|
|
cmd = 'cd {} \
|
|
&& git clone --depth 1 {} \
|
|
&& cd PaddleCustomDevice \
|
|
&& git fetch origin \
|
|
&& git checkout {} -b dev \
|
|
&& cd backends/custom_cpu \
|
|
&& mkdir build && cd build && cmake .. -DPython_EXECUTABLE={} -DWITH_TESTING=OFF && make -j8'.format(
|
|
self.temp_dir.name,
|
|
os.getenv('PLUGIN_URL'),
|
|
os.getenv('PLUGIN_TAG'),
|
|
sys.executable,
|
|
)
|
|
os.system(cmd)
|
|
|
|
# set environment for loading and registering compiled custom kernels
|
|
# only valid in current process
|
|
os.environ['CUSTOM_DEVICE_ROOT'] = os.path.join(
|
|
cur_dir,
|
|
f'{self.temp_dir.name}/PaddleCustomDevice/backends/custom_cpu/build',
|
|
)
|
|
|
|
def tearDown(self):
|
|
self.temp_dir.cleanup()
|
|
del os.environ['CUSTOM_DEVICE_ROOT']
|
|
|
|
def test_custom_device(self):
|
|
self._test_custom_device_dataloader()
|
|
self._test_custom_device_mnist()
|
|
self._test_eager_backward_api()
|
|
self._test_eager_copy_to()
|
|
self._test_fallback_kernel()
|
|
self._test_scalar()
|
|
self._test_custom_device_py_api()
|
|
self._test_custom_device_mix_precision()
|
|
|
|
def _test_custom_device_dataloader(self):
|
|
import paddle
|
|
|
|
paddle.set_device('custom_cpu')
|
|
dataset = paddle.vision.datasets.MNIST(
|
|
mode='test',
|
|
transform=paddle.vision.transforms.Compose(
|
|
[
|
|
paddle.vision.transforms.CenterCrop(20),
|
|
paddle.vision.transforms.RandomResizedCrop(14),
|
|
paddle.vision.transforms.Normalize(),
|
|
paddle.vision.transforms.ToTensor(),
|
|
]
|
|
),
|
|
)
|
|
loader = paddle.io.DataLoader(
|
|
dataset, batch_size=32, num_workers=1, shuffle=True
|
|
)
|
|
for image, label in loader:
|
|
self.assertTrue(image.place.is_custom_place())
|
|
self.assertTrue(label.place.is_custom_place())
|
|
break
|
|
|
|
def _test_custom_device_mnist(self):
|
|
import paddle
|
|
|
|
class MNIST(paddle.nn.Layer):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.shape = 1 * 28 * 28
|
|
self.size = 10
|
|
self.output_weight = self.create_parameter(
|
|
[self.shape, self.size]
|
|
)
|
|
self.accuracy = paddle.metric.Accuracy()
|
|
|
|
def forward(self, inputs, label=None):
|
|
x = paddle.reshape(inputs, shape=[-1, self.shape])
|
|
x = paddle.matmul(x, self.output_weight)
|
|
x = paddle.nn.functional.softmax(x)
|
|
if label is not None:
|
|
self.accuracy.reset()
|
|
correct = self.accuracy.compute(x, label)
|
|
self.accuracy.update(correct)
|
|
acc = self.accuracy.accumulate()
|
|
return x, acc
|
|
else:
|
|
return x
|
|
|
|
paddle.set_device('custom_cpu')
|
|
dataset = paddle.vision.datasets.MNIST(
|
|
mode='train',
|
|
transform=paddle.vision.transforms.Compose(
|
|
[paddle.vision.transforms.ToTensor()]
|
|
),
|
|
)
|
|
loader = paddle.io.DataLoader(
|
|
dataset, batch_size=64, num_workers=1, shuffle=True
|
|
)
|
|
|
|
mnist = MNIST()
|
|
sgd = paddle.optimizer.SGD(
|
|
learning_rate=0.01, parameters=mnist.parameters()
|
|
)
|
|
|
|
data = next(loader())
|
|
img = data[0]
|
|
label = data[1]
|
|
label_int32 = paddle.cast(label, 'int32')
|
|
|
|
pred, acc = mnist(img, label_int32)
|
|
avg_loss = paddle.nn.functional.cross_entropy(pred, label_int32)
|
|
avg_loss.backward()
|
|
sgd.step()
|
|
sgd.clear_grad()
|
|
|
|
self.assertTrue(pred.place.is_custom_place())
|
|
|
|
def _test_eager_backward_api(self):
|
|
x = np.random.random([2, 2]).astype("float32")
|
|
y = np.random.random([2, 2]).astype("float32")
|
|
grad = np.ones([2, 2]).astype("float32")
|
|
|
|
import paddle
|
|
|
|
paddle.set_device('custom_cpu')
|
|
paddle.device.get_available_device()
|
|
x_tensor = paddle.to_tensor(x, stop_gradient=False)
|
|
y_tensor = paddle.to_tensor(y)
|
|
z1_tensor = paddle.matmul(x_tensor, y_tensor)
|
|
z2_tensor = paddle.matmul(x_tensor, y_tensor)
|
|
|
|
grad_tensor = paddle.to_tensor(grad)
|
|
paddle.autograd.backward([z1_tensor, z2_tensor], [grad_tensor, None])
|
|
|
|
self.assertTrue(x_tensor.grad.place.is_custom_place())
|
|
|
|
def _test_eager_copy_to(self):
|
|
import paddle
|
|
|
|
x = np.random.random([2, 2]).astype("float32")
|
|
# cpu -> custom
|
|
cpu_tensor = paddle.to_tensor(
|
|
x, dtype='float32', place=paddle.CPUPlace()
|
|
)
|
|
custom_cpu_tensor = cpu_tensor._copy_to(
|
|
paddle.CustomPlace('custom_cpu', 0), True
|
|
)
|
|
np.testing.assert_array_equal(custom_cpu_tensor, x)
|
|
self.assertTrue(custom_cpu_tensor.place.is_custom_place())
|
|
# custom -> custom
|
|
another_custom_cpu_tensor = custom_cpu_tensor._copy_to(
|
|
paddle.CustomPlace('custom_cpu', 0), True
|
|
)
|
|
np.testing.assert_array_equal(another_custom_cpu_tensor, x)
|
|
self.assertTrue(another_custom_cpu_tensor.place.is_custom_place())
|
|
# custom -> cpu
|
|
another_cpu_tensor = custom_cpu_tensor._copy_to(paddle.CPUPlace(), True)
|
|
np.testing.assert_array_equal(another_cpu_tensor, x)
|
|
self.assertTrue(another_cpu_tensor.place.is_cpu_place())
|
|
# custom -> custom self
|
|
another_custom_cpu_tensor = another_custom_cpu_tensor._copy_to(
|
|
paddle.CustomPlace('custom_cpu', 0), True
|
|
)
|
|
np.testing.assert_array_equal(another_custom_cpu_tensor, x)
|
|
self.assertTrue(another_custom_cpu_tensor.place.is_custom_place())
|
|
|
|
def _test_fallback_kernel(self):
|
|
# using (custom_cpu, add, int16) which is not registered
|
|
import paddle
|
|
|
|
r = np.array([6, 6, 6], 'int16')
|
|
x = paddle.to_tensor([5, 4, 3], 'int16')
|
|
y = paddle.to_tensor([1, 2, 3], 'int16')
|
|
z = paddle.add(x, y)
|
|
np.testing.assert_array_equal(z, r)
|
|
|
|
def _test_scalar(self):
|
|
import paddle
|
|
|
|
data_1 = paddle.to_tensor(
|
|
[[[[1.0, 4.0, 5.0, 7.0], [3.0, 4.0, 5.0, 6.0]]]]
|
|
)
|
|
k_t = paddle.to_tensor([3], dtype="int32")
|
|
value_1, indices_1 = paddle.topk(data_1, k=k_t)
|
|
|
|
def _test_custom_device_gradient_accumulation(self):
|
|
import paddle
|
|
|
|
class MNIST(paddle.nn.Layer):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.shape = 1 * 28 * 28
|
|
self.size = 10
|
|
self.output_weight = self.create_parameter(
|
|
[self.shape, self.size]
|
|
)
|
|
self.accuracy = paddle.metric.Accuracy()
|
|
|
|
def forward(self, inputs, label=None):
|
|
x = paddle.reshape(inputs, shape=[-1, self.shape])
|
|
x = paddle.matmul(x, self.output_weight)
|
|
x = paddle.nn.functional.softmax(x)
|
|
if label is not None:
|
|
self.accuracy.reset()
|
|
correct = self.accuracy.compute(x, label)
|
|
self.accuracy.update(correct)
|
|
acc = self.accuracy.accumulate()
|
|
return x, acc
|
|
else:
|
|
return x
|
|
|
|
paddle.set_device('custom_cpu')
|
|
dataset = paddle.vision.datasets.MNIST(
|
|
mode='train',
|
|
transform=paddle.vision.transforms.Compose(
|
|
[paddle.vision.transforms.ToTensor()]
|
|
),
|
|
)
|
|
loader = paddle.io.DataLoader(
|
|
dataset, batch_size=64, num_workers=1, shuffle=True
|
|
)
|
|
|
|
mnist = MNIST()
|
|
sgd = paddle.optimizer.SGD(
|
|
learning_rate=0.01, parameters=mnist.parameters()
|
|
)
|
|
|
|
data = next(loader())
|
|
img = data[0]
|
|
label = data[1]
|
|
label_int32 = paddle.cast(label, 'int32')
|
|
|
|
pred, acc = mnist(img, label_int32)
|
|
avg_loss = paddle.nn.functional.cross_entropy(pred, label_int32)
|
|
avg_loss.backward(retain_graph=True)
|
|
avg_loss = paddle.nn.functional.cross_entropy(pred, label_int32)
|
|
avg_loss.backward()
|
|
sgd.step()
|
|
|
|
def _test_custom_device_mix_precision(self):
|
|
import tempfile
|
|
|
|
import paddle
|
|
from paddle.inference import (
|
|
PlaceType,
|
|
PrecisionType,
|
|
convert_to_mixed_precision,
|
|
)
|
|
from paddle.jit import to_static
|
|
from paddle.static import InputSpec
|
|
from paddle.vision.models import resnet50
|
|
|
|
self.temp_dir = tempfile.TemporaryDirectory()
|
|
model = resnet50(True)
|
|
net = to_static(
|
|
model,
|
|
input_spec=[InputSpec(shape=[None, 3, 224, 224], name='x')],
|
|
full_graph=True,
|
|
)
|
|
paddle.jit.save(
|
|
net, os.path.join(self.temp_dir.name, 'resnet50/inference')
|
|
)
|
|
if paddle.framework.use_pir_api():
|
|
return
|
|
|
|
convert_to_mixed_precision(
|
|
os.path.join(self.temp_dir.name, 'resnet50/inference.pdmodel'),
|
|
os.path.join(self.temp_dir.name, 'resnet50/inference.pdiparams'),
|
|
os.path.join(
|
|
self.temp_dir.name, 'mixed_precision/inference.pdmodel'
|
|
),
|
|
os.path.join(
|
|
self.temp_dir.name, 'mixed_precision/inference.pdiparams'
|
|
),
|
|
backend=PlaceType.CUSTOM,
|
|
mixed_precision=PrecisionType.Half,
|
|
)
|
|
self.temp_dir.cleanup()
|
|
|
|
def _test_custom_device_py_api(self):
|
|
import paddle
|
|
|
|
p = paddle.set_device('custom_cpu')
|
|
paddle.device.synchronize('custom_cpu')
|
|
|
|
s1 = paddle.device.Stream()
|
|
s2 = paddle.device.Stream(p)
|
|
|
|
s1 = paddle.device.current_stream()
|
|
s2 = paddle.device.current_stream(p)
|
|
|
|
e1 = paddle.device.Event()
|
|
e2 = paddle.device.Event(p)
|
|
|
|
s = paddle.device.Stream()
|
|
e = paddle.device.Event()
|
|
s.query()
|
|
s.synchronize()
|
|
s.wait_event(e)
|
|
s.record_event(e)
|
|
s.wait_stream(s)
|
|
paddle.device.set_stream(s)
|
|
|
|
e.query()
|
|
e.synchronize()
|
|
e.record(s)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
if os.name == 'nt' or sys.platform.startswith('darwin'):
|
|
# only support Linux now
|
|
sys.exit()
|
|
unittest.main()
|