wehub-resource-sync
362 lines
13 KiB
Python
362 lines
13 KiB
Python
# Copyright (c) 2021 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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from utils import check_output, check_output_allclose
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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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from paddle.vision.transforms import Compose, Normalize
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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_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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paddle.static.program_guard(paddle.static.Program()),
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):
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x = paddle.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 = paddle.static.Executor()
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exe.run(paddle.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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paddle.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_static_inference(func, device, np_data, np_label, path_prefix):
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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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# simple module
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data = static.data(
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name='data', shape=[None, 1, 28, 28], dtype='float32'
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)
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label = static.data(name='label', shape=[None, 1], dtype='int64')
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hidden = static.nn.fc(data, size=128)
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hidden = func(hidden)
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hidden = static.nn.fc(hidden, size=128)
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predict = static.nn.fc(hidden, size=10, activation='softmax')
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loss = paddle.nn.functional.cross_entropy(input=hidden, label=label)
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avg_loss = paddle.mean(loss)
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opt = paddle.optimizer.SGD(learning_rate=0.1)
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opt.minimize(avg_loss)
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# run start up model
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exe = static.Executor()
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exe.run(static.default_startup_program())
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# train
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for i in range(4):
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avg_loss_v = exe.run(
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static.default_main_program(),
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feed={'data': np_data, 'label': np_label},
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fetch_list=[avg_loss],
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)
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# save inference model
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static.save_inference_model(path_prefix, [data], [predict], exe)
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# get train predict value
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predict_v = exe.run(
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static.default_main_program(),
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feed={'data': np_data, 'label': np_label},
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fetch_list=[predict],
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)
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return predict_v
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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 TestNewCustomOpSetUpInstall(unittest.TestCase):
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def setUp(self):
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cur_dir = os.path.dirname(os.path.abspath(__file__))
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# compile, install the custom op egg into site-packages under background
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if os.name == 'nt':
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cmd = f'cd /d {cur_dir} && python custom_relu_setup.py install'
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else:
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site_dir = site.getsitepackages()[0]
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cmd = f'cd {cur_dir} && {sys.executable} custom_relu_setup.py install --install-lib={site_dir}'
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run_cmd(cmd)
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# NOTE(Aurelius84): Normally, it's no need to add following codes for users.
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# But we simulate to pip install in current process, so interpreter don't snap
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# sys.path has been updated. So we update it manually.
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# See: https://stackoverflow.com/questions/56974185/import-runtime-installed-module-using-pip-in-python-3
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if os.name == 'nt':
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# NOTE(zhouwei25): getsitepackages on windows will return a list: [python install dir, site packages dir]
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site_dir = site.getsitepackages()[1]
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else:
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site_dir = site.getsitepackages()[0]
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custom_install_path = [
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x for x in os.listdir(site_dir) if 'custom_relu_module_setup' 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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# usage: import the package directly
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import custom_relu_module_setup
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# `custom_relu_dup` is same as `custom_relu_dup`
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self.custom_ops = [
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custom_relu_module_setup.custom_relu,
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custom_relu_module_setup.custom_relu_dup,
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]
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self.dtypes = ['float32', 'float64']
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if paddle.is_compiled_with_cuda():
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self.dtypes.append('float16')
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self.devices = ['cpu']
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if paddle.is_compiled_with_cuda():
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self.devices.append('gpu')
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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 test_all(self):
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self._test_static()
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self._test_dynamic()
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self._test_debug_tools()
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self._test_static_save_and_load_inference_model()
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self._test_static_save_and_run_inference_predictor()
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self._test_double_grad_dynamic()
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self._test_with_dataloader()
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def _test_static(self):
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for device in self.devices:
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for dtype in self.dtypes:
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if device == 'cpu' and dtype == 'float16':
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continue
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x = np.random.uniform(-1, 1, [4, 8]).astype(dtype)
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for custom_op in self.custom_ops:
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out = custom_relu_static(custom_op, device, dtype, x)
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pd_out = custom_relu_static(
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custom_op, device, dtype, x, False
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)
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check_output(out, pd_out, "out")
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def _test_dynamic(self):
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for device in self.devices:
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for dtype in self.dtypes:
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if device == 'cpu' and dtype == 'float16':
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continue
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x = np.random.uniform(-1, 1, [4, 8]).astype(dtype)
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for custom_op in self.custom_ops:
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out, x_grad = custom_relu_dynamic(
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custom_op, device, dtype, x
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)
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pd_out, pd_x_grad = custom_relu_dynamic(
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custom_op, device, dtype, x, False
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)
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check_output(out, pd_out, "out")
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check_output(x_grad, pd_x_grad, "x_grad")
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def _test_static_save_and_load_inference_model(self):
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paddle.enable_static()
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np_data = np.random.random((1, 1, 28, 28)).astype("float32")
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np_label = np.random.random((1, 1)).astype("int64")
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path_prefix = "custom_op_inference/custom_relu"
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for device in self.devices:
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predict = custom_relu_static_inference(
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self.custom_ops[0], device, np_data, np_label, path_prefix
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)
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# load inference model
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with static.scope_guard(static.Scope()):
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exe = static.Executor()
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[
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inference_program,
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feed_target_names,
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fetch_targets,
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] = static.load_inference_model(path_prefix, exe)
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predict_infer = exe.run(
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inference_program,
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feed={feed_target_names[0]: np_data},
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fetch_list=fetch_targets,
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)
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check_output(predict, predict_infer, "predict")
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paddle.disable_static()
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def _test_static_save_and_run_inference_predictor(self):
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paddle.enable_static()
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with paddle.pir_utils.OldIrGuard():
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np_data = np.random.random((1, 1, 28, 28)).astype("float32")
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np_label = np.random.random((1, 1)).astype("int64")
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path_prefix = "custom_op_inference/custom_relu"
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from paddle.inference import Config, create_predictor
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for device in self.devices:
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predict = custom_relu_static_inference(
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self.custom_ops[0], device, np_data, np_label, path_prefix
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)
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# load inference model
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config = Config(
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path_prefix + ".pdmodel", path_prefix + ".pdiparams"
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)
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predictor = create_predictor(config)
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input_tensor = predictor.get_input_handle(
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predictor.get_input_names()[0]
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)
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input_tensor.reshape(np_data.shape)
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input_tensor.copy_from_cpu(np_data.copy())
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predictor.run()
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output_tensor = predictor.get_output_handle(
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predictor.get_output_names()[0]
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)
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predict_infer = output_tensor.copy_to_cpu()
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predict = np.array(predict).flatten()
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predict_infer = np.array(predict_infer).flatten()
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check_output_allclose(predict, predict_infer, "predict")
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paddle.disable_static()
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def _test_double_grad_dynamic(self):
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for device in self.devices:
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for dtype in self.dtypes:
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if device == 'cpu' and dtype == 'float16':
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continue
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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_ops[0], 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_ops[0], device, dtype, x, False
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)
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check_output(out, pd_out, "out")
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check_output(dx_grad, pd_dx_grad, "dx_grad")
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def _test_with_dataloader(self):
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for device in self.devices:
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paddle.set_device(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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image = paddle.to_tensor(image)
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out = self.custom_ops[0](image)
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pd_out = paddle.nn.functional.relu(image)
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check_output(out, pd_out, "out")
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if batch_id == 5:
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break
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def _test_debug_tools(self):
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# Test the debug utils on custom op
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# It is only necessary to test whether any error occur,
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# and there is no need to verify the results
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paddle.set_flags(
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{"FLAGS_tensor_md5_checksum_output_path": "./tmp_md5.txt"}
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)
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with (
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paddle.utils.capture_forward_subgraph_guard("./tmp_subgraph"),
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paddle.utils.capture_backward_subgraph_guard("./tmp_debug_info"),
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):
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x = paddle.randn([5, 5])
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x.stop_gradient = False
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y = paddle.randn([5, 5])
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y.stop_gradient = False
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z = x + y
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func = self.custom_ops[0]
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out = func(z)
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loss = out.sum()
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loss.backward()
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if __name__ == '__main__':
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unittest.main()
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