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467 lines
17 KiB
467 lines
17 KiB
# 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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from __future__ import print_function
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import unittest
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import numpy as np
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import paddle
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import paddle.fluid.core as core
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import paddle.fluid as fluid
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import six
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from fake_reader import fake_imdb_reader
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def bow_net(data,
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label,
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dict_dim,
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emb_dim=128,
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hid_dim=128,
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hid_dim2=96,
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class_dim=2):
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"""
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BOW net
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This model is from https://github.com/PaddlePaddle/models:
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fluid/PaddleNLP/text_classification/nets.py
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"""
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emb = fluid.layers.embedding(
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input=data, is_sparse=True, size=[dict_dim, emb_dim])
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bow = fluid.layers.sequence_pool(input=emb, pool_type='sum')
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bow_tanh = fluid.layers.tanh(bow)
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fc_1 = fluid.layers.fc(input=bow_tanh, size=hid_dim, act="tanh")
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fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh")
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prediction = fluid.layers.fc(input=[fc_2], size=class_dim, act="softmax")
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cost = fluid.layers.cross_entropy(input=prediction, label=label)
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avg_cost = fluid.layers.mean(x=cost)
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return avg_cost
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class TestGradientClip(unittest.TestCase):
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def setUp(self):
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self.word_dict_len = 5147
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self.BATCH_SIZE = 2
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reader = fake_imdb_reader(self.word_dict_len, self.BATCH_SIZE * 100)
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self.train_data = paddle.batch(reader, batch_size=self.BATCH_SIZE)
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self.init()
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def init(self):
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pass
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def get_places(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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return places
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def clip_gradient(self, params_grads):
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pass
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def check_clip_result(self, out, out_clip):
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pass
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def check_gradient_clip(self, place):
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prog = fluid.Program()
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startup_program = fluid.Program()
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with fluid.program_guard(
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main_program=prog, startup_program=startup_program):
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image = fluid.data(name="a", shape=[-1, 784], dtype='float32')
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label = fluid.data(name="b", shape=[-1, 1], dtype='int64')
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hidden = fluid.layers.fc(input=image, size=32, act='relu')
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predict = fluid.layers.fc(input=hidden, size=10, act='softmax')
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cost = fluid.layers.cross_entropy(input=predict, label=label)
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avg_cost = fluid.layers.mean(cost)
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prog_clip = prog.clone()
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avg_cost_clip = prog_clip.block(0).var(avg_cost.name)
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p_g = fluid.backward.append_backward(loss=avg_cost)
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p_g_clip = fluid.backward.append_backward(loss=avg_cost_clip)
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p_g = sorted(p_g, key=lambda x: x[0].name)
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p_g_clip = sorted(p_g_clip, key=lambda x: x[0].name)
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with fluid.program_guard(
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main_program=prog_clip, startup_program=startup_program):
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p_g_clip = self.clip_gradient(p_g_clip)
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grad_list = [elem[1] for elem in p_g]
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grad_clip_list = [elem[1] for elem in p_g_clip]
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train_reader = paddle.batch(paddle.dataset.mnist.train(), batch_size=3)
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exe = fluid.Executor(place)
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feeder = fluid.DataFeeder(feed_list=[image, label], place=place)
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exe.run(startup_program)
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data = next(train_reader())
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out = exe.run(prog, feed=feeder.feed(data), fetch_list=grad_list)
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out_clip = exe.run(prog_clip,
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feed=feeder.feed(data),
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fetch_list=grad_clip_list)
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self.check_clip_result(out, out_clip)
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def check_sparse_gradient_clip(self, place):
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prog = fluid.Program()
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startup_program = fluid.Program()
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with fluid.program_guard(
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main_program=prog, startup_program=startup_program):
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data = fluid.data(
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name="words", shape=[-1, 1], dtype="int64", lod_level=1)
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label = fluid.data(name="label", shape=[-1, 1], dtype="int64")
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cost = bow_net(data, label, self.word_dict_len)
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self.backward_and_optimize(cost)
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exe = fluid.Executor(place)
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feeder = fluid.DataFeeder(feed_list=[data, label], place=place)
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exe.run(startup_program)
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data = next(self.train_data())
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val = exe.run(prog, feed=feeder.feed(data), fetch_list=[cost])[0]
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self.assertEqual((1, ), val.shape)
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print(val)
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self.assertFalse(np.isnan(val))
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def backward_and_optimize(cost):
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pass
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class TestGradientClipByGlobalNorm(TestGradientClip):
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def init(self):
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self.clip_norm = 0.2
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def clip_gradient(self, params_grads):
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clip = fluid.clip.GradientClipByGlobalNorm(clip_norm=self.clip_norm)
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print(clip)
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return clip(params_grads)
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def check_clip_result(self, out, out_clip):
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global_norm = 0
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for v in out:
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global_norm += np.sum(np.power(v, 2))
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global_norm = np.sqrt(global_norm)
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scale = self.clip_norm / np.maximum(self.clip_norm, global_norm)
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res = []
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for i in range(len(out)):
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out[i] = scale * out[i]
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for u, v in zip(out, out_clip):
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self.assertTrue(
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np.allclose(
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a=u, b=v, rtol=1e-5, atol=1e-8),
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"gradient clip by global norm has wrong results!")
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# test whether the ouput is right when use 'set_gradient_clip'
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def test_old_gradient_clip(self):
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def func(params_grads):
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clip = fluid.clip.GradientClipByGlobalNorm(clip_norm=self.clip_norm)
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fluid.clip.set_gradient_clip(clip)
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return fluid.clip.append_gradient_clip_ops(params_grads)
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self.clip_gradient = func
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self.check_gradient_clip(fluid.CPUPlace())
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# test whether the ouput is right when use grad_clip
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def test_new_gradient_clip(self):
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def func(params_grads):
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clip = fluid.clip.GradientClipByGlobalNorm(clip_norm=self.clip_norm)
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print(clip)
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return clip(params_grads)
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self.clip_gradient = func
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self.check_gradient_clip(fluid.CPUPlace())
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# invoke 'set_gradient_clip' in a wrong order
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def test_wrong_API_order(self):
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def backward_func(cost):
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# no clip gradient
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def fileter_func(param):
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return param.name == "fc.w_0"
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clip = fluid.clip.GradientClipByGlobalNorm(
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clip_norm=5.0, need_clip=fileter_func)
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fluid.clip.set_gradient_clip(clip)
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sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.01,
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grad_clip=clip)
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# if 'set_gradient_clip' and 'optimize(grad_clip)' together, 'set_gradient_clip' will be ineffective
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sgd_optimizer.minimize(cost)
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# 'set_gradient_clip' must before 'minimize', otherwise, 'set_gradient_clip' will be ineffective
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fluid.clip.set_gradient_clip(clip)
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self.backward_and_optimize = backward_func
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for place in self.get_places():
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self.check_sparse_gradient_clip(place)
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# if grad is None or not need clip
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def test_none_grad(self):
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def fileter_func(param):
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return param.name == "x"
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clip = fluid.clip.GradientClipByGlobalNorm(
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self.clip_norm, need_clip=fileter_func)
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x = fluid.default_main_program().global_block().create_parameter(
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name="x", shape=[2, 3], dtype="float32")
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y = fluid.default_main_program().global_block().create_parameter(
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name="y", shape=[2, 3], dtype="float32")
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# (x, None) should not be returned
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params_grads = [(x, None), (x, y), (y, x)]
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params_grads = clip(params_grads)
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self.assertTrue(
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len(clip(params_grads)) == 2,
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"ClipByGlobalNorm: when grad is None, it shouldn't be returned by gradient clip!"
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)
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self.assertTrue(
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params_grads[0][1].name != 'y',
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"ClipByGlobalNorm: param_grad (x, y) should be clipped!")
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# raise typeError
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def test_tpyeError(self):
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# the type of need_clip must be an funciton
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with self.assertRaises(TypeError):
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clip = fluid.clip.GradientClipByGlobalNorm(
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clip_norm=self.clip_norm, need_clip="test")
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# the type of optimizer(grad_clip=) must be an instance of GradientClipBase's derived class
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with self.assertRaises(TypeError):
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sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.1,
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grad_clip="test")
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class TestGradientClipByNorm(TestGradientClip):
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def init(self):
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self.clip_norm = 0.2
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def clip_gradient(self, params_grads):
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clip = fluid.clip.GradientClipByNorm(clip_norm=self.clip_norm)
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print(clip)
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return clip(params_grads)
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def check_clip_result(self, out, out_clip):
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for u, v in zip(out, out_clip):
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norm = np.sqrt(np.sum(np.power(u, 2)))
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scale = self.clip_norm / np.maximum(self.clip_norm, norm)
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u = u * scale
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self.assertTrue(
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np.allclose(
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a=u, b=v, rtol=1e-5, atol=1e-8),
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"gradient clip by norm has wrong results!")
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# test whether the ouput is right when use grad_clip
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def test_gradient_clip(self):
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self.check_gradient_clip(fluid.CPUPlace())
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# if grad is None or not need clip
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def test_none_grad(self):
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def fileter_func(param):
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return param.name == "z"
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clip = fluid.clip.GradientClipByNorm(
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self.clip_norm, need_clip=fileter_func)
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x = fluid.default_main_program().global_block().create_parameter(
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name="x", shape=[2, 3], dtype="float32")
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y = fluid.default_main_program().global_block().create_parameter(
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name="y", shape=[2, 3], dtype="float32")
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# (x, None) should not be returned
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params_grads = [(x, None), (x, y)]
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params_grads = clip(params_grads)
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self.assertTrue(
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len(clip(params_grads)) == 1,
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"ClipByNorm: when grad is None, it shouldn't be returned by gradient clip!"
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)
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self.assertTrue(
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params_grads[0][1].name == 'y',
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"ClipByNorm: grad should not be clipped when filtered out!")
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class TestGradientClipByValue(TestGradientClip):
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def init(self):
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self.max = 0.2
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self.min = 0.1
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def clip_gradient(self, params_grads):
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clip = fluid.clip.GradientClipByValue(max=self.max, min=self.min)
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print(clip)
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return clip(params_grads)
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def check_clip_result(self, out, out_clip):
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for i, v in enumerate(out):
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out[i] = np.clip(v, self.min, self.max)
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for u, v in zip(out, out_clip):
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u = np.clip(u, self.min, self.max)
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self.assertTrue(
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np.allclose(
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a=u, b=v, rtol=1e-6, atol=1e-8),
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"gradient clip by value has wrong results!")
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# test whether the ouput is right when use grad_clip
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def test_gradient_clip(self):
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self.check_gradient_clip(fluid.CPUPlace())
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# if grad is None or not need clip
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def test_none_grad(self):
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def fileter_func(param):
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return param.name == "z"
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clip = fluid.clip.GradientClipByValue(
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self.max, self.min, need_clip=fileter_func)
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x = fluid.default_main_program().global_block().create_parameter(
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name="x", shape=[2, 3], dtype="float32")
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y = fluid.default_main_program().global_block().create_parameter(
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name="y", shape=[2, 3], dtype="float32")
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# (x, None) should not be returned
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params_grads = [(x, None), (x, y)]
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params_grads = clip(params_grads)
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self.assertTrue(
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len(clip(params_grads)) == 1,
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"ClipByValue: when grad is None, it shouldn't be returned by gradient clip!"
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)
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self.assertTrue(
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params_grads[0][1].name == 'y',
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"ClipByValue: grad should not be clipped when filtered out!")
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class TestDygraphGradientClip(unittest.TestCase):
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def test_gradient_clip(self):
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with fluid.dygraph.guard():
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linear = fluid.dygraph.Linear(5, 5)
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inputs = fluid.layers.uniform_random(
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[16, 5], min=-10, max=10).astype('float32')
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out = linear(fluid.dygraph.to_variable(inputs))
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loss = fluid.layers.reduce_mean(out)
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loss.backward()
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sgd_optimizer = fluid.optimizer.SGD(
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learning_rate=0.0,
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parameter_list=linear.parameters(),
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grad_clip=fluid.clip.GradientClipByGlobalNorm(0.1))
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self.check_clip_result(loss, sgd_optimizer)
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def check_clip_result(self, loss, optimizer):
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pass
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class TestDygraphGradientClipByGlobalNorm(TestDygraphGradientClip):
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def setUp(self):
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# only clip gradient of x (ParamBase)
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def fileter_func(param):
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return param.name == "x"
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self.clip_norm = 0.8
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self.clip1 = fluid.clip.GradientClipByGlobalNorm(
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clip_norm=self.clip_norm, need_clip=fileter_func)
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self.clip2 = fluid.clip.GradientClipByGlobalNorm(
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clip_norm=self.clip_norm)
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def check_clip_result(self, loss, optimizer):
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# if grad is None
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x = fluid.dygraph.to_variable(
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np.array([2, 3]).astype("float32"), name="x")
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y = fluid.dygraph.to_variable(
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np.array([3, 4]).astype("float32"), name="y")
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assert len(self.clip1([(x, x), (x, y), (x, None)])) == 2
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# get params and grads from network
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opt, params_grads = optimizer.minimize(loss)
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_, grads = zip(*params_grads)
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params_grads = self.clip2(params_grads)
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_, grads_clip = zip(*params_grads)
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global_norm = 0
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for u in grads:
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u = u.numpy()
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global_norm += np.sum(np.power(u, 2))
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global_norm = np.sqrt(global_norm)
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global_norm_clip = 0
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for v in grads_clip:
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v = v.numpy()
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global_norm_clip += np.sum(np.power(v, 2))
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global_norm_clip = np.sqrt(global_norm_clip)
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a = np.minimum(global_norm, self.clip_norm)
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b = global_norm_clip
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self.assertTrue(
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np.isclose(
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a=a, b=b, rtol=1e-6, atol=1e-8),
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"gradient clip by global norm has wrong results, expetcd:%f, but recieved:%f"
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% (a, b))
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class TestDygraphGradientClipByNorm(TestDygraphGradientClip):
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def setUp(self):
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# only clip gradient of linear_0.w_0 (ParamBase)
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def fileter_func(param):
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return param.name == "linear_0.w_0"
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self.clip_norm = 0.8
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self.clip = fluid.clip.GradientClipByNorm(
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clip_norm=self.clip_norm, need_clip=fileter_func)
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def check_clip_result(self, loss, optimizer):
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# if grad is None
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x = fluid.dygraph.to_variable(np.array([2, 3]).astype("float32"))
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assert len(self.clip([(x, None)])) == 0
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# get params and grads from network
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self.clip([(fluid.dygraph.to_variable(np.array([2, 3])), None)])
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opt, params_grads = optimizer.minimize(loss)
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_, grads = zip(*params_grads)
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params_grads = self.clip(params_grads)
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_, grads_clip = zip(*params_grads)
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for u, v in zip(grads, grads_clip):
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u = u.numpy()
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v = v.numpy()
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a = np.sqrt(np.sum(np.power(u, 2)))
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a = np.minimum(a, self.clip_norm)
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b = np.sqrt(np.sum(np.power(v, 2)))
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self.assertTrue(
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np.isclose(
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a=a, b=b, rtol=1e-6, atol=1e-8),
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"gradient clip by norm has wrong results, expetcd:%f, but recieved:%f"
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% (a, b))
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class TestDygraphGradientClipByValue(TestDygraphGradientClip):
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def setUp(self):
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# only clip gradient of linear_0.w_0 (ParamBase)
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def fileter_func(param):
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return param.name == "linear_0.w_0"
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self.max = 0.2
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self.min = 0.1
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self.clip = fluid.clip.GradientClipByValue(
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max=self.max, min=self.min, need_clip=fileter_func)
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def check_clip_result(self, loss, optimizer):
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# if grad is None
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x = fluid.dygraph.to_variable(np.array([2, 3]).astype("float32"))
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assert len(self.clip([(x, None)])) == 0
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# get params and grads from network
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opt, params_grads = optimizer.minimize(loss)
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_, grads = zip(*params_grads)
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params_grads = self.clip(params_grads)
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_, grads_clip = zip(*params_grads)
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for u, v in zip(grads, grads_clip):
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u = np.clip(u.numpy(), self.min, self.max)
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v = v.numpy()
|
|
self.assertTrue(
|
|
np.allclose(
|
|
a=u, b=v, rtol=1e-6, atol=1e-8),
|
|
"gradient clip by value has wrong results!")
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|