wehub-resource-sync
245 lines
7.5 KiB
Python
245 lines
7.5 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 tempfile
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import unittest
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from functools import partial
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import numpy as np
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from op_test_ipu import IPUOpTest
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import paddle
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import paddle.optimizer
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import paddle.static
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class TestBase(IPUOpTest):
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def setUp(self):
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self.set_atol()
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self.set_data_feed()
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self.set_feed_attr()
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self.set_attrs()
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self.set_optimizer()
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def set_data_feed(self):
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data = np.random.uniform(size=[1, 3, 10, 10])
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self.feed_fp32 = {"in_0": data.astype(np.float32)}
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self.feed_fp16 = {"in_0": data.astype(np.float16)}
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def set_feed_attr(self):
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self.feed_shape = [x.shape for x in self.feed_fp32.values()]
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self.feed_list = list(self.feed_fp32.keys())
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def set_attrs(self):
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self.attrs = {}
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self.attrs['steps'] = 100
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self.attrs['save_at_step'] = 20
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self.attrs['model_path'] = tempfile.TemporaryDirectory()
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def set_optimizer(self):
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self.optimizer = partial(paddle.optimizer.SGD, learning_rate=1e-1)
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@IPUOpTest.static_graph
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def build_model(self):
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generator = paddle.base.unique_name.UniqueNameGenerator()
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with paddle.base.unique_name.guard(generator):
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x = paddle.static.data(
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name=self.feed_list[0],
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shape=self.feed_shape[0],
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dtype='float32',
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)
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conv1 = paddle.nn.Conv2D(
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in_channels=x.shape[1],
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out_channels=3,
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kernel_size=3,
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bias_attr=False,
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)(x)
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loss = paddle.mean(conv1)
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# apply optimizer
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self.optimizer().minimize(loss)
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self.fetch_list = [loss]
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def run_model(self, exec_mode, save_otherwise_load):
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self.build_model()
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place = paddle.IPUPlace()
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exe = paddle.static.Executor(place)
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exe.run(self.startup_prog)
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if not save_otherwise_load:
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paddle.static.load(self.main_prog, self.attrs['model_path'].name)
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ipu_strategy = paddle.static.IpuStrategy()
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ipu_strategy.set_graph_config(is_training=True)
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if self.is_fp16_mode(exec_mode):
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ipu_strategy.set_precision_config(enable_fp16=True)
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IPUOpTest.cast_model_to_fp16(self.main_prog)
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ipu_compiler = paddle.static.IpuCompiledProgram(
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self.main_prog, ipu_strategy=ipu_strategy
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)
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program = ipu_compiler.compile(self.feed_list, self.fetch_list)
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feed = self.feed_fp32
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if self.is_fp16_mode(exec_mode):
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feed = self.feed_fp16
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result = []
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run_steps = (
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self.attrs['steps']
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if save_otherwise_load
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else self.attrs['steps'] - self.attrs['save_at_step']
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)
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for i in range(run_steps):
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tmp = exe.run(program, feed=feed, fetch_list=self.fetch_list)
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if save_otherwise_load and i == self.attrs['save_at_step'] - 1:
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ipu_compiler._backend.weights_to_host()
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paddle.static.save(
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self.main_prog, self.attrs['model_path'].name
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)
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if save_otherwise_load and i >= self.attrs['save_at_step']:
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result.append(tmp)
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elif not save_otherwise_load:
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result.append(tmp)
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return np.asarray(result)
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def test_base(self):
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res0 = self.run_model(IPUOpTest.ExecutionMode.IPU_FP32, True)
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res1 = self.run_model(IPUOpTest.ExecutionMode.IPU_FP32, False)
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np.testing.assert_allclose(
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res0.flatten(), res1.flatten(), rtol=1e-05, atol=self.atol
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)
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self.attrs['model_path'].cleanup()
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class TestMomentum(TestBase):
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def set_optimizer(self):
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self.optimizer = partial(paddle.optimizer.Momentum, learning_rate=1e-1)
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class TestAdam(TestBase):
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def set_optimizer(self):
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self.optimizer = partial(paddle.optimizer.Adam, learning_rate=1e-1)
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class TestLamb(TestBase):
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def set_optimizer(self):
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self.optimizer = partial(paddle.optimizer.Lamb, learning_rate=1e-1)
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class TestAdamW(TestBase):
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def set_optimizer(self):
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self.optimizer = partial(paddle.optimizer.AdamW, learning_rate=1e-1)
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class TestAdamax(TestBase):
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def set_optimizer(self):
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self.optimizer = partial(paddle.optimizer.Adamax, learning_rate=1e-1)
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class TestAdagrad(TestBase):
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def set_optimizer(self):
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self.optimizer = partial(paddle.optimizer.Adagrad, learning_rate=1e-1)
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class TestAdadelta(TestBase):
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def set_optimizer(self):
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self.optimizer = partial(paddle.optimizer.Adagrad, learning_rate=1e-1)
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class TestRMSProp(TestBase):
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def set_optimizer(self):
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self.optimizer = partial(paddle.optimizer.RMSProp, learning_rate=1e-1)
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class TestCenteredRMSProp(TestBase):
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def set_optimizer(self):
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self.optimizer = partial(
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paddle.optimizer.RMSProp, learning_rate=1e-1, centered=True
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)
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@unittest.skipIf(IPUOpTest.use_ipumodel(), "skip for ipumodel")
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class TestSGDFP16(TestBase):
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def set_attrs(self):
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self.attrs = {}
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self.attrs['steps'] = 100
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self.attrs['save_at_step'] = 20
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self.attrs['model_path'] = tempfile.TemporaryDirectory()
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def set_optimizer(self):
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self.optimizer = partial(paddle.optimizer.SGD, learning_rate=1e-1)
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def test_base(self):
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res0 = self.run_model(IPUOpTest.ExecutionMode.IPU_FP16, True)
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res1 = self.run_model(IPUOpTest.ExecutionMode.IPU_FP16, False)
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np.testing.assert_allclose(
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res0.flatten(), res1.flatten(), rtol=1e-05, atol=self.atol
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)
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self.attrs['model_path'].cleanup()
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class TestMomentumFp16(TestSGDFP16):
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def set_optimizer(self):
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self.optimizer = partial(paddle.optimizer.Momentum, learning_rate=1e-1)
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class TestAdamFP16(TestSGDFP16):
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def set_optimizer(self):
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self.optimizer = partial(paddle.optimizer.Adam, learning_rate=1e-1)
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class TestLambFP16(TestSGDFP16):
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def set_optimizer(self):
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self.optimizer = partial(paddle.optimizer.Lamb, learning_rate=1e-1)
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class TestAdamWFP16FP16(TestSGDFP16):
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def set_optimizer(self):
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self.optimizer = partial(paddle.optimizer.AdamW, learning_rate=1e-1)
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class TestAdamaxFP16(TestSGDFP16):
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def set_optimizer(self):
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self.optimizer = partial(paddle.optimizer.Adamax, learning_rate=1e-1)
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class TestAdagradFP16(TestSGDFP16):
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def set_optimizer(self):
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self.optimizer = partial(paddle.optimizer.Adagrad, learning_rate=1e-1)
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class TestAdadeltaFP16(TestSGDFP16):
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def set_optimizer(self):
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self.optimizer = partial(paddle.optimizer.Adagrad, learning_rate=1e-1)
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class TestRMSPropFP16(TestSGDFP16):
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def set_optimizer(self):
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self.optimizer = partial(paddle.optimizer.RMSProp, learning_rate=1e-1)
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class TestCenteredRMSPropFP16(TestSGDFP16):
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def set_optimizer(self):
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self.optimizer = partial(
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paddle.optimizer.RMSProp, learning_rate=1e-1, centered=True
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)
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if __name__ == "__main__":
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unittest.main()
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