chore: import upstream snapshot with attribution
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wehub-resource-sync
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import sys
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import tempfile
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import unittest
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import numpy as np
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class TestCustomCPUPlugin(unittest.TestCase):
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def setUp(self):
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# compile so and set to current path
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cur_dir = os.path.dirname(os.path.abspath(__file__))
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self.temp_dir = tempfile.TemporaryDirectory()
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cmd = 'cd {} \
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&& git clone --depth 1 {} \
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&& cd PaddleCustomDevice \
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&& git fetch origin \
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&& git checkout {} -b dev \
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&& cd backends/custom_cpu \
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&& mkdir build && cd build && cmake .. -DPython_EXECUTABLE={} -DWITH_TESTING=OFF && make -j8'.format(
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self.temp_dir.name,
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os.getenv('PLUGIN_URL'),
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os.getenv('PLUGIN_TAG'),
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sys.executable,
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)
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os.system(cmd)
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# set environment for loading and registering compiled custom kernels
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# only valid in current process
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os.environ['CUSTOM_DEVICE_ROOT'] = os.path.join(
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cur_dir,
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f'{self.temp_dir.name}/PaddleCustomDevice/backends/custom_cpu/build',
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)
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def tearDown(self):
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self.temp_dir.cleanup()
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del os.environ['CUSTOM_DEVICE_ROOT']
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def test_custom_device(self):
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self._test_custom_device_dataloader()
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self._test_custom_device_mnist()
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self._test_eager_backward_api()
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self._test_eager_copy_to()
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self._test_fallback_kernel()
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self._test_scalar()
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self._test_custom_device_py_api()
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self._test_custom_device_mix_precision()
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def _test_custom_device_dataloader(self):
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import paddle
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paddle.set_device('custom_cpu')
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dataset = paddle.vision.datasets.MNIST(
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mode='test',
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transform=paddle.vision.transforms.Compose(
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[
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paddle.vision.transforms.CenterCrop(20),
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paddle.vision.transforms.RandomResizedCrop(14),
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paddle.vision.transforms.Normalize(),
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paddle.vision.transforms.ToTensor(),
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]
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),
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)
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loader = paddle.io.DataLoader(
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dataset, batch_size=32, num_workers=1, shuffle=True
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)
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for image, label in loader:
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self.assertTrue(image.place.is_custom_place())
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self.assertTrue(label.place.is_custom_place())
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break
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def _test_custom_device_mnist(self):
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import paddle
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class MNIST(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.shape = 1 * 28 * 28
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self.size = 10
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self.output_weight = self.create_parameter(
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[self.shape, self.size]
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)
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self.accuracy = paddle.metric.Accuracy()
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def forward(self, inputs, label=None):
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x = paddle.reshape(inputs, shape=[-1, self.shape])
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x = paddle.matmul(x, self.output_weight)
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x = paddle.nn.functional.softmax(x)
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if label is not None:
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self.accuracy.reset()
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correct = self.accuracy.compute(x, label)
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self.accuracy.update(correct)
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acc = self.accuracy.accumulate()
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return x, acc
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else:
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return x
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paddle.set_device('custom_cpu')
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dataset = paddle.vision.datasets.MNIST(
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mode='train',
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transform=paddle.vision.transforms.Compose(
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[paddle.vision.transforms.ToTensor()]
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),
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)
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loader = paddle.io.DataLoader(
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dataset, batch_size=64, num_workers=1, shuffle=True
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)
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mnist = MNIST()
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sgd = paddle.optimizer.SGD(
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learning_rate=0.01, parameters=mnist.parameters()
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)
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data = next(loader())
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img = data[0]
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label = data[1]
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label_int32 = paddle.cast(label, 'int32')
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pred, acc = mnist(img, label_int32)
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avg_loss = paddle.nn.functional.cross_entropy(pred, label_int32)
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avg_loss.backward()
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sgd.step()
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sgd.clear_grad()
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self.assertTrue(pred.place.is_custom_place())
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def _test_eager_backward_api(self):
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x = np.random.random([2, 2]).astype("float32")
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y = np.random.random([2, 2]).astype("float32")
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grad = np.ones([2, 2]).astype("float32")
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import paddle
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paddle.set_device('custom_cpu')
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paddle.device.get_available_device()
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x_tensor = paddle.to_tensor(x, stop_gradient=False)
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y_tensor = paddle.to_tensor(y)
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z1_tensor = paddle.matmul(x_tensor, y_tensor)
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z2_tensor = paddle.matmul(x_tensor, y_tensor)
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grad_tensor = paddle.to_tensor(grad)
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paddle.autograd.backward([z1_tensor, z2_tensor], [grad_tensor, None])
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self.assertTrue(x_tensor.grad.place.is_custom_place())
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def _test_eager_copy_to(self):
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import paddle
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x = np.random.random([2, 2]).astype("float32")
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# cpu -> custom
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cpu_tensor = paddle.to_tensor(
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x, dtype='float32', place=paddle.CPUPlace()
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)
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custom_cpu_tensor = cpu_tensor._copy_to(
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paddle.CustomPlace('custom_cpu', 0), True
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)
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np.testing.assert_array_equal(custom_cpu_tensor, x)
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self.assertTrue(custom_cpu_tensor.place.is_custom_place())
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# custom -> custom
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another_custom_cpu_tensor = custom_cpu_tensor._copy_to(
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paddle.CustomPlace('custom_cpu', 0), True
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)
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np.testing.assert_array_equal(another_custom_cpu_tensor, x)
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self.assertTrue(another_custom_cpu_tensor.place.is_custom_place())
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# custom -> cpu
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another_cpu_tensor = custom_cpu_tensor._copy_to(paddle.CPUPlace(), True)
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np.testing.assert_array_equal(another_cpu_tensor, x)
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self.assertTrue(another_cpu_tensor.place.is_cpu_place())
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# custom -> custom self
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another_custom_cpu_tensor = another_custom_cpu_tensor._copy_to(
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paddle.CustomPlace('custom_cpu', 0), True
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)
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np.testing.assert_array_equal(another_custom_cpu_tensor, x)
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self.assertTrue(another_custom_cpu_tensor.place.is_custom_place())
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def _test_fallback_kernel(self):
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# using (custom_cpu, add, int16) which is not registered
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import paddle
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r = np.array([6, 6, 6], 'int16')
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x = paddle.to_tensor([5, 4, 3], 'int16')
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y = paddle.to_tensor([1, 2, 3], 'int16')
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z = paddle.add(x, y)
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np.testing.assert_array_equal(z, r)
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def _test_scalar(self):
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import paddle
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data_1 = paddle.to_tensor(
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[[[[1.0, 4.0, 5.0, 7.0], [3.0, 4.0, 5.0, 6.0]]]]
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)
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k_t = paddle.to_tensor([3], dtype="int32")
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value_1, indices_1 = paddle.topk(data_1, k=k_t)
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def _test_custom_device_gradient_accumulation(self):
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import paddle
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class MNIST(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.shape = 1 * 28 * 28
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self.size = 10
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self.output_weight = self.create_parameter(
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[self.shape, self.size]
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)
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self.accuracy = paddle.metric.Accuracy()
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def forward(self, inputs, label=None):
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x = paddle.reshape(inputs, shape=[-1, self.shape])
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x = paddle.matmul(x, self.output_weight)
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x = paddle.nn.functional.softmax(x)
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if label is not None:
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self.accuracy.reset()
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correct = self.accuracy.compute(x, label)
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self.accuracy.update(correct)
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acc = self.accuracy.accumulate()
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return x, acc
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else:
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return x
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paddle.set_device('custom_cpu')
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dataset = paddle.vision.datasets.MNIST(
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mode='train',
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transform=paddle.vision.transforms.Compose(
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[paddle.vision.transforms.ToTensor()]
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),
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)
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loader = paddle.io.DataLoader(
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dataset, batch_size=64, num_workers=1, shuffle=True
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)
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mnist = MNIST()
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sgd = paddle.optimizer.SGD(
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learning_rate=0.01, parameters=mnist.parameters()
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)
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data = next(loader())
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img = data[0]
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label = data[1]
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label_int32 = paddle.cast(label, 'int32')
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pred, acc = mnist(img, label_int32)
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avg_loss = paddle.nn.functional.cross_entropy(pred, label_int32)
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avg_loss.backward(retain_graph=True)
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avg_loss = paddle.nn.functional.cross_entropy(pred, label_int32)
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avg_loss.backward()
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sgd.step()
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def _test_custom_device_mix_precision(self):
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import tempfile
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import paddle
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from paddle.inference import (
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PlaceType,
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PrecisionType,
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convert_to_mixed_precision,
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)
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from paddle.jit import to_static
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from paddle.static import InputSpec
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from paddle.vision.models import resnet50
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self.temp_dir = tempfile.TemporaryDirectory()
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model = resnet50(True)
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net = to_static(
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model,
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input_spec=[InputSpec(shape=[None, 3, 224, 224], name='x')],
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full_graph=True,
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)
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paddle.jit.save(
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net, os.path.join(self.temp_dir.name, 'resnet50/inference')
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)
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if paddle.framework.use_pir_api():
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return
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convert_to_mixed_precision(
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os.path.join(self.temp_dir.name, 'resnet50/inference.pdmodel'),
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os.path.join(self.temp_dir.name, 'resnet50/inference.pdiparams'),
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os.path.join(
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self.temp_dir.name, 'mixed_precision/inference.pdmodel'
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),
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os.path.join(
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self.temp_dir.name, 'mixed_precision/inference.pdiparams'
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),
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backend=PlaceType.CUSTOM,
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mixed_precision=PrecisionType.Half,
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)
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self.temp_dir.cleanup()
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def _test_custom_device_py_api(self):
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import paddle
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p = paddle.set_device('custom_cpu')
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paddle.device.synchronize('custom_cpu')
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s1 = paddle.device.Stream()
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s2 = paddle.device.Stream(p)
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s1 = paddle.device.current_stream()
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s2 = paddle.device.current_stream(p)
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e1 = paddle.device.Event()
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e2 = paddle.device.Event(p)
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s = paddle.device.Stream()
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e = paddle.device.Event()
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s.query()
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s.synchronize()
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s.wait_event(e)
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s.record_event(e)
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s.wait_stream(s)
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paddle.device.set_stream(s)
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e.query()
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e.synchronize()
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e.record(s)
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if __name__ == '__main__':
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if os.name == 'nt' or sys.platform.startswith('darwin'):
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# only support Linux now
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sys.exit()
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unittest.main()
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