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# Benchmark
Machine:
- Server: Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz, 2 Sockets, 20 Cores per socket
- Laptop: TBD
System: CentOS release 6.3 (Final), Docker 1.12.1.
PaddlePaddle:
- paddlepaddle/paddle:0.11.0 (for MKLML and MKL-DNN)
- MKL-DNN tag v0.11
- MKLML 2018.0.1.20171007
- paddlepaddle/paddle:0.11.0-openblas (for OpenBLAS)
- OpenBLAS v0.2.20
On each machine, we will test and compare the performance of training on single node using MKL-DNN / MKLML / OpenBLAS respectively.
## Benchmark Model
### Server
#### Training
Test on batch size 64, 128, 256 on Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
Pay attetion that the speed below includes forward, backward and parameter update time. So we can not directly compare the data with the benchmark of caffe `time` [command](https://github.com/PaddlePaddle/Paddle/blob/develop/benchmark/caffe/image/run.sh#L9), which only contain forward and backward. The updating time of parameter would become very heavy when the weight size are large, especially on alexnet.
Input image size - 3 * 224 * 224, Time: images/second
- VGG-19
| BatchSize | 64 | 128 | 256 |
|--------------|-------| -----| --------|
| OpenBLAS | 7.80 | 9.00 | 10.80 |
| MKLML | 12.12 | 13.70 | 16.18 |
| MKL-DNN | 28.46 | 29.83 | 30.44 |
<img src="figs/vgg-cpu-train.png" width="500">
- ResNet-50
| BatchSize | 64 | 128 | 256 |
|--------------|-------| ------| -------|
| OpenBLAS | 25.22 | 25.68 | 27.12 |
| MKLML | 32.52 | 31.89 | 33.12 |
| MKL-DNN | 81.69 | 82.35 | 84.08 |
<img src="figs/resnet-cpu-train.png" width="500">
- GoogLeNet
| BatchSize | 64 | 128 | 256 |
|--------------|-------| ------| -------|
| OpenBLAS | 89.52 | 96.97 | 108.25 |
| MKLML | 128.46| 137.89| 158.63 |
| MKL-DNN     | 250.46| 264.83| 269.50 |
<img src="figs/googlenet-cpu-train.png" width="500">
- AlexNet
| BatchSize | 64 | 128 | 256 |
|--------------|--------| ------ | -------|
| OpenBLAS | 45.62 | 72.79 | 107.22 |
| MKLML | 66.37 | 105.60 | 144.04 |
| MKL-DNN | 399.00 | 498.94 | 626.53 |
<img src="figs/alexnet-cpu-train.png" width="500">
#### Inference
Test on batch size 1, 2, 4, 8, 16 on Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
- VGG-19
| BatchSize | 1 | 2 | 4 | 8 | 16 |
|-----------|-------|-------|-------|-------|-------|
| OpenBLAS | 1.10 | 1.96 | 3.62 | 3.63 | 2.25 |
| MKLML | 5.58 | 9.80 | 15.15 | 21.21 | 28.67 |
| MKL-DNN | 75.07 | 88.64 | 82.58 | 92.29 | 96.75 |
<img src="figs/vgg-cpu-infer.png" width="500">
- ResNet-50
| BatchSize | 1 | 2 | 4 | 8 | 16 |
|-----------|-------|--------|--------|--------|--------|
| OpenBLAS | 3.31 | 6.72 | 11.59 | 13.17 | 9.27 |
| MKLML | 6.33 | 12.02 | 22.88 | 40.53 | 63.09 |
| MKL-DNN | 107.83| 148.84 | 177.78 | 189.35 | 217.69 |
<img src="figs/resnet-cpu-infer.png" width="500">
- GoogLeNet
| BatchSize | 1 | 2 | 4 | 8 | 16 |
|-----------|--------|--------|--------|--------|--------|
| OpenBLAS | 12.06 | 23.56 | 34.48 | 36.45 | 23.12 |
| MKLML | 22.74 | 41.56 | 81.22 | 133.47 | 210.53 |
| MKL-DNN | 175.10 | 272.92 | 450.70 | 512.00 | 600.94 |
<img src="figs/googlenet-cpu-infer.png" width="500">
- AlexNet
| BatchSize | 1 | 2 | 4 | 8 | 16 |
|-----------|--------|--------|--------|--------|--------|
| OpenBLAS | 3.53 | 6.23 | 15.04 | 26.06 | 31.62 |
| MKLML | 21.32 | 36.55 | 73.06 | 131.15 | 192.77 |
| MKL-DNN | 442.91 | 656.41 | 719.10 | 847.68 | 850.51 |
<img src="figs/alexnet-cpu-infer.png" width="500">
### Laptop
TBD
benchmark/README.md 已删除 100644 → 0
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# Benchmark
Machine:
- CPU: 12-core Intel(R) Xeon(R) CPU E5-2620 v2 @2.10GHz
- GPU: Tesla K40m
- cuDNN: v5.1
- system: Docker 1.12.1, all platforms are tested in docker environment.
Platforms:
- PaddlePaddle: paddledev/paddle:gpu-devel-v0.9.0a0
- Tensorflow: gcr.io/tensorflow/tensorflow:0.11.0rc0-gpu
- Caffe: kaixhin/cuda-caffe
Several convolutional neural networks and recurrent neural networks are used to test.
## Image
### Benchmark Model
AlexNet, GoogleNet and a small network used in Caffe.
- [AlexNet](https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet): but the group size is one.
- [GoogleNet](https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet): but remove loss1 and loss2 when testing benchmark.
- [SmallNet](https://github.com/BVLC/caffe/blob/master/examples/cifar10/cifar10\_quick\_train\_test.prototxt)
### Single-GPU
- AlexNet: input - 3 * 227 * 227, Time: ms/batch
| BatchSize | 64 | 128 | 256 | 512 |
|--------------|-----| -----| ------| -----|
| PaddlePaddle | 195 | 334 | 602 | 1629 |
| TensorFlow | 223 | 364 | 645 | 1235 |
| Caffe | 324 | 627 | 1232 | 2513 |
**Notation**
All platforms use cuDNN-v5.1. We see that caffe is slower in this experiment, because its workspace limit size of cuDNN-conv interface is 8 * 1024 * 1024, which is smaller in PaddlePaddle and TensorFlow. Note that Caffe will be faster if increasing the workspace limit size.
- GoogletNet: input - 3 * 224 * 224, Time: ms/batch
| BatchSize | 64 | 128 | 256 |
|--------------|-------| -------| --------|
| PaddlePaddle | 613 | 1149 | 2348 |
| TensorFlow | 644 | 1176 | 2219 |
| Caffe | 694 | 1364 | out of memory |
- SmallNet: input - 3 * 32 * 32, Time ms/batch
| BatchSize | 64 | 128 | 256 | 512 |
|--------------|--------| -------- | --------|---------|
| PaddlePaddle | 10.463 | 18.184 | 33.113 | 63.039 |
| TensorFlow | 9 | 15 | 28 | 59 |
| Caffe | 9.373 | 16.6606 | 31.4797 | 59.719 |
**Notation**
All the single-GPU experiments in caffe use `caffe time` to calculate elapsed time, which does not include parameter updating time. However, both PaddlePaddle and TensorFlow experiments contain the parameter updating time. As compared with the total time, this part is relatively little on single machine, we can ignore it.
In Tensorflow, they implement algorithm searching method instead of using the algorithm searching interface in cuDNN.
### Multi-GPU: 4 GPUs
- AlexNet, ms / batch
| total-BatchSize | 128 * 4 | 256 * 4 |
|------------------|----------| -----------|
| PaddlePaddle | 347 | 622 |
| TensorFlow | 377 | 675 |
| Caffe | 1229 | 2435 |
For example, if `total-BatchSize = 128 * 4`, the speedup ratio is calculated by
```
time_at_1gpu_batch_128 * 4 / time_at_4gpu_total_batch_512
= (334 * 4)/347
= 3.85
```
<img src="figs/alexnet-4gpu.png" width="420">
- GoogleNet, ms / batch
| total-BatchSize | 128 * 4 | 256 * 4 |
|-------------------|--------------| ----------- |
| PaddlePaddle | 1178 | 2367 |
| TensorFlow | 1210 | 2292 |
| Caffe | 2007 | out of memory |
<img src="figs/googlenet-4gpu.png" width="420">
## RNN
We use lstm network for text classfication to test benchmark.
### Dataset
- [IMDB](http://www.iro.umontreal.ca/~lisa/deep/data/imdb.pkl)
- Sequence length is 100. In fact, PaddlePaddle supports training with variable-length sequence, but TensorFlow needs to pad. Thus, we also pad sequence length to 100 in PaddlePaddle in order to compare.
- Dictionary size=30000
- Peephole connection is used in `lstmemory` by default in PaddlePaddle. It is also configured in TensorFlow.
### Single-GPU
#### LSTM in Text Classification
Testing `2 lstm layer + fc` network with different hidden size and batch size.
- Batch size = 64, ms / batch
| hidden_size | 256 | 512 | 1280 |
|--------------|-------| -------| --------|
| PaddlePaddle | 83 | 184 | 641 |
| TensorFlow | 175 | 280 | 818 |
- Batch size = 128, ms / batch
| hidden_size | 256 | 512 | 1280 |
|--------------|------- | -------| --------|
| PaddlePaddle | 110 | 261 | 1007 |
| TensorFlow | 181 | 361 | 1237 |
- Batch size = 256, ms / batch
| hidden_size | 256 | 512 | 1280 |
|--------------|-------| -------| --------|
| PaddlePaddle | 170 | 414 | 1655 |
| TensorFlow | 238 | 536 | 1905 |
<img src="figs/rnn_lstm_cls.png" width="600">
#### Seq2Seq
The benchmark of sequence-to-sequence network will be added later.
### Multi GPU: 4 GPUs
#### LSTM in Text Classification
- hidden_size = 256, ms / batch
| batch_size | 256 | 512 |
|--------------| -------| --------|
| PaddlePaddle | 90 | 118 |
| TensorFlow | 226 | 118 |
- hidden_size = 512, ms / batch
| batch_size | 256 | 512 |
|--------------| -------| --------|
| PaddlePaddle | 189 | 268 |
| TensorFlow | 297 | 383 |
<img src="figs/rnn_lstm_4gpus.png" width="420">
#### Seq2Seq
The benchmark of sequence-to-sequence network will be added later.
benchmark/fluid/Dockerfile
浏览文件 @ 2ccbcb15
......@@ -15,9 +15,6 @@ RUN ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.7 /usr/lib/libcudnn.so && ln -s
RUN pip install -U pip
RUN pip install -U kubernetes paddlepaddle
RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.cifar.train10()\npaddle.dataset.flowers.fetch()" | python'
RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.mnist.train()\npaddle.dataset.mnist.test()\npaddle.dataset.imdb.fetch()" | python'
RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.imikolov.fetch()" | python'
RUN pip uninstall -y paddlepaddle && mkdir /workspace
ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/paddle_k8s /usr/bin
......
benchmark/paddle/image/alexnet.py 已删除 100644 → 0
浏览文件 @ a3a3d3d8
# 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.
from paddle.trainer_config_helpers import *
height = 227
width = 227
num_class = 1000
batch_size = get_config_arg('batch_size', int, 128)
gp = get_config_arg('layer_num', int, 1)
is_infer = get_config_arg("is_infer", bool, False)
num_samples = get_config_arg('num_samples', int, 2560)
args = {
'height': height,
'width': width,
'color': True,
'num_class': num_class,
'is_infer': is_infer,
'num_samples': num_samples
}
define_py_data_sources2(
"train.list" if not is_infer else None,
"test.list" if is_infer else None,
module="provider",
obj="process",
args=args)
settings(
batch_size=batch_size,
learning_rate=0.01 / batch_size,
learning_method=MomentumOptimizer(0.9),
regularization=L2Regularization(0.0005 * batch_size))
# conv1
net = data_layer('data', size=height * width * 3)
net = img_conv_layer(
input=net,
filter_size=11,
num_channels=3,
num_filters=96,
stride=4,
padding=1)
net = img_cmrnorm_layer(input=net, size=5, scale=0.0001, power=0.75)
net = img_pool_layer(input=net, pool_size=3, stride=2)
# conv2
net = img_conv_layer(
input=net, filter_size=5, num_filters=256, stride=1, padding=2, groups=gp)
net = img_cmrnorm_layer(input=net, size=5, scale=0.0001, power=0.75)
net = img_pool_layer(input=net, pool_size=3, stride=2)
# conv3
net = img_conv_layer(
input=net, filter_size=3, num_filters=384, stride=1, padding=1)
# conv4
net = img_conv_layer(
input=net, filter_size=3, num_filters=384, stride=1, padding=1, groups=gp)
# conv5
net = img_conv_layer(
input=net, filter_size=3, num_filters=256, stride=1, padding=1, groups=gp)
net = img_pool_layer(input=net, pool_size=3, stride=2)
net = fc_layer(
input=net,
size=4096,
act=ReluActivation(),
layer_attr=ExtraAttr(drop_rate=0.5))
net = fc_layer(
input=net,
size=4096,
act=ReluActivation(),
layer_attr=ExtraAttr(drop_rate=0.5))
net = fc_layer(input=net, size=1000, act=SoftmaxActivation())
if is_infer:
outputs(net)
else:
lab = data_layer('label', num_class)
loss = cross_entropy(input=net, label=lab)
outputs(loss)
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浏览文件 @ a3a3d3d8
#!/usr/bin/env python
from paddle.trainer_config_helpers import *
height = 224
width = 224
num_class = 1000
batch_size = get_config_arg('batch_size', int, 128)
use_gpu = get_config_arg('use_gpu', bool, True)
is_infer = get_config_arg("is_infer", bool, False)
num_samples = get_config_arg('num_samples', int, 2560)
args = {
'height': height,
'width': width,
'color': True,
'num_class': num_class,
'is_infer': is_infer,
'num_samples': num_samples
}
define_py_data_sources2(
"train.list" if not is_infer else None,
"test.list" if is_infer else None,
module="provider",
obj="process",
args=args)
settings(
batch_size=batch_size,
learning_rate=0.01 / batch_size,
learning_method=MomentumOptimizer(0.9),
regularization=L2Regularization(0.0005 * batch_size))
conv_projection = conv_projection if use_gpu else img_conv_layer
def inception2(name, input, channels, \
filter1,
filter3R, filter3,
filter5R, filter5,
proj):
conv1 = name + '_1'
conv3r = name + '_3r'
conv3 = name + '_3'
conv5r = name + '_5r'
conv5 = name + '_5'
maxpool = name + '_max'
convproj = name + '_proj'
cov1 = img_conv_layer(
name=conv1,
input=input,
filter_size=1,
num_channels=channels,
num_filters=filter1,
stride=1,
padding=0)
cov3r = img_conv_layer(
name=conv3r,
input=input,
filter_size=1,
num_channels=channels,
num_filters=filter3R,
stride=1,
padding=0)
cov3 = img_conv_layer(
name=conv3,
input=cov3r,
filter_size=3,
num_filters=filter3,
stride=1,
padding=1)
cov5r = img_conv_layer(
name=conv5r,
input=input,
filter_size=1,
num_channels=channels,
num_filters=filter5R,
stride=1,
padding=0)
cov5 = img_conv_layer(
name=conv5,
input=cov5r,
filter_size=5,
num_filters=filter5,
stride=1,
padding=2)
pool1 = img_pool_layer(
name=maxpool,
input=input,
pool_size=3,
num_channels=channels,
stride=1,
padding=1)
covprj = img_conv_layer(
name=convproj,
input=pool1,
filter_size=1,
num_filters=proj,
stride=1,
padding=0)
cat = concat_layer(name=name, input=[cov1, cov3, cov5, covprj])
return cat
def inception(name, input, channels, \
filter1,
filter3R, filter3,
filter5R, filter5,
proj):
cov1 = conv_projection(
input=input,
filter_size=1,
num_channels=channels,
num_filters=filter1,
stride=1,
padding=0)
cov3r = img_conv_layer(
name=name + '_3r',
input=input,
filter_size=1,
num_channels=channels,
num_filters=filter3R,
stride=1,
padding=0)
cov3 = conv_projection(
input=cov3r, filter_size=3, num_filters=filter3, stride=1, padding=1)
cov5r = img_conv_layer(
name=name + '_5r',
input=input,
filter_size=1,
num_channels=channels,
num_filters=filter5R,
stride=1,
padding=0)
cov5 = conv_projection(
input=cov5r, filter_size=5, num_filters=filter5, stride=1, padding=2)
pool1 = img_pool_layer(
name=name + '_max',
input=input,
pool_size=3,
num_channels=channels,
stride=1,
padding=1)
covprj = conv_projection(
input=pool1, filter_size=1, num_filters=proj, stride=1, padding=0)
cat = concat_layer(
name=name,
input=[cov1, cov3, cov5, covprj],
bias_attr=True if use_gpu else False,
act=ReluActivation())
return cat
data = data_layer(name="input", size=3 * height * width)
# stage 1
conv1 = img_conv_layer(
name="conv1",
input=data,
filter_size=7,
num_channels=3,
num_filters=64,
stride=2,
padding=3)
pool1 = img_pool_layer(
name="pool1", input=conv1, pool_size=3, num_channels=64, stride=2)
# stage 2
conv2_1 = img_conv_layer(
name="conv2_1",
input=pool1,
filter_size=1,
num_filters=64,
stride=1,
padding=0)
conv2_2 = img_conv_layer(
name="conv2_2",
input=conv2_1,
filter_size=3,
num_filters=192,
stride=1,
padding=1)
pool2 = img_pool_layer(
name="pool2", input=conv2_2, pool_size=3, num_channels=192, stride=2)
# stage 3
ince3a = inception("ince3a", pool2, 192, 64, 96, 128, 16, 32, 32)
ince3b = inception("ince3b", ince3a, 256, 128, 128, 192, 32, 96, 64)
pool3 = img_pool_layer(
name="pool3", input=ince3b, num_channels=480, pool_size=3, stride=2)
# stage 4
ince4a = inception("ince4a", pool3, 480, 192, 96, 208, 16, 48, 64)
ince4b = inception("ince4b", ince4a, 512, 160, 112, 224, 24, 64, 64)
ince4c = inception("ince4c", ince4b, 512, 128, 128, 256, 24, 64, 64)
ince4d = inception("ince4d", ince4c, 512, 112, 144, 288, 32, 64, 64)
ince4e = inception("ince4e", ince4d, 528, 256, 160, 320, 32, 128, 128)
pool4 = img_pool_layer(
name="pool4", input=ince4e, num_channels=832, pool_size=3, stride=2)
# stage 5
ince5a = inception("ince5a", pool4, 832, 256, 160, 320, 32, 128, 128)
ince5b = inception("ince5b", ince5a, 832, 384, 192, 384, 48, 128, 128)
pool5 = img_pool_layer(
name="pool5",
input=ince5b,
num_channels=1024,
pool_size=7,
stride=7,
pool_type=AvgPooling())
# We remove loss1 and loss2 for all system when testing benchmark
# output 1
# pool_o1 = img_pool_layer(name="pool_o1", input=ince4a, num_channels=512, pool_size=5, stride=3, pool_type=AvgPooling())
# conv_o1 = img_conv_layer(name="conv_o1", input=pool_o1, filter_size=1, num_filters=128, stride=1, padding=0)
# fc_o1 = fc_layer(name="fc_o1", input=conv_o1, size=1024, layer_attr=ExtraAttr(drop_rate=0.7), act=ReluActivation())
# out1 = fc_layer(name="output1", input=fc_o1, size=1000, act=SoftmaxActivation())
# loss1 = cross_entropy(name='loss1', input=out1, label=lab, coeff=0.3)
# output 2
#pool_o2 = img_pool_layer(name="pool_o2", input=ince4d, num_channels=528, pool_size=5, stride=3, pool_type=AvgPooling())
#conv_o2 = img_conv_layer(name="conv_o2", input=pool_o2, filter_size=1, num_filters=128, stride=1, padding=0)
#fc_o2 = fc_layer(name="fc_o2", input=conv_o2, size=1024, layer_attr=ExtraAttr(drop_rate=0.7), act=ReluActivation())
#out2 = fc_layer(name="output2", input=fc_o2, size=1000, act=SoftmaxActivation())
#loss2 = cross_entropy(name='loss2', input=out2, label=lab, coeff=0.3)
# output 3
dropout = dropout_layer(name="dropout", input=pool5, dropout_rate=0.4)
out3 = fc_layer(
name="output3", input=dropout, size=1000, act=SoftmaxActivation())
if is_infer:
outputs(out3)
else:
lab = data_layer(name="label", size=num_class)
loss3 = cross_entropy(name='loss3', input=out3, label=lab)
outputs(loss3)
benchmark/paddle/image/plotlog.py 已删除 100644 → 0
浏览文件 @ a3a3d3d8
# Copyright (c) 2016 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 sys
import argparse
import matplotlib.pyplot as plt
def parse_args():
parser = argparse.ArgumentParser('Parse Log')
parser.add_argument(
'--file_path', '-f', type=str, help='the path of the log file')
parser.add_argument(
'--sample_rate',
'-s',
type=float,
default=1.0,
help='the rate to take samples from log')
parser.add_argument(
'--log_period', '-p', type=int, default=1, help='the period of log')
args = parser.parse_args()
return args
def parse_file(file_name):
loss = []
error = []
with open(file_name) as f:
for i, line in enumerate(f):
line = line.strip()
if not line.startswith('pass'):
continue
line_split = line.split(' ')
if len(line_split) != 5:
continue
loss_str = line_split[2][:-1]
cur_loss = float(loss_str.split('=')[-1])
loss.append(cur_loss)
err_str = line_split[3][:-1]
cur_err = float(err_str.split('=')[-1])
error.append(cur_err)
accuracy = [1.0 - err for err in error]
return loss, accuracy
def sample(metric, sample_rate):
interval = int(1.0 / sample_rate)
if interval > len(metric):
return metric[:1]
num = len(metric) / interval
idx = [interval * i for i in range(num)]
metric_sample = [metric[id] for id in idx]
return metric_sample
def plot_metric(metric,
batch_id,
graph_title,
line_style='b-',
line_label='y',
line_num=1):
plt.figure()
plt.title(graph_title)
if line_num == 1:
plt.plot(batch_id, metric, line_style, label=line_label)
else:
for i in range(line_num):
plt.plot(batch_id, metric[i], line_style[i], label=line_label[i])
plt.xlabel('batch')
plt.ylabel(graph_title)
plt.legend()
plt.savefig(graph_title + '.jpg')
plt.close()
def main():
args = parse_args()
assert args.sample_rate > 0. and args.sample_rate <= 1.0, "The sample rate should in the range (0, 1]."
loss, accuracy = parse_file(args.file_path)
batch = [args.log_period * i for i in range(len(loss))]
batch_sample = sample(batch, args.sample_rate)
loss_sample = sample(loss, args.sample_rate)
accuracy_sample = sample(accuracy, args.sample_rate)
plot_metric(loss_sample, batch_sample, 'loss', line_label='loss')
plot_metric(
accuracy_sample,
batch_sample,
'accuracy',
line_style='g-',
line_label='accuracy')
if __name__ == '__main__':
main()
benchmark/paddle/image/provider.py 已删除 100644 → 0
浏览文件 @ a3a3d3d8
# 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 io, os
import random
import numpy as np
from paddle.trainer.PyDataProvider2 import *
def initHook(settings, height, width, color, num_class, **kwargs):
settings.height = height
settings.width = width
settings.color = color
settings.num_class = num_class
if settings.color:
settings.data_size = settings.height * settings.width * 3
else:
settings.data_size = settings.height * settings.width
settings.is_infer = kwargs.get('is_infer', False)
settings.num_samples = kwargs.get('num_samples', 2560)
if settings.is_infer:
settings.slots = [dense_vector(settings.data_size)]
else:
settings.slots = [dense_vector(settings.data_size), integer_value(1)]
@provider(
init_hook=initHook, min_pool_size=-1, cache=CacheType.CACHE_PASS_IN_MEM)
def process(settings, file_list):
for i in xrange(settings.num_samples):
img = np.random.rand(1, settings.data_size).reshape(-1, 1).flatten()
if settings.is_infer:
yield img.astype('float32')
else:
lab = random.randint(0, settings.num_class - 1)
yield img.astype('float32'), int(lab)
benchmark/paddle/image/resnet.py 已删除 100644 → 0
浏览文件 @ a3a3d3d8
#!/usr/bin/env python
from paddle.trainer_config_helpers import *
height = 224
width = 224
num_class = 1000
batch_size = get_config_arg('batch_size', int, 64)
layer_num = get_config_arg("layer_num", int, 50)
is_infer = get_config_arg("is_infer", bool, False)
num_samples = get_config_arg('num_samples', int, 2560)
args = {
'height': height,
'width': width,
'color': True,
'num_class': num_class,
'is_infer': is_infer,
'num_samples': num_samples
}
define_py_data_sources2(
"train.list" if not is_infer else None,
"test.list" if is_infer else None,
module="provider",
obj="process",
args=args)
settings(
batch_size=batch_size,
learning_rate=0.01 / batch_size,
learning_method=MomentumOptimizer(0.9),
regularization=L2Regularization(0.0005 * batch_size))
#######################Network Configuration #############
def conv_bn_layer(name,
input,
filter_size,
num_filters,
stride,
padding,
channels=None,
active_type=ReluActivation()):
"""
A wrapper for conv layer with batch normalization layers.
Note:
conv layer has no activation.
"""
tmp = img_conv_layer(
name=name + "_conv",
input=input,
filter_size=filter_size,
num_channels=channels,
num_filters=num_filters,
stride=stride,
padding=padding,
act=LinearActivation(),
bias_attr=False)
return batch_norm_layer(
name=name + "_bn",
input=tmp,
act=active_type,
use_global_stats=is_infer)
def bottleneck_block(name, input, num_filters1, num_filters2):
"""
A wrapper for bottlenect building block in ResNet.
Last conv_bn_layer has no activation.
Addto layer has activation of relu.
"""
last_name = conv_bn_layer(
name=name + '_branch2a',
input=input,
filter_size=1,
num_filters=num_filters1,
stride=1,
padding=0)
last_name = conv_bn_layer(
name=name + '_branch2b',
input=last_name,
filter_size=3,
num_filters=num_filters1,
stride=1,
padding=1)
last_name = conv_bn_layer(
name=name + '_branch2c',
input=last_name,
filter_size=1,
num_filters=num_filters2,
stride=1,
padding=0,
active_type=LinearActivation())
return addto_layer(
name=name + "_addto", input=[input, last_name], act=ReluActivation())
def mid_projection(name, input, num_filters1, num_filters2, stride=2):
"""
A wrapper for middile projection in ResNet.
projection shortcuts are used for increasing dimensions,
and other shortcuts are identity
branch1: projection shortcuts are used for increasing
dimensions, has no activation.
branch2x: bottleneck building block, shortcuts are identity.
"""
# stride = 2
branch1 = conv_bn_layer(
name=name + '_branch1',
input=input,
filter_size=1,
num_filters=num_filters2,
stride=stride,
padding=0,
active_type=LinearActivation())
last_name = conv_bn_layer(
name=name + '_branch2a',
input=input,
filter_size=1,
num_filters=num_filters1,
stride=stride,
padding=0)
last_name = conv_bn_layer(
name=name + '_branch2b',
input=last_name,
filter_size=3,
num_filters=num_filters1,
stride=1,
padding=1)
last_name = conv_bn_layer(
name=name + '_branch2c',
input=last_name,
filter_size=1,
num_filters=num_filters2,
stride=1,
padding=0,
active_type=LinearActivation())
return addto_layer(
name=name + "_addto", input=[branch1, last_name], act=ReluActivation())
img = data_layer(name='image', size=height * width * 3)
def deep_res_net(res2_num=3, res3_num=4, res4_num=6, res5_num=3):
"""
A wrapper for 50,101,152 layers of ResNet.
res2_num: number of blocks stacked in conv2_x
res3_num: number of blocks stacked in conv3_x
res4_num: number of blocks stacked in conv4_x
res5_num: number of blocks stacked in conv5_x
"""
# For ImageNet
# conv1: 112x112
tmp = conv_bn_layer(
"conv1",
input=img,
filter_size=7,
channels=3,
num_filters=64,
stride=2,
padding=3)
tmp = img_pool_layer(name="pool1", input=tmp, pool_size=3, stride=2)
# conv2_x: 56x56
tmp = mid_projection(
name="res2_1", input=tmp, num_filters1=64, num_filters2=256, stride=1)
for i in xrange(2, res2_num + 1, 1):
tmp = bottleneck_block(
name="res2_" + str(i), input=tmp, num_filters1=64, num_filters2=256)
# conv3_x: 28x28
tmp = mid_projection(
name="res3_1", input=tmp, num_filters1=128, num_filters2=512)
for i in xrange(2, res3_num + 1, 1):
tmp = bottleneck_block(
name="res3_" + str(i),
input=tmp,
num_filters1=128,
num_filters2=512)
# conv4_x: 14x14
tmp = mid_projection(
name="res4_1", input=tmp, num_filters1=256, num_filters2=1024)
for i in xrange(2, res4_num + 1, 1):
tmp = bottleneck_block(
name="res4_" + str(i),
input=tmp,
num_filters1=256,
num_filters2=1024)
# conv5_x: 7x7
tmp = mid_projection(
name="res5_1", input=tmp, num_filters1=512, num_filters2=2048)
for i in xrange(2, res5_num + 1, 1):
tmp = bottleneck_block(
name="res5_" + str(i),
input=tmp,
num_filters1=512,
num_filters2=2048)
tmp = img_pool_layer(
name='avgpool',
input=tmp,
pool_size=7,
stride=1,
pool_type=AvgPooling())
return fc_layer(input=tmp, size=num_class, act=SoftmaxActivation())
if layer_num == 50:
resnet = deep_res_net(3, 4, 6, 3)
elif layer_num == 101:
resnet = deep_res_net(3, 4, 23, 3)
elif layer_num == 152:
resnet = deep_res_net(3, 8, 36, 3)
else:
print("Wrong layer number.")
if is_infer:
outputs(resnet)
else:
lbl = data_layer(name="label", size=num_class)
loss = cross_entropy(name='loss', input=resnet, label=lbl)
outputs(loss)
benchmark/paddle/image/run.sh 已删除 100755 → 0
浏览文件 @ a3a3d3d8
#!/bin/bash
set -e
function train() {
cfg=$1
thread=$2
bz=$3
args="batch_size=$3"
prefix=$4
paddle train --job=time \
--config=$cfg \
--use_gpu=True \
--trainer_count=$thread \
--log_period=10 \
--test_period=100 \
--config_args=$args \
> logs/$prefix-${thread}gpu-$bz.log 2>&1
}
if [ ! -d "train.list" ]; then
echo " " > train.list
fi
if [ ! -d "logs" ]; then
mkdir logs
fi
#========single-gpu=========#
# alexnet
train alexnet.py 1 64 alexnet
train alexnet.py 1 128 alexnet
train alexnet.py 1 256 alexnet
train alexnet.py 1 512 alexnet
# googlenet
train googlenet.py 1 64 googlenet
train googlenet.py 1 128 googlenet
train googlenet.py 1 256 googlenet
# smallnet
train smallnet_mnist_cifar.py 1 64 smallnet
train smallnet_mnist_cifar.py 1 128 smallnet
train smallnet_mnist_cifar.py 1 256 smallnet
train smallnet_mnist_cifar.py 1 512 smallnet
############################
#========multi-gpus=========#
train alexnet.py 4 512 alexnet
train alexnet.py 4 1024 alexnet
train googlenet.py 4 512 googlenet
train googlenet.py 4 1024 googlenet
benchmark/paddle/image/run_mkl_infer.sh 已删除 100755 → 0
浏览文件 @ a3a3d3d8
#!/bin/bash
set -e
function clock_to_seconds() {
hours=`echo $1 | awk -F ':' '{print $1}'`
mins=`echo $1 | awk -F ':' '{print $2}'`
secs=`echo $1 | awk -F ':' '{print $3}'`
echo `awk 'BEGIN{printf "%.2f",('$secs' + '$mins' * 60 + '$hours' * 3600)}'`
}
function infer() {
unset OMP_NUM_THREADS MKL_NUM_THREADS OMP_DYNAMIC KMP_AFFINITY
topology=$1
layer_num=$2
bs=$3
use_mkldnn=$4
if [ $4 == "True" ]; then
thread=1
log="logs/infer-${topology}-${layer_num}-mkldnn-${bs}.log"
elif [ $4 == "False" ]; then
thread=`nproc`
if [ $thread -gt $bs ]; then
thread=$bs
fi
log="logs/infer-${topology}-${layer_num}-${thread}mklml-${bs}.log"
else
echo "Wrong input $4, use True or False."
exit 0
fi
models_in="models/${topology}-${layer_num}/pass-00000/"
if [ ! -d $models_in ]; then
echo "Training model ${topology}_${layer_num}"
paddle train --job=train \
--config="${topology}.py" \
--use_mkldnn=True \
--use_gpu=False \
--trainer_count=1 \
--num_passes=1 \
--save_dir="models/${topology}-${layer_num}" \
--config_args="batch_size=128,layer_num=${layer_num},num_samples=256" \
> /dev/null 2>&1
echo "Done"
fi
log_period=$((256 / bs))
paddle train --job=test \
--config="${topology}.py" \
--use_mkldnn=$use_mkldnn \
--use_gpu=False \
--trainer_count=$thread \
--log_period=$log_period \
--config_args="batch_size=${bs},layer_num=${layer_num},is_infer=True" \
--init_model_path=$models_in \
2>&1 | tee ${log}
# calculate the last 5 logs period time of 1280 samples,
# the time before are burning time.
start=`tail ${log} -n 7 | head -n 1 | awk -F ' ' '{print $2}' | xargs`
end=`tail ${log} -n 2 | head -n 1 | awk -F ' ' '{print $2}' | xargs`
start_sec=`clock_to_seconds $start`
end_sec=`clock_to_seconds $end`
fps=`awk 'BEGIN{printf "%.2f",(1280 / ('$end_sec' - '$start_sec'))}'`
echo "Last 1280 samples start: ${start}(${start_sec} sec), end: ${end}(${end_sec} sec;" >> ${log}
echo "FPS: $fps images/sec" 2>&1 | tee -a ${log}
}
if [ ! -f "train.list" ]; then
echo " " > train.list
fi
if [ ! -f "test.list" ]; then
echo " " > test.list
fi
if [ ! -d "logs" ]; then
mkdir logs
fi
if [ ! -d "models" ]; then
mkdir -p models
fi
# inference benchmark
for use_mkldnn in True False; do
for batchsize in 1 2 4 8 16; do
infer vgg 19 $batchsize $use_mkldnn
infer resnet 50 $batchsize $use_mkldnn
infer googlenet v1 $batchsize $use_mkldnn
infer alexnet 2 $batchsize $use_mkldnn
done
done
benchmark/paddle/image/run_mkl_train.sh 已删除 100755 → 0
浏览文件 @ a3a3d3d8
#!/bin/bash
set -e
function train() {
unset OMP_NUM_THREADS MKL_NUM_THREADS OMP_DYNAMIC KMP_AFFINITY
topology=$1
layer_num=$2
bs=$3
use_mkldnn=$4
if [ $4 == "True" ]; then
thread=1
log="logs/train-${topology}-${layer_num}-mkldnn-${bs}.log"
elif [ $4 == "False" ]; then
thread=`nproc`
# each trainer_count use only 1 core to avoid conflict
log="logs/train-${topology}-${layer_num}-${thread}mklml-${bs}.log"
else
echo "Wrong input $4, use True or False."
exit 0
fi
args="batch_size=${bs},layer_num=${layer_num}"
config="${topology}.py"
paddle train --job=time \
--config=$config \
--use_mkldnn=$use_mkldnn \
--use_gpu=False \
--trainer_count=$thread \
--log_period=10 \
--test_period=100 \
--config_args=$args \
2>&1 | tee ${log}
avg_time=`tail ${log} -n 1 | awk -F ' ' '{print $8}' | sed 's/avg=//'`
fps=`awk 'BEGIN{printf "%.2f",('$bs' / '$avg_time' * 1000)}'`
echo "FPS: $fps images/sec" 2>&1 | tee -a ${log}
}
if [ ! -f "train.list" ]; then
echo " " > train.list
fi
if [ ! -d "logs" ]; then
mkdir logs
fi
# training benchmark
for use_mkldnn in True False; do
for batchsize in 64 128 256; do
train vgg 19 $batchsize $use_mkldnn
train resnet 50 $batchsize $use_mkldnn
train googlenet v1 $batchsize $use_mkldnn
train alexnet 2 $batchsize $use_mkldnn
done
done
benchmark/paddle/image/run_openblas_infer.sh 已删除 100755 → 0
浏览文件 @ a3a3d3d8
#!/bin/bash
set -e
function clock_to_seconds() {
hours=`echo $1 | awk -F ':' '{print $1}'`
mins=`echo $1 | awk -F ':' '{print $2}'`
secs=`echo $1 | awk -F ':' '{print $3}'`
echo `awk 'BEGIN{printf "%.2f",('$secs' + '$mins' * 60 + '$hours' * 3600)}'`
}
function infer() {
export OPENBLAS_MAIN_FREE=1
topology=$1
layer_num=$2
bs=$3
trainers=`nproc`
if [ $trainers -gt $bs ]; then
trainers=$bs
fi
log="logs/infer-${topology}-${layer_num}-${trainers}openblas-${bs}.log"
threads=$((`nproc` / trainers))
if [ $threads -eq 0 ]; then
threads=1
fi
export OPENBLAS_NUM_THREADS=$threads
models_in="models/${topology}-${layer_num}/pass-00000/"
if [ ! -d $models_in ]; then
echo "./run_mkl_infer.sh to save the model first"
exit 0
fi
log_period=$((32 / bs))
paddle train --job=test \
--config="${topology}.py" \
--use_mkldnn=False \
--use_gpu=False \
--trainer_count=$trainers \
--log_period=$log_period \
--config_args="batch_size=${bs},layer_num=${layer_num},is_infer=True,num_samples=256" \
--init_model_path=$models_in \
2>&1 | tee ${log}
# calculate the last 5 logs period time of 160(=32*5) samples,
# the time before are burning time.
start=`tail ${log} -n 7 | head -n 1 | awk -F ' ' '{print $2}' | xargs`
end=`tail ${log} -n 2 | head -n 1 | awk -F ' ' '{print $2}' | xargs`
start_sec=`clock_to_seconds $start`
end_sec=`clock_to_seconds $end`
fps=`awk 'BEGIN{printf "%.2f",(160 / ('$end_sec' - '$start_sec'))}'`
echo "Last 160 samples start: ${start}(${start_sec} sec), end: ${end}(${end_sec} sec;" >> ${log}
echo "FPS: $fps images/sec" 2>&1 | tee -a ${log}
}
if [ ! -f "train.list" ]; then
echo " " > train.list
fi
if [ ! -f "test.list" ]; then
echo " " > test.list
fi
if [ ! -d "logs" ]; then
mkdir logs
fi
# inference benchmark
for batchsize in 1 2 4 8 16; do
infer vgg 19 $batchsize
infer resnet 50 $batchsize
infer googlenet v1 $batchsize
infer alexnet 2 $batchsize
done
benchmark/paddle/image/run_openblas_train.sh 已删除 100755 → 0
浏览文件 @ a3a3d3d8
#!/bin/bash
set -e
function train() {
export OPENBLAS_NUM_THREADS=1
topology=$1
layer_num=$2
bs=$3
thread=`nproc`
# each trainer_count use only 1 core to avoid conflict
log="logs/train-${topology}-${layer_num}-${thread}openblas-${bs}.log"
args="batch_size=${bs},layer_num=${layer_num}"
config="${topology}.py"
paddle train --job=time \
--config=$config \
--use_mkldnn=False \
--use_gpu=False \
--trainer_count=$thread \
--log_period=3 \
--test_period=30 \
--config_args=$args \
2>&1 | tee ${log}
avg_time=`tail ${log} -n 1 | awk -F ' ' '{print $8}' | sed 's/avg=//'`
fps=`awk 'BEGIN{printf "%.2f",('$bs' / '$avg_time' * 1000)}'`
echo "FPS: $fps images/sec" 2>&1 | tee -a ${log}
}
if [ ! -f "train.list" ]; then
echo " " > train.list
fi
if [ ! -d "logs" ]; then
mkdir logs
fi
# training benchmark
for batchsize in 64 128 256; do
train vgg 19 $batchsize
train resnet 50 $batchsize
train googlenet v1 $batchsize
train alexnet 2 $batchsize
done
benchmark/paddle/image/smallnet_mnist_cifar.py 已删除 100644 → 0
浏览文件 @ a3a3d3d8
#!/usr/bin/env python
from paddle.trainer_config_helpers import *
height = 32
width = 32
num_class = 10
batch_size = get_config_arg('batch_size', int, 128)
args = {'height': height, 'width': width, 'color': True, 'num_class': num_class}
define_py_data_sources2(
"train.list", None, module="provider", obj="process", args=args)
settings(
batch_size=batch_size,
learning_rate=0.01 / batch_size,
learning_method=MomentumOptimizer(0.9),
regularization=L2Regularization(0.0005 * batch_size))
# conv1
net = data_layer('data', size=height * width * 3)
net = img_conv_layer(
input=net,
filter_size=5,
num_channels=3,
num_filters=32,
stride=1,
padding=2)
net = img_pool_layer(input=net, pool_size=3, stride=2, padding=1)
# conv2
net = img_conv_layer(
input=net, filter_size=5, num_filters=32, stride=1, padding=2)
net = img_pool_layer(
input=net, pool_size=3, stride=2, padding=1, pool_type=AvgPooling())
# conv3
net = img_conv_layer(
input=net, filter_size=3, num_filters=64, stride=1, padding=1)
net = img_pool_layer(
input=net, pool_size=3, stride=2, padding=1, pool_type=AvgPooling())
net = fc_layer(input=net, size=64, act=ReluActivation())
net = fc_layer(input=net, size=10, act=SoftmaxActivation())
lab = data_layer('label', num_class)
loss = classification_cost(input=net, label=lab)
outputs(loss)
benchmark/paddle/image/vgg.py 已删除 100644 → 0
浏览文件 @ a3a3d3d8
#!/usr/bin/env python
from paddle.trainer_config_helpers import *
height = 224
width = 224
num_class = 1000
batch_size = get_config_arg('batch_size', int, 64)
layer_num = get_config_arg('layer_num', int, 19)
is_infer = get_config_arg("is_infer", bool, False)
num_samples = get_config_arg('num_samples', int, 2560)
args = {
'height': height,
'width': width,
'color': True,
'num_class': num_class,
'is_infer': is_infer,
'num_samples': num_samples
}
define_py_data_sources2(
"train.list" if not is_infer else None,
"test.list" if is_infer else None,
module="provider",
obj="process",
args=args)
settings(
batch_size=batch_size,
learning_rate=0.001 / batch_size,
learning_method=MomentumOptimizer(0.9),
regularization=L2Regularization(0.0005 * batch_size))
img = data_layer(name='image', size=height * width * 3)
def vgg_network(vgg_num=3):
tmp = img_conv_group(
input=img,
num_channels=3,
conv_padding=1,
conv_num_filter=[64, 64],
conv_filter_size=3,
conv_act=ReluActivation(),
pool_size=2,
pool_stride=2,
pool_type=MaxPooling())
tmp = img_conv_group(
input=tmp,
conv_num_filter=[128, 128],
conv_padding=1,
conv_filter_size=3,
conv_act=ReluActivation(),
pool_stride=2,
pool_type=MaxPooling(),
pool_size=2)
channels = []
for i in range(vgg_num):
channels.append(256)
tmp = img_conv_group(
input=tmp,
conv_num_filter=channels,
conv_padding=1,
conv_filter_size=3,
conv_act=ReluActivation(),
pool_stride=2,
pool_type=MaxPooling(),
pool_size=2)
channels = []
for i in range(vgg_num):
channels.append(512)
tmp = img_conv_group(
input=tmp,
conv_num_filter=channels,
conv_padding=1,
conv_filter_size=3,
conv_act=ReluActivation(),
pool_stride=2,
pool_type=MaxPooling(),
pool_size=2)
tmp = img_conv_group(
input=tmp,
conv_num_filter=channels,
conv_padding=1,
conv_filter_size=3,
conv_act=ReluActivation(),
pool_stride=2,
pool_type=MaxPooling(),
pool_size=2)
tmp = fc_layer(
input=tmp,
size=4096,
act=ReluActivation(),
layer_attr=ExtraAttr(drop_rate=0.5))
tmp = fc_layer(
input=tmp,
size=4096,
act=ReluActivation(),
layer_attr=ExtraAttr(drop_rate=0.5))
return fc_layer(input=tmp, size=num_class, act=SoftmaxActivation())
if layer_num == 16:
vgg = vgg_network(3)
elif layer_num == 19:
vgg = vgg_network(4)
else:
print("Wrong layer number.")
if is_infer:
outputs(vgg)
else:
lab = data_layer('label', num_class)
loss = cross_entropy(input=vgg, label=lab)
outputs(loss)
benchmark/paddle/rnn/imdb.py 已删除 100755 → 0
浏览文件 @ a3a3d3d8
# 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.
from __future__ import print_function
import six.moves.cPickle as pickle
import gzip
import os
import numpy
def get_dataset_file(dataset, default_dataset, origin):
data_dir, data_file = os.path.split(dataset)
if (not os.path.isfile(dataset)) and data_file == default_dataset:
from six.moves import urllib
print('Downloading data from %s' % origin)
urllib.request.urlretrieve(origin, dataset)
return dataset
def create_data(path="imdb.pkl"):
if (not os.path.isfile('imdb.train.pkl')):
path = get_dataset_file(
path, "imdb.pkl",
"http://www.iro.umontreal.ca/~lisa/deep/data/imdb.pkl")
if path.endswith(".gz"):
f = gzip.open(path, 'rb')
else:
f = open(path, 'rb')
train_set = pickle.load(f)
test_set = pickle.load(f)
f.close()
pickle.dump(train_set, open('imdb.train.pkl', 'wb'))
pickle.dump(test_set, open('imdb.test.pkl', 'wb'))
if (not os.path.isfile('train.list')):
file('train.list', 'w').write('imdb.train.pkl\n')
def main():
create_data('imdb.pkl')
if __name__ == "__main__":
main()
benchmark/paddle/rnn/provider.py 已删除 100644 → 0
浏览文件 @ a3a3d3d8
# 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 io, os
import random
import numpy as np
import six.moves.cPickle as pickle
from paddle.trainer.PyDataProvider2 import *
def remove_unk(x, n_words):
return [[1 if w >= n_words else w for w in sen] for sen in x]
# ==============================================================
# tensorflow uses fixed length, but PaddlePaddle can process
# variable-length. Padding is used in benchmark in order to
# compare with other platform.
# ==============================================================
def pad_sequences(sequences,
maxlen=None,
dtype='int32',
padding='post',
truncating='post',
value=0.):
lengths = [len(s) for s in sequences]
nb_samples = len(sequences)
if maxlen is None:
maxlen = np.max(lengths)
x = (np.ones((nb_samples, maxlen)) * value).astype(dtype)
for idx, s in enumerate(sequences):
if len(s) == 0:
continue # empty list was found
if truncating == 'pre':
trunc = s[-maxlen:]
elif truncating == 'post':
trunc = s[:maxlen]
else:
raise ValueError("Truncating type '%s' not understood" % padding)
if padding == 'post':
x[idx, :len(trunc)] = trunc
elif padding == 'pre':
x[idx, -len(trunc):] = trunc
else:
raise ValueError("Padding type '%s' not understood" % padding)
return x
def initHook(settings, vocab_size, pad_seq, maxlen, **kwargs):
settings.vocab_size = vocab_size
settings.pad_seq = pad_seq
settings.maxlen = maxlen
settings.input_types = [
integer_value_sequence(vocab_size), integer_value(2)
]
@provider(
init_hook=initHook, min_pool_size=-1, cache=CacheType.CACHE_PASS_IN_MEM)
def process(settings, file):
f = open(file, 'rb')
train_set = pickle.load(f)
f.close()
x, y = train_set
# remove unk, namely remove the words out of dictionary
x = remove_unk(x, settings.vocab_size)
if settings.pad_seq:
x = pad_sequences(x, maxlen=settings.maxlen, value=0.)
for i in range(len(y)):
yield map(int, x[i]), int(y[i])
benchmark/paddle/rnn/rnn.py 已删除 100755 → 0
浏览文件 @ a3a3d3d8
#!/usr/bin/env python
from paddle.trainer_config_helpers import *
import imdb
num_class = 2
vocab_size = 30000
fixedlen = 100
batch_size = get_config_arg('batch_size', int, 128)
lstm_num = get_config_arg('lstm_num', int, 1)
hidden_size = get_config_arg('hidden_size', int, 128)
# whether to pad sequence into fixed length
pad_seq = get_config_arg('pad_seq', bool, True)
imdb.create_data('imdb.pkl')
args = {'vocab_size': vocab_size, 'pad_seq': pad_seq, 'maxlen': fixedlen}
define_py_data_sources2(
"train.list", None, module="provider", obj="process", args=args)
settings(
batch_size=batch_size,
learning_rate=2e-3,
learning_method=AdamOptimizer(),
regularization=L2Regularization(8e-4),
gradient_clipping_threshold=25)
net = data_layer('data', size=vocab_size)
net = embedding_layer(input=net, size=128)
for i in xrange(lstm_num):
net = simple_lstm(input=net, size=hidden_size)
net = last_seq(input=net)
net = fc_layer(input=net, size=2, act=SoftmaxActivation())
lab = data_layer('label', num_class)
loss = classification_cost(input=net, label=lab)
outputs(loss)
benchmark/paddle/rnn/run.sh 已删除 100755 → 0
浏览文件 @ a3a3d3d8
#!/bin/bash
set -e
function train() {
cfg=$1
thread=$2
args="lstm_num=${3},seq_pad=${4},hidden_size=${5},batch_size=${6}"
paddle train --job=time \
--config=$cfg \
--use_gpu=1 \
--trainer_count=$thread \
--log_period=10 \
--test_period=100 \
--num_passes=1 \
--feed_data=1 \
--config_args=$args \
>logs/rnn-pad${4}-${thread}gpu-lstm${3}-batch${6}-hid${5}.log 2>&1
}
if [ ! -d "logs" ]; then
mkdir logs
fi
## padding, single gpu
#-----config--gpu--lstm_num--padding--hidden_size--batch_size
## lstm_num=2, batch_size=64
train rnn.py 1 2 1 256 64
train rnn.py 1 2 1 512 64
train rnn.py 1 2 1 1280 64
## lstm_num=2, batch_size=128
train rnn.py 1 2 1 256 128
train rnn.py 1 2 1 512 128
train rnn.py 1 2 1 1280 128
## lstm_num=4, batch_size=256
train rnn.py 1 2 1 256 256
train rnn.py 1 2 1 512 256
train rnn.py 1 2 1 1280 256
#==================multi gpus=====================#
# hidden_size=256, lstm_num=2, different batch size
train rnn.py 4 2 1 256 128
train rnn.py 4 2 1 256 256
train rnn.py 4 2 1 256 512
# hidden_size=512, lstm_num=4, different batch size
train rnn.py 4 2 1 512 128
train rnn.py 4 2 1 512 256
train rnn.py 4 2 1 512 512
benchmark/tensorflow/machine_translation.py
浏览文件 @ 2ccbcb15
......@@ -35,8 +35,6 @@ import os
import argparse
import time
import paddle.v2 as paddle
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--embedding_dim",
......
benchmark/tensorflow/mnist.py
浏览文件 @ 2ccbcb15
......@@ -21,7 +21,6 @@ import time
import numpy as np
import tensorflow as tf
import paddle.v2 as paddle
DTYPE = tf.float32
......
benchmark/tensorflow/resnet.py
浏览文件 @ 2ccbcb15
......@@ -27,7 +27,6 @@ import argparse
import time
import numpy as np
import paddle.v2 as paddle
import tensorflow as tf
DTYPE = tf.float32
......
benchmark/tensorflow/stacked_dynamic_lstm.py
浏览文件 @ 2ccbcb15
......@@ -21,8 +21,6 @@ import argparse
import time
import tensorflow as tf
import paddle.v2 as paddle
def parse_args():
parser = argparse.ArgumentParser("LSTM model benchmark.")
......
benchmark/tensorflow/vgg.py
浏览文件 @ 2ccbcb15
......@@ -13,7 +13,6 @@
# limitations under the License.
"""VGG16 benchmark in TensorFlow"""
import tensorflow as tf
import paddle.v2 as paddle
import numpy as np
import argparse
import time
......
paddle/fluid/framework/ir/identity_scale_op_clean_pass.cc
浏览文件 @ 2ccbcb15
......@@ -38,9 +38,13 @@ std::unique_ptr<ir::Graph> IdentityScaleOpCleanPass::ApplyImpl(
->assert_is_op("scale")
->assert_op_attr<float>("scale", 1.)
->assert_op_attr<float>("bias", 0.);
auto scale_out = detector.mutable_pattern()
->NewNode("scale_out")
->assert_is_op_output("scale");
auto scale_out =
detector.mutable_pattern()
->NewNode("scale_out")
->assert_is_op_output("scale")
// scale's output var should has only one consumer, or it can't be
// removed.
->assert_more([](Node* x) { return x->outputs.size() == 1UL; });
pre_op->LinksTo({scale_in});
scale_op->LinksFrom({scale_in}).LinksTo({scale_out});
......
paddle/fluid/imperative/layer.cc
浏览文件 @ 2ccbcb15
......@@ -207,7 +207,7 @@ framework::LoDTensor& VarBase::GradValue() {
std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
if (grad_op_descs_.empty() && backward_id_ <= 0) {
LOG(WARNING) << "op with no grad: " << op_desc_->Type();
VLOG(3) << "op with no grad: " << op_desc_->Type();
return {};
}
......
paddle/fluid/inference/analysis/ir_passes/subgraph_detector.cc
浏览文件 @ 2ccbcb15
......@@ -460,77 +460,6 @@ inline bool CheckNodeIndegreeEquals(const Node &node, size_t n) {
return node.inputs.size() == n;
}
NodesTSIterator::NodesTSIterator(const std::vector<Node *> &source) {
PADDLE_ENFORCE(!source.empty(),
"Start points of topological sorting should not be empty!");
// CHECK all the inputs' in-degree is 0
for (auto *node : source) {
PADDLE_ENFORCE(CheckNodeIndegreeEquals(*node, 0));
}
std::unordered_set<Node *> visited;
std::unordered_set<Node *> to_visit{source.begin(), source.end()};
std::vector<Node *> inlink_visited;
while (!to_visit.empty()) {
std::vector<Node *> queue(to_visit.begin(), to_visit.end());
for (auto *p : queue) {
if (Agent(p).deleted()) {
visited.insert(p);
to_visit.erase(p);
}
inlink_visited.clear();
std::copy_if(p->inputs.begin(), p->inputs.end(),
std::back_inserter(inlink_visited),
[&](Node *x) -> bool { return visited.count(x) != 0; });
if (inlink_visited.size() == p->inputs.size()) {
sorted_.push_back(p);
for (auto *_ : p->outputs) {
if (!visited.count(_)) {
to_visit.insert(_);
}
}
to_visit.erase(p);
visited.insert(p);
}
}
}
}
NodesTSIterator::NodesTSIterator(const NodesTSIterator &other)
: sorted_(other.sorted_), cursor_(other.cursor_) {}
Node &NodesTSIterator::operator*() {
PADDLE_ENFORCE_LT(cursor_, sorted_.size());
return *sorted_[cursor_];
}
NodesTSIterator &NodesTSIterator::operator++() {
if (++cursor_ >= sorted_.size()) {
sorted_.clear();
cursor_ = 0;
}
return *this;
}
NodesTSIterator &NodesTSIterator::operator=(const NodesTSIterator &other) {
cursor_ = other.cursor_;
sorted_ = other.sorted_;
return *this;
}
bool NodesTSIterator::operator==(const NodesTSIterator &other) {
return sorted_ == other.sorted_ && cursor_ == other.cursor_;
}
Node *NodesTSIterator::operator->() {
PADDLE_ENFORCE_LT(cursor_, sorted_.size());
return sorted_[cursor_];
}
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/analysis/ir_passes/subgraph_detector.h
浏览文件 @ 2ccbcb15
......@@ -30,6 +30,7 @@ namespace inference {
namespace analysis {
using framework::ir::Graph;
using framework::ir::NodesTSIterator;
const char kIsFunctionNode[] = "__is_function_node__";
const char kFunctionNodeSubGraph[] = "__function_node_sub_graph__";
......@@ -132,32 +133,6 @@ struct Agent {
framework::ir::Node *x_;
};
// Topological sorting iterator on nodes.
struct NodesTSIterator
: public std::iterator<std::forward_iterator_tag, framework::ir::Node *> {
NodesTSIterator() = default;
explicit NodesTSIterator(const std::vector<framework::ir::Node *> &source);
NodesTSIterator(NodesTSIterator &&other)
: sorted_(std::move(other.sorted_)), cursor_(other.cursor_) {
other.cursor_ = 0;
}
NodesTSIterator(const NodesTSIterator &other);
framework::ir::Node &operator*();
NodesTSIterator &operator++();
// TODO(Superjomn) current implementation just compare the first
// element, need to compare the graph and all the elements in the queue and
// set.
NodesTSIterator &operator=(const NodesTSIterator &other);
bool operator==(const NodesTSIterator &other);
bool operator!=(const NodesTSIterator &other) { return !(*this == other); }
framework::ir::Node *operator->();
private:
std::vector<framework::ir::Node *> sorted_;
size_t cursor_{0};
};
// The nodes those have no input will be treated as start points.
static std::vector<framework::ir::Node *> ExtractStartPoints(const Graph &g) {
std::vector<framework::ir::Node *> result;
......
paddle/fluid/operators/detection/density_prior_box_op.h
浏览文件 @ 2ccbcb15
......@@ -72,7 +72,7 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for
#endif
for (int i = 0; i < fixed_ratios.size(); i++) {
for (size_t i = 0; i < fixed_ratios.size(); i++) {
sqrt_fixed_ratios.push_back(sqrt(fixed_ratios[i]));
}
......@@ -115,11 +115,10 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
}
}
if (clip) {
platform::Transform<platform::CPUDeviceContext> trans;
ClipFunctor<T> clip_func;
trans(ctx.template device_context<platform::CPUDeviceContext>(),
boxes->data<T>(), boxes->data<T>() + boxes->numel(),
boxes->data<T>(), clip_func);
T* dt = boxes->data<T>();
std::transform(dt, dt + boxes->numel(), dt, [](T v) -> T {
return std::min<T>(std::max<T>(v, 0.), 1.);
});
}
framework::Tensor var_t;
var_t.mutable_data<T>(
......@@ -141,7 +140,7 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
#pragma omp parallel for collapse(2)
#endif
for (int i = 0; i < box_num; ++i) {
for (int j = 0; j < variances.size(); ++j) {
for (size_t j = 0; j < variances.size(); ++j) {
e_vars(i, j) = variances[j];
}
}
......
paddle/fluid/operators/detection/prior_box_op.h
浏览文件 @ 2ccbcb15
......@@ -46,13 +46,6 @@ inline void ExpandAspectRatios(const std::vector<float>& input_aspect_ratior,
}
}
template <typename T>
struct ClipFunctor {
HOSTDEVICE inline T operator()(T in) const {
return std::min<T>(std::max<T>(in, 0.), 1.);
}
};
template <typename T>
class PriorBoxOpKernel : public framework::OpKernel<T> {
public:
......@@ -101,31 +94,30 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
boxes->mutable_data<T>(ctx.GetPlace());
vars->mutable_data<T>(ctx.GetPlace());
auto e_boxes = framework::EigenTensor<T, 4>::From(*boxes);
T* b_t = boxes->data<T>();
for (int h = 0; h < feature_height; ++h) {
for (int w = 0; w < feature_width; ++w) {
T center_x = (w + offset) * step_width;
T center_y = (h + offset) * step_height;
T box_width, box_height;
int idx = 0;
for (size_t s = 0; s < min_sizes.size(); ++s) {
auto min_size = min_sizes[s];
if (min_max_aspect_ratios_order) {
box_width = box_height = min_size / 2.;
e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
idx++;
b_t[0] = (center_x - box_width) / img_width;
b_t[1] = (center_y - box_height) / img_height;
b_t[2] = (center_x + box_width) / img_width;
b_t[3] = (center_y + box_height) / img_height;
b_t += 4;
if (max_sizes.size() > 0) {
auto max_size = max_sizes[s];
// square prior with size sqrt(minSize * maxSize)
box_width = box_height = sqrt(min_size * max_size) / 2.;
e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
idx++;
b_t[0] = (center_x - box_width) / img_width;
b_t[1] = (center_y - box_height) / img_height;
b_t[2] = (center_x + box_width) / img_width;
b_t[3] = (center_y + box_height) / img_height;
b_t += 4;
}
// priors with different aspect ratios
for (size_t r = 0; r < aspect_ratios.size(); ++r) {
......@@ -135,11 +127,11 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
}
box_width = min_size * sqrt(ar) / 2.;
box_height = min_size / sqrt(ar) / 2.;
e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
idx++;
b_t[0] = (center_x - box_width) / img_width;
b_t[1] = (center_y - box_height) / img_height;
b_t[2] = (center_x + box_width) / img_width;
b_t[3] = (center_y + box_height) / img_height;
b_t += 4;
}
} else {
// priors with different aspect ratios
......@@ -147,21 +139,21 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
float ar = aspect_ratios[r];
box_width = min_size * sqrt(ar) / 2.;
box_height = min_size / sqrt(ar) / 2.;
e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
idx++;
b_t[0] = (center_x - box_width) / img_width;
b_t[1] = (center_y - box_height) / img_height;
b_t[2] = (center_x + box_width) / img_width;
b_t[3] = (center_y + box_height) / img_height;
b_t += 4;
}
if (max_sizes.size() > 0) {
auto max_size = max_sizes[s];
// square prior with size sqrt(minSize * maxSize)
box_width = box_height = sqrt(min_size * max_size) / 2.;
e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
idx++;
b_t[0] = (center_x - box_width) / img_width;
b_t[1] = (center_y - box_height) / img_height;
b_t[2] = (center_x + box_width) / img_width;
b_t[3] = (center_y + box_height) / img_height;
b_t += 4;
}
}
}
......@@ -169,11 +161,10 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
}
if (clip) {
platform::Transform<platform::CPUDeviceContext> trans;
ClipFunctor<T> clip_func;
trans(ctx.template device_context<platform::CPUDeviceContext>(),
boxes->data<T>(), boxes->data<T>() + boxes->numel(),
boxes->data<T>(), clip_func);
T* dt = boxes->data<T>();
std::transform(dt, dt + boxes->numel(), dt, [](T v) -> T {
return std::min<T>(std::max<T>(v, 0.), 1.);
});
}
framework::Tensor var_t;
......
paddle/fluid/operators/group_norm_op.cc
浏览文件 @ 2ccbcb15
......@@ -170,13 +170,48 @@ class GroupNormGradMaker : public framework::SingleGradOpDescMaker {
}
};
class GroupNormInplaceInToOut : public framework::InplaceInToOut {
public:
using InplaceInToOut::InplaceInToOut;
protected:
std::unordered_map<std::string, std::string> Apply(
const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {
return {{"X", "Y"}};
}
};
class GroupNormGradInplaceInToOut : public framework::InplaceInToOut {
public:
using InplaceInToOut::InplaceInToOut;
protected:
std::unordered_map<std::string, std::string> Apply(
const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {
return {{framework::GradVarName("Y"), framework::GradVarName("X")}};
}
};
class GroupNormOpInferVarType
: public framework::PassInDtypeAndVarTypeToOutput {
protected:
std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
const override {
return {{"X", /*->*/ "Y"}};
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(group_norm, ops::GroupNormOp, ops::GroupNormOpMaker,
ops::GroupNormGradMaker);
REGISTER_OPERATOR(group_norm_grad, ops::GroupNormGradOp);
ops::GroupNormOpInferVarType, ops::GroupNormGradMaker,
ops::GroupNormInplaceInToOut);
REGISTER_OPERATOR(group_norm_grad, ops::GroupNormGradOp,
ops::GroupNormGradInplaceInToOut);
REGISTER_OP_CPU_KERNEL(
group_norm, ops::GroupNormKernel<paddle::platform::CPUDeviceContext, float>,
ops::GroupNormKernel<paddle::platform::CPUDeviceContext, double>);
......
paddle/fluid/operators/load_combine_op.cc
浏览文件 @ 2ccbcb15
......@@ -64,7 +64,7 @@ class LoadCombineOp : public framework::OperatorBase {
auto *tensor = out_var->GetMutable<framework::LoDTensor>();
// Error checking
PADDLE_ENFORCE(static_cast<bool>(buffer), "Cannot read more");
PADDLE_ENFORCE(static_cast<bool>(*buffer), "Cannot read more");
// Get data from fin to tensor
DeserializeFromStream(*buffer, tensor, dev_ctx);
......@@ -90,6 +90,10 @@ class LoadCombineOp : public framework::OperatorBase {
tensor->ShareDataWith(fp16_tensor);
}
}
buffer->peek();
PADDLE_ENFORCE(buffer->eof(),
"You are not allowed to load partial data via "
"load_combine_op, use load_op instead.");
}
};
......
paddle/fluid/operators/lstm_op.h
浏览文件 @ 2ccbcb15
......@@ -311,6 +311,10 @@ class LSTMGradKernel : public framework::OpKernel<T> {
lstm_grad.prev_state_grad = c0_g ? ordered_c0_g.data<T>() : nullptr;
}
// lstm_value.output_value not used in bp, set to nullptr
// lstm_grad.state_active_grad not used in bp, set to nullptr
lstm_value.output_value = nullptr;
lstm_grad.state_active_grad = nullptr;
int cur_batch_size = bend - bstart;
math::LstmUnitGradFunctor<DeviceContext, T>::compute(
device_ctx, lstm_value, lstm_grad, frame_size, cur_batch_size,
......
paddle/fluid/operators/lstmp_op.h
浏览文件 @ 2ccbcb15
......@@ -405,6 +405,11 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
}
int cur_batch_size = bend - bstart;
// lstmp_value.output_value not used in bp, set to null
// lstmp_grad.state_active_grad not used in bp, set to null
lstmp_value.output_value = nullptr;
lstmp_grad.state_active_grad = nullptr;
math::LstmUnitGradFunctor<DeviceContext, T>::compute(
device_ctx, lstmp_value, lstmp_grad, frame_size, cur_batch_size,
gate_act, cell_act, cand_act);
......
paddle/fluid/operators/row_conv_op.cc
浏览文件 @ 2ccbcb15
......@@ -109,23 +109,23 @@ from future subsequences in a computationally efficient manner to improve
unidirectional recurrent neural networks. The row convolution operator is
different from the 1D sequence convolution, and is computed as follows:
Given an input sequence $in$ of length $t$ and input dimension $d$,
and a filter ($W$) of size $context \times d$,
Given an input sequence $X$ of length $t$ and input dimension $D$,
and a filter ($W$) of size $context \times D$,
the output sequence is convolved as:
$$
out_{i, :} = \\sum_{j=i}^{i + context} in_{j,:} \\cdot W_{i-j, :}
out_{i} = \\sum_{j=i}^{i + context - 1} X_{j} \\cdot W_{j-i}
$$
In the above equation:
* $Out_{i}$: The i-th row of output variable with shape [1, D].
* $\\tau$: Future context size.
* $context$: Future context size.
* $X_{j}$: The j-th row of input variable with shape [1, D].
* $W_{i-j}$: The (i-j)-th row of parameters with shape [1, D].
* $W_{j-i}$: The (j-i)-th row of parameters with shape [1, D].
More details about row_conv please refer to
the design document
......
paddle/fluid/platform/enforce.h
浏览文件 @ 2ccbcb15
......@@ -233,9 +233,11 @@ inline void throw_on_error(ncclResult_t stat, const std::string& msg) {
#endif // __APPLE__ and windows
#endif // PADDLE_WITH_CUDA
#define PADDLE_THROW(...) \
throw ::paddle::platform::EnforceNotMet( \
::paddle::string::Sprintf(__VA_ARGS__), __FILE__, __LINE__)
#define PADDLE_THROW(...) \
do { \
throw ::paddle::platform::EnforceNotMet( \
::paddle::string::Sprintf(__VA_ARGS__), __FILE__, __LINE__); \
} while (0)
#define PADDLE_ENFORCE(COND, ...) \
do { \
......@@ -270,23 +272,25 @@ inline void throw_on_error(ncclResult_t stat, const std::string& msg) {
* extra messages is also supported, for example:
* PADDLE_ENFORCE(a, b, "some simple enforce failed between %d numbers", 2)
*/
#define PADDLE_ENFORCE_NOT_NULL(__VAL, ...) \
do { \
if (UNLIKELY(nullptr == (__VAL))) { \
PADDLE_THROW(#__VAL " should not be null\n%s", \
paddle::string::Sprintf("" __VA_ARGS__)); \
} \
#define PADDLE_ENFORCE_NOT_NULL(__VAL, ...) \
do { \
if (UNLIKELY(nullptr == (__VAL))) { \
PADDLE_THROW(#__VAL " should not be null\n%s", \
::paddle::string::Sprintf(__VA_ARGS__)); \
} \
} while (0)
#define __PADDLE_BINARY_COMPARE(__VAL0, __VAL1, __CMP, __INV_CMP, ...) \
do { \
if (UNLIKELY(!((__VAL0)__CMP(__VAL1)))) { \
auto __cond1__ = (__VAL0); \
auto __cond2__ = (__VAL1); \
if (UNLIKELY(!((__cond1__)__CMP(__cond2__)))) { \
PADDLE_THROW("Enforce failed. Expected %s " #__CMP \
" %s, but received %s:%s " #__INV_CMP " %s:%s.\n%s", \
#__VAL0, #__VAL1, #__VAL0, \
paddle::string::to_string(__VAL0), #__VAL1, \
paddle::string::to_string(__VAL1), \
paddle::string::Sprintf("" __VA_ARGS__)); \
::paddle::string::to_string(__cond1__), #__VAL1, \
::paddle::string::to_string(__cond2__), \
::paddle::string::Sprintf(__VA_ARGS__)); \
} \
} while (0)
......
paddle/fluid/pybind/ir.cc
浏览文件 @ 2ccbcb15
......@@ -13,10 +13,12 @@
// limitations under the License.
#include "paddle/fluid/pybind/ir.h"
#include <algorithm>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
#include "paddle/fluid/framework/ir/node.h"
#include "paddle/fluid/framework/op_desc.h"
......@@ -27,6 +29,10 @@ namespace py = pybind11;
using paddle::framework::ir::Graph;
using paddle::framework::ir::Node;
using paddle::framework::ir::GraphSafeRemoveNodes;
using paddle::framework::ir::HasCircle;
using paddle::framework::ir::GraphNum;
using paddle::framework::ir::TopologySortOperations;
using paddle::framework::ir::BuildOperationAdjList;
using paddle::framework::OpDesc;
using paddle::framework::ProgramDesc;
using paddle::framework::VarDesc;
......@@ -36,6 +42,12 @@ namespace paddle {
namespace pybind {
void BindGraph(py::module *m) {
m->def("graph_safe_remove_nodes", GraphSafeRemoveNodes);
m->def("has_circle", HasCircle);
m->def("graph_num", GraphNum);
m->def("topology_sort", TopologySortOperations,
return_value_policy::reference);
m->def("build_adjacency_list", BuildOperationAdjList,
return_value_policy::reference);
py::class_<Graph, std::shared_ptr<Graph>>(
*m, "Graph",
"The graph is a Directed Acyclic Single Static Assignment Graph, see "
......@@ -46,7 +58,6 @@ void BindGraph(py::module *m) {
.def("get_float", &Graph::Get<float>)
.def("get_double", &Graph::Get<double>)
.def("get_string", &Graph::Get<std::string>)
.def("get_program", &Graph::Get<ProgramDesc>)
.def("get_marked_nodes", &Graph::Get<std::unordered_set<const Node *>>)
.def("set", [](Graph &self, const std::string &attr_name,
int attr) { return self.Set(attr_name, new int(attr)); })
......@@ -63,11 +74,6 @@ void BindGraph(py::module *m) {
[](Graph &self, const std::string &attr_name, double attr) {
return self.Set(attr_name, new double(attr));
})
.def("set",
[](Graph &self, const std::string &attr_name,
const ProgramDesc &attr) {
return self.Set(attr_name, new ProgramDesc(attr));
})
.def("set",
[](Graph &self, const std::string &attr_name,
const std::unordered_set<const Node *> &attr) {
......@@ -108,42 +114,42 @@ void BindNode(py::module *m) {
.def("is_op", &Node::IsOp)
.def("is_var", &Node::IsVar)
.def("is_ctrl_var", &Node::IsCtrlVar)
.def("clear_inputs", [](Node &self) { self.inputs.clear(); })
.def("inputs_remove",
[](Node &self, int node_id) {
for (auto it = self.inputs.begin(); it != self.inputs.end();
it++) {
if ((*it)->id() == node_id) {
self.inputs.erase(it);
}
auto pos = std::find_if(
self.inputs.begin(), self.inputs.end(),
[&node_id](const Node *n) { return n->id() == node_id; });
if (pos != self.inputs.end()) {
self.inputs.erase(pos);
}
})
.def("inputs_remove",
[](Node &self, Node &node) {
for (auto it = self.inputs.begin(); it != self.inputs.end();
it++) {
if (*it == &node) {
self.inputs.erase(it);
}
auto pos =
std::find(self.inputs.begin(), self.inputs.end(), &node);
if (pos != self.inputs.end()) {
self.inputs.erase(pos);
}
})
.def("inputs_append",
[](Node &self, Node &node) { self.inputs.push_back(&node); })
.def("clear_outputs", [](Node &self) { self.outputs.clear(); })
.def("outputs_remove",
[](Node &self, int node_id) {
for (auto it = self.outputs.begin(); it != self.outputs.end();
it++) {
if ((*it)->id() == node_id) {
self.outputs.erase(it);
}
auto pos = std::find_if(
self.outputs.begin(), self.outputs.end(),
[&node_id](const Node *n) { return n->id() == node_id; });
if (pos != self.outputs.end()) {
self.outputs.erase(pos);
}
})
.def("outputs_remove",
[](Node &self, Node &node) {
for (auto it = self.outputs.begin(); it != self.outputs.end();
it++) {
if (*it == &node) {
self.outputs.erase(it);
}
auto pos =
std::find(self.outputs.begin(), self.outputs.end(), &node);
if (pos != self.outputs.end()) {
self.outputs.erase(pos);
}
})
.def("outputs_append",
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ 2ccbcb15
......@@ -829,8 +829,7 @@ All parameter, weight, gradient are variables in Paddle.
m.def("disable_profiler", platform::DisableProfiler);
m.def("is_profiler_enabled", platform::IsProfileEnabled);
m.def("reset_profiler", platform::ResetProfiler);
m.def("get_pass", [](const py::bytes &binary_str) {
std::string pass_type(binary_str);
m.def("get_pass", [](const std::string &pass_type) {
auto pass = framework::ir::PassRegistry::Instance().Get(pass_type);
return std::shared_ptr<framework::ir::Pass>(std::move(pass));
});
......@@ -838,10 +837,9 @@ All parameter, weight, gradient are variables in Paddle.
py::class_<ir::Pass, std::shared_ptr<ir::Pass>> pass(m, "Pass");
pass.def(py::init())
.def("has", &ir::Pass::Has)
.def("set",
[](ir::Pass &self, const std::string &attr_name,
const ProgramDesc &attr) {
return self.Set(attr_name, new ProgramDesc(attr));
.def("set_not_owned",
[](ir::Pass &self, const std::string &attr_name, ProgramDesc &attr) {
self.SetNotOwned<ProgramDesc>(attr_name, &attr);
})
.def(
"set",
......@@ -850,7 +848,6 @@ All parameter, weight, gradient are variables in Paddle.
})
.def("set", [](ir::Pass &self, const std::string &name,
int val) { self.Set<const int>(name, new int(val)); })
.def("get_program", &ir::Pass::Get<ProgramDesc>)
.def("type", &ir::Pass::Type)
.def("apply", [](ir::Pass &self, std::shared_ptr<ir::Graph> graph) {
std::unique_ptr<ir::Graph> origin_graph(graph.get());
......
python/CMakeLists.txt
浏览文件 @ 2ccbcb15
......@@ -64,6 +64,7 @@ if (WITH_TESTING)
add_subdirectory(paddle/dataset/tests)
add_subdirectory(paddle/fluid/tests)
add_subdirectory(paddle/fluid/contrib/tests)
add_subdirectory(paddle/fluid/contrib/slim/tests)
endif()
install(DIRECTORY ${PADDLE_PYTHON_PACKAGE_DIR}
DESTINATION opt/paddle/share/wheels
......
python/paddle/fluid/contrib/int8_inference/README.md
浏览文件 @ 2ccbcb15
......@@ -63,10 +63,10 @@ Notes:
## 4. How to reproduce the results
* Small dataset
```bash
python python/paddle/fluid/contrib/tests/test_calibration.py
FLAGS_use_mkldnn=true python python/paddle/fluid/contrib/tests/test_calibration.py
```
* Full dataset
```bash
DATASET=full python python/paddle/fluid/contrib/tests/test_calibration.py
FLAGS_use_mkldnn=true DATASET=full python python/paddle/fluid/contrib/tests/test_calibration.py
```
python/paddle/fluid/contrib/slim/tests/CMakeLists.txt 0 → 100644
浏览文件 @ 2ccbcb15
file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py)
endforeach()
python/paddle/fluid/contrib/slim/unitest/configs/config.yaml → python/paddle/fluid/contrib/slim/tests/configs/config.yaml
浏览文件 @ 2ccbcb15
version: 1.0
include: ["./unitest/configs/pruners.yaml", "./unitest/configs/pruners_0.yaml"]
include: ["./configs/pruners.yaml", "./configs/pruners_0.yaml"]
pruners:
pruner_1:
class: 'RatioPruner'
......
python/paddle/fluid/contrib/slim/unitest/test_factory.py → python/paddle/fluid/contrib/slim/tests/test_factory.py
浏览文件 @ 2ccbcb15
......@@ -18,7 +18,7 @@ import unittest
class TestFactory(unittest.TestCase):
def test_parse(self):
factory = ConfigFactory('./unitest/configs/config.yaml')
factory = ConfigFactory('./configs/config.yaml')
pruner = factory.instance('pruner_1')
self.assertEquals(pruner.ratios['conv1_1.w'], 0.3)
......
python/paddle/fluid/contrib/slim/tests/test_graph.py 0 → 100644
浏览文件 @ 2ccbcb15
# 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.
from __future__ import print_function
import unittest
import paddle.fluid as fluid
import six
from paddle.fluid.framework import IrGraph
from paddle.fluid import core
def residual_block(num):
def conv_bn_layer(input,
ch_out,
filter_size,
stride,
padding,
act='relu',
bias_attr=False):
tmp = fluid.layers.conv2d(
input=input,
filter_size=filter_size,
num_filters=ch_out,
stride=stride,
padding=padding,
act=None,
bias_attr=bias_attr)
return fluid.layers.batch_norm(input=tmp, act=act)
data = fluid.layers.data(name='image', shape=[1, 32, 32], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
hidden = data
for _ in six.moves.xrange(num):
conv = conv_bn_layer(hidden, 16, 3, 1, 1, act=None, bias_attr=True)
short = conv_bn_layer(hidden, 16, 1, 1, 0, act=None)
hidden = fluid.layers.elementwise_add(x=conv, y=short, act='relu')
fc = fluid.layers.fc(input=hidden, size=10)
loss = fluid.layers.cross_entropy(input=fc, label=label)
loss = fluid.layers.mean(loss)
return loss
class TestGraph(unittest.TestCase):
def test_graph_functions(self):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
loss = residual_block(2)
opt = fluid.optimizer.Adam(learning_rate=0.001)
opt.minimize(loss)
graph = IrGraph(core.Graph(main.desc), for_test=False)
marked_nodes = set()
for op in graph.all_ops():
if op.name().find('conv2d') > -1:
marked_nodes.add(op)
graph.draw('.', 'residual', marked_nodes)
self.assertFalse(graph.has_circle())
self.assertEqual(graph.graph_num(), 1)
nodes = graph.topology_sort()
self.assertEqual(len(nodes), len(graph.all_ops()))
nodes_map = graph.build_adjacency_list()
self.assertEqual(len(nodes_map), len(graph.all_ops()))
nodes_num = len(graph.all_nodes())
graph.safe_remove_nodes(marked_nodes)
self.assertEqual(len(graph.all_nodes()), nodes_num - len(marked_nodes))
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/contrib/slim/unitest/test_quantization_pass.py → python/paddle/fluid/contrib/slim/tests/test_quantization_pass.py
浏览文件 @ 2ccbcb15
......@@ -17,9 +17,12 @@ import random
import numpy as np
import paddle.fluid as fluid
import six
from paddle.fluid.framework import Program
import paddle
from paddle.fluid.framework import IrGraph
from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
from paddle.fluid.contrib.slim.quantization import QuantizationFreezePass
from paddle.fluid.contrib.slim.quantization import ConvertToInt8Pass
from paddle.fluid.contrib.slim.quantization import TransformForMobilePass
from paddle.fluid import core
......@@ -65,6 +68,28 @@ def residual_block(num):
return loss
def conv_net(img, label):
conv_pool_1 = fluid.nets.simple_img_conv_pool(
input=img,
filter_size=5,
num_filters=20,
pool_size=2,
pool_stride=2,
act="relu")
conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=50,
pool_size=2,
pool_stride=2,
act="relu")
prediction = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=prediction, label=label)
avg_loss = fluid.layers.mean(loss)
return avg_loss
class TestQuantizationTransformPass(unittest.TestCase):
def setUp(self):
self.quantizable_op_and_inputs = {
......@@ -171,5 +196,177 @@ class TestQuantizationTransformPass(unittest.TestCase):
self.residual_block_quant('range_abs_max')
class TestQuantizationFreezePass(unittest.TestCase):
def freeze_graph(self, use_cuda, seed, quant_type):
def build_program(main, startup, is_test):
main.random_seed = seed
startup.random_seed = seed
with fluid.unique_name.guard():
with fluid.program_guard(main, startup):
img = fluid.layers.data(
name='image', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(
name='label', shape=[1], dtype='int64')
loss = conv_net(img, label)
if not is_test:
opt = fluid.optimizer.Adam(learning_rate=0.001)
opt.minimize(loss)
return [img, label], loss
random.seed(0)
np.random.seed(0)
main = fluid.Program()
startup = fluid.Program()
test_program = fluid.Program()
feeds, loss = build_program(main, startup, False)
build_program(test_program, startup, True)
test_program = test_program.clone(for_test=True)
main_graph = IrGraph(core.Graph(main.desc), for_test=False)
test_graph = IrGraph(core.Graph(test_program.desc), for_test=True)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
scope = fluid.Scope()
with fluid.scope_guard(scope):
exe.run(startup)
transform_pass = QuantizationTransformPass(
scope=scope, program_exe=exe, activation_quantize_type=quant_type)
transform_pass.apply(main_graph)
transform_pass.apply(test_graph)
dev_name = '_gpu_' if use_cuda else '_cpu_'
marked_nodes = set()
for op in main_graph.all_ops():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
main_graph.draw('.', 'main' + dev_name + quant_type, marked_nodes)
marked_nodes = set()
for op in test_graph.all_ops():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test' + dev_name + quant_type, marked_nodes)
quantized_main_program = main_graph.to_program()
quantized_test_program = test_graph.to_program()
iters = 5
batch_size = 8
#train_exe = fluid.ParallelExecutor(
# main_program=quantized_main_program,
# use_cuda=bool(use_cuda),
# loss_name=loss.name,
# scope=scope)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=batch_size)
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=batch_size)
feeder = fluid.DataFeeder(feed_list=feeds, place=place)
with fluid.scope_guard(scope):
for _ in range(iters):
data = next(train_reader())
loss_v = exe.run(program=quantized_main_program,
feed=feeder.feed(data),
fetch_list=[loss])
#loss_v = train_exe.run(feed=feeder.feed(data),
# fetch_list=[loss.name])
#print('{}: {}'.format('loss' + dev_name + quant_type, loss_v))
test_data = next(test_reader())
with fluid.program_guard(quantized_test_program):
w_var = fluid.framework._get_var('conv2d_1.w_0.quantized',
quantized_test_program)
# Testing
with fluid.scope_guard(scope):
test_loss1, w_quant = exe.run(program=quantized_test_program,
feed=feeder.feed(test_data),
fetch_list=[loss, w_var])
# Freeze graph for inference, but the weight of fc/conv is still float type.
freeze_pass = QuantizationFreezePass(scope=scope, place=place)
freeze_pass.apply(test_graph)
marked_nodes = set()
for op in test_graph.all_ops():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_freeze' + dev_name + quant_type,
marked_nodes)
server_program = test_graph.to_program()
with fluid.scope_guard(scope):
test_loss2, = exe.run(program=server_program,
feed=feeder.feed(test_data),
fetch_list=[loss])
self.assertAlmostEqual(test_loss1, test_loss2, delta=5e-3)
#print('{}: {}'.format('test_loss1' + dev_name + quant_type, test_loss1))
#print('{}: {}'.format('test_loss2' + dev_name + quant_type, test_loss2))
w_freeze = np.array(scope.find_var('conv2d_1.w_0').get_tensor())
# Maybe failed, this is due to the calculation precision
# self.assertAlmostEqual(np.sum(w_freeze), np.sum(w_quant))
#print('{}: {}'.format('w_freeze' + dev_name + quant_type,
# np.sum(w_freeze)))
#print('{}: {}'.format('w_quant' + dev_name + quant_type,
# np.sum(w_quant)))
# Convert parameter to 8-bit.
convert_int8_pass = ConvertToInt8Pass(scope=scope, place=place)
convert_int8_pass.apply(test_graph)
marked_nodes = set()
for op in test_graph.all_ops():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_int8' + dev_name + quant_type, marked_nodes)
server_program_int8 = test_graph.to_program()
# Save the 8-bit parameter and model file.
with fluid.scope_guard(scope):
fluid.io.save_inference_model('server_int8' + dev_name + quant_type,
['image', 'label'], [loss], exe,
server_program_int8)
# Test whether the 8-bit parameter and model file can be loaded successfully.
[infer, feed, fetch] = fluid.io.load_inference_model(
'server_int8' + dev_name + quant_type, exe)
# Check the loaded 8-bit weight.
w_8bit = np.array(scope.find_var('conv2d_1.w_0.int8').get_tensor())
self.assertEqual(w_8bit.dtype, np.int8)
self.assertEqual(np.sum(w_8bit), np.sum(w_freeze))
#print('{}: {}'.format('w_8bit' + dev_name + quant_type, np.sum(w_8bit)))
#print('{}: {}'.format('w_freeze' + dev_name + quant_type,
# np.sum(w_freeze)))
mobile_pass = TransformForMobilePass()
mobile_pass.apply(test_graph)
marked_nodes = set()
for op in test_graph.all_ops():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_mobile' + dev_name + quant_type,
marked_nodes)
mobile_program = test_graph.to_program()
with fluid.scope_guard(scope):
fluid.io.save_inference_model('mobile_int8' + dev_name + quant_type,
['image', 'label'], [loss], exe,
mobile_program)
def test_freeze_graph_cuda_dynamic(self):
if fluid.core.is_compiled_with_cuda():
with fluid.unique_name.guard():
self.freeze_graph(True, seed=1, quant_type='abs_max')
def test_freeze_graph_cpu_dynamic(self):
with fluid.unique_name.guard():
self.freeze_graph(False, seed=2, quant_type='abs_max')
def test_freeze_graph_cuda_static(self):
if fluid.core.is_compiled_with_cuda():
with fluid.unique_name.guard():
self.freeze_graph(True, seed=1, quant_type='range_abs_max')
def test_freeze_graph_cpu_static(self):
with fluid.unique_name.guard():
self.freeze_graph(False, seed=2, quant_type='range_abs_max')
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/contrib/tests/CMakeLists.txt
浏览文件 @ 2ccbcb15
......@@ -6,5 +6,9 @@ if(APPLE OR WIN32 OR NOT WITH_MKL)
endif()
foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py)
if(src MATCHES "test_calibration")
py_test(${src} SRCS ${src}.py ENVS FLAGS_use_mkldnn=true)
else()
py_test(${src} SRCS ${src}.py)
endif()
endforeach()
python/paddle/fluid/contrib/tests/test_calibration.py
浏览文件 @ 2ccbcb15
......@@ -199,7 +199,6 @@ class TestCalibrationForResnet50(unittest.TestCase):
def run_program(self, model_path, generate_int8=False, algo='direct'):
image_shape = [3, 224, 224]
os.environ['FLAGS_use_mkldnn'] = 'True'
fluid.memory_optimize(fluid.default_main_program())
......@@ -241,9 +240,6 @@ class TestCalibrationForResnet50(unittest.TestCase):
label = label.reshape([-1, 1])
running_program = calibrator.sampling_program.clone(
) if generate_int8 else infer_program.clone()
for op in running_program.current_block().ops:
if op.has_attr("use_mkldnn"):
op._set_attr("use_mkldnn", True)
t1 = time.time()
_, acc1, _ = exe.run(
......
python/paddle/fluid/contrib/tests/test_quantize_transpiler.py
浏览文件 @ 2ccbcb15
......@@ -204,9 +204,11 @@ class TestQuantizeTranspiler(unittest.TestCase):
build_program(test_program, startup, True)
test_program = test_program.clone(for_test=True)
quant_transpiler = QuantizeTranspiler()
quant_transpiler.training_transpile(main)
quant_transpiler.training_transpile(test_program)
quant_type = 'range_abs_max' # 'range_abs_max' or 'abs_max'
quant_transpiler = QuantizeTranspiler(
activation_quantize_type=quant_type)
quant_transpiler.training_transpile(main, startup)
quant_transpiler.training_transpile(test_program, startup)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
......
python/paddle/fluid/framework.py
浏览文件 @ 2ccbcb15
......@@ -16,6 +16,8 @@ from __future__ import print_function
import collections
from collections import defaultdict
from collections import Iterable
import contextlib
from .wrapped_decorator import signature_safe_contextmanager
import os
import re
......@@ -1529,12 +1531,16 @@ class Block(object):
class IrGraph(object):
"""
IrGraph uses core.Graph as the delegation to accomplish the manipulation.
Python IrGraph. Beneath it is a core.Graph, which is used for
create a c++ Ir Pass Graph. An IrGraph is just a graph view of
a Program. In an IrGraph, both Variables and Operators are graph
nodes.
"""
def __init__(self, graph, for_test=False):
"""
Construct the IrGraph using core.Graph.
Construct an IrGraph using core.Graph.
Args:
graph(core.Graph): C++ Graph.
for_test(bool): True for the test graph and false for the train graph.
......@@ -1545,23 +1551,81 @@ class IrGraph(object):
self._for_test = for_test
def is_test(self):
"""
If the graph is used for testing, the function returns true. Otherwise, returns false.
"""
return self._for_test
def all_parameters(self):
param_nodes = set()
for node in self.graph.nodes():
if node.is_var() and node.var() is not None and node.var(
).persistable():
param_nodes.add(node)
return param_nodes
def all_nodes(self):
"""
Return all nodes included in the graph as a set.
"""
return {node for node in self.graph.nodes()}
def all_vars(self):
"""
Return all variable nodes included in the graph as a set.
"""
return {node for node in self.graph.nodes() if node.is_var()}
def all_persistable_vars(self):
"""
Return all persistable variable nodes included in the graph as a set.
"""
persistable_nodes = set()
for node in self.graph.nodes():
if node.is_var() and node.var() is not None and node.var(
).persistable():
persistable_nodes.add(node)
return persistable_nodes
def all_ops(self):
"""
Return all operator nodes included in the graph as a set.
"""
return {node for node in self.graph.nodes() if node.is_op()}
def var_node(self, name):
"""
Get a variable node by name from the graph.
Args:
name(str): the name of the variable node.
Raises:
ValueError: The If input's type is not str, or this graph
doesn't have a variable with the giving name.
Returns:
core.Node: the variable node with the giving name.
"""
if not isinstance(name, six.string_types):
raise TypeError(
"var require string as parameter, but get %s instead." %
(type(name)))
target_var_node = None
var_nodes = self.all_vars()
for var_node in var_nodes:
if var_node.name() == name:
target_var_node = var_node
if target_var_node is None:
raise ValueError("var_node %s not in this graph" % name)
return target_var_node
def create_param_node(self, name, var_type, shape, var_dtype):
"""
Create a persistable variable node in the graph. In IrGraph,
it can not distinguish between persistable variables and parameters.
Args:
name(str): the name of the persistable variable node.
vart_type(core.VarDesc.VarType): the type of the persistable variable node.
shape(list): the shape of the persistable variable node.
var_dtype(core.VarDesc.VarType): the data type of the persistable variable node.
Returns:
core.Node: the created persistable variable node.
"""
var_desc = core.VarDesc(name)
var_desc.set_type(var_type)
var_desc.set_shape(shape)
......@@ -1570,6 +1634,20 @@ class IrGraph(object):
return self.graph.create_var_node(var_desc)
def create_var_node(self, name, var_type, shape, var_dtype):
"""
Create a variable node in the graph. The created variable node is
not persistable.
Args:
name(str): the name of the variable node.
vart_type(core.VarDesc.VarType): the type of the variable node.
shape(list): the shape of the variable node.
var_dtype(core.VarDesc.VarType): the data type of the variable node.
Returns:
core.Node: the created variable node.
"""
var_desc = core.VarDesc(name)
var_desc.set_type(var_type)
var_desc.set_shape(shape)
......@@ -1577,19 +1655,41 @@ class IrGraph(object):
return self.graph.create_var_node(var_desc)
def create_var_node_from_desc(self, var_desc):
"""
Create a variable node by using an existing VarDesc in the graph.
Depend on the giving VarDesc, the created variable node may be persistable.
Args:
var_desc(core.VarDesc): the giving variable description.
Returns:
core.Node: the created variable node.
"""
return self.graph.create_var_node(var_desc)
def create_op_node(self, op_type, attrs, inputs, outputs):
"""
Create a operator node in the graph.
Args:
op_type(str): the type of the operator node.
attrs(dict): the attributes of the operator node.
inputs(dict): the inputs of the operator node.
outputs(dict): the outpus of the operator node.
Returns:
core.Node: the created operator node.
"""
op_desc = core.OpDesc()
op_desc.set_type(op_type)
for attr, value in attrs.iteritems():
for attr, value in six.iteritems(attrs):
self._update_desc_attr(op_desc, attr, value)
for input_name, var_nodes in inputs.iteritems():
for input_name, var_nodes in six.iteritems(inputs):
if not isinstance(var_nodes, list):
var_nodes = [var_nodes]
op_desc.set_input(input_name,
[var_node.name() for var_node in var_nodes])
for output_name, var_nodes in outputs.iteritems():
for output_name, var_nodes in six.iteritems(outputs):
if not isinstance(var_nodes, list):
var_nodes = [var_nodes]
op_desc.set_output(output_name,
......@@ -1597,11 +1697,29 @@ class IrGraph(object):
return self.graph.create_op_node(op_desc)
def create_op_node_from_desc(self, op_desc):
"""
Create a operator node by using an existing OpDesc in the graph.
Args:
op_desc(core.VarDesc): the giving operator description.
Returns:
core.Node: the created operator node.
"""
return self.graph.create_op_node(op_desc)
def update_input_link(self, old_input_node, new_input_node, op_node):
assert old_input_node in self.graph.nodes() and new_input_node in self.graph.nodes() and \
op_node in self.graph.nodes(), 'Th three arguments must be in the graph nodes.'
"""
Update the input's link of a operator node.
Args:
old_input_node(core.Node): the old input node of the giving op_node.
new_input_node(core.Node): the new input node of the giving op_node.
op_node(core.Node): the operator node that is needed to update input's link.
"""
assert old_input_node in self.graph.nodes() and new_input_node in \
self.graph.nodes() and op_node in self.graph.nodes(), \
'The three arguments(old_input_node&new_input_node&op_node) must be in the graph nodes.'
old_input_node.outputs_remove(op_node)
op_node.inputs_remove(old_input_node)
new_input_node.outputs_append(op_node)
......@@ -1609,17 +1727,85 @@ class IrGraph(object):
op_node.op()._rename_input(old_input_node.name(), new_input_node.name())
def link_to(self, node_in, node_out):
"""
Connect two nodes.
Args:
node_in(core.Node): the input node.
node_out(core.Node): the output node.
"""
assert node_in in self.graph.nodes() and node_out in self.graph.nodes(), \
'Th two arguments must be in the graph nodes.'
'The two arguments(node_in&node_out) must be in the graph nodes.'
node_in.outputs_append(node_out)
node_out.inputs_append(node_in)
def safe_remove_nodes(self, remove_nodes):
"""
Remove nodes safely since links connected to these removed nodes are
also removed.
Args:
remove_nodes(set): the nodes prepared to be removed.
"""
if not isinstance(remove_nodes, set):
remove_nodes = set(remove_nodes)
if isinstance(remove_nodes, Iterable):
remove_nodes = set(remove_nodes)
else:
remove_nodes = {remove_nodes}
core.graph_safe_remove_nodes(self.graph, remove_nodes)
def draw(self, save_path, name, marked_nodes=None):
def has_circle(self):
"""
Check if the graph has a circle.
Returns:
bool: True if the graph has a circle else False.
"""
return core.has_circle(self.graph)
def graph_num(self):
"""
Count the number of unconnected graphs in this graph.
Returns:
int: the number of unconnected graphs.
"""
return core.graph_num(self.graph)
def topology_sort(self):
"""
Perform the topology sort operation on the graph.
Notes: the `graph` cannot contain a circle.
Returns:
set(core.Node): nodes in topology order.
"""
return core.topology_sort(self.graph)
def build_adjacency_list(self):
"""
Build an adjacency list of operations for the `graph`.
Returns:
dict{core.Node: set(core.Node)}: the adjacency list.
"""
return core.build_adjacency_list(self.graph)
def draw(self, save_path, name, marked_nodes=None, remove_ctr_var=True):
"""
Draw the graph. If `dot` command is installed, the drawn graph
will be saved as pdf file type, otherwise dot file type is used.
Args:
save_path(str): the save path of drawn graph.
name(str): the name of drawn graph.
marked_nodes(set(core.Node)): nodes that are needed to be marked.
Default value is None.
remove_ctr_var(bool): If it is set True, all control variable nodes
in the graph will be removed. Default value is True.
"""
def _convert_to_pdf(dot_file_path):
pdf_save_path = os.path.splitext(dot_file_path)[0] + '.pdf'
exited_code = subprocess.call('dot -Tpdf ' + dot_file_path \
......@@ -1629,15 +1815,17 @@ class IrGraph(object):
print('The {} is saved as the dot filetype.'.format(
dot_file_path))
remove_ctr_vars = set()
if remove_ctr_var:
remove_ctr_vars = set()
for node in self.graph.nodes():
if node.is_ctrl_var():
remove_ctr_vars.add(node)
self.safe_remove_nodes(remove_ctr_vars)
ops_num = 0
for node in self.graph.nodes():
if node.is_ctrl_var():
remove_ctr_vars.add(node)
elif node.is_op():
if node.is_op():
ops_num += 1
print('Total ops num = {}.'.format(ops_num))
self.safe_remove_nodes(remove_ctr_vars)
if marked_nodes is not None:
if not isinstance(marked_nodes, set):
marked_nodes = set(marked_nodes)
......@@ -1652,10 +1840,20 @@ class IrGraph(object):
_convert_to_pdf(viz_dot_path)
def to_program(self):
"""
Convert the graph into a Program.
Notes: When the graph includes backward operator nodes, the
conversion process may be failed. Usually, this function is
only used to convert a test graph.
Returns:
Program: a program converted from the graph.
"""
convert_pass = core.get_pass('graph_to_program_pass')
convert_pass.set('program', Program().desc)
desc = core.ProgramDesc()
convert_pass.set_not_owned('program', desc)
convert_pass.apply(self.graph)
desc = convert_pass.get_program('program')
program = Program._construct_from_desc(desc)
return program
......
python/paddle/fluid/imperative/layers.py
浏览文件 @ 2ccbcb15
......@@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import contextlib
import sys
import numpy as np
......@@ -30,31 +31,45 @@ class Layer(core.Layer):
def __init__(self, dtype=core.VarDesc.VarType.FP32, name=None):
self._built = False
self._dtype = dtype
self._parameters = collections.OrderedDict()
self._sub_layers = collections.OrderedDict()
def parameters(self, include_sublayers=True):
"""Returns a list of Parameters from current and sub-layers.
Args:
include_sublayers: If true, also include the parameters from
sublayers.
Returns a list of Parameters.
"""
ret = [p for p in self._parameters.values()]
if include_sublayers:
for l in self._sub_layers.values():
for p in l.parameters(include_sublayers):
ret.append(p)
return ret
def sublayers(self, include_sublayers=True):
"""Returns a list of sub layers.
def parameters(self):
params = []
for key in self.__dict__.keys():
value = self.__dict__[key]
if isinstance(value, framework.Parameter):
params.append(value)
elif isinstance(value, core.Layer):
params.extend(value.parameters())
elif isinstance(value, collections.Container):
if len(value) == 0:
continue
if isinstance(value[0], framework.Parameter):
params.extend(value)
elif isinstance(value[0], core.Layer):
for v in value:
params.extend(v.parameters())
return params
Args:
include_sublayers: If true, also include the layers from sublayers.
Returns a list of sub layers.
"""
ret = [l for l in self._sub_layers.values()]
if include_sublayers:
for l in self._sub_layers.values():
for sub_l in l.sublayers(include_sublayers):
ret.append(sub_l)
return ret
def clear_gradients(self):
for p in self.parameters():
p._clear_gradient()
def _build_once(self, inputs):
def _build_once(self, *args):
pass
def __call__(self, *inputs):
......@@ -71,6 +86,66 @@ class Layer(core.Layer):
def backward(self, *inputs):
raise ValueError("Layer shouldn't implement backward")
def add_sublayer(self, name, sublayer):
"""Adds a sub Layer instance.
Added sublayer can be access like self.name.
Args:
name: name of this sublayer.
sublayer: an instance of Layer.
Returns:
the sublayer passed in.
"""
assert isinstance(sublayer, core.Layer)
self._sub_layers[name] = sublayer
return sublayer
def add_parameter(self, name, parameter):
"""Adds a Parameter instance.
Added parameter can be access like self.name.
Args:
name: name of this sublayer.
parameter: an instance of Parameter.
Returns:
the parameter passed in.
"""
assert isinstance(parameter, framework.Parameter)
self._parameters[name] = parameter
return parameter
def __getattr__(self, name):
if name in self._parameters:
return self._parameters[name]
elif name in self._sub_layers:
return self._sub_layers[name]
def __setattr__(self, name, value):
if isinstance(value, framework.Parameter):
params = self.__dict__.get('_parameters', None)
if params is None:
raise ValueError(
"super(YourLayer, self).__init__() should be called first")
params[name] = value
elif isinstance(value, core.Layer):
layers = self.__dict__.get('_sub_layers', None)
if layers is None:
raise ValueError(
"super(YourLayer, self).__init__() should be called first")
layers[name] = value
else:
object.__setattr__(self, name, value)
def __delattr__(self, name):
if name in self._parameters:
del self._parameters[name]
elif name in self._sub_layers:
del self._sub_layers[name]
else:
object.__delattr__(self, name)
class PyLayer(core.PyLayer):
"""Layers composed of user-defined python codes."""
......
python/paddle/fluid/imperative/nn.py
浏览文件 @ 2ccbcb15
......@@ -225,9 +225,6 @@ class FC(layers.Layer):
act=act,
name=name)
def parameters(self):
return [self._w, self._b]
def _build_once(self, input):
input_shape = input.shape
param_shape = [
......@@ -478,9 +475,6 @@ class Embedding(layers.Layer):
dtype=self._dtype,
is_bias=False)
def parameters(self):
return [self._w]
def forward(self, input):
out = self._helper.create_variable_for_type_inference(self._dtype)
self._helper.append_op(
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 2ccbcb15
......@@ -3236,7 +3236,7 @@ def group_norm(input,
# create output
mean_out = helper.create_variable(dtype=dtype, stop_gradient=True)
variance_out = helper.create_variable(dtype=dtype, stop_gradient=True)
group_norm_out = helper.create_variable(dtype)
group_norm_out = helper.create_variable(dtype=dtype)
helper.append_op(
type="group_norm",
......@@ -5936,13 +5936,10 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None):
than :attr:`shape`.
act (str): The non-linear activation to be applied to the reshaped tensor
variable.
inplace(bool): Must use :attr:`False` if :attr:`x` is used in multiple
operators. If this flag is set :attr:`True`, reuse input
:attr:`x` to reshape, which will change the shape of
tensor variable :attr:`x` and might cause errors when
:attr:`x` is used in multiple operators. If :attr:`False`,
preserve the shape :attr:`x` and create a new output tensor
variable whose data is copied from input x but reshaped.
inplace(bool): If ``inplace`` is `True`, the input and output of ``layers.reshape``
are the same variable, otherwise, the input and output of
``layers.reshape`` are different variables. Note that if :attr:`x`
is more than one layer's input, ``inplace`` must be :attr:`False`.
name (str): The name of this layer. It is optional.
Returns:
......@@ -8335,6 +8332,46 @@ def stack(x, axis=0):
If :code:`axis` < 0, it would be replaced with :code:`axis+rank(x[0])+1`.
If :code:`axis` is None, it would be replaced with 0.
For Example:
.. code-block:: text
Case 1:
Input:
x[0].data = [ [1.0 , 2.0 ] ]
x[0].dims = [1, 2]
x[1].data = [ [3.0 , 4.0 ] ]
x[1].dims = [1, 2]
x[2].data = [ [5.0 , 6.0 ] ]
x[2].dims = [1, 2]
Attrs:
axis = 0
Output:
Out.data =[ [ [1.0, 2.0] ],
[ [3.0, 4.0] ],
[ [5.0, 6.0] ] ]
Out.dims = [3, 1, 2]
Case 2:
Given
x[0].data = [ [1.0 , 2.0 ] ]
x[0].dims = [1, 2]
x[1].data = [ [3.0 , 4.0 ] ]
x[1].dims = [1, 2]
x[2].data = [ [5.0 , 6.0 ] ]
x[2].dims = [1, 2]
Attrs:
axis = 1 or axis = -2
Output:
Out.data =[ [ [1.0, 2.0]
[3.0, 4.0]
[5.0, 6.0] ] ]
Out.dims = [1, 3, 2]
Args:
x (Variable|list(Variable)|tuple(Variable)): Input variables.
axis (int|None): The axis along which all inputs are stacked.
......
python/paddle/fluid/layers/tensor.py
浏览文件 @ 2ccbcb15
......@@ -567,7 +567,7 @@ def ones(shape, dtype, force_cpu=False):
It also sets *stop_gradient* to True.
Args:
shape(tuple|list|None): Shape of output tensor
shape(tuple|list): Shape of output tensor
dtype(np.dtype|core.VarDesc.VarType|str): Data type of output tensor
Returns:
......@@ -578,6 +578,10 @@ def ones(shape, dtype, force_cpu=False):
data = fluid.layers.ones(shape=[1], dtype='int64')
"""
assert isinstance(shape, list) or isinstance(
shape, tuple), "The shape's type should be list or tuple."
assert reduce(lambda x, y: x * y,
shape) > 0, "The shape is invalid: %s." % (str(shape))
return fill_constant(value=1.0, **locals())
......
python/paddle/fluid/tests/demo/file_reader/convert_data_to_recordio.py
浏览文件 @ 2ccbcb15
......@@ -16,7 +16,6 @@ from __future__ import print_function
import sys
import paddle.fluid as fluid
import paddle.v2 as paddle
def load_vocab(filename):
......
python/paddle/fluid/tests/demo/pyreader.py
浏览文件 @ 2ccbcb15
......@@ -20,7 +20,6 @@ import six
import paddle
import paddle.dataset.mnist as mnist
import paddle.fluid as fluid
import paddle.v2
def network(is_train):
......@@ -72,7 +71,7 @@ def main():
use_cuda=use_cuda, share_vars_from=trainer, main_program=test_prog)
train_reader.decorate_paddle_reader(
paddle.v2.reader.shuffle(
paddle.reader.shuffle(
paddle.batch(mnist.train(), 512), buf_size=8192))
test_reader.decorate_paddle_reader(paddle.batch(mnist.test(), 512))
......
python/paddle/fluid/tests/unittests/test_base_layer.py 0 → 100644
浏览文件 @ 2ccbcb15
# 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 unittest
import numpy as np
import paddle.fluid as fluid
from paddle.fluid.layer_helper import LayerHelper
class L1(fluid.imperative.Layer):
def __init__(self):
super(L1, self).__init__()
self._helper = LayerHelper(
'MyLayer',
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.1)))
self.w1 = self._helper.create_parameter(
attr=self._helper.param_attr,
shape=[2, 2],
dtype='float32',
is_bias=False)
self.w2 = self._helper.create_parameter(
attr=self._helper.param_attr,
shape=[2, 2],
dtype='float32',
is_bias=False)
def forward(self):
return self.w1 + self.w2
class L2(fluid.imperative.Layer):
def __init__(self):
super(L2, self).__init__()
self.layer1 = L1()
self.layer2 = L1()
def forward(self):
return self.layer1() + self.layer2()
class L3(fluid.imperative.Layer):
def __init__(self):
super(L3, self).__init__()
self.layer1 = L2()
self.layer2 = L2()
def forward(self):
return self.layer1() + self.layer2()
class TestBaseLayer(unittest.TestCase):
def test_one_level(self):
with fluid.imperative.guard():
l = L1()
ret = l()
self.assertEqual(l.w1.name, "MyLayer_0.w_0")
self.assertEqual(l.w2.name, "MyLayer_0.w_1")
self.assertTrue(np.allclose(ret._numpy(), 0.2 * np.ones([2, 2])))
def test_three_level(self):
with fluid.imperative.guard():
l = L3()
ret = l()
self.assertTrue(np.allclose(ret._numpy(), 0.8 * np.ones([2, 2])))
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_dist_transpiler.py
浏览文件 @ 2ccbcb15
......@@ -22,6 +22,9 @@ import six
import unittest
import numpy as np
import gc
gc.set_debug(gc.DEBUG_COLLECTABLE)
import paddle.fluid as fluid
......@@ -99,6 +102,12 @@ class TranspilerTest(unittest.TestCase):
with fluid.unique_name.guard():
with fluid.program_guard(main, startup):
self.transpiler_test_impl()
# NOTE: run gc.collect to eliminate pybind side objects to
# prevent random double-deallocate when inherited in python.
del self.transpiler
del main
del startup
gc.collect()
class TestBasicModel(TranspilerTest):
......@@ -797,6 +806,7 @@ class TestNCCL2Transpile(TranspilerTest):
print([op.type for op in startup.global_block().ops])
self.assertEqual(startup.global_block().ops[-1].type, "gen_nccl_id")
self.assertIsNotNone(startup.global_block().vars.get("NCCLID"))
gc.collect()
else:
pass
......
python/paddle/fluid/tests/unittests/test_imperative.py
浏览文件 @ 2ccbcb15
......@@ -333,6 +333,18 @@ class TestImperative(unittest.TestCase):
self.assertTrue(np.allclose(dy_out, static_out))
self.assertTrue(np.allclose(dy_grad, static_grad))
params = mlp.parameters(True)
self.assertEqual("FC_0.w_0", params[0].name)
self.assertEqual("FC_0.b_0", params[1].name)
self.assertEqual("FC_1.w_0", params[2].name)
self.assertEqual("FC_1.b_0", params[3].name)
self.assertEqual(len(params), 4)
sublayers = mlp.sublayers(True)
self.assertEqual(mlp._fc1, sublayers[0])
self.assertEqual(mlp._fc2, sublayers[1])
self.assertEqual(len(sublayers), 2)
def test_rnn(self):
np_inp = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0],
[10.0, 11.0, 12.0]])
......
python/paddle/fluid/tests/unittests/test_imperative_gan.py
浏览文件 @ 2ccbcb15
......@@ -33,9 +33,6 @@ class Discriminator(fluid.imperative.Layer):
self._fc1 = FC(size=32, act='elu', name="d_fc1")
self._fc2 = FC(size=1, name="d_fc2")
def parameters(self):
return self._fc1.parameters() + self._fc2.parameters()
def forward(self, inputs):
x = self._fc1(inputs)
return self._fc2(x)
......@@ -48,10 +45,6 @@ class Generator(fluid.imperative.Layer):
self._fc2 = FC(size=64, act='elu', name="g_fc2")
self._fc3 = FC(size=1, name="g_fc3")
def parameters(self):
return self._fc1.parameters() + self._fc2.parameters(
) + self._fc3.parameters()
def forward(self, inputs):
x = self._fc1(inputs)
x = self._fc2(x)
......
python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py
浏览文件 @ 2ccbcb15
......@@ -75,16 +75,6 @@ class SimpleLSTMRNN(fluid.imperative.Layer):
self.hidden_array.append(pre_hidden)
self.cell_array.append(pre_cell)
def parameters(self):
parameters = list()
for param in self.weight_1_arr:
parameters.append(param)
for param in self.weight_2_arr:
parameters.append(param)
for bias in self.bias_arr:
parameters.append(bias)
return parameters
def forward(self, input_embedding, init_hidden=None, init_cell=None):
res = []
for index in range(self._num_steps):
......@@ -177,12 +167,6 @@ class PtbModel(fluid.imperative.Layer):
def _build_once(self, input, label, init_hidden, init_cell):
pass
def parameters(self):
parameters = self.simple_lstm_rnn.parameters() + [
self.softmax_weight, self.softmax_bias
] + self.embedding.parameters()
return parameters
def forward(self, input, label, init_hidden, init_cell):
init_h = fluid.layers.reshape(
......
python/paddle/fluid/tests/unittests/test_imperative_resnet.py
浏览文件 @ 2ccbcb15
......@@ -21,7 +21,6 @@ import paddle
import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.optimizer import SGDOptimizer
from paddle.fluid.imperative.nn import Conv2D, Pool2D, BatchNorm, FC
from paddle.fluid.imperative.base import to_variable
from test_imperative_base import new_program_scope
......@@ -173,11 +172,13 @@ class ResNet(fluid.imperative.Layer):
for block in range(len(depth)):
shortcut = False
for i in range(depth[block]):
bottleneck_block = BottleneckBlock(
num_channels=num_channels,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
shortcut=shortcut)
bottleneck_block = self.add_sublayer(
'bb_%d_%d' % (block, i),
BottleneckBlock(
num_channels=num_channels,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
shortcut=shortcut))
num_channels = bottleneck_block._num_channels_out
self.bottleneck_block_list.append(bottleneck_block)
shortcut = True
......@@ -223,8 +224,7 @@ class TestImperativeResnet(unittest.TestCase):
batch_size=batch_size)
dy_param_init_value = {}
for param in fluid.default_main_program().global_block(
).all_parameters():
for param in resnet.parameters():
dy_param_init_value[param.name] = param._numpy()
for batch_id, data in enumerate(train_reader()):
......@@ -247,16 +247,14 @@ class TestImperativeResnet(unittest.TestCase):
dy_out = avg_loss._numpy()
if batch_id == 0:
for param in fluid.default_main_program().global_block(
).all_parameters():
for param in resnet.parameters():
if param.name not in dy_param_init_value:
dy_param_init_value[param.name] = param._numpy()
avg_loss._backward()
dy_grad_value = {}
for param in fluid.default_main_program().global_block(
).all_parameters():
for param in resnet.parameters():
if not param.stop_gradient:
np_array = np.array(param._ivar._grad_ivar().value()
.get_tensor())
......@@ -267,8 +265,7 @@ class TestImperativeResnet(unittest.TestCase):
resnet.clear_gradients()
dy_param_value = {}
for param in fluid.default_main_program().global_block(
).all_parameters():
for param in resnet.parameters():
dy_param_value[param.name] = param._numpy()
with new_program_scope():
......@@ -349,6 +346,7 @@ class TestImperativeResnet(unittest.TestCase):
self.assertTrue(np.allclose(static_out, dy_out))
self.assertEqual(len(dy_param_init_value), len(static_param_init_value))
for key, value in six.iteritems(static_param_init_value):
self.assertTrue(np.allclose(value, dy_param_init_value[key]))
self.assertTrue(np.isfinite(value.all()))
......
python/paddle/fluid/transpiler/memory_optimization_transpiler.py
浏览文件 @ 2ccbcb15
......@@ -355,6 +355,10 @@ class ControlFlowGraph(object):
is_forward).dtype()
cache_dtype = self._find_var(block_desc, cache_var,
is_forward).dtype()
if x_dtype != cache_dtype:
if PRINT_LOG:
print("x_dtype and cache_dtype are different")
continue
if not compare_shape(x_shape, cache_shape, level):
continue
......
python/paddle/utils/dump_config.py 已删除 100644 → 0
浏览文件 @ a3a3d3d8
# Copyright (c) 2016 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 paddle.trainer.config_parser import parse_config
from paddle.proto import TrainerConfig_pb2
import sys
__all__ = []
if __name__ == '__main__':
whole_conf = False
binary = False
if len(sys.argv) == 2:
conf = parse_config(sys.argv[1], '')
elif len(sys.argv) == 3:
conf = parse_config(sys.argv[1], sys.argv[2])
elif len(sys.argv) == 4:
conf = parse_config(sys.argv[1], sys.argv[2])
if sys.argv[3] == '--whole':
whole_conf = True
elif sys.argv[3] == '--binary':
binary = True
else:
raise RuntimeError()
assert isinstance(conf, TrainerConfig_pb2.TrainerConfig)
if whole_conf:
print(conf)
else:
if binary:
sys.stdout.write(conf.model_config.SerializeToString())
else:
print(conf.model_config)
python/paddle/utils/dump_v2_config.py 已删除 100644 → 0
浏览文件 @ a3a3d3d8
# Copyright (c) 2016 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 collections
from paddle.trainer_config_helpers.layers import LayerOutput
from paddle.v2.layer import parse_network
from paddle.proto import TrainerConfig_pb2
__all__ = ["dump_v2_config"]
def dump_v2_config(topology, save_path, binary=False):
""" Dump the network topology to a specified file.
This function is only used to dump network defined by using PaddlePaddle V2
APIs. This function will NOT dump configurations related to PaddlePaddle
optimizer.
:param topology: The output layers (can be more than one layers given in a
Python List or Tuple) of the entire network. Using the
specified layers (if more than one layer is given) as root,
traversing back to the data layer(s), all the layers
connected to the specified output layers will be dumped.
Layers not connceted to the specified will not be dumped.
:type topology: LayerOutput|List|Tuple
:param save_path: The path to save the dumped network topology.
:type save_path: str
:param binary: Whether to dump the serialized network topology or not.
The default value is false. NOTE that, if you call this
function to generate network topology for PaddlePaddle C-API,
a serialized version of network topology is required. When
using PaddlePaddle C-API, this flag MUST be set to True.
:type binary: bool
"""
if isinstance(topology, LayerOutput):
topology = [topology]
elif isinstance(topology, collections.Sequence):
for out_layer in topology:
assert isinstance(out_layer, LayerOutput), (
"The type of each element in the parameter topology "
"should be LayerOutput.")
else:
raise RuntimeError("Error input type for parameter topology.")
model_str = parse_network(topology)
with open(save_path, "w") as fout:
if binary:
fout.write(model_str.SerializeToString())
else:
fout.write(str(model_str))
python/paddle/utils/image_multiproc.py 已删除 100644 → 0
浏览文件 @ a3a3d3d8
# 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 os, sys
import numpy as np
from PIL import Image
import six
from six.moves import cStringIO as StringIO
import multiprocessing
import functools
import itertools
from paddle.utils.image_util import *
from paddle.trainer.config_parser import logger
try:
import cv2
except ImportError:
logger.warning("OpenCV2 is not installed, using PIL to process")
cv2 = None
__all__ = ["CvTransformer", "PILTransformer", "MultiProcessImageTransformer"]
class CvTransformer(ImageTransformer):
"""
CvTransformer used python-opencv to process image.
"""
def __init__(
self,
min_size=None,
crop_size=None,
transpose=(2, 0, 1), # transpose to C * H * W
channel_swap=None,
mean=None,
is_train=True,
is_color=True):
ImageTransformer.__init__(self, transpose, channel_swap, mean, is_color)
self.min_size = min_size
self.crop_size = crop_size
self.is_train = is_train
def resize(self, im, min_size):
row, col = im.shape[:2]
new_row, new_col = min_size, min_size
if row > col:
new_row = min_size * row / col
else:
new_col = min_size * col / row
im = cv2.resize(im, (new_row, new_col), interpolation=cv2.INTER_CUBIC)
return im
def crop_and_flip(self, im):
"""
Return cropped image.
The size of the cropped image is inner_size * inner_size.
im: (H x W x K) ndarrays
"""
row, col = im.shape[:2]
start_h, start_w = 0, 0
if self.is_train:
start_h = np.random.randint(0, row - self.crop_size + 1)
start_w = np.random.randint(0, col - self.crop_size + 1)
else:
start_h = (row - self.crop_size) / 2
start_w = (col - self.crop_size) / 2
end_h, end_w = start_h + self.crop_size, start_w + self.crop_size
if self.is_color:
im = im[start_h:end_h, start_w:end_w, :]
else:
im = im[start_h:end_h, start_w:end_w]
if (self.is_train) and (np.random.randint(2) == 0):
if self.is_color:
im = im[:, ::-1, :]
else:
im = im[:, ::-1]
return im
def transform(self, im):
im = self.resize(im, self.min_size)
im = self.crop_and_flip(im)
# transpose, swap channel, sub mean
im = im.astype('float32')
ImageTransformer.transformer(self, im)
return im
def load_image_from_string(self, data):
flag = cv2.CV_LOAD_IMAGE_COLOR if self.is_color else cv2.CV_LOAD_IMAGE_GRAYSCALE
im = cv2.imdecode(np.fromstring(data, np.uint8), flag)
return im
def transform_from_string(self, data):
im = self.load_image_from_string(data)
return self.transform(im)
def load_image_from_file(self, file):
flag = cv2.CV_LOAD_IMAGE_COLOR if self.is_color else cv2.CV_LOAD_IMAGE_GRAYSCALE
im = cv2.imread(file, flag)
return im
def transform_from_file(self, file):
im = self.load_image_from_file(file)
return self.transform(im)
class PILTransformer(ImageTransformer):
"""
PILTransformer used PIL to process image.
"""
def __init__(
self,
min_size=None,
crop_size=None,
transpose=(2, 0, 1), # transpose to C * H * W
channel_swap=None,
mean=None,
is_train=True,
is_color=True):
ImageTransformer.__init__(self, transpose, channel_swap, mean, is_color)
self.min_size = min_size
self.crop_size = crop_size
self.is_train = is_train
def resize(self, im, min_size):
row, col = im.size[:2]
new_row, new_col = min_size, min_size
if row > col:
new_row = min_size * row / col
else:
new_col = min_size * col / row
im = im.resize((new_row, new_col), Image.ANTIALIAS)
return im
def crop_and_flip(self, im):
"""
Return cropped image.
The size of the cropped image is inner_size * inner_size.
"""
row, col = im.size[:2]
start_h, start_w = 0, 0
if self.is_train:
start_h = np.random.randint(0, row - self.crop_size + 1)
start_w = np.random.randint(0, col - self.crop_size + 1)
else:
start_h = (row - self.crop_size) / 2
start_w = (col - self.crop_size) / 2
end_h, end_w = start_h + self.crop_size, start_w + self.crop_size
im = im.crop((start_h, start_w, end_h, end_w))
if (self.is_train) and (np.random.randint(2) == 0):
im = im.transpose(Image.FLIP_LEFT_RIGHT)
return im
def transform(self, im):
im = self.resize(im, self.min_size)
im = self.crop_and_flip(im)
im = np.array(im, dtype=np.float32) # convert to numpy.array
# transpose, swap channel, sub mean
ImageTransformer.transformer(self, im)
return im
def load_image_from_string(self, data):
im = Image.open(StringIO(data))
return im
def transform_from_string(self, data):
im = self.load_image_from_string(data)
return self.transform(im)
def load_image_from_file(self, file):
im = Image.open(file)
return im
def transform_from_file(self, file):
im = self.load_image_from_file(file)
return self.transform(im)
def job(is_img_string, transformer, data_label_pack):
(data, label) = data_label_pack
if is_img_string:
return transformer.transform_from_string(data), label
else:
return transformer.transform_from_file(data), label
class MultiProcessImageTransformer(object):
def __init__(self,
procnum=10,
resize_size=None,
crop_size=None,
transpose=(2, 0, 1),
channel_swap=None,
mean=None,
is_train=True,
is_color=True,
is_img_string=True):
"""
Processing image with multi-process. If it is used in PyDataProvider,
the simple usage for CNN is as follows:
.. code-block:: python
def hool(settings, is_train, **kwargs):
settings.is_train = is_train
settings.mean_value = np.array([103.939,116.779,123.68], dtype=np.float32)
settings.input_types = [
dense_vector(3 * 224 * 224),
integer_value(1)]
settings.transformer = MultiProcessImageTransformer(
procnum=10,
resize_size=256,
crop_size=224,
transpose=(2, 0, 1),
mean=settings.mean_values,
is_train=settings.is_train)
@provider(init_hook=hook, pool_size=20480)
def process(settings, file_list):
with open(file_list, 'r') as fdata:
for line in fdata:
data_dic = np.load(line.strip()) # load the data batch pickled by Pickle.
data = data_dic['data']
labels = data_dic['label']
labels = np.array(labels, dtype=np.float32)
for im, lab in settings.dp.run(data, labels):
yield [im.astype('float32'), int(lab)]
:param procnum: processor number.
:type procnum: int
:param resize_size: the shorter edge size of image after resizing.
:type resize_size: int
:param crop_size: the croping size.
:type crop_size: int
:param transpose: the transpose order, Paddle only allow C * H * W order.
:type transpose: tuple or list
:param channel_swap: the channel swap order, RGB or BRG.
:type channel_swap: tuple or list
:param mean: the mean values of image, per-channel mean or element-wise mean.
:type mean: array, The dimension is 1 for per-channel mean.
The dimension is 3 for element-wise mean.
:param is_train: training peroid or testing peroid.
:type is_train: bool.
:param is_color: the image is color or gray.
:type is_color: bool.
:param is_img_string: The input can be the file name of image or image string.
:type is_img_string: bool.
"""
self.procnum = procnum
self.pool = multiprocessing.Pool(procnum)
self.is_img_string = is_img_string
if cv2 is not None:
self.transformer = CvTransformer(resize_size, crop_size, transpose,
channel_swap, mean, is_train,
is_color)
else:
self.transformer = PILTransformer(resize_size, crop_size, transpose,
channel_swap, mean, is_train,
is_color)
def run(self, data, label):
fun = functools.partial(job, self.is_img_string, self.transformer)
return self.pool.imap_unordered(
fun, six.moves.zip(data, label), chunksize=100 * self.procnum)
python/paddle/utils/make_model_diagram.py 已删除 100644 → 0
浏览文件 @ a3a3d3d8
# Copyright (c) 2016 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.
# Generate dot diagram file for the given paddle model config
# The generated file can be viewed using Graphviz (http://graphviz.org)
from __future__ import print_function
import six
import sys
import traceback
from paddle.trainer.config_parser import parse_config
def make_layer_label(layer_config):
label = '%s type=%s' % (layer_config.name, layer_config.type)
if layer_config.reversed:
label += ' <=='
label2 = ''
if layer_config.active_type:
label2 += 'act=%s ' % layer_config.active_type
if layer_config.bias_parameter_name:
label2 += 'bias=%s ' % layer_config.bias_parameter_name
if label2:
label += '\l' + label2
return label
def make_diagram(config_file, dot_file, config_arg_str):
config = parse_config(config_file, config_arg_str)
make_diagram_from_proto(config.model_config, dot_file)
def make_diagram_from_proto(model_config, dot_file):
# print >> sys.stderr, config
name2id = {}
f = open(dot_file, 'w')
submodel_layers = set()
def make_link(link):
return 'l%s -> l%s;' % (name2id[link.layer_name],
name2id[link.link_name])
def make_mem(mem):
s = ''
if mem.boot_layer_name:
s += 'l%s -> l%s;\n' % (name2id[mem.boot_layer_name],
name2id[mem.layer_name])
s += 'l%s -> l%s [style=dashed];' % (name2id[mem.layer_name],
name2id[mem.link_name])
return s
print('digraph graphname {', file=f)
print('node [width=0.375,height=0.25];', file=f)
for i in six.moves.xrange(len(model_config.layers)):
l = model_config.layers[i]
name2id[l.name] = i
i = 0
for sub_model in model_config.sub_models:
if sub_model.name == 'root':
continue
print('subgraph cluster_%s {' % i, file=f)
print('style=dashed;', file=f)
label = '%s ' % sub_model.name
if sub_model.reversed:
label += '<=='
print('label = "%s";' % label, file=f)
i += 1
submodel_layers.add(sub_model.name)
for layer_name in sub_model.layer_names:
submodel_layers.add(layer_name)
lid = name2id[layer_name]
layer_config = model_config.layers[lid]
label = make_layer_label(layer_config)
print('l%s [label="%s", shape=box];' % (lid, label), file=f)
print('}', file=f)
for i in six.moves.xrange(len(model_config.layers)):
l = model_config.layers[i]
if l.name not in submodel_layers:
label = make_layer_label(l)
print('l%s [label="%s", shape=box];' % (i, label), file=f)
for sub_model in model_config.sub_models:
if sub_model.name == 'root':
continue
for link in sub_model.in_links:
print(make_link(link), file=f)
for link in sub_model.out_links:
print(make_link(link), file=f)
for mem in sub_model.memories:
print(make_mem(mem), file=f)
for i in six.moves.xrange(len(model_config.layers)):
for l in model_config.layers[i].inputs:
print(
'l%s -> l%s [label="%s"];' % (name2id[l.input_layer_name], i,
l.input_parameter_name),
file=f)
print('}', file=f)
f.close()
def usage():
print(
("Usage: python show_model_diagram.py" +
" CONFIG_FILE DOT_FILE [config_str]"),
file=sys.stderr)
exit(1)
if __name__ == '__main__':
if len(sys.argv) < 3 or len(sys.argv) > 4:
usage()
config_file = sys.argv[1]
dot_file = sys.argv[2]
config_arg_str = sys.argv[3] if len(sys.argv) == 4 else ''
try:
make_diagram(config_file, dot_file, config_arg_str)
except:
traceback.print_exc()
raise
python/paddle/utils/merge_model.py 已删除 100644 → 0
浏览文件 @ a3a3d3d8
# Copyright (c) 2016 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 gzip
import struct
import os
from paddle.trainer_config_helpers.layers import LayerOutput
from paddle.v2.parameters import Parameters
from paddle.proto import ModelConfig_pb2
from paddle.v2.topology import Topology
def merge_v2_model(net, param_file, output_file):
'''Merge the model config and parameters into one file.
The model configuration file describes the model structure which
ends with .py. The parameters file stores the parameters of the model
which ends with .tar.gz.
@param net The output layer of the network for inference.
@param param_file Path of the parameters (.tar.gz) which is stored by
v2 api.
@param output_file Path of the merged file which will be generated.
Usage:
from paddle.utils.merge_model import merge_v2_model
# import your network configuration
from example_net import net_conf
net = net_conf(is_predict=True)
param_file = './param_pass_00000.tar.gz'
output_file = './output.paddle'
merge_v2_model(net, param_file, output_file)
'''
assert isinstance(net, LayerOutput), \
"The net should be the output of the network for inference"
assert os.path.exists(param_file), \
"The model parameters file %s does not exists " % (param_file)
model_proto = Topology(net).proto()
assert isinstance(model_proto, ModelConfig_pb2.ModelConfig)
with gzip.open(param_file) as f:
params = Parameters.from_tar(f)
if os.path.exists(output_file):
os.remove(output_file)
with open(output_file, 'w') as f:
param_names = [param.name for param in model_proto.parameters]
conf_str = model_proto.SerializeToString()
f.write(struct.pack('q', len(conf_str)))
f.write(conf_str)
for pname in param_names:
params.serialize(pname, f)
print('Generate %s success!' % (output_file))
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