wehub-resource-sync
199 lines
6.7 KiB
Python
199 lines
6.7 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 json
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import os
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import tempfile
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import unittest
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import warnings
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import numpy as np
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from op_test import get_device_place, is_custom_device
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import paddle
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class SimpleNet(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.conv = paddle.nn.Conv2D(1, 2, (3, 3))
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def forward(self, image, label=None):
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return self.conv(image)
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def train_dygraph(net, data):
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data.stop_gradient = False
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out = net(data)
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loss = paddle.mean(out)
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adam = paddle.optimizer.Adam(parameters=net.parameters())
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out.backward()
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adam.step()
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adam.clear_grad()
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def static_program(net, data):
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data.stop_gradient = False
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out = net(data)
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loss = paddle.mean(out)
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adam = paddle.optimizer.Adam()
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adam.minimize(loss)
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return loss
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class TestAutoTune(unittest.TestCase):
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def set_flags(self, enable_autotune):
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if paddle.is_compiled_with_cuda() or is_custom_device():
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if enable_autotune:
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paddle.set_flags({'FLAGS_conv_workspace_size_limit': -1})
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else:
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paddle.set_flags({'FLAGS_conv_workspace_size_limit': 512})
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def get_flags(self, name):
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res = paddle.get_flags(name)
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return res[name]
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def get_expected_res(self, step_id, enable_autotune):
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expected_res = {
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"step_id": step_id,
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"cache_size": 0,
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"cache_hit_rate": 0,
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}
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if paddle.is_compiled_with_cuda() or is_custom_device():
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# Total 3 * num_iters cache accesses, only iter 2 hits the cache.
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expected_res["cache_size"] = 3
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expected_res["cache_hit_rate"] = (step_id + 0.0) / (step_id + 1.0)
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return expected_res
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def test_autotune(self):
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paddle.incubate.autotune.set_config(
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config={"kernel": {"enable": False}}
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)
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self.assertEqual(self.get_flags("FLAGS_use_autotune"), False)
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paddle.incubate.autotune.set_config(config={"kernel": {"enable": True}})
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self.assertEqual(self.get_flags("FLAGS_use_autotune"), True)
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def check_status(self, expected_res):
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status = paddle.base.core.autotune_status()
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for key in status.keys():
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v = status[key]
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if key == "cache_hit_rate":
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np.testing.assert_allclose(v, expected_res[key])
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else:
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np.testing.assert_array_equal(v, expected_res[key])
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class TestDygraphAutoTuneStatus(TestAutoTune):
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def run_program(self, enable_autotune):
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self.set_flags(enable_autotune)
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if enable_autotune:
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paddle.incubate.autotune.set_config(
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config={"kernel": {"enable": True, "tuning_range": [1, 2]}}
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)
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else:
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paddle.incubate.autotune.set_config(
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config={"kernel": {"enable": False}}
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)
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x_var = paddle.uniform((1, 1, 8, 8), dtype='float32', min=-1.0, max=1.0)
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net = SimpleNet()
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for i in range(3):
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train_dygraph(net, x_var)
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expected_res = self.get_expected_res(i, enable_autotune)
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self.check_status(expected_res)
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def test_enable_autotune(self):
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self.run_program(enable_autotune=True)
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def test_disable_autotune(self):
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self.run_program(enable_autotune=False)
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class TestStaticAutoTuneStatus(TestAutoTune):
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def run_program(self, enable_autotune):
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with paddle.pir_utils.OldIrGuard():
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paddle.enable_static()
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data_shape = [1, 1, 8, 8]
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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data = paddle.static.data(
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name='X', shape=data_shape, dtype='float32'
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)
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net = SimpleNet()
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loss = static_program(net, data)
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place = get_device_place()
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exe = paddle.static.Executor(place)
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exe.run(startup_program)
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x = np.random.random(size=data_shape).astype('float32')
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# Node(tizheng): warmup run to make sure the following runs
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# are in the same thread. Necessary for CUDNNv8 tests
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exe.run(program=main_program, feed={'X': x}, fetch_list=[loss])
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self.set_flags(enable_autotune)
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if enable_autotune:
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config = {"kernel": {"enable": True, "tuning_range": [1, 2]}}
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tfile = tempfile.NamedTemporaryFile(mode="w+", delete=False)
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json.dump(config, tfile)
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tfile.close()
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paddle.incubate.autotune.set_config(tfile.name)
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os.remove(tfile.name)
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else:
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paddle.incubate.autotune.set_config(
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config={"kernel": {"enable": False, "tuning_range": [1, 2]}}
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)
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for i in range(3):
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exe.run(program=main_program, feed={'X': x}, fetch_list=[loss])
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status = paddle.base.core.autotune_status()
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expected_res = self.get_expected_res(i, enable_autotune)
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self.check_status(expected_res)
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paddle.disable_static()
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def func_enable_autotune(self):
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self.run_program(enable_autotune=True)
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def test_enable_autotune(self):
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self.func_enable_autotune()
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def func_disable_autotune(self):
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self.run_program(enable_autotune=False)
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def test_disable_autotune(self):
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self.func_disable_autotune()
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class TestAutoTuneAPI(unittest.TestCase):
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def test_set_config_warnings(self):
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with warnings.catch_warnings(record=True) as w:
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config = {"kernel": {"enable": 1, "tuning_range": 1}}
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tfile = tempfile.NamedTemporaryFile(mode="w+", delete=False)
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json.dump(config, tfile)
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tfile.close()
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paddle.incubate.autotune.set_config(tfile.name)
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os.remove(tfile.name)
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self.assertTrue(len(w) == 2)
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def test_set_config_attr(self):
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paddle.incubate.autotune.set_config(config=None)
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self.assertEqual(
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paddle.get_flags("FLAGS_use_autotune")["FLAGS_use_autotune"], True
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)
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if __name__ == '__main__':
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unittest.main()
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