chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) 2018 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 contextlib
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import random
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import unittest
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import numpy as np
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from op_test import get_places
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import paddle
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from paddle import base, regularizer
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class TestL1Decay(unittest.TestCase):
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def test_l1decay_regularizer(self):
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with paddle.pir_utils.IrGuard():
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main_program = paddle.static.Program()
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with paddle.static.program_guard(main_program):
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block = main_program.global_block()
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mul_x = paddle.pir.core.create_parameter(
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dtype="float32",
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shape=[5, 10],
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name="mul.x",
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regularizer=regularizer.L1Decay(0.5),
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initializer=paddle.nn.initializer.Constant(1),
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)
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self.assertIsNotNone(mul_x.regularizer)
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self.assertTrue(
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isinstance(mul_x.regularizer, regularizer.L1Decay)
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)
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mul_y = paddle.static.data(
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dtype="float32", shape=[10, 8], name="mul.y"
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)
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mul_out = paddle.matmul(mul_x, mul_y)
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mean_out = paddle.mean(mul_out)
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grads = paddle.autograd.ir_backward.grad(mean_out, [mul_x])
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params_grads = [(mul_x, grads[0])]
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self.assertEqual(len(params_grads), 1)
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count_ops = len(block.ops)
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optimizer = paddle.optimizer.Adam()
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params_grads = optimizer.append_regularization_ops(params_grads)
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self.assertEqual(len(params_grads), 1)
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self.assertEqual(len(block.ops), count_ops + 5)
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self.assertEqual(block.ops[-1].name(), 'pd_op.add_n')
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self.assertEqual(block.ops[-3].name(), 'pd_op.scale')
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self.assertEqual(block.ops[-5].name(), 'pd_op.sign')
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class TestRegularizer(unittest.TestCase):
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def setUp(self):
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self.word_len = 1500
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self.train_data = [
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[(random.sample(range(1000), 10), [0])] for _ in range(2)
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]
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def get_places(self):
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return get_places()
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@contextlib.contextmanager
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def scope_prog_guard(self, main_prog, startup_prog):
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scope = base.core.Scope()
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with (
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base.unique_name.guard(),
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base.scope_guard(scope),
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base.program_guard(main_prog, startup_prog),
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):
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yield
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def run_program(self, place, feed_list):
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exe = base.Executor(place)
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feeder = base.DataFeeder(feed_list=feed_list, place=place)
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exe.run(base.default_startup_program())
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main_prog = base.default_main_program()
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param_list = [var.name for var in main_prog.block(0).all_parameters()]
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param_sum = []
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for data in self.train_data:
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out = exe.run(
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main_prog, feed=feeder.feed(data), fetch_list=param_list
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)
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p_sum = 0
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for v in out:
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p_sum += np.sum(np.abs(v))
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param_sum.append(p_sum)
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return param_sum
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def check_l2decay_regularizer(self, place, model):
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paddle.seed(1)
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paddle.framework.random._manual_program_seed(1)
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main_prog = base.framework.Program()
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startup_prog = base.framework.Program()
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with self.scope_prog_guard(
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main_prog=main_prog, startup_prog=startup_prog
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):
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data = paddle.static.data(
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name="words", shape=[-1, 1], dtype="int64"
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)
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label = paddle.static.data(
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name="label", shape=[-1, 1], dtype="int64"
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)
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avg_cost = model(data, label, self.word_len)
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optimizer = paddle.optimizer.Adagrad(
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learning_rate=0.1,
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weight_decay=paddle.regularizer.L2Decay(1.0),
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)
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optimizer.minimize(avg_cost)
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param_sum = self.run_program(place, [data, label])
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return param_sum
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def check_l2decay(self, place, model):
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paddle.seed(1)
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paddle.framework.random._manual_program_seed(1)
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main_prog = base.framework.Program()
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startup_prog = base.framework.Program()
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with self.scope_prog_guard(
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main_prog=main_prog, startup_prog=startup_prog
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):
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data = paddle.static.data(
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name="words", shape=[-1, 1], dtype="int64"
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)
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label = paddle.static.data(
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name="label", shape=[-1, 1], dtype="int64"
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)
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avg_cost_l2 = model(data, label, self.word_len)
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param_list = base.default_main_program().block(0).all_parameters()
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para_sum = []
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for para in param_list:
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para_mul = paddle.square(x=para)
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para_sum.append(paddle.sum(para_mul))
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avg_cost_l2 += paddle.add_n(para_sum) * 0.5
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optimizer = paddle.optimizer.Adagrad(learning_rate=0.1)
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optimizer.minimize(avg_cost_l2)
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param_sum = self.run_program(place, [data, label])
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return param_sum
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def test_repeated_regularization(self):
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l1 = paddle.regularizer.L1Decay(coeff=0.1)
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l2 = paddle.regularizer.L2Decay(coeff=0.01)
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fc_param_attr = paddle.ParamAttr(
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regularizer=paddle.regularizer.L1Decay()
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)
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x = paddle.uniform([2, 2, 3])
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linear = paddle.nn.Linear(3, 5, weight_attr=fc_param_attr)
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out = linear(x)
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loss = paddle.sum(out)
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sgd = paddle.optimizer.SGD(learning_rate=0.1, weight_decay=l2)
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sgd.minimize(loss)
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with base.dygraph.guard():
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input = paddle.to_tensor(np.random.randn(3, 2).astype('float32'))
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paddle.seed(1)
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if paddle.framework.use_pir_api():
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with paddle.pir_utils.OldIrGuard():
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paddle.framework.random._manual_program_seed(1)
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else:
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paddle.framework.random._manual_program_seed(1)
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linear1 = paddle.nn.Linear(
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2, 2, weight_attr=fc_param_attr, bias_attr=fc_param_attr
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)
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linear2 = paddle.nn.Linear(
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2, 2, weight_attr=fc_param_attr, bias_attr=fc_param_attr
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)
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loss1 = linear1(input)
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loss1.backward()
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# set l2 regularizer in optimizer, but l1 in base.ParamAttr
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paddle.optimizer.SGD(
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parameters=linear1.parameters(),
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learning_rate=1e-2,
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weight_decay=l2,
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).minimize(loss1)
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# only set l1 in base.ParamAttr
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loss2 = linear2(input)
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loss2.backward()
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paddle.optimizer.SGD(
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parameters=linear2.parameters(), learning_rate=1e-2
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).minimize(loss2)
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# they should both be applied by l1, and keep the same
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np.testing.assert_allclose(
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linear1.weight.numpy(),
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linear2.weight.numpy(),
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rtol=1e-05,
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err_msg='weight should use the regularization in base.ParamAttr!',
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)
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np.testing.assert_allclose(
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linear1.bias.numpy(),
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linear2.bias.numpy(),
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rtol=1e-05,
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err_msg='bias should use the regularization in base.ParamAttr!',
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)
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if __name__ == '__main__':
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unittest.main()
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