wehub-resource-sync
229 lines
7.1 KiB
Python
229 lines
7.1 KiB
Python
# Copyright (c) 2023 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 site
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import sys
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import unittest
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import numpy as np
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import paddle
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from paddle import static
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from paddle.utils.cpp_extension.extension_utils import run_cmd
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def custom_relu_static(
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func, device, dtype, np_x, use_func=True, test_infer=False
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):
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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 graph 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_dynamic(func, device, dtype, np_x, use_func=True):
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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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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_double_grad_dynamic(func, device, dtype, np_x, use_func=True):
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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 TestCppExtensionSetupInstall(unittest.TestCase):
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"""
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Tests setup install cpp extensions.
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"""
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def setUp(self):
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cur_dir = os.path.dirname(os.path.abspath(__file__))
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# install mixed custom_op and extension
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# compile, install the custom op egg into site-packages under background
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site_dir = site.getsitepackages()[0]
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cmd = f'cd {cur_dir} && {sys.executable} mix_relu_and_extension_setup.py install'
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if os.name != 'nt':
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cmd += f' --install-lib={site_dir}'
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run_cmd(cmd)
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custom_install_path = [
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x for x in os.listdir(site_dir) if 'mix_relu_extension' in x
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]
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assert len(custom_install_path) == 2, (
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f"Matched egg number is {len(custom_install_path)}."
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)
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sys.path.append(os.path.join(site_dir, custom_install_path[0]))
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#################################
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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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self.dtypes = ['float32', 'float64']
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def tearDown(self):
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pass
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def test_cpp_extension(self):
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# Extension
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self._test_extension_function_mixed()
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# Custom op
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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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def _test_extension_function_mixed(self):
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import mix_relu_extension
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for dtype in self.dtypes:
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np_x = np.random.uniform(-1, 1, [4, 8]).astype(dtype)
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x = paddle.to_tensor(np_x, dtype=dtype)
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np_y = np.random.uniform(-1, 1, [4, 8]).astype(dtype)
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y = paddle.to_tensor(np_y, dtype=dtype)
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# Test mix_relu_extension
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out = mix_relu_extension.custom_add2(x, y)
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target_out = np.exp(np_x) + np.exp(np_y)
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np.testing.assert_allclose(out.numpy(), target_out, atol=1e-5)
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# Test we can call a method not defined in the main C++ file.
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out = mix_relu_extension.custom_sub2(x, y)
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target_out = np.exp(np_x) - np.exp(np_y)
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np.testing.assert_allclose(out.numpy(), target_out, atol=1e-5)
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def _test_static(self):
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import mix_relu_extension
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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(
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mix_relu_extension.custom_relu, "CPU", dtype, x
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)
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pd_out = custom_relu_static(
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mix_relu_extension.custom_relu, "CPU", 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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import mix_relu_extension
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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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mix_relu_extension.custom_relu, "CPU", dtype, x
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)
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pd_out, pd_x_grad = custom_relu_dynamic(
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mix_relu_extension.custom_relu, "CPU", 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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import mix_relu_extension
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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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mix_relu_extension.custom_relu, "CPU", dtype, x
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)
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pd_out, pd_dx_grad = custom_relu_double_grad_dynamic(
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mix_relu_extension.custom_relu, "CPU", 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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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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