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guoshengCSguoshengCS
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Merge branch 'develop' of https://github.com/PaddlePaddle/paddle into add-ReduceOp
2 parents 477a6a0 + dcfd31d commit 99b8dbb

132 files changed

Lines changed: 5304 additions & 1386 deletions

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‎benchmark/paddle/image/provider.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -22,5 +22,5 @@ def initHook(settings, height, width, color, num_class, **kwargs):
2222
def process(settings, file_list):
2323
for i in xrange(1024):
2424
img = np.random.rand(1, settings.data_size).reshape(-1, 1).flatten()
25-
lab = random.randint(0, settings.num_class)
25+
lab = random.randint(0, settings.num_class - 1)
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yield img.astype('float32'), int(lab)

‎benchmark/paddle/image/run_mkldnn.sh‎

Lines changed: 48 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,48 @@
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set -e
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3+
unset OMP_NUM_THREADS MKL_NUM_THREADS
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export OMP_DYNAMIC="FALSE"
5+
export KMP_AFFINITY="granularity=fine,compact,0,0"
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7+
function train() {
8+
topology=$1
9+
bs=$2
10+
use_mkldnn=$3
11+
if [ $3 == "True" ]; then
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thread=1
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log="logs/${topology}-mkldnn-${bs}.log"
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elif [ $3 == "False" ]; then
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thread=`nproc`
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log="logs/${topology}-${thread}mklml-${bs}.log"
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else
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echo "Wrong input $3, use True or False."
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fi
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args="batch_size=${bs}"
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config="${topology}.py"
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paddle train --job=time \
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--config=$config \
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--use_mkldnn=$use_mkldnn \
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--use_gpu=False \
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--trainer_count=$thread \
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--log_period=10 \
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--test_period=100 \
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--config_args=$args \
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2>&1 | tee ${log}
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}
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if [ ! -d "train.list" ]; then
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echo " " > train.list
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fi
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if [ ! -d "logs" ]; then
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mkdir logs
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fi
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#========== mkldnn ==========#
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train vgg 64 True
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train vgg 128 True
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train vgg 256 True
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#========== mklml ===========#
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train vgg 64 False
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train vgg 128 False
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train vgg 256 False

‎benchmark/paddle/image/vgg.py‎

Lines changed: 103 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,103 @@
1+
#!/usr/bin/env python
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from paddle.trainer_config_helpers import *
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height = 224
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width = 224
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num_class = 1000
7+
batch_size = get_config_arg('batch_size', int, 64)
8+
layer_num = get_config_arg('layer_num', int, 19)
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args = {'height': height, 'width': width, 'color': True, 'num_class': num_class}
11+
define_py_data_sources2(
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"train.list", None, module="provider", obj="process", args=args)
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settings(
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batch_size=batch_size,
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learning_rate=0.01 / batch_size,
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learning_method=MomentumOptimizer(0.9),
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regularization=L2Regularization(0.0005 * batch_size))
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img = data_layer(name='image', size=height * width * 3)
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def vgg_network(vgg_num=3):
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tmp = img_conv_group(
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input=img,
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num_channels=3,
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conv_padding=1,
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conv_num_filter=[64, 64],
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conv_filter_size=3,
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conv_act=ReluActivation(),
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pool_size=2,
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pool_stride=2,
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pool_type=MaxPooling())
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tmp = img_conv_group(
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input=tmp,
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conv_num_filter=[128, 128],
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conv_padding=1,
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conv_filter_size=3,
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conv_act=ReluActivation(),
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pool_stride=2,
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pool_type=MaxPooling(),
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pool_size=2)
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channels = []
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for i in range(vgg_num):
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channels.append(256)
48+
tmp = img_conv_group(
49+
input=tmp,
50+
conv_num_filter=channels,
51+
conv_padding=1,
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conv_filter_size=3,
53+
conv_act=ReluActivation(),
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pool_stride=2,
55+
pool_type=MaxPooling(),
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pool_size=2)
57+
channels = []
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for i in range(vgg_num):
59+
channels.append(512)
60+
tmp = img_conv_group(
61+
input=tmp,
62+
conv_num_filter=channels,
63+
conv_padding=1,
64+
conv_filter_size=3,
65+
conv_act=ReluActivation(),
66+
pool_stride=2,
67+
pool_type=MaxPooling(),
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pool_size=2)
69+
tmp = img_conv_group(
70+
input=tmp,
71+
conv_num_filter=channels,
72+
conv_padding=1,
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conv_filter_size=3,
74+
conv_act=ReluActivation(),
75+
pool_stride=2,
76+
pool_type=MaxPooling(),
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pool_size=2)
78+
79+
tmp = fc_layer(
80+
input=tmp,
81+
size=4096,
82+
act=ReluActivation(),
83+
layer_attr=ExtraAttr(drop_rate=0.5))
84+
85+
tmp = fc_layer(
86+
input=tmp,
87+
size=4096,
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act=ReluActivation(),
89+
layer_attr=ExtraAttr(drop_rate=0.5))
90+
91+
return fc_layer(input=tmp, size=num_class, act=SoftmaxActivation())
92+
93+
94+
if layer_num == 16:
95+
vgg = vgg_network(3)
96+
elif layer_num == 19:
97+
vgg = vgg_network(4)
98+
else:
99+
print("Wrong layer number.")
100+
101+
lab = data_layer('label', num_class)
102+
loss = cross_entropy(input=vgg, label=lab)
103+
outputs(loss)

‎cmake/generic.cmake‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -253,7 +253,7 @@ function(nv_library TARGET_NAME)
253253
foreach(source_file ${nv_library_SRCS})
254254
string(REGEX REPLACE "\\.[^.]*$" "" source ${source_file})
255255
if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${source}.h)
256-
list(APPEND cc_library_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/${source}.h)
256+
list(APPEND nv_library_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/${source}.h)
257257
endif()
258258
endforeach()
259259
add_style_check_target(${TARGET_NAME} ${nv_library_SRCS} ${nv_library_HEADERS})

‎cmake/util.cmake‎

Lines changed: 4 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -97,6 +97,10 @@ function(link_paddle_exe TARGET_NAME)
9797
target_link_libraries(${TARGET_NAME} log)
9898
endif(ANDROID)
9999

100+
if(WITH_MKLDNN AND WITH_MKLML AND MKLDNN_IOMP_DIR)
101+
target_link_libraries(${TARGET_NAME} "-L${MKLDNN_IOMP_DIR} -liomp5 -Wl,--as-needed")
102+
endif()
103+
100104
add_dependencies(${TARGET_NAME} ${external_project_dependencies})
101105
endfunction()
102106

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