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* add ipu uts * fix ut * split PR * fix ut * rm ut
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python/paddle/fluid/tests/unittests/ipu/test_eval_model_ipu.py
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
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import unittest | ||
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import numpy as np | ||
import paddle | ||
import paddle.static | ||
from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest | ||
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@unittest.skipIf(not paddle.is_compiled_with_ipu(), | ||
"core is not compiled with IPU") | ||
class TestBase(IPUOpTest): | ||
def setUp(self): | ||
self.set_atol() | ||
self.set_data_feed() | ||
self.set_feed_attr() | ||
self.set_attrs() | ||
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def set_atol(self): | ||
self.atol = 1e-4 | ||
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def set_data_feed(self): | ||
self.feed = { | ||
"image": np.random.uniform(size=[1, 3, 10, 10]).astype('float32'), | ||
} | ||
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def set_feed_attr(self): | ||
self.feed_shape = [x.shape for x in self.feed.values()] | ||
self.feed_list = list(self.feed.keys()) | ||
self.feed_dtype = [x.dtype for x in self.feed.values()] | ||
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def set_attrs(self): | ||
self.attrs = { | ||
"optimizer": 'lamb', | ||
"weight_decay": 2.0, | ||
} | ||
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def _test_optimizer(self, run_ipu=True): | ||
scope = paddle.static.Scope() | ||
main_prog = paddle.static.Program() | ||
startup_prog = paddle.static.Program() | ||
main_prog.random_seed = self.SEED | ||
startup_prog.random_seed = self.SEED | ||
np.random.seed(self.SEED) | ||
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with paddle.static.scope_guard(scope): | ||
with paddle.static.program_guard(main_prog, startup_prog): | ||
image = paddle.static.data( | ||
name='image', shape=[1, 3, 10, 10], dtype='float32') | ||
conv1 = paddle.static.nn.conv2d( | ||
image, num_filters=3, filter_size=3, bias_attr=False) | ||
loss = paddle.mean(conv1) | ||
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weight_decay = self.attrs['weight_decay'] | ||
opt = paddle.optimizer.SGD(learning_rate=1e-1, | ||
weight_decay=weight_decay) | ||
if self.attrs['optimizer'] == 'adam': | ||
opt = paddle.optimizer.Adam( | ||
learning_rate=1e-1, weight_decay=weight_decay) | ||
elif self.attrs['optimizer'] == 'lamb': | ||
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opt = paddle.optimizer.Lamb( | ||
learning_rate=1e-1, lamb_weight_decay=weight_decay) | ||
opt.minimize(loss) | ||
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if run_ipu: | ||
place = paddle.IPUPlace() | ||
else: | ||
place = paddle.CPUPlace() | ||
exe = paddle.static.Executor(place) | ||
exe.run(startup_prog) | ||
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if run_ipu: | ||
feed_list = [image.name] | ||
fetch_list = [loss.name] | ||
ipu_strategy = paddle.static.IpuStrategy() | ||
ipu_strategy.set_graph_config(is_training=True) | ||
ipu_strategy.set_options({"runtime_options.enable_eval": True}) | ||
program = paddle.static.IpuCompiledProgram( | ||
main_prog, ipu_strategy=ipu_strategy).compile(feed_list, | ||
fetch_list) | ||
else: | ||
program = main_prog | ||
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result = [] | ||
if run_ipu: | ||
for epoch in range(200): | ||
if epoch == 100: | ||
ipu_strategy.set_options({ | ||
"runtime_options.enable_eval": False | ||
}) | ||
loss_res = exe.run(program, | ||
feed=self.feed, | ||
fetch_list=[loss]) | ||
result.append(loss_res) | ||
else: | ||
for epoch in range(100): | ||
loss_res = exe.run(program, | ||
feed=self.feed, | ||
fetch_list=[loss]) | ||
result.append(loss_res) | ||
return np.array(result) | ||
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def test(self): | ||
# cpu and ipu dimenstion mismatch, cpu:(100, 1, 1), ipu:(100, 1) | ||
ipu_loss = self._test_optimizer(True).flatten() | ||
cpu_loss = self._test_optimizer(False).flatten() | ||
self.assertTrue(ipu_loss[0] == ipu_loss[99]) | ||
self.assertTrue(np.allclose(ipu_loss[100:], cpu_loss, atol=self.atol)) | ||
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if __name__ == "__main__": | ||
unittest.main() |
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