You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
134 lines
4.8 KiB
134 lines
4.8 KiB
# Copyright (c) 2019 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.
|
|
|
|
from __future__ import print_function
|
|
|
|
import unittest
|
|
import paddle.fluid as fluid
|
|
from paddle.fluid.dygraph.base import to_variable
|
|
from paddle.fluid.dygraph.nn import Embedding
|
|
from paddle.fluid.optimizer import SGDOptimizer
|
|
import numpy as np
|
|
import paddle.fluid.core as core
|
|
|
|
|
|
class SimpleNet(fluid.Layer):
|
|
def __init__(self, vocab_size, hidden_size, dtype):
|
|
super(SimpleNet, self).__init__()
|
|
self.emb = fluid.dygraph.Embedding(
|
|
size=[vocab_size, hidden_size],
|
|
dtype=dtype,
|
|
param_attr='emb.w',
|
|
is_sparse=True)
|
|
|
|
def forward(self, input):
|
|
input_emb = self.emb(input)
|
|
return input_emb, self.emb
|
|
|
|
|
|
class TestSimpleNet(unittest.TestCase):
|
|
def test_selectedrows_gradient1(self):
|
|
places = [fluid.CPUPlace()]
|
|
if core.is_compiled_with_cuda():
|
|
places.append(fluid.CUDAPlace(0))
|
|
|
|
for place in places:
|
|
for dtype in ["float32", "float64"]:
|
|
for sort_sum_gradient in [True, False]:
|
|
with fluid.dygraph.guard(place):
|
|
backward_strategy = fluid.dygraph.BackwardStrategy()
|
|
backward_strategy.sort_sum_gradient = sort_sum_gradient
|
|
adam = SGDOptimizer(learning_rate=0.001)
|
|
# grad_clip = fluid.dygraph_grad_clip.GradClipByGlobalNorm(5.0)
|
|
|
|
input_word = np.array([[1, 2], [2, 1]]).astype('int64')
|
|
input = to_variable(input_word)
|
|
|
|
simplenet = SimpleNet(20, 32, dtype)
|
|
input_emb, emb = simplenet(input)
|
|
|
|
try:
|
|
emb._w.gradient()
|
|
except ValueError as e:
|
|
pass
|
|
try:
|
|
input_emb.gradient()
|
|
except ValueError as e:
|
|
pass
|
|
|
|
input_emb.backward(backward_strategy)
|
|
adam.minimize(input_emb) # grad_clip=grad_clip
|
|
emb._w.gradient()
|
|
|
|
emb.clear_gradients()
|
|
try:
|
|
emb._w.gradient()
|
|
except ValueError as e:
|
|
pass
|
|
|
|
input_emb.clear_gradient()
|
|
try:
|
|
input_emb.gradient()
|
|
except ValueError as e:
|
|
pass
|
|
|
|
def test_selectedrows_gradient2(self):
|
|
places = [fluid.CPUPlace()]
|
|
if core.is_compiled_with_cuda():
|
|
places.append(fluid.CUDAPlace(0))
|
|
|
|
for place in places:
|
|
for sort_sum_gradient in [True, False]:
|
|
with fluid.dygraph.guard(place):
|
|
backward_strategy = fluid.dygraph.BackwardStrategy()
|
|
backward_strategy.sort_sum_gradient = sort_sum_gradient
|
|
adam = SGDOptimizer(learning_rate=0.001)
|
|
grad_clip = fluid.dygraph_grad_clip.GradClipByGlobalNorm(
|
|
5.0)
|
|
|
|
input_word = np.array([[1, 2], [2, 1]]).astype('int64')
|
|
input = to_variable(input_word)
|
|
|
|
simplenet = SimpleNet(20, 32, "float32")
|
|
input_emb, emb = simplenet(input)
|
|
|
|
try:
|
|
emb._w.gradient()
|
|
except ValueError as e:
|
|
pass
|
|
try:
|
|
input_emb.gradient()
|
|
except ValueError as e:
|
|
pass
|
|
|
|
input_emb.backward(backward_strategy)
|
|
adam.minimize(input_emb, grad_clip=grad_clip)
|
|
emb._w.gradient()
|
|
|
|
emb.clear_gradients()
|
|
try:
|
|
emb._w.gradient()
|
|
except ValueError as e:
|
|
pass
|
|
|
|
input_emb.clear_gradient()
|
|
try:
|
|
input_emb.gradient()
|
|
except ValueError as e:
|
|
pass
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|