wehub-resource-sync
281 lines
8.7 KiB
Python
281 lines
8.7 KiB
Python
# 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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from pathlib import Path
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from site import getsitepackages
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import numpy as np
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from paddle.utils.cpp_extension.extension_utils import (
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_get_all_paddle_includes_from_include_root,
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)
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def custom_relu_dynamic(func, device, dtype, np_x, use_func=True):
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import paddle
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paddle.set_device(device)
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t = paddle.to_tensor(np_x, dtype=dtype)
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t.stop_gradient = False
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t.retain_grads()
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sys.stdout.flush()
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out = func(t) if use_func else paddle.nn.functional.relu(t)
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out.stop_gradient = False
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out.backward()
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if t.grad is None:
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return out.numpy(), t.grad
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else:
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return out.numpy(), t.grad.numpy()
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def custom_relu_static(func, device, dtype, np_x, use_func=True):
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import paddle
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from paddle import static
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paddle.enable_static()
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paddle.set_device(device)
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with (
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static.scope_guard(static.Scope()),
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static.program_guard(static.Program()),
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):
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x = static.data(name="X", shape=[None, 8], dtype=dtype)
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x.stop_gradient = False
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out = func(x) if use_func else paddle.nn.functional.relu(x)
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static.append_backward(out)
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exe = static.Executor()
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exe.run(static.default_startup_program())
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# in static mode, x data has been covered by out
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out_v = exe.run(
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static.default_main_program(),
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feed={"X": np_x},
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fetch_list=[out],
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)
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paddle.disable_static()
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return out_v
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def custom_relu_double_grad_dynamic(func, device, dtype, np_x, use_func=True):
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import paddle
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paddle.set_device(device)
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t = paddle.to_tensor(np_x, dtype=dtype, stop_gradient=False)
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t.retain_grads()
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out = func(t) if use_func else paddle.nn.functional.relu(t)
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out.retain_grads()
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dx = paddle.grad(
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outputs=out,
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inputs=t,
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grad_outputs=paddle.ones_like(t),
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create_graph=True,
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retain_graph=True,
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)
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ddout = paddle.grad(
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outputs=dx[0],
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inputs=out.grad,
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grad_outputs=paddle.ones_like(t),
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create_graph=False,
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)
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assert ddout[0].numpy() is not None
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return dx[0].numpy(), ddout[0].numpy()
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class TestNewCustomOpSetUpInstall(unittest.TestCase):
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def setUp(self):
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# compile so and set to current path
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self.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 \
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&& cd {}'.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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self.cur_dir,
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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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self.cur_dir,
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f'{self.temp_dir.name}/PaddleCustomDevice/backends/custom_cpu/build',
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)
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# `import paddle` loads custom_cpu.so, hence we must import paddle after finishing build PaddleCustomDevice
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import paddle
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# [Why specific paddle_includes directory?]
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# Add paddle_includes to pass CI, for more details,
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# please refer to the comments in `paddle/tests/custom_op/utils.py``
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paddle_includes = []
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for site_packages_path in getsitepackages():
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paddle_include_dir = Path(site_packages_path) / "paddle/include"
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paddle_includes.extend(
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_get_all_paddle_includes_from_include_root(
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str(paddle_include_dir)
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)
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)
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custom_module = paddle.utils.cpp_extension.load(
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name='custom_device',
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sources=['custom_op.cc'],
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extra_include_paths=paddle_includes, # add for Coverage CI
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extra_cxx_cflags=["-w", "-g"], # test for cc flags
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# build_directory=self.cur_dir,
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verbose=True,
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)
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self.custom_op = custom_module.custom_relu
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self.custom_stream_op = custom_module.custom_stream
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self.dtypes = ["float32", "float64"]
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self.device = "custom_cpu"
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# config seed
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SEED = 2021
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paddle.seed(SEED)
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paddle.framework.random._manual_program_seed(SEED)
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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_static()
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self._test_dynamic()
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self._test_double_grad_dynamic()
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self._test_with_dataloader()
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self._test_stream()
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def _test_static(self):
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for dtype in self.dtypes:
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x = np.random.uniform(-1, 1, [4, 8]).astype(dtype)
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out = custom_relu_static(self.custom_op, self.device, dtype, x)
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pd_out = custom_relu_static(
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self.custom_op, self.device, dtype, x, False
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)
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np.testing.assert_array_equal(
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out,
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pd_out,
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err_msg=f"custom op out: {out},\n paddle api out: {pd_out}",
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)
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def _test_dynamic(self):
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for dtype in self.dtypes:
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x = np.random.uniform(-1, 1, [4, 8]).astype(dtype)
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out, x_grad = custom_relu_dynamic(
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self.custom_op, self.device, dtype, x
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)
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pd_out, pd_x_grad = custom_relu_dynamic(
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self.custom_op, self.device, dtype, x, False
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)
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np.testing.assert_array_equal(
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out,
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pd_out,
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err_msg=f"custom op out: {out},\n paddle api out: {pd_out}",
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)
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np.testing.assert_array_equal(
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x_grad,
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pd_x_grad,
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err_msg=f"custom op x grad: {x_grad},\n paddle api x grad: {pd_x_grad}",
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)
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def _test_double_grad_dynamic(self):
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for dtype in self.dtypes:
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x = np.random.uniform(-1, 1, [4, 8]).astype(dtype)
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out, dx_grad = custom_relu_double_grad_dynamic(
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self.custom_op, self.device, dtype, x
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)
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pd_out, pd_dx_grad = custom_relu_double_grad_dynamic(
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self.custom_op, self.device, dtype, x, False
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)
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np.testing.assert_array_equal(
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out,
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pd_out,
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err_msg=f"custom op out: {out},\n paddle api out: {pd_out}",
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)
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np.testing.assert_array_equal(
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dx_grad,
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pd_dx_grad,
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err_msg=f"custom op dx grad: {dx_grad},\n paddle api dx grad: {pd_dx_grad}",
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)
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def _test_with_dataloader(self):
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import paddle
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from paddle.vision.transforms import Compose, Normalize
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paddle.set_device(self.device)
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# data loader
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transform = Compose(
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[Normalize(mean=[127.5], std=[127.5], data_format="CHW")]
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)
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train_dataset = paddle.vision.datasets.MNIST(
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mode="train", transform=transform
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)
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train_loader = paddle.io.DataLoader(
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train_dataset,
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batch_size=64,
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shuffle=True,
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drop_last=True,
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num_workers=0,
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)
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for batch_id, (image, _) in enumerate(train_loader()):
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out = self.custom_op(image)
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pd_out = paddle.nn.functional.relu(image)
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np.testing.assert_array_equal(
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out,
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pd_out,
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err_msg=f"custom op out: {out},\n paddle api out: {pd_out}",
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)
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if batch_id == 5:
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break
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def _test_stream(self):
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import paddle
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paddle.set_device(self.device)
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x = paddle.ones([2, 2], dtype='float32')
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out = self.custom_stream_op(x)
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np.testing.assert_array_equal(x.numpy(), out.numpy())
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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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