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.
456 lines
17 KiB
456 lines
17 KiB
# Copyright (c) 2018 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.
|
|
|
|
import numpy
|
|
import unittest
|
|
|
|
import paddle.fluid as fluid
|
|
import paddle
|
|
import paddle.dataset.mnist as mnist
|
|
import paddle.dataset.wmt16 as wmt16
|
|
|
|
|
|
def simple_fc_net(use_feed):
|
|
if use_feed:
|
|
img = fluid.layers.data(name='image', shape=[784], dtype='float32')
|
|
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
|
|
else:
|
|
reader = fluid.layers.open_recordio_file(
|
|
filename='./mnist.recordio',
|
|
shapes=[[-1, 784], [-1, 1]],
|
|
lod_levels=[0, 0],
|
|
dtypes=['float32', 'int64'])
|
|
img, label = fluid.layers.read_file(reader)
|
|
hidden = img
|
|
for _ in xrange(4):
|
|
hidden = fluid.layers.fc(
|
|
hidden,
|
|
size=200,
|
|
act='tanh',
|
|
bias_attr=fluid.ParamAttr(
|
|
initializer=fluid.initializer.Constant(value=1.0)))
|
|
prediction = fluid.layers.fc(hidden, size=10, act='softmax')
|
|
loss = fluid.layers.cross_entropy(input=prediction, label=label)
|
|
loss = fluid.layers.mean(loss)
|
|
return loss
|
|
|
|
|
|
def fc_with_batchnorm(use_feed):
|
|
if use_feed:
|
|
img = fluid.layers.data(name='image', shape=[784], dtype='float32')
|
|
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
|
|
else:
|
|
reader = fluid.layers.open_recordio_file(
|
|
filename='./mnist.recordio',
|
|
shapes=[[-1, 784], [-1, 1]],
|
|
lod_levels=[0, 0],
|
|
dtypes=['float32', 'int64'])
|
|
img, label = fluid.layers.read_file(reader)
|
|
|
|
hidden = img
|
|
for _ in xrange(1):
|
|
hidden = fluid.layers.fc(
|
|
hidden,
|
|
size=200,
|
|
act='tanh',
|
|
bias_attr=fluid.ParamAttr(
|
|
initializer=fluid.initializer.Constant(value=1.0)))
|
|
|
|
hidden = fluid.layers.batch_norm(input=hidden)
|
|
|
|
prediction = fluid.layers.fc(hidden, size=10, act='softmax')
|
|
loss = fluid.layers.cross_entropy(input=prediction, label=label)
|
|
loss = fluid.layers.mean(loss)
|
|
return loss
|
|
|
|
|
|
def squeeze_excitation(input, num_channels, reduction_ratio):
|
|
# pool = fluid.layers.pool2d(
|
|
# input=input, pool_size=0, pool_type='avg', global_pooling=True)
|
|
conv = input
|
|
shape = conv.shape
|
|
reshape = fluid.layers.reshape(
|
|
x=conv, shape=[-1, shape[1], shape[2] * shape[3]])
|
|
pool = fluid.layers.reduce_mean(input=reshape, dim=2)
|
|
|
|
squeeze = fluid.layers.fc(input=pool,
|
|
size=num_channels / reduction_ratio,
|
|
act='relu')
|
|
excitation = fluid.layers.fc(input=squeeze,
|
|
size=num_channels,
|
|
act='sigmoid')
|
|
scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
|
|
return scale
|
|
|
|
|
|
def conv_bn_layer(input, num_filters, filter_size, stride=1, groups=1,
|
|
act=None):
|
|
conv = fluid.layers.conv2d(
|
|
input=input,
|
|
num_filters=num_filters,
|
|
filter_size=filter_size,
|
|
stride=stride,
|
|
padding=(filter_size - 1) / 2,
|
|
groups=groups,
|
|
act=None,
|
|
bias_attr=False)
|
|
return fluid.layers.batch_norm(input=conv, act=act, momentum=0.1)
|
|
|
|
|
|
def shortcut(input, ch_out, stride):
|
|
ch_in = input.shape[1]
|
|
if ch_in != ch_out:
|
|
if stride == 1:
|
|
filter_size = 1
|
|
else:
|
|
filter_size = 3
|
|
return conv_bn_layer(input, ch_out, filter_size, stride)
|
|
else:
|
|
return input
|
|
|
|
|
|
def bottleneck_block(input, num_filters, stride, cardinality, reduction_ratio):
|
|
# The number of first 1x1 convolutional channels for each bottleneck build block
|
|
# was halved to reduce the compution cost.
|
|
conv0 = conv_bn_layer(
|
|
input=input, num_filters=num_filters, filter_size=1, act='relu')
|
|
conv1 = conv_bn_layer(
|
|
input=conv0,
|
|
num_filters=num_filters * 2,
|
|
filter_size=3,
|
|
stride=stride,
|
|
groups=cardinality,
|
|
act='relu')
|
|
conv2 = conv_bn_layer(
|
|
input=conv1, num_filters=num_filters * 2, filter_size=1, act=None)
|
|
scale = squeeze_excitation(
|
|
input=conv2,
|
|
num_channels=num_filters * 2,
|
|
reduction_ratio=reduction_ratio)
|
|
|
|
short = shortcut(input, num_filters * 2, stride)
|
|
|
|
return fluid.layers.elementwise_add(x=short, y=scale, act='relu')
|
|
|
|
|
|
def SE_ResNeXt50Small(batch_size=2, use_feed=False):
|
|
assert not use_feed, "SE_ResNeXt doesn't support feed yet"
|
|
|
|
img = fluid.layers.fill_constant(
|
|
shape=[batch_size, 3, 224, 224], dtype='float32', value=0.0)
|
|
label = fluid.layers.fill_constant(
|
|
shape=[batch_size, 1], dtype='int64', value=0.0)
|
|
|
|
conv = conv_bn_layer(
|
|
input=img, num_filters=16, filter_size=3, stride=2, act='relu')
|
|
conv = conv_bn_layer(
|
|
input=conv, num_filters=16, filter_size=3, stride=1, act='relu')
|
|
conv = conv_bn_layer(
|
|
input=conv, num_filters=16, filter_size=3, stride=1, act='relu')
|
|
conv = fluid.layers.pool2d(
|
|
input=conv, pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
|
|
|
|
cardinality = 32
|
|
reduction_ratio = 16
|
|
depth = [3, 4, 6, 3]
|
|
num_filters = [128, 256, 512, 1024]
|
|
|
|
for block in range(len(depth)):
|
|
for i in range(depth[block]):
|
|
conv = bottleneck_block(
|
|
input=conv,
|
|
num_filters=num_filters[block],
|
|
stride=2 if i == 0 and block != 0 else 1,
|
|
cardinality=cardinality,
|
|
reduction_ratio=reduction_ratio)
|
|
|
|
shape = conv.shape
|
|
reshape = fluid.layers.reshape(
|
|
x=conv, shape=[-1, shape[1], shape[2] * shape[3]])
|
|
pool = fluid.layers.reduce_mean(input=reshape, dim=2)
|
|
dropout = fluid.layers.dropout(x=pool, dropout_prob=0.2)
|
|
# Classifier layer:
|
|
prediction = fluid.layers.fc(input=dropout, size=1000, act='softmax')
|
|
loss = fluid.layers.cross_entropy(input=prediction, label=label)
|
|
loss = fluid.layers.mean(loss)
|
|
return loss
|
|
|
|
|
|
import time
|
|
|
|
|
|
class TestParallelExecutorBase(unittest.TestCase):
|
|
def check_network_convergence(self,
|
|
method,
|
|
memory_opt=True,
|
|
iter=10,
|
|
batch_size=None,
|
|
allow_op_delay=False,
|
|
feed_dict={}):
|
|
main = fluid.Program()
|
|
startup = fluid.Program()
|
|
with fluid.program_guard(main, startup):
|
|
loss = method(use_feed=len(feed_dict) > 0)
|
|
adam = fluid.optimizer.Adam()
|
|
adam.minimize(loss)
|
|
if memory_opt:
|
|
fluid.memory_optimize(main)
|
|
|
|
exe = fluid.ParallelExecutor(loss_name=loss.name, use_cuda=True)
|
|
if batch_size is not None:
|
|
batch_size *= fluid.core.get_cuda_device_count()
|
|
begin = time.time()
|
|
first_loss, = exe.run([loss.name], feed_dict=feed_dict)
|
|
first_loss = numpy.array(first_loss)
|
|
|
|
for i in xrange(iter):
|
|
exe.run([], feed_dict=feed_dict)
|
|
|
|
last_loss, = exe.run([loss.name], feed_dict=feed_dict)
|
|
end = time.time()
|
|
|
|
if batch_size is not None:
|
|
print "%.4f Instance per second" % (
|
|
(batch_size * iter + 2) / (end - begin))
|
|
|
|
last_loss = numpy.array(last_loss)
|
|
|
|
print first_loss, last_loss
|
|
# self.assertGreater(first_loss[0], last_loss[0])
|
|
|
|
|
|
class TestMNIST(TestParallelExecutorBase):
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
# Convert mnist to recordio file
|
|
with fluid.program_guard(fluid.Program(), fluid.Program()):
|
|
reader = paddle.batch(mnist.train(), batch_size=4)
|
|
feeder = fluid.DataFeeder(
|
|
feed_list=[ # order is image and label
|
|
fluid.layers.data(
|
|
name='image', shape=[784]),
|
|
fluid.layers.data(
|
|
name='label', shape=[1], dtype='int64'),
|
|
],
|
|
place=fluid.CPUPlace())
|
|
fluid.recordio_writer.convert_reader_to_recordio_file(
|
|
'./mnist.recordio', reader, feeder)
|
|
|
|
def test_simple_fc(self):
|
|
self.check_network_convergence(simple_fc_net)
|
|
self.check_network_convergence(simple_fc_net, allow_op_delay=True)
|
|
|
|
img = numpy.zeros(shape=[32, 784], dtype='float32')
|
|
label = numpy.ones(shape=[32, 1], dtype='int64')
|
|
self.check_network_convergence(
|
|
simple_fc_net, feed_dict={"image": img,
|
|
"label": label})
|
|
|
|
def test_batchnorm_fc(self):
|
|
self.check_network_convergence(fc_with_batchnorm)
|
|
img = numpy.zeros(shape=[32, 784], dtype='float32')
|
|
label = numpy.ones(shape=[32, 1], dtype='int64')
|
|
self.check_network_convergence(
|
|
fc_with_batchnorm, feed_dict={"image": img,
|
|
"label": label})
|
|
|
|
|
|
class TestResnet(TestParallelExecutorBase):
|
|
# @classmethod
|
|
# def setUpClass(cls):
|
|
# # import os
|
|
# # if os.path.exists('./flowers.recordio'):
|
|
# # return
|
|
# with fluid.program_guard(fluid.Program(), fluid.Program()):
|
|
# reader = paddle.batch(flowers.train(), batch_size=4)
|
|
# feeder = fluid.DataFeeder(
|
|
# feed_list=[
|
|
# fluid.layers.data(
|
|
# name='image', shape=[3, 224, 224]),
|
|
# fluid.layers.data(
|
|
# name='label', shape=[1], dtype='int64'),
|
|
# ],
|
|
# place=fluid.CPUPlace())
|
|
# fluid.recordio_writer.convert_reader_to_recordio_file(
|
|
# "./flowers.recordio", reader, feeder, compressor=fluid.core.RecordIOWriter.Compressor.NoCompress)
|
|
|
|
def test_resnet(self):
|
|
import functools
|
|
batch_size = 2
|
|
self.check_network_convergence(
|
|
functools.partial(
|
|
SE_ResNeXt50Small, batch_size=batch_size),
|
|
iter=20,
|
|
batch_size=batch_size)
|
|
|
|
|
|
class ModelHyperParams(object):
|
|
# Dictionary size for source and target language. This model directly uses
|
|
# paddle.dataset.wmt16 in which <bos>, <eos> and <unk> token has
|
|
# alreay been added, but the <pad> token is not added. Transformer requires
|
|
# sequences in a mini-batch are padded to have the same length. A <pad> token is
|
|
# added into the original dictionary in paddle.dateset.wmt16.
|
|
|
|
# size of source word dictionary.
|
|
src_vocab_size = 10000
|
|
# index for <pad> token in source language.
|
|
src_pad_idx = src_vocab_size
|
|
|
|
# size of target word dictionay
|
|
trg_vocab_size = 10000
|
|
# index for <pad> token in target language.
|
|
trg_pad_idx = trg_vocab_size
|
|
|
|
# position value corresponding to the <pad> token.
|
|
pos_pad_idx = 0
|
|
|
|
# max length of sequences. It should plus 1 to include position
|
|
# padding token for position encoding.
|
|
max_length = 50
|
|
|
|
# the dimension for word embeddings, which is also the last dimension of
|
|
# the input and output of multi-head attention, position-wise feed-forward
|
|
# networks, encoder and decoder.
|
|
|
|
d_model = 512
|
|
# size of the hidden layer in position-wise feed-forward networks.
|
|
d_inner_hid = 1024
|
|
# the dimension that keys are projected to for dot-product attention.
|
|
d_key = 64
|
|
# the dimension that values are projected to for dot-product attention.
|
|
d_value = 64
|
|
# number of head used in multi-head attention.
|
|
n_head = 8
|
|
# number of sub-layers to be stacked in the encoder and decoder.
|
|
n_layer = 6
|
|
# dropout rate used by all dropout layers.
|
|
dropout = 0.1
|
|
|
|
|
|
import numpy as np
|
|
|
|
|
|
def prepare_batch_input(insts, src_pad_idx, trg_pad_idx, n_head):
|
|
"""
|
|
Pad the instances to the max sequence length in batch, and generate the
|
|
corresponding position data and attention bias. Then, convert the numpy
|
|
data to tensors and return a dict mapping names to tensors.
|
|
"""
|
|
|
|
def __pad_batch_data(insts,
|
|
pad_idx,
|
|
is_target=False,
|
|
return_pos=True,
|
|
return_attn_bias=True,
|
|
return_max_len=True):
|
|
"""
|
|
Pad the instances to the max sequence length in batch, and generate the
|
|
corresponding position data and attention bias.
|
|
"""
|
|
return_list = []
|
|
max_len = max(len(inst) for inst in insts)
|
|
inst_data = np.array(
|
|
[inst + [pad_idx] * (max_len - len(inst)) for inst in insts])
|
|
return_list += [inst_data.astype("int64").reshape([-1, 1])]
|
|
if return_pos:
|
|
inst_pos = np.array([[
|
|
pos_i + 1 if w_i != pad_idx else 0
|
|
for pos_i, w_i in enumerate(inst)
|
|
] for inst in inst_data])
|
|
|
|
return_list += [inst_pos.astype("int64").reshape([-1, 1])]
|
|
if return_attn_bias:
|
|
if is_target:
|
|
# This is used to avoid attention on paddings and subsequent
|
|
# words.
|
|
slf_attn_bias_data = np.ones((inst_data.shape[0], max_len,
|
|
max_len))
|
|
slf_attn_bias_data = np.triu(slf_attn_bias_data, 1).reshape(
|
|
[-1, 1, max_len, max_len])
|
|
slf_attn_bias_data = np.tile(slf_attn_bias_data,
|
|
[1, n_head, 1, 1]) * [-1e9]
|
|
else:
|
|
# This is used to avoid attention on paddings.
|
|
slf_attn_bias_data = np.array([[0] * len(inst) + [-1e9] *
|
|
(max_len - len(inst))
|
|
for inst in insts])
|
|
slf_attn_bias_data = np.tile(
|
|
slf_attn_bias_data.reshape([-1, 1, 1, max_len]),
|
|
[1, n_head, max_len, 1])
|
|
return_list += [slf_attn_bias_data.astype("float32")]
|
|
if return_max_len:
|
|
return_list += [max_len]
|
|
return return_list if len(return_list) > 1 else return_list[0]
|
|
|
|
def data_to_tensor(data_list, name_list, input_dict, place):
|
|
assert len(data_list) == len(name_list)
|
|
for i in range(len(name_list)):
|
|
tensor = fluid.LoDTensor()
|
|
tensor.set(data_list[i], place)
|
|
input_dict[name_list[i]] = tensor
|
|
|
|
src_word, src_pos, src_slf_attn_bias, src_max_len = __pad_batch_data(
|
|
[inst[0] for inst in insts], src_pad_idx, is_target=False)
|
|
trg_word, trg_pos, trg_slf_attn_bias, trg_max_len = __pad_batch_data(
|
|
[inst[1] for inst in insts], trg_pad_idx, is_target=True)
|
|
trg_src_attn_bias = np.tile(src_slf_attn_bias[:, :, ::src_max_len, :],
|
|
[1, 1, trg_max_len, 1]).astype("float32")
|
|
lbl_word = __pad_batch_data([inst[2] for inst in insts], trg_pad_idx, False,
|
|
False, False, False)
|
|
lbl_weight = (lbl_word != trg_pad_idx).astype("float32").reshape([-1, 1])
|
|
|
|
return [
|
|
src_word, src_pos, trg_word, trg_pos, src_slf_attn_bias,
|
|
trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight
|
|
]
|
|
|
|
|
|
import transformer_model
|
|
|
|
|
|
def transformer(use_feed):
|
|
assert not use_feed, "transfomer doesn't support feed yet"
|
|
return transformer_model.transformer(
|
|
ModelHyperParams.src_vocab_size + 1,
|
|
ModelHyperParams.trg_vocab_size + 1, ModelHyperParams.max_length + 1,
|
|
ModelHyperParams.n_layer, ModelHyperParams.n_head,
|
|
ModelHyperParams.d_key, ModelHyperParams.d_value,
|
|
ModelHyperParams.d_model, ModelHyperParams.d_inner_hid,
|
|
ModelHyperParams.dropout, ModelHyperParams.src_pad_idx,
|
|
ModelHyperParams.trg_pad_idx, ModelHyperParams.pos_pad_idx)
|
|
|
|
|
|
class TestTransformer(TestParallelExecutorBase):
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
reader = paddle.batch(
|
|
wmt16.train(ModelHyperParams.src_vocab_size,
|
|
ModelHyperParams.trg_vocab_size),
|
|
batch_size=transformer_model.batch_size)
|
|
|
|
with fluid.recordio_writer.create_recordio_writer(
|
|
"./wmt16.recordio") as writer:
|
|
for batch in reader():
|
|
for tensor in prepare_batch_input(
|
|
batch, ModelHyperParams.src_pad_idx,
|
|
ModelHyperParams.trg_pad_idx, ModelHyperParams.n_head):
|
|
t = fluid.LoDTensor()
|
|
t.set(tensor, fluid.CPUPlace())
|
|
writer.append_tensor(t)
|
|
writer.complete_append_tensor()
|
|
|
|
@unittest.skip("transformer is buggy in multi gpu")
|
|
def test_main(self):
|
|
self.check_network_convergence(transformer)
|