wehub-resource-sync
143 lines
4.8 KiB
Python
143 lines
4.8 KiB
Python
# Copyright (c) 2020 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 unittest
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import numpy as np
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import paddle
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from paddle import nn, static
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paddle.enable_static()
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class SimpleNet(nn.Layer):
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def __init__(self, input_size, output_size):
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super().__init__()
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self.linear1 = nn.Linear(input_size, output_size)
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self.relu1 = nn.ReLU()
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self.linear2 = nn.Linear(input_size, output_size)
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self.relu2 = nn.ReLU()
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self.linear3 = nn.Linear(input_size, output_size)
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def forward(self, x):
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x = self.linear1(x)
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# currently, paddle's relu may hide nan/inf, relu(nan) = 0, relu(inf)= inf
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# so, do not use it here.
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# x = self.relu1(x)
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x = self.linear2(x)
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# x = self.relu2(x)
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x = self.linear3(x)
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return x
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class AMPTest(unittest.TestCase):
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def setUp(self):
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self.place = paddle.CUDAPlace(0)
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def net(self):
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input_size = 4096
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output_size = 4096
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x = static.data(name='X', shape=[1000, 4096], dtype='float32')
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label = static.data(name='Y', shape=[1000, 4096], dtype='float32')
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model = SimpleNet(input_size, output_size) # 定义模型
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mse = paddle.nn.MSELoss()
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out = model(x)
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loss = mse(out, label)
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opt = paddle.optimizer.Adam(
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learning_rate=0.0001, parameters=model.parameters()
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) # 定义优化器
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opt = paddle.static.amp.decorate(
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opt, init_loss_scaling=128.0, use_dynamic_loss_scaling=True
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)
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opt.minimize(loss)
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return model, loss, opt
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def test_skip_update(self):
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input_size = 4096
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output_size = 4096
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batch_size = 1000
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nums_batch = 10
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startup_prog = paddle.static.Program()
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main_prog = paddle.static.Program()
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with static.program_guard(main_prog, startup_prog):
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model, loss, opt = self.net()
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weight = model.linear1.weight
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moment1 = opt._optimizer._get_accumulator(
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opt._optimizer._moment1_acc_str, weight
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)
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beta_pow1 = opt._optimizer._get_accumulator(
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opt._optimizer._beta1_pow_acc_str, weight
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)
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fetch_list = [
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loss,
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weight,
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moment1,
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beta_pow1,
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'find_infinite_scale.tmp_0',
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]
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exe = paddle.static.Executor(self.place)
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train_data = [
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np.random.rand(batch_size, input_size).astype(np.float32)
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for _ in range(nums_batch)
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]
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labels = [
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np.random.rand(batch_size, output_size).astype(np.float32)
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for _ in range(nums_batch)
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]
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weight_, moment1_, beta_pow1_ = exe.run(
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startup_prog, fetch_list=[weight, moment1, beta_pow1]
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)
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pre_weight_, pre_moment1_, pre_beta_pow1_ = (
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weight_,
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moment1_,
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beta_pow1_,
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)
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for i in range(nums_batch):
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if i % 2:
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train_data[i][10] = np.inf
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loss_, weight_, moment1_, beta_pow1_, found_inf = exe.run(
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main_prog,
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feed={"X": train_data[i], "Y": labels[i]},
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fetch_list=fetch_list,
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)
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print(
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loss_, weight_[0][0], moment1_[0][0], beta_pow1_, found_inf
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)
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if i % 2:
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self.assertTrue(found_inf)
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np.testing.assert_array_equal(weight_, pre_weight_)
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np.testing.assert_array_equal(moment1_, pre_moment1_)
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np.testing.assert_array_equal(beta_pow1_, pre_beta_pow1_)
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else:
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self.assertFalse(found_inf)
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self.assertFalse(np.array_equal(weight_, pre_weight_))
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self.assertFalse(np.array_equal(moment1_, pre_moment1_))
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self.assertFalse(np.array_equal(beta_pow1_, pre_beta_pow1_))
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pre_weight_, pre_moment1_, pre_beta_pow1_ = (
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weight_,
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moment1_,
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beta_pow1_,
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)
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if __name__ == '__main__':
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unittest.main()
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