Skip to content

  • 体验新版
    • 正在加载...
  • 登录
  • PaddlePaddle
  • Paddle
  • Issue
  • #27522

P
Paddle
  • 项目概览

PaddlePaddle / Paddle
大约 2 年 前同步成功

通知 2325
Star 20933
Fork 5424
  • 代码
    • 文件
    • 提交
    • 分支
    • Tags
    • 贡献者
    • 分支图
    • Diff
  • Issue 1423
    • 列表
    • 看板
    • 标记
    • 里程碑
  • 合并请求 543
  • Wiki 0
    • Wiki
  • 分析
    • 仓库
    • DevOps
  • 项目成员
  • Pages
P
Paddle
  • 项目概览
    • 项目概览
    • 详情
    • 发布
  • 仓库
    • 仓库
    • 文件
    • 提交
    • 分支
    • 标签
    • 贡献者
    • 分支图
    • 比较
  • Issue 1,423
    • Issue 1,423
    • 列表
    • 看板
    • 标记
    • 里程碑
  • 合并请求 543
    • 合并请求 543
  • Pages
  • 分析
    • 分析
    • 仓库分析
    • DevOps
  • Wiki 0
    • Wiki
  • 成员
    • 成员
  • 收起侧边栏
  • 动态
  • 分支图
  • 创建新Issue
  • 提交
  • Issue看板
已关闭
开放中
Opened 9月 23, 2020 by saxon_zh@saxon_zhGuest

关于gpu加速问题

Created by: ARDUJS

环境

  • paddle 1.7.2
  • python 3.7.5

问题

神经网络层使用多个crf,gpu使用率明显下降,对于crf运算是否没法加速,或是我哪没有设置

复现代码

# 导入 PaddlePaddle 函数库.
import paddle
from paddle import fluid
import paddle.fluid.layers as layers
# In[2]:

maxlen = 256
char_size = 128
char2id_num = 3172
word_vector_dim = 300
class_num = 110


# In[3]:

#栈式双向LSTM
def stacked_lstm_net(emb, hid_dim, stacked_num):
    fc1 = fluid.layers.fc(input=emb, size=hid_dim)
    #lstm层
    lstm1, cell1 = fluid.layers.dynamic_lstm(input=fc1, size=hid_dim)

    inputs = [fc1, lstm1]
    # print("fc1", fc1.shape, "lstm1", lstm1.shape)
    #其余的所有栈结构
    for i in range(2, stacked_num + 1):
        fc = fluid.layers.fc(input=inputs, size=hid_dim)
        lstm, cell = fluid.layers.dynamic_lstm(
            input=fc, size=hid_dim, is_reverse=(i % 2) == 0)
        inputs = [fc, lstm]

    # layers.Print(inputs[0])
    #池化层
    # fc_last = fluid.layers.sequence_pool(input=inputs[0], pool_type='max')
    # lstm_last = fluid.layers.sequence_pool(input=inputs[1], pool_type='max')
    fc_last = inputs[0]
    lstm_last = inputs[1]
    # #全连接层,softmax预测
    # prediction = fluid.layers.fc(
    #     input=[fc_last, lstm_last], size=class_dim, act='softmax')
    return (fc_last, lstm_last)


t_char = fluid.data(name='t_char', shape=[None, maxlen], dtype='int64')
t_word = fluid.data(name='t_word', shape=[None, maxlen, word_vector_dim], dtype='float32')
t_posid = fluid.data(name='pos_id', shape=[None, maxlen], dtype='int64')
seq_len = fluid.data(name='seq_len', shape=[None, 1], dtype='int64', lod_level=0)


seq_len_used = fluid.layers.squeeze(seq_len, axes=[1])

char_em = fluid.embedding(input=t_char, size=(char2id_num+2, char_size)) ## batch * 256 * 128
word_em = fluid.layers.fc(size=char_size,input=t_word, num_flatten_dims=2) ## batch * 256 * 128
# layers.Print(t_posid)
pv = fluid.embedding(input=t_posid, size=(maxlen, char_size)) ## batch * 256 * 128
# layers.Print(char_em)
# layers.Print(seq_len_used)
char_em = fluid.layers.sequence_unpad(char_em, length=seq_len_used)
word_em = fluid.layers.sequence_unpad(word_em, length=seq_len_used)
pv = fluid.layers.sequence_unpad(pv, length=seq_len_used)

t = char_em + word_em + pv

t = fluid.layers.dropout(t, dropout_prob=0.25)
hidden = stacked_lstm_net(t, 256, 2)

l1 = fluid.layers.data(name="l1", shape=[None, maxlen, 1], dtype='int64')
l2 = fluid.layers.data(
    name="l2", shape=[None, maxlen, 1], dtype='int64')
l3 = fluid.layers.data(
    name="l3", shape=[None, maxlen, 1], dtype='int64')
l4 = fluid.layers.data(
    name="l4", shape=[None, maxlen, 1], dtype='int64')
l5 = fluid.layers.data(
    name="l5", shape=[None, maxlen, 1], dtype='int64')

labels = [l1, l2, l3, l4, l5]

crf_num = 3
emissions =  [""  for i in range(crf_num+1)]
ret_infers =  [""  for i in range(crf_num+1)]
infers = [""  for i in range(crf_num+1)]
losses = [""  for i in range(crf_num+1)]
emissions[0] = hidden
# In[5]:
res = []
Loss = 0
for i in range(1, crf_num+1):
    emissions[i] = fluid.layers.fc(size=class_num,input=emissions[i-1],param_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-0.1, high=0.1), regularizer=fluid.regularizer.L2DecayRegularizer(regularization_coeff=1e-4)))
    size = emissions[1].shape[1]

    fluid.layers.create_parameter(shape=[size + 2, size], dtype=emissions[1].dtype, name='crfw_'+str(i))

    labels[i-1] = fluid.layers.sequence_unpad(labels[i-1], seq_len_used)
    losses[i] = fluid.layers.linear_chain_crf(input=emissions[i],label=labels[i-1], param_attr=fluid.ParamAttr(name='crfw_'+str(i)))
    losses[i] = fluid.layers.mean(losses[i])
    Loss += losses[i]


# In[8]:


def optimizer_func():
    learning_rate = 1e-4
    return fluid.optimizer.Adam(learning_rate=learning_rate)


# ### 创建执行器

# In[10]:

test_program = fluid.default_main_program().clone(for_test=True)
sgd_optimizer = optimizer_func()#训练优化函数
sgd_optimizer.minimize(Loss)


use_cuda = True
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
main_program = fluid.default_main_program()
exe.run(fluid.default_startup_program())



feeder = fluid.DataFeeder(feed_list=['t_char', 't_word', 'pos_id', 'seq_len','l1', 'l2', 'l3','l4', 'l5'], place=place)


import numpy as np

T1 = np.random.randint(0,10,(32,256))
T2 = np.random.rand(32,256, 300)
L1 = np.random.randint(0,10,(32,256, 1))
L2 = np.random.randint(0,10,(32,256, 1))
L3 = np.random.randint(0,10,(32,256, 1))
L4 = np.random.randint(0,10,(32,256, 1))
L5 = np.random.randint(0,10,(32,256, 1))
Posid = np.random.randint(0,10,(32,256))
Seq_len = np.random.randint(0,256,(32,1))
batch_id = 0

# import reader_5 as R
# train_data = R.train_data
# dev_data = R.dev_data
# train_D = R.data_generator(train_data, batch_size=32, epoch=130, shuffle=True)

# st = tim
# for T1, T2,  L1, L2, L3, L4, L5, Posid,  Seq_len, Text, Ans, _ in train_D.__iter__():
for i in range(1000):
    metrics = exe.run(main_program,
                feed=feeder.feed([[T1, T2, Posid, Seq_len,  L1, L2, L3, L4, L5]]),
                fetch_list=[Loss])
    if batch_id % 10 == 0:
        print("loss {}".format(metrics[0][0]))
    batch_id += 1

修改crf_num 变量, 范围[1, 5],越大gpu使用率越低。

指派人
分配到
无
里程碑
无
分配里程碑
工时统计
无
截止日期
无
标识: paddlepaddle/Paddle#27522
渝ICP备2023009037号

京公网安备11010502055752号

网络110报警服务 Powered by GitLab CE v13.7
开源知识
Git 入门 Pro Git 电子书 在线学 Git
Markdown 基础入门 IT 技术知识开源图谱
帮助
使用手册 反馈建议 博客
《GitCode 隐私声明》 《GitCode 服务条款》 关于GitCode
Powered by GitLab CE v13.7