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提交 2a4e7add 编写于 作者: Y Yu Yang
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into feature/add_global_step

上级 5f70629f f0548679
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Showing with 2870 addition and 2357 deletion
CMakeLists.txt
浏览文件 @ 2a4e7add
......@@ -19,7 +19,7 @@ set(PADDLE_BINARY_DIR ${CMAKE_CURRENT_BINARY_DIR})
include(system)
project(paddle CXX C Go)
project(paddle CXX C)
message(STATUS "CXX compiler: ${CMAKE_CXX_COMPILER}, version: "
"${CMAKE_CXX_COMPILER_ID} ${CMAKE_CXX_COMPILER_VERSION}")
message(STATUS "C compiler: ${CMAKE_C_COMPILER}, version: "
......@@ -60,7 +60,7 @@ option(USE_NNPACK "Compile PaddlePaddle with NNPACK library" OFF)
option(WITH_DISTRIBUTE "Compile with grpc distributed support" OFF)
option(USE_EIGEN_FOR_BLAS "Use matrix multiplication in Eigen" OFF)
option(WITH_ARM_FP16 "Use half precision support on armv8.2-a cpu" OFF)
option(WITH_FAST_BUNDLE_TEST "Bundle tests that can be run in a single process together to reduce launch overhead" ON)
option(WITH_FAST_BUNDLE_TEST "Bundle tests that can be run in a single process together to reduce launch overhead" OFF)
# CMAKE_BUILD_TYPE
if(NOT CMAKE_BUILD_TYPE)
......@@ -146,6 +146,7 @@ include(external/cares)
include(external/grpc)
include(cudnn) # set cudnn libraries, must before configure
include(cupti)
include(configure) # add paddle env configuration
include(generic) # simplify cmake module
include(package) # set paddle packages
......@@ -174,7 +175,7 @@ set(EXTERNAL_LIBS
)
if(WITH_GPU)
include(cuda)
include(cuda)
endif(WITH_GPU)
if(WITH_MKLML)
......@@ -201,17 +202,18 @@ endif()
# "add_subdirectory(paddle)" and "add_subdirectory(python)" should be
# placed after this block, because they depends on it.
if(WITH_GOLANG)
enable_language(Go)
add_subdirectory(go)
endif(WITH_GOLANG)
set(PADDLE_PYTHON_BUILD_DIR "${CMAKE_CURRENT_BINARY_DIR}/python/build")
SET(CMAKE_CXX_FLAGS_RELWITHDEBINFO "-O3 -g -DNDEBUG")
SET(CMAKE_C_FLAGS_RELWITHDEBINFO "-O3 -g -DNDEBUG")
set(CMAKE_CXX_FLAGS_RELWITHDEBINFO "-O3 -g -DNDEBUG")
set(CMAKE_C_FLAGS_RELWITHDEBINFO "-O3 -g -DNDEBUG")
add_subdirectory(paddle)
if(WITH_PYTHON)
add_subdirectory(python)
add_subdirectory(python)
endif()
if(WITH_DOC)
......
Dockerfile.android
浏览文件 @ 2a4e7add
......@@ -21,16 +21,6 @@ RUN apt-get update && \
wget curl tar unzip gcc g++ locales clang-format-3.8 swig cmake && \
apt-get clean -y
# Install Go and glide
RUN wget -qO- go.tgz https://storage.googleapis.com/golang/go1.8.1.linux-amd64.tar.gz | \
tar -xz -C /usr/local && \
mkdir /root/gopath && \
mkdir /root/gopath/bin && \
mkdir /root/gopath/src
ENV GOROOT=/usr/local/go GOPATH=/root/gopath
# should not be in the same line with GOROOT definition, otherwise docker build could not find GOROOT.
ENV PATH=${PATH}:${GOROOT}/bin:${GOPATH}/bin
# git credential to skip password typing
RUN git config --global credential.helper store
......
benchmark/cluster/vgg16/Dockerfile
浏览文件 @ 2a4e7add
#FROM python:2.7.14
FROM nvidia/cuda:8.0-cudnn5-runtime-ubuntu16.04
RUN apt-get update && apt-get install -y python
RUN pip install -U kubernetes opencv-python && apt-get update -y && apt-get install -y iputils-ping libgtk2.0-dev
# NOTE: By default CI built wheel packages turn WITH_DISTRIBUTE=OFF,
# so we must build one with distribute support to install in this image.
# you can get mirror list here:
# https://launchpad.net/ubuntu/+archivemirrors
ARG UBUNTU_MIRROR
RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com/ubuntu#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi'
RUN apt-get update && apt-get install -y python python-dev python-pip iputils-ping libgtk2.0-dev
RUN pip install -U kubernetes opencv-python
RUN pip install paddlepaddle
# if network is slowly, you may need to add proxy here.
# ENV https_proxy=
RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.cifar.train10()" | python'
RUN pip uninstall -y paddlepaddle
# unset proxy if it is setted.
# ENV https_proxy=""
# NOTE: By default CI built wheel packages turn WITH_DISTRIBUTE=OFF,
# so we must build one with distribute support to install in this image.
ADD *.whl /
RUN pip install /*.whl && rm -f /*.whl
ENV LD_LIBRARY_PATH=/usr/local/lib
# tf k8s
RUN pip install tensorflow==1.4.0
ADD tf_k8s /usr/bin
RUN chmod +x /usr/bin/tf_k8s
ADD vgg16_tf.py /workspace/
# below lines may change a lot for debugging
ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/paddle_k8s /usr/bin
ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/k8s_tools.py /root
ADD *.whl /
RUN pip install /*.whl && rm -f /*.whl && \
chmod +x /usr/bin/paddle_k8s
ENV LD_LIBRARY_PATH=/usr/local/lib
RUN chmod +x /usr/bin/paddle_k8s
ADD vgg16_fluid.py vgg16_v2.py /workspace/
benchmark/cluster/vgg16/fluid_trainer.yaml
浏览文件 @ 2a4e7add
......@@ -11,7 +11,7 @@ spec:
paddle-job: vgg16job
spec:
imagePullSecrets:
- name: job-registry-secret
- name: job-registry-secret
hostNetwork: true
containers:
- name: trainer
......
benchmark/cluster/vgg16/tf_k8s 0 → 100644
浏览文件 @ 2a4e7add
#!/bin/bash
check_trainer_ret() {
ret=$1
stdbuf -oL echo "job returned $ret...setting pod return message..."
stdbuf -oL echo "==============================="
if [ $ret -eq 136 ] ; then
echo "Error Arithmetic Operation(Floating Point Exception)" > /dev/termination-log
elif [ $ret -eq 139 ] ; then
echo "Segmentation Fault" > /dev/termination-log
elif [ $ret -eq 1 ] ; then
echo "General Error" > /dev/termination-log
elif [ $ret -eq 134 ] ; then
echo "Program Abort" > /dev/termination-log
fi
stdbuf -oL echo "termination log wroted..."
exit $ret
}
g_pservers=""
g_trainers=""
wait_running_pods(){
pserver_label="tf-job-pserver=${JOB_NAME}"
trainer_label="tf-job-trainer=${JOB_NAME}"
stdbuf -oL python /root/k8s_tools.py wait_pods_running ${pserver_label} ${PSERVERS_NUM}
stdbuf -oL python /root/k8s_tools.py wait_pods_running ${trainer_label} ${TRAINERS_NUM}
g_pservers=$(python /root/k8s_tools.py fetch_endpoints ${pserver_label} ${PORT})
g_trainers=$(python /root/k8s_tools.py fetch_endpoints ${trainer_label} ${PORT})
}
start_tf_pserver(){
wait_running_pods
label="tf-job-pserver=${JOB_NAME}"
pserver_id=$(python /root/k8s_tools.py fetch_id ${label})
cmd="${ENTRY} --ps_hosts=${g_pservers} --worker_hosts=${g_trainers} \
--job_name=${TF_JOB_NAME} --task_index=${pserver_id}"
stdbuf -oL sh -c "cd ${TRAINER_PACKAGE} && ${cmd}"
}
start_tf_trainer(){
wait_running_pods
label="tf-job-trainer=${JOB_NAME}"
trainer_id=$(python /root/k8s_tools.py fetch_id ${label})
cmd="${ENTRY} --ps_hosts=${g_pservers} --worker_hosts=${g_trainers} \
--job_name=${TF_JOB_NAME} --task_index=${trainer_id} --batch_size=${BATCH_SIZE}"
stdbuf -oL sh -c "cd ${TRAINER_PACKAGE} && ${cmd}"
check_trainer_ret $?
}
start_tf(){
if [[ "${TF_JOB_NAME}" == "worker" ]]; then
start_tf_trainer
else
start_tf_pserver
fi
}
usage() {
echo "usage: tf_k8s [<args>]:"
echo " start_tf Start tensorflow jobs"
}
case "$1" in
start_tf)
start_tf
;;
--help)
usage
;;
*)
usage
;;
esac
benchmark/cluster/vgg16/tf_pserver.yaml 0 → 100644
浏览文件 @ 2a4e7add
apiVersion: extensions/v1beta1
kind: ReplicaSet
metadata:
name: vgg16job-tf-pserver
spec:
replicas: 10
template:
metadata:
labels:
tf-job-pserver: vgg16job-tf
spec:
hostNetwork: true
imagePullSecrets:
- name: job-registry-secret
containers:
- name: pserver
image: "registry.baidu.com/paddlepaddle/fluid_benchmark_tf:vgg16"
imagePullPolicy: Always
command: ["tf_k8s", "start_tf"]
ports:
- name: jobport-30236
containerPort: 30236
env:
- name: PORT
value: "32036"
- name: ENTRY
value: "python vgg16_tf.py"
- name: JOB_NAME
value: vgg16job-tf
- name: PSERVERS_NUM
value: "10"
- name: TF_JOB_NAME
value: "ps"
- name: TRAINERS_NUM
value: "20"
- name: BATCH_SIZE
value: "128"
- name: TRAINER_PACKAGE
value: "/workspace"
- name: NUM_PASSES
value: "1"
- name: NAMESPACE
valueFrom:
fieldRef:
fieldPath: "metadata.namespace"
- name: POD_IP
valueFrom:
fieldRef:
fieldPath: "status.podIP"
resources:
requests:
memory: 10Gi
cpu: 4
limits:
memory: 10Gi
cpu: 4
benchmark/cluster/vgg16/tf_trainer.yaml 0 → 100644
浏览文件 @ 2a4e7add
apiVersion: batch/v1
kind: Job
metadata:
name: vgg16job-tf-trainer
spec:
parallelism: 20
completions: 20
template:
metadata:
labels:
tf-job-trainer: vgg16job-tf
spec:
imagePullSecrets:
- name: job-registry-secret
hostNetwork: true
containers:
- name: trainer
image: "registry.baidu.com/paddlepaddle/fluid_benchmark_tf:vgg16"
imagePullPolicy: Always
command: ["tf_k8s", "start_tf"]
ports:
- name: jobport-30236
containerPort: 30236
env:
- name: PORT
value: "32036"
- name: JOB_NAME
value: vgg16job-tf
- name: TF_JOB_NAME
value: "worker"
- name: ENTRY
value: "python vgg16_tf.py"
- name: PSERVERS_NUM
value: "10"
- name: BATCH_SIZE
value: "128"
- name: TRAINERS_NUM
value: "20"
- name: TRAINER_PACKAGE
value: "/workspace"
- name: NUM_PASSES
value: "1"
- name: NAMESPACE
valueFrom:
fieldRef:
fieldPath: "metadata.namespace"
- name: POD_IP
valueFrom:
fieldRef:
fieldPath: "status.podIP"
resources:
requests:
memory: 40Gi
cpu: 2
limits:
memory: 40Gi
cpu: 2
restartPolicy: Never
benchmark/cluster/vgg16/vgg16_fluid.py
浏览文件 @ 2a4e7add
......@@ -68,6 +68,21 @@ parser.add_argument(
type=str2bool,
default=True,
help='Whether to run as local mode.')
parser.add_argument(
"--ps_hosts",
type=str,
default="",
help="Comma-separated list of hostname:port pairs")
parser.add_argument(
"--trainer_hosts",
type=str,
default="",
help="Comma-separated list of hostname:port pairs")
# Flags for defining the tf.train.Server
parser.add_argument(
"--task_index", type=int, default=0, help="Index of task within the job")
args = parser.parse_args()
......@@ -180,8 +195,9 @@ def main():
iters += 1
num_samples += len(data)
print(
"Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, spent %f"
% (pass_id, iters, loss, acc, time.time() - ts)
"Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, Speed = %.2f img/s"
% (pass_id, iters, loss, acc,
len(data) / (time.time() - ts))
) # The accuracy is the accumulation of batches, but not the current batch.
pass_elapsed = time.time() - start_time
......@@ -209,27 +225,24 @@ def main():
batch_size=args.batch_size)
train_loop(exe, fluid.default_main_program())
else:
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # all pserver endpoints
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, "6174"]))
pserver_endpoints = ",".join(eplist)
print("pserver endpoints: ", pserver_endpoints)
trainers = int(os.getenv("TRAINERS")) # total trainer count
print("trainers total: ", trainers)
current_endpoint = os.getenv(
"POD_IP") + ":6174" # current pserver endpoint
training_role = os.getenv(
"TRAINING_ROLE",
"TRAINER") # get the training role: trainer/pserver
t = fluid.DistributeTranspiler()
t.transpile(
optimize_ops,
params_grads,
pservers=pserver_endpoints,
trainer_id=args.task_index,
pservers=args.ps_hosts,
trainers=trainers)
if training_role == "PSERVER":
current_endpoint = os.getenv("POD_IP") + ":" + os.getenv(
"PADDLE_INIT_PORT")
if not current_endpoint:
print("need env SERVER_ENDPOINT")
exit(1)
......
benchmark/cluster/vgg16/vgg16_tf.py 0 → 100644
浏览文件 @ 2a4e7add
# 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.
"""VGG16 benchmark in TensorFlow
You can get distribution example template structure here:
https://medium.com/clusterone/how-to-write-distributed-tensorflow-code-with-an-example-on-tensorport-70bf3306adcb
https://www.tensorflow.org/deploy/distributed
"""
import tensorflow as tf
import paddle.v2 as paddle
import numpy as np
import argparse
import time
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
'--batch_size', type=int, default=128, help="Batch size for training.")
parser.add_argument(
'--learning_rate',
type=float,
default=1e-3,
help="Learning rate for training.")
parser.add_argument('--num_passes', type=int, default=50, help="No. of passes.")
parser.add_argument(
'--device',
type=str,
default='CPU',
choices=['CPU', 'GPU'],
help="The device type.")
parser.add_argument(
'--data_format',
type=str,
default='NHWC',
choices=['NCHW', 'NHWC'],
help='The data order, NCHW=[batch, channels, height, width].'
'Only support NHWC right now.')
parser.add_argument(
'--data_set',
type=str,
default='cifar10',
choices=['cifar10', 'flowers'],
help='Optional dataset for benchmark.')
parser.add_argument(
"--ps_hosts",
type=str,
default="",
help="Comma-separated list of hostname:port pairs")
parser.add_argument(
"--worker_hosts",
type=str,
default="",
help="Comma-separated list of hostname:port pairs")
parser.add_argument(
"--job_name", type=str, default="", help="One of 'worker', 'ps'")
# Flags for defining the tf.train.Server
parser.add_argument(
"--task_index", type=int, default=0, help="Index of task within the job")
args = parser.parse_args()
class VGG16Model(object):
def __init__(self):
self.parameters = []
def batch_norm_relu(self, inputs, is_training):
"""Performs a batch normalization followed by a ReLU."""
# We set fused=True for a significant speed boost. See
# https://www.tensorflow.org/speed/speed_guide#common_fused_ops
inputs = tf.layers.batch_normalization(
inputs=inputs,
axis=1 if args.data_format == 'NCHW' else -1,
momentum=0.9,
epsilon=1e-05,
center=True,
scale=True,
training=is_training,
fused=True)
inputs = tf.nn.relu(inputs)
return inputs
def conv_bn_layer(self,
name,
images,
kernel_shape,
is_training,
drop_rate=0.0):
with tf.name_scope(name) as scope:
kernel = tf.Variable(
tf.truncated_normal(
kernel_shape, dtype=tf.float32, stddev=1e-1),
name='weights')
conv = tf.nn.conv2d(
images,
kernel, [1, 1, 1, 1],
data_format=args.data_format,
padding='SAME')
biases = tf.Variable(
tf.constant(
0.0, shape=[kernel_shape[-1]], dtype=tf.float32),
trainable=True,
name='biases')
out = tf.nn.bias_add(conv, biases)
out = self.batch_norm_relu(out, is_training)
out = tf.layers.dropout(out, rate=drop_rate, training=is_training)
return out
def fc_layer(self, name, inputs, shape):
with tf.name_scope(name) as scope:
fc_w = tf.Variable(
tf.truncated_normal(
shape, dtype=tf.float32, stddev=1e-1),
name='weights')
fc_b = tf.Variable(
tf.constant(
0.0, shape=[shape[-1]], dtype=tf.float32),
trainable=True,
name='biases')
out = tf.nn.bias_add(tf.matmul(inputs, fc_w), fc_b)
return out
def network(self, images, class_dim, is_training):
""" VGG16 model structure.
TODO(kuke): enable this network to support the 'NCHW' data format
"""
# conv1
conv1_1 = self.conv_bn_layer(
'conv1_1', images, [3, 3, 3, 64], is_training, drop_rate=0.3)
conv1_2 = self.conv_bn_layer(
'conv1_2', conv1_1, [3, 3, 64, 64], is_training, drop_rate=0.0)
# pool1
pool1 = tf.nn.max_pool(
conv1_2,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool1')
# conv2
conv2_1 = self.conv_bn_layer(
'conv2_1', pool1, [3, 3, 64, 128], is_training, drop_rate=0.4)
conv2_2 = self.conv_bn_layer(
'conv2_2', conv2_1, [3, 3, 128, 128], is_training, drop_rate=0.0)
# pool2
pool2 = tf.nn.max_pool(
conv2_2,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool2')
# conv3
conv3_1 = self.conv_bn_layer(
'conv3_1', pool2, [3, 3, 128, 256], is_training, drop_rate=0.4)
conv3_2 = self.conv_bn_layer(
'conv3_2', conv3_1, [3, 3, 256, 256], is_training, drop_rate=0.4)
conv3_3 = self.conv_bn_layer(
'conv3_3', conv3_2, [3, 3, 256, 256], is_training, drop_rate=0.0)
# pool3
pool3 = tf.nn.max_pool(
conv3_3,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool3')
# conv4
conv4_1 = self.conv_bn_layer(
'conv4_1', pool3, [3, 3, 256, 512], is_training, drop_rate=0.4)
conv4_2 = self.conv_bn_layer(
'conv4_2', conv4_1, [3, 3, 512, 512], is_training, drop_rate=0.4)
conv4_3 = self.conv_bn_layer(
'conv4_3', conv4_2, [3, 3, 512, 512], is_training, drop_rate=0.0)
# pool4
pool4 = tf.nn.max_pool(
conv4_3,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool4')
# conv5
conv5_1 = self.conv_bn_layer(
'conv5_1', pool4, [3, 3, 512, 512], is_training, drop_rate=0.4)
conv5_2 = self.conv_bn_layer(
'conv5_2', conv5_1, [3, 3, 512, 512], is_training, drop_rate=0.4)
conv5_3 = self.conv_bn_layer(
'conv5_3', conv5_2, [3, 3, 512, 512], is_training, drop_rate=0.0)
# pool5
pool5 = tf.nn.max_pool(
conv5_3,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool4')
# flatten
shape = int(np.prod(pool5.get_shape()[1:]))
pool5_flat = tf.reshape(pool5, [-1, shape])
# fc1
drop = tf.layers.dropout(pool5_flat, rate=0.5, training=is_training)
fc1 = self.fc_layer('fc1', drop, [shape, 512])
# fc2
bn = self.batch_norm_relu(fc1, is_training)
drop = tf.layers.dropout(bn, rate=0.5, training=is_training)
fc2 = self.fc_layer('fc2', drop, [512, 512])
fc3 = self.fc_layer('fc3', fc2, [512, class_dim])
return fc3
def run_benchmark(cluster_spec, server):
"""Run benchmark on cifar10 or flowers."""
if args.data_set == "cifar10":
class_dim = 10
raw_shape = (3, 32, 32)
dat_shape = (None, 32, 32, 3) if args.data_format == 'NHWC' else (
None, 3, 32, 32)
else:
class_dim = 102
raw_shape = (3, 224, 224)
dat_shape = (None, 224, 224, 3) if args.data_format == 'NHWC' else (
None, 3, 224, 224)
device = tf.train.replica_device_setter(
worker_device="/job:worker/task:{}".format(args.task_index),
cluster=cluster_spec)
with tf.device(device):
images = tf.placeholder(tf.float32, shape=dat_shape)
labels = tf.placeholder(tf.int64, shape=(None, ))
is_training = tf.placeholder('bool')
onehot_labels = tf.one_hot(labels, depth=class_dim)
vgg16 = VGG16Model()
logits = vgg16.network(images, class_dim, is_training)
loss = tf.losses.softmax_cross_entropy(
onehot_labels=onehot_labels, logits=logits)
avg_loss = tf.reduce_mean(loss)
correct = tf.equal(tf.argmax(logits, 1), labels)
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
optimizer = tf.train.AdamOptimizer(learning_rate=args.learning_rate)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
global_step = tf.Variable(0, name='global_step', trainable=False)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(avg_loss, global_step=global_step)
summary_op = tf.summary.merge_all()
init_op = tf.global_variables_initializer()
# data reader
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.cifar.train10()
if args.data_set == 'cifar10' else paddle.dataset.flowers.train(),
buf_size=5120),
batch_size=args.batch_size)
test_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.cifar.test10()
if args.data_set == 'cifar10' else paddle.dataset.flowers.test(),
buf_size=5120),
batch_size=args.batch_size)
# test
def test():
test_accs = []
for batch_id, data in enumerate(test_reader()):
test_images = np.array(
map(lambda x: np.transpose(x[0].reshape(raw_shape),
axes=[1, 2, 0]) if args.data_format == 'NHWC' else x[0], data)).astype("float32")
test_labels = np.array(map(lambda x: x[1], data)).astype('int64')
test_accs.append(
accuracy.eval(feed_dict={
images: test_images,
labels: test_labels,
is_training: False
}))
return np.mean(test_accs)
config = tf.ConfigProto(
intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
config.gpu_options.allow_growth = True
hooks = [tf.train.StopAtStepHook(last_step=1000000)]
with tf.train.MonitoredTrainingSession(
master=server.target, is_chief=(args.task_index == 0),
hooks=hooks) as sess:
iters, num_samples, start_time = 0, 0, 0.0
for pass_id in range(args.num_passes):
# train
num_samples = 0
start_time = time.time()
for batch_id, data in enumerate(train_reader()):
train_images = np.array(
map(lambda x: np.transpose(x[0].reshape(raw_shape),
axes=[1, 2, 0]) if args.data_format == 'NHWC' else x[0], data)).astype("float32")
train_labels = np.array(map(lambda x: x[1], data)).astype(
'int64')
iter_begin_time = time.time()
_, loss, acc = sess.run([train_op, avg_loss, accuracy],
feed_dict={
images: train_images,
labels: train_labels,
is_training: True
})
iters += 1
print(
"Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, Speed=%.2f imgs/sec"
% (pass_id, iters, loss, acc,
len(data) / (time.time() - iter_begin_time)))
num_samples += len(data)
train_elapsed = time.time() - start_time
# test
pass_test_acc = test()
print("Pass = %d, Train speed = %f imgs/s, Test accuracy = %f\n" %
(pass_id, num_samples / train_elapsed, pass_test_acc))
def print_arguments():
print('----------- Configuration Arguments -----------')
for arg, value in sorted(vars(args).iteritems()):
print('%s: %s' % (arg, value))
print('------------------------------------------------')
if __name__ == '__main__':
print_arguments()
ps_hosts = args.ps_hosts.split(",")
worker_hosts = args.worker_hosts.split(",")
# Create a cluster from the parameter server and worker hosts.
cluster_spec = tf.train.ClusterSpec({
"ps": ps_hosts,
"worker": worker_hosts
})
# Create and start a server for the local task.
server = tf.train.Server(
cluster_spec, job_name=args.job_name, task_index=args.task_index)
if args.job_name == "ps":
print("start pserver")
server.join()
elif args.job_name == "worker":
print("start worker")
run_benchmark(cluster_spec, server)
cmake/configure.cmake
浏览文件 @ 2a4e7add
......@@ -59,6 +59,7 @@ endif(NOT WITH_GOLANG)
if(NOT WITH_GPU)
add_definitions(-DHPPL_STUB_FUNC)
add_definitions("-DCUPTI_LIB_PATH=\"\"")
list(APPEND CMAKE_CXX_SOURCE_FILE_EXTENSIONS cu)
else()
......@@ -73,7 +74,14 @@ else()
if(NOT CUDNN_FOUND)
message(FATAL_ERROR "Paddle needs cudnn to compile")
endif()
if(CUPTI_FOUND)
include_directories(${CUPTI_INCLUDE_DIR})
add_definitions(-DPADDLE_WITH_CUPTI)
add_definitions("-DCUPTI_LIB_PATH=\"${CUPTI_LIBRARY_PATH}\"")
else()
add_definitions("-DCUPTI_LIB_PATH=\"\"")
message(STATUS "Cannot find CUPTI, GPU Profiling is incorrect.")
endif()
set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-Xcompiler ${SIMD_FLAG}")
# Include cuda and cudnn
......
cmake/cuda.cmake
浏览文件 @ 2a4e7add
......@@ -155,7 +155,8 @@ endif()
include_directories(${CUDA_INCLUDE_DIRS})
list(APPEND EXTERNAL_LIBS ${CUDA_LIBRARIES} ${CUDA_rt_LIBRARY})
if(NOT WITH_DSO)
list(APPEND EXTERNAL_LIBS ${CUDNN_LIBRARY} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_curand_LIBRARY} ${NCCL_LIBRARY})
# TODO(panyx0718): CUPTI only allows DSO?
list(APPEND EXTERNAL_LIBS ${CUDNN_LIBRARY} ${CUPTI_LIBRARY} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_curand_LIBRARY} ${NCCL_LIBRARY})
endif(NOT WITH_DSO)
# setting nvcc arch flags
......
cmake/cupti.cmake 0 → 100644
浏览文件 @ 2a4e7add
if(NOT WITH_GPU)
return()
endif()
set(CUPTI_ROOT "/usr" CACHE PATH "CUPTI ROOT")
find_path(CUPTI_INCLUDE_DIR cupti.h
PATHS ${CUPTI_ROOT} ${CUPTI_ROOT}/include
$ENV{CUPTI_ROOT} $ENV{CUPTI_ROOT}/include
${CUDA_TOOLKIT_ROOT_DIR}/extras/CUPTI/include
NO_DEFAULT_PATH
)
get_filename_component(__libpath_hist ${CUDA_CUDART_LIBRARY} PATH)
set(TARGET_ARCH "x86_64")
if(NOT ${CMAKE_SYSTEM_PROCESSOR})
set(TARGET_ARCH ${CMAKE_SYSTEM_PROCESSOR})
endif()
list(APPEND CUPTI_CHECK_LIBRARY_DIRS
${CUPTI_ROOT}
${CUPTI_ROOT}/lib64
${CUPTI_ROOT}/lib
${CUPTI_ROOT}/lib/${TARGET_ARCH}-linux-gnu
$ENV{CUPTI_ROOT}
$ENV{CUPTI_ROOT}/lib64
$ENV{CUPTI_ROOT}/lib
/usr/lib
${CUDA_TOOLKIT_ROOT_DIR}/extras/CUPTI/lib64)
find_library(CUPTI_LIBRARY NAMES libcupti.so libcupti.dylib # libcupti_static.a
PATHS ${CUPTI_CHECK_LIBRARY_DIRS} ${CUPTI_INCLUDE_DIR} ${__libpath_hist}
NO_DEFAULT_PATH
DOC "Path to cuPTI library.")
get_filename_component(CUPTI_LIBRARY_PATH ${CUPTI_LIBRARY} DIRECTORY)
if(CUPTI_INCLUDE_DIR AND CUPTI_LIBRARY)
set(CUPTI_FOUND ON)
else()
set(CUPTI_FOUND OFF)
endif()
doc/build_and_install/build_from_source_cn.rst
浏览文件 @ 2a4e7add
......@@ -189,7 +189,7 @@ PaddlePaddle的编译选项，包括生成CPU/GPU二进制文件、链接何种B
"WITH_TESTING", "是否开启单元测试", "OFF"
"WITH_DOC", "是否编译中英文文档", "OFF"
"WITH_SWIG_PY", "是否编译PYTHON的SWIG接口，该接口可用于预测和定制化训练", "Auto"
"WITH_GOLANG", "是否编译go语言的可容错parameter server", "ON"
"WITH_GOLANG", "是否编译go语言的可容错parameter server", "OFF"
"WITH_MKL", "是否使用MKL数学库，如果为否则是用OpenBLAS", "ON"
BLAS
......
doc/build_and_install/build_from_source_en.rst
浏览文件 @ 2a4e7add
......@@ -191,7 +191,7 @@ You can add :code:`-D` argument to pass such options, like:
"WITH_TESTING", "Build unit tests", "OFF"
"WITH_DOC", "Build documentations", "OFF"
"WITH_SWIG_PY", "Build Python SWIG interface for V2 API", "Auto"
"WITH_GOLANG", "Build fault-tolerant parameter server written in go", "ON"
"WITH_GOLANG", "Build fault-tolerant parameter server written in go", "OFF"
"WITH_MKL", "Use MKL as BLAS library, else use OpenBLAS", "ON"
......
doc/howto/capi/workflow_of_capi_cn.md
浏览文件 @ 2a4e7add
......@@ -65,6 +65,7 @@
output_file = "output.paddle.model"
merge_v2_model(net, param_file, output_file)
```
对[手写数字识别](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense)这个示例，可直接运行 `python` [merge_v2_model.py](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense/merge_v2_model.py)。序列化结果会写入当前运行目录下的`output.paddle.model`文件中。使用这种方式，运行时C-API可以通过指定`output.paddle.model`文件的路径来加载预测模型。
#### 注意事项
......
doc/howto/cluster/multi_cluster/index_cn.rst
浏览文件 @ 2a4e7add
在不同集群中运行
================
用户的集群环境不尽相同，为了方便大家的部署，我们提供了多种的集群部署方式，方便提交集群训练任务，以下将一一介绍:
PaddlePaddle可以使用多种分布式计算平台构建分布式计算任务，包括：
- `Kubernetes <http://kubernetes.io>`_ Google开源的容器集群的调度框架，支持大规模集群生产环境的完整集群方案。
- `OpenMPI <https://www.open-mpi.org>`_ 成熟的高性能并行计算框架。
- `Fabric <http://www.fabfile.org>`_ 集群管理工具。可以使用`Fabric`编写集群任务提交和管理脚本。
`Kubernetes <http://kubernetes.io>`_ 是Google开源的容器集群的调度框架，支持大规模集群生产环境的完整集群方案。以下指南展示了PaddlePaddle对Kubernetes的支持：
对于不同的集群平台，会分别介绍集群作业的启动和停止方法。这些例子都可以在 `cluster_train_v2 <https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/scripts/cluster_train_v2>`_ 找到。
.. toctree::
:maxdepth: 1
k8s_cn.md
k8s_distributed_cn.md
在使用分布式计算平台进行训练时，任务被调度在集群中时，分布式计算平台通常会通过API或者环境变量提供任务运行需要的参数，比如节点的ID、IP和任务节点个数等。
`OpenMPI <https://www.open-mpi.org>`_ 是成熟的高性能并行计算框架，在HPC领域使用非常的广泛。以下指南介绍了如何使用OpenMPI来搭建PaddlePaddle的集群训练任务:
.. toctree::
:maxdepth: 1
fabric_cn.md
openmpi_cn.md
k8s_cn.md
k8s_distributed_cn.md
`Fabric <http://www.fabfile.org>`_ 是一个方便的程序部署和管理工具。我们提供了使用Fabric 进行部署、管理的方法，如果想详细了解，请阅读以下指南:
.. toctree::
:maxdepth: 1
fabric_cn.md
我们也支持在AWS上部署PaddlePaddle，详细请了解:
.. toctree::
:maxdepth: 1
k8s_aws_cn.md
您可以在 `cluster_train_v2 <https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/scripts/cluster_train_v2>`_ 找到以上相关的例子。
doc/mobile/cross_compiling_for_android_cn.md
浏览文件 @ 2a4e7add
......@@ -23,6 +23,12 @@ $ docker build -t username/paddle-android:dev . -f Dockerfile.android
$ docker pull paddlepaddle/paddle:latest-dev-android
```
对于国内用户，我们提供了加速访问的镜像源：
```bash
$ docker pull docker.paddlepaddlehub.com/paddle:latest-dev-android
```
### 编译PaddlePaddle C-API库
构建好开发镜像后，即可使用开发镜像来编译Android版PaddlePaddle C-API库。
Android的Docker开发镜像向用户提供两个可配置的参数：
......@@ -56,15 +62,15 @@ Android的Docker开发镜像向用户提供两个可配置的参数：
- 编译`armeabi-v7a`，`Android API 21`的PaddlePaddle库
```bash
$ docker run -it --rm -v $PWD:/paddle -e "ANDROID_ABI=armeabi-v7a" -e "ANDROID_API=21" username/paddle-android:dev
```
```bash
$ docker run -it --rm -v $PWD:/paddle -e "ANDROID_ABI=armeabi-v7a" -e "ANDROID_API=21" username/paddle-android:dev
```
- 编译`arm64-v8a`，`Android API 21`的PaddlePaddle库
```bash
$ docker run -it --rm -v $PWD:/paddle -e "ANDROID_ABI=arm64-v8a" -e "ANDROID_API=21" username/paddle-android:dev
```
```bash
$ docker run -it --rm -v $PWD:/paddle -e "ANDROID_ABI=arm64-v8a" -e "ANDROID_API=21" username/paddle-android:dev
```
执行上述`docker run`命令时，容器默认执行[paddle/scripts/docker/build_android.sh](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build_android.sh)脚本。该脚本中记录了交叉编译Android版PaddlePaddle库常用的CMake配置，并且会根据`ANDROID_ABI`和`ANDROID_API`自动构建独立工具链、进行编译和安装。由于arm64架构要求Android API不小于21。因此当`ANDROID_ABI=arm64-v8a`，`ANDROID_API<21`时，Docker容器中将默认使用`Android API 21`的编译工具链。用户可以参考下文[配置交叉编译参数](#配置交叉编译参数)章节，根据个人的需求修改定制Docker容器所执行的脚本。编译安装结束之后，PaddlePaddle的C-API库将被安装到`$PWD/install_android`目录，所依赖的第三方库同时也被安装到`$PWD/install_android/third_party`目录。
......@@ -155,7 +161,11 @@ cmake -DCMAKE_SYSTEM_NAME=Android \
..
```
用户还可根据自己的需求设置其他编译参数。比如希望最小化生成的库的大小，可以设置`CMAKE_BUILD_TYPE`为`MinSizeRel`；若希望最快的执行速度，则可设置`CMAKE_BUILD_TYPE`为`Release`。亦可以通过手动设置`CMAKE_C/CXX_FLAGS`来影响PaddlePaddle的编译过程。
用户还可根据自己的需求设置其他编译参数。
- 设置`CMAKE_BUILD_TYPE`为`MinSizeRel`，最小化生成的库的大小。
- 设置`CMAKE_BUILD_TYPE`为`Release`，获得最快的执行速度，
- 用户亦可以通过手动设置`CMAKE_C/CXX_FLAGS`来影响PaddlePaddle的编译过程。
**性能TIPS**，为了达到最快的计算速度，在CMake参数配置上，有以下建议：
......
doc/mobile/cross_compiling_for_android_en.md
浏览文件 @ 2a4e7add
......@@ -25,6 +25,12 @@ Users can directly use the published Docker image.
$ docker pull paddlepaddle/paddle:latest-dev-android
```
For users in China, we provide a faster mirror.
```bash
$ docker pull docker.paddlepaddlehub.com/paddle:latest-dev-android
```
### Build the Inference Library
We can run the Docker image we just created to build the inference library of PaddlePaddle for Android using the command below:
......@@ -86,19 +92,19 @@ Android NDK includes everything we need to build the [*standalone toolchain*](ht
- To build the standalone toolchain for `armeabi-v7a` and Android API level 21:
```bash
your/path/to/android-ndk-r14b-linux-x86_64/build/tools/make-standalone-toolchain.sh \
--arch=arm --platform=android-21 --install-dir=your/path/to/arm_standalone_toolchain
```
```bash
your/path/to/android-ndk-r14b-linux-x86_64/build/tools/make-standalone-toolchain.sh \
--arch=arm --platform=android-21 --install-dir=your/path/to/arm_standalone_toolchain
```
The generated standalone toolchain will be in `your/path/to/arm_standalone_toolchain`.
- To build the standalone toolchain for `arm64-v8a` and Android API level 21:
```bash
your/path/to/android-ndk-r14b-linux-x86_64/build/tools/make-standalone-toolchain.sh \
--arch=arm64 --platform=android-21 --install-dir=your/path/to/arm64_standalone_toolchain
```
```bash
your/path/to/android-ndk-r14b-linux-x86_64/build/tools/make-standalone-toolchain.sh \
--arch=arm64 --platform=android-21 --install-dir=your/path/to/arm64_standalone_toolchain
```
The generated standalone toolchain will be in `your/path/to/arm64_standalone_toolchain`.
......
doc/templates/conf.py.cn.in
浏览文件 @ 2a4e7add
......@@ -18,7 +18,6 @@ import shlex
from recommonmark import parser, transform
import paddle
import paddle.v2
import paddle.fluid
MarkdownParser = parser.CommonMarkParser
AutoStructify = transform.AutoStructify
......
doc/templates/conf.py.en.in
浏览文件 @ 2a4e7add
......@@ -18,7 +18,6 @@ import shlex
from recommonmark import parser, transform
import paddle
import paddle.v2
import paddle.fluid
MarkdownParser = parser.CommonMarkParser
......
paddle/fluid/framework/executor.cc
浏览文件 @ 2a4e7add
......@@ -58,13 +58,13 @@ static void CreateTensor(Variable* var, proto::VarType::Type var_type) {
var->GetMutable<ReaderHolder>();
} else if (var_type == proto::VarType::CHANNEL) {
var->GetMutable<ChannelHolder>();
} else if (var_type == proto::VarType::NCCL_COM) {
// GetMutable will be called in ncclInit
} else if (var_type == proto::VarType::RAW) {
// GetMutable will be called in operator
} else {
PADDLE_THROW(
"Variable type %d is not in "
"[LOD_TENSOR, SELECTED_ROWS, FEED_MINIBATCH, FETCH_LIST, "
"LOD_RANK_TABLE, PLACE_LIST, READER, CHANNEL, NCCL_COM]",
"LOD_RANK_TABLE, PLACE_LIST, READER, CHANNEL, RAW]",
var_type);
}
}
......@@ -127,7 +127,9 @@ void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
auto op = paddle::framework::OpRegistry::CreateOp(*op_desc);
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
platform::RecordEvent record_event(op->Type(), pool.Get(place_));
// TODO(panyx0718): Need a program id to distinguish programs.
platform::RecordEvent record_event(op->Type(), pool.Get(place_),
op_desc->Block()->ID());
VLOG(3) << place_ << " " << op->DebugStringEx(local_scope);
op->Run(*local_scope, place_);
......
paddle/fluid/framework/framework.proto
浏览文件 @ 2a4e7add
......@@ -113,7 +113,10 @@ message VarType {
PLACE_LIST = 14;
READER = 15;
CHANNEL = 16;
NCCL_COM = 17;
// Any runtime decided variable type is raw
// raw variables should manage their own allocations
// in operators like nccl_op
RAW = 17;
}
required Type type = 1;
......@@ -164,4 +167,6 @@ message BlockDesc {
// Please refer to
// https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/program.md
// for more details.
// TODO(panyx0718): A model can have multiple programs. Need a
// way to distinguish them. Maybe ID or name?
message ProgramDesc { repeated BlockDesc blocks = 1; }
paddle/fluid/framework/lod_tensor.cc
浏览文件 @ 2a4e7add
......@@ -31,8 +31,14 @@ std::ostream &operator<<(std::ostream &os, const LoD &lod) {
os << "{";
for (auto &v : lod) {
os << "{";
bool is_first = true;
for (auto &i : v) {
os << i << ",";
if (is_first) {
os << i;
is_first = false;
} else {
os << ", " << i;
}
}
os << "}";
}
......
paddle/fluid/framework/op_desc.h
浏览文件 @ 2a4e7add
......@@ -125,6 +125,8 @@ class OpDesc {
BlockDesc *Block() { return this->block_; }
const BlockDesc &BlockRef() const { return *this->block_; }
void SetBlock(BlockDesc *block) { this->block_ = block; }
private:
......
paddle/fluid/inference/io.cc
浏览文件 @ 2a4e7add
......@@ -32,23 +32,11 @@ void ReadBinaryFile(const std::string& filename, std::string& contents) {
inputfs.close();
}
bool IsParameter(const framework::VarDesc* var,
const framework::ProgramDesc& main_program) {
if (var->Persistable()) {
// There are many unreachable variables in the program
for (size_t i = 0; i < main_program.Size(); ++i) {
const framework::BlockDesc& block = main_program.Block(i);
for (auto* op : block.AllOps()) {
if (op->Type() == framework::kFeedOpType) {
continue;
}
for (auto input_argument_name : op->InputArgumentNames()) {
if (input_argument_name == var->Name()) {
return true;
}
}
}
}
bool IsPersistable(const framework::VarDesc* var) {
if (var->Persistable() &&
var->GetType() != framework::proto::VarType::FEED_MINIBATCH &&
var->GetType() != framework::proto::VarType::FETCH_LIST) {
return true;
}
return false;
}
......@@ -65,8 +53,8 @@ void LoadPersistables(framework::Executor& executor,
std::vector<std::string> paramlist;
for (auto* var : global_block.AllVars()) {
if (IsParameter(var, main_program)) {
VLOG(3) << "parameter's name: " << var->Name();
if (IsPersistable(var)) {
VLOG(3) << "persistable variable's name: " << var->Name();
framework::VarDesc* new_var = load_block->Var(var->Name());
new_var->SetShape(var->GetShape());
......@@ -101,7 +89,6 @@ void LoadPersistables(framework::Executor& executor,
executor.Run(*load_program, &scope, 0, true, true);
VLOG(3) << "Ran loading successfully";
delete load_program;
}
......
paddle/fluid/inference/tests/book/CMakeLists.txt
浏览文件 @ 2a4e7add
......@@ -30,5 +30,5 @@ inference_test(label_semantic_roles)
inference_test(recognize_digits ARGS mlp conv)
inference_test(recommender_system)
#inference_test(rnn_encoder_decoder)
inference_test(understand_sentiment)
inference_test(understand_sentiment ARGS conv)
inference_test(word2vec)
paddle/fluid/inference/tests/book/test_inference_label_semantic_roles.cc
浏览文件 @ 2a4e7add
......@@ -32,16 +32,42 @@ TEST(inference, label_semantic_roles) {
paddle::framework::LoDTensor word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1,
ctx_p2, mark;
paddle::framework::LoD lod{{0, 4, 10}};
SetupLoDTensor(word, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
SetupLoDTensor(
predicate, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
SetupLoDTensor(ctx_n2, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
SetupLoDTensor(ctx_n1, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
SetupLoDTensor(ctx_0, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
SetupLoDTensor(ctx_p1, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
SetupLoDTensor(ctx_p2, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
SetupLoDTensor(mark, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
int64_t word_dict_len = 44068;
int64_t predicate_dict_len = 3162;
int64_t mark_dict_len = 2;
SetupLoDTensor(word,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(predicate,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(predicate_dict_len - 1));
SetupLoDTensor(ctx_n2,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(ctx_n1,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(ctx_0,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(ctx_p1,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(ctx_p2,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(mark,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(mark_dict_len - 1));
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&word);
......
paddle/fluid/inference/tests/book/test_inference_understand_sentiment.cc
浏览文件 @ 2a4e7add
......@@ -31,7 +31,12 @@ TEST(inference, understand_sentiment) {
paddle::framework::LoDTensor words;
paddle::framework::LoD lod{{0, 4, 10}};
SetupLoDTensor(words, lod, static_cast<int64_t>(0), static_cast<int64_t>(10));
int64_t word_dict_len = 5147;
SetupLoDTensor(words,
lod,
static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&words);
......
paddle/fluid/inference/tests/book/test_inference_word2vec.cc
浏览文件 @ 2a4e7add
......@@ -31,12 +31,12 @@ TEST(inference, word2vec) {
paddle::framework::LoDTensor first_word, second_word, third_word, fourth_word;
paddle::framework::LoD lod{{0, 1}};
int64_t dict_size = 2072; // Hard-coding the size of dictionary
int64_t dict_size = 2073; // The size of dictionary
SetupLoDTensor(first_word, lod, static_cast<int64_t>(0), dict_size);
SetupLoDTensor(second_word, lod, static_cast<int64_t>(0), dict_size);
SetupLoDTensor(third_word, lod, static_cast<int64_t>(0), dict_size);
SetupLoDTensor(fourth_word, lod, static_cast<int64_t>(0), dict_size);
SetupLoDTensor(first_word, lod, static_cast<int64_t>(0), dict_size - 1);
SetupLoDTensor(second_word, lod, static_cast<int64_t>(0), dict_size - 1);
SetupLoDTensor(third_word, lod, static_cast<int64_t>(0), dict_size - 1);
SetupLoDTensor(fourth_word, lod, static_cast<int64_t>(0), dict_size - 1);
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&first_word);
......
paddle/fluid/inference/tests/test_helper.h
浏览文件 @ 2a4e7add
......@@ -101,8 +101,8 @@ void TestInference(const std::string& dirname,
if (IsCombined) {
// All parameters are saved in a single file.
// Hard-coding the file names of program and parameters in unittest.
// Users are free to specify different filename
// (provided: the filenames are changed in the python api as well: io.py)
// The file names should be consistent with that used in Python API
// `fluid.io.save_inference_model`.
std::string prog_filename = "__model_combined__";
std::string param_filename = "__params_combined__";
inference_program = paddle::inference::Load(executor,
......
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ 2a4e7add
......@@ -11,6 +11,8 @@ function(op_library TARGET)
set(cc_srcs)
set(cu_srcs)
set(cu_cc_srcs)
set(cudnn_cu_cc_srcs)
set(CUDNN_FILE)
set(op_common_deps operator op_registry math_function)
set(options "")
set(oneValueArgs "")
......@@ -30,10 +32,16 @@ function(op_library TARGET)
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${TARGET}.cu)
list(APPEND cu_srcs ${TARGET}.cu)
endif()
string(REPLACE "_op" "_cudnn_op" CUDNN_FILE "${TARGET}")
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${CUDNN_FILE}.cu.cc)
list(APPEND cudnn_cu_cc_srcs ${CUDNN_FILE}.cu.cc)
endif()
else()
foreach(src ${op_library_SRCS})
if (${src} MATCHES ".*\\.cu$")
list(APPEND cu_srcs ${src})
elseif(${src} MATCHES ".*_cudnn_op.cu.cc$")
list(APPEND cudnn_cu_cc_srcs ${src})
elseif(${src} MATCHES ".*\\.cu.cc$")
list(APPEND cu_cc_srcs ${src})
elseif(${src} MATCHES ".*\\.cc$")
......@@ -54,7 +62,7 @@ function(op_library TARGET)
set(DEPS_OPS ${TARGET} ${DEPS_OPS} PARENT_SCOPE)
endif()
if (WITH_GPU)
nv_library(${TARGET} SRCS ${cc_srcs} ${cu_cc_srcs} ${cu_srcs} DEPS ${op_library_DEPS}
nv_library(${TARGET} SRCS ${cc_srcs} ${cu_cc_srcs} ${cudnn_cu_cc_srcs} ${cu_srcs} DEPS ${op_library_DEPS}
${op_common_deps})
else()
cc_library(${TARGET} SRCS ${cc_srcs} DEPS ${op_library_DEPS}
......@@ -98,6 +106,12 @@ function(op_library TARGET)
set(pybind_flag 1)
endif()
# pybind USE_OP_DEVICE_KERNEL for CUDNN
list(LENGTH cudnn_cu_cc_srcs cudnn_cu_cc_srcs_len)
if (WITH_GPU AND ${cudnn_cu_cc_srcs_len} GREATER 0)
file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(${TARGET}, CUDNN);\n")
endif()
# pybind USE_OP
if (${pybind_flag} EQUAL 0)
file(APPEND ${pybind_file} "USE_OP(${TARGET});\n")
......@@ -141,6 +155,7 @@ op_library(print_op DEPS lod_tensor)
op_library(adagrad_op DEPS selected_rows_functor)
op_library(maxout_op DEPS maxouting)
op_library(unpool_op DEPS unpooling)
op_library(pool_op DEPS pooling)
op_library(pool_with_index_op DEPS pooling)
op_library(lod_rank_table_op DEPS lod_rank_table)
op_library(lod_tensor_to_array_op DEPS lod_rank_table_op)
......@@ -152,43 +167,17 @@ op_library(lstm_op DEPS sequence2batch lstm_compute)
op_library(lstmp_op DEPS sequence2batch lstm_compute)
op_library(gru_op DEPS sequence2batch gru_compute)
op_library(recurrent_op DEPS executor)
op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale math_function)
op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale)
op_library(cos_sim_op DEPS cos_sim_functor)
op_library(parallel_do_op DEPS executor)
op_library(create_reader_op DEPS reader)
# Regist multiple Kernel to pybind
if (WITH_GPU)
op_library(conv_op SRCS conv_op.cc conv_op.cu.cc conv_cudnn_op.cu.cc DEPS
vol2col depthwise_conv)
op_library(edit_distance_op SRCS edit_distance_op.cc edit_distance_op.cu DEPS math_function)
op_library(pool_op SRCS pool_op.cc pool_op.cu.cc pool_cudnn_op.cu.cc DEPS pooling)
op_library(conv_transpose_op SRCS conv_transpose_op.cc conv_transpose_op.cu.cc
conv_transpose_cudnn_op.cu.cc DEPS vol2col)
file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(conv2d, CUDNN);\n")
file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(pool2d, CUDNN);\n")
file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(conv2d_transpose, CUDNN);\n")
op_library(conv_op DEPS vol2col depthwise_conv)
else()
op_library(conv_op SRCS conv_op.cc DEPS vol2col)
op_library(pool_op SRCS pool_op.cc DEPS pooling)
op_library(conv_transpose_op SRCS conv_transpose_op.cc DEPS vol2col)
op_library(conv_op DEPS vol2col)
endif()
cc_library(batch_size_like SRCS batch_size_like.cc DEPS op_registry)
op_library(fill_constant_batch_size_like_op
SRCS fill_constant_batch_size_like_op.cc fill_constant_batch_size_like_op.cu.cc
DEPS batch_size_like)
op_library(uniform_random_batch_size_like_op
SRCS uniform_random_batch_size_like_op.cc
DEPS batch_size_like uniform_random_op)
op_library(gaussian_random_batch_size_like_op
SRCS gaussian_random_batch_size_like_op.cc
DEPS batch_size_like gaussian_random_op)
op_library(conv_transpose_op DEPS vol2col)
# FIXME(typhoonzero): save/load depends lodtensor serialization functions
op_library(save_op DEPS lod_tensor)
......
paddle/fluid/operators/batch_size_like.cc 已删除 100644 → 0
浏览文件 @ 5f70629f
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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. */
#include "paddle/fluid/operators/batch_size_like.h"
namespace paddle {
namespace operators {
void BatchSizeLikeOp::InferShape(framework::InferShapeContext *ctx) const {
PADDLE_ENFORCE(ctx->HasInput("Input"),
"Input(Input) of %s should not be null.", Type());
PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) of %s should not be null.",
Type());
auto &shape = ctx->Attrs().Get<std::vector<int>>("shape");
PADDLE_ENFORCE_GT(shape.size(), 0);
std::vector<int64_t> shape_int64(shape.size(), 0);
std::transform(shape.begin(), shape.end(), shape_int64.begin(),
[](int a) { return static_cast<int64_t>(a); });
auto output_dim = framework::make_ddim(shape_int64);
int input_dim_idx = ctx->Attrs().Get<int>("input_dim_idx");
PADDLE_ENFORCE_GE(input_dim_idx, 0);
PADDLE_ENFORCE_GT(ctx->GetInputDim("Input").size(), input_dim_idx);
int output_dim_idx = ctx->Attrs().Get<int>("output_dim_idx");
PADDLE_ENFORCE_GE(output_dim_idx, 0);
PADDLE_ENFORCE_GT(static_cast<int>(shape.size()), output_dim_idx);
output_dim[output_dim_idx] = ctx->GetInputDim("Input")[input_dim_idx];
ctx->SetOutputDim("Out", output_dim);
}
BatchSizeLikeOpMaker::BatchSizeLikeOpMaker(OpProto *proto,
OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Input",
"(Tensor) Tensor "
"whose input_dim_idx'th dimension specifies the batch_size");
AddOutput("Out",
"(Tensor) Tensor of specified shape will be filled "
"with the specified value");
AddAttr<std::vector<int>>("shape", "(vector<int>) The shape of the output");
AddAttr<int>("input_dim_idx",
"(int, default 0) The index of input's batch size dimension")
.SetDefault(0);
AddAttr<int>("output_dim_idx",
"(int, default 0) The index of output's batch size dimension")
.SetDefault(0);
}
} // namespace operators
} // namespace paddle
paddle/fluid/operators/batch_size_like.h
浏览文件 @ 2a4e7add
......@@ -24,12 +24,50 @@ class BatchSizeLikeOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Input"),
"Input(Input) of %s should not be null.", Type());
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of %s should not be null.", Type());
auto &shape = ctx->Attrs().Get<std::vector<int>>("shape");
PADDLE_ENFORCE_GT(shape.size(), 0);
std::vector<int64_t> shape_int64(shape.size(), 0);
std::transform(shape.begin(), shape.end(), shape_int64.begin(),
[](int a) { return static_cast<int64_t>(a); });
auto output_dim = framework::make_ddim(shape_int64);
int input_dim_idx = ctx->Attrs().Get<int>("input_dim_idx");
PADDLE_ENFORCE_GE(input_dim_idx, 0);
PADDLE_ENFORCE_GT(ctx->GetInputDim("Input").size(), input_dim_idx);
int output_dim_idx = ctx->Attrs().Get<int>("output_dim_idx");
PADDLE_ENFORCE_GE(output_dim_idx, 0);
PADDLE_ENFORCE_GT(static_cast<int>(shape.size()), output_dim_idx);
output_dim[output_dim_idx] = ctx->GetInputDim("Input")[input_dim_idx];
ctx->SetOutputDim("Out", output_dim);
}
};
class BatchSizeLikeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
BatchSizeLikeOpMaker(OpProto *proto, OpAttrChecker *op_checker);
BatchSizeLikeOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Input",
"(Tensor) Tensor "
"whose input_dim_idx'th dimension specifies the batch_size");
AddOutput("Out",
"(Tensor) Tensor of specified shape will be filled "
"with the specified value");
AddAttr<std::vector<int>>("shape", "(vector<int>) The shape of the output");
AddAttr<int>("input_dim_idx",
"(int, default 0) The index of input's batch size dimension")
.SetDefault(0);
AddAttr<int>("output_dim_idx",
"(int, default 0) The index of output's batch size dimension")
.SetDefault(0);
}
};
} // namespace operators
......
paddle/fluid/operators/bipartite_match_op.cc
浏览文件 @ 2a4e7add
......@@ -94,6 +94,38 @@ class BipartiteMatchKernel : public framework::OpKernel<T> {
}
}
void ArgMaxMatch(const Tensor& dist, int* match_indices, T* match_dist,
T overlap_threshold) const {
constexpr T kEPS = static_cast<T>(1e-6);
int64_t row = dist.dims()[0];
int64_t col = dist.dims()[1];
auto* dist_data = dist.data<T>();
for (int64_t j = 0; j < col; ++j) {
if (match_indices[j] != -1) {
// the j-th column has been matched to one entity.
continue;
}
int max_row_idx = -1;
T max_dist = -1;
for (int i = 0; i < row; ++i) {
T dist = dist_data[i * col + j];
if (dist < kEPS) {
// distance is 0 between m-th row and j-th column
continue;
}
if (dist >= overlap_threshold && dist > max_dist) {
max_row_idx = i;
max_dist = dist;
}
}
if (max_row_idx != -1) {
PADDLE_ENFORCE_EQ(match_indices[j], -1);
match_indices[j] = max_row_idx;
match_dist[j] = max_dist;
}
}
}
void Compute(const framework::ExecutionContext& context) const override {
auto* dist_mat = context.Input<LoDTensor>("DistMat");
auto* match_indices = context.Output<Tensor>("ColToRowMatchIndices");
......@@ -120,13 +152,21 @@ class BipartiteMatchKernel : public framework::OpKernel<T> {
int* indices = match_indices->data<int>();
T* dist = match_dist->data<T>();
auto type = context.Attr<std::string>("match_type");
auto threshold = context.Attr<float>("dist_threshold");
if (n == 1) {
BipartiteMatch(*dist_mat, indices, dist);
if (type == "per_prediction") {
ArgMaxMatch(*dist_mat, indices, dist, threshold);
}
} else {
auto lod = dist_mat->lod().back();
for (size_t i = 0; i < lod.size() - 1; ++i) {
Tensor one_ins = dist_mat->Slice(lod[i], lod[i + 1]);
BipartiteMatch(one_ins, indices + i * col, dist + i * col);
if (type == "per_prediction") {
ArgMaxMatch(one_ins, indices + i * col, dist + i * col, threshold);
}
}
}
}
......@@ -147,6 +187,19 @@ class BipartiteMatchOpMaker : public framework::OpProtoAndCheckerMaker {
"This tensor can contain LoD information to represent a batch of "
"inputs. One instance of this batch can contain different numbers of "
"entities.");
AddAttr<std::string>(
"match_type",
"(string, defalut: per_prediction) "
"The type of matching method, should be 'bipartite' or "
"'per_prediction', 'bipartite' by defalut.")
.SetDefault("bipartite")
.InEnum({"bipartite", "per_prediction"});
AddAttr<float>(
"dist_threshold",
"(float, defalut: 0.5) "
"If `match_type` is 'per_prediction', this threshold is to determine "
"the extra matching bboxes based on the maximum distance.")
.SetDefault(0.5);
AddOutput("ColToRowMatchIndices",
"(Tensor) A 2-D Tensor with shape [N, M] in int type. "
"N is the batch size. If ColToRowMatchIndices[i][j] is -1, it "
......@@ -168,10 +221,10 @@ distance matrix. For input 2D matrix, the bipartite matching algorithm can
find the matched column for each row, also can find the matched row for
each column. And this operator only calculate matched indices from column
to row. For each instance, the number of matched indices is the number of
of columns of the input ditance matrix.
of columns of the input distance matrix.
There are two outputs to save matched indices and distance.
A simple description, this algothrim matched the best (maximum distance)
A simple description, this algorithm matched the best (maximum distance)
row entity to the column entity and the matched indices are not duplicated
in each row of ColToRowMatchIndices. If the column entity is not matched
any row entity, set -1 in ColToRowMatchIndices.
......
paddle/fluid/operators/concat_op.h
浏览文件 @ 2a4e7add
......@@ -14,6 +14,7 @@ limitations under the License. */
#pragma once
#include <utility>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/strided_memcpy.h"
......@@ -34,12 +35,46 @@ class ConcatKernel : public framework::OpKernel<T> {
auto out_stride = framework::stride_numel(out->dims());
size_t output_offset = 0;
for (auto* in : ins) {
auto in_stride = framework::stride_numel(in->dims());
StridedNumelCopyWithAxis<T>(ctx.device_context(), axis,
out->data<T>() + output_offset, out_stride,
in->data<T>(), in_stride, in_stride[axis]);
output_offset += in_stride[axis];
// If axis >=1, copy to out immediately need to call many times
// of cuda memcpy. Copy the input to cpu and do the stride copy,
// then copy to gpu output.
if (platform::is_gpu_place(place) && axis >= 1) {
platform::CPUPlace copy_place;
auto& cpu_ctx = *platform::DeviceContextPool::Instance().Get(copy_place);
framework::Tensor cpu_out;
cpu_out.Resize(out->dims());
cpu_out.mutable_data<T>(copy_place);
auto& dev_ctx = ctx.device_context();
std::vector<std::unique_ptr<framework::Tensor>> cpu_ins;
for (auto* in : ins) {
std::unique_ptr<framework::Tensor> cpu_in(new framework::Tensor);
framework::TensorCopy(*in, copy_place, dev_ctx, cpu_in.get());
cpu_ins.emplace_back(std::move(cpu_in));
}
// TODO(dzhwinter): overlap copy and compute stream
// https://devblogs.nvidia.com/how-overlap-data-transfers-cuda-cc/
dev_ctx.Wait();
for (auto& in : cpu_ins) {
auto& cpu_in = *in.get();
auto in_stride = framework::stride_numel(cpu_in.dims());
StridedNumelCopyWithAxis<T>(
cpu_ctx, axis, cpu_out.data<T>() + output_offset, out_stride,
cpu_in.data<T>(), in_stride, in_stride[axis]);
output_offset += in_stride[axis];
}
framework::TensorCopy(cpu_out, place, dev_ctx, out);
} else {
for (auto* in : ins) {
auto in_stride = framework::stride_numel(in->dims());
StridedNumelCopyWithAxis<T>(ctx.device_context(), axis,
out->data<T>() + output_offset, out_stride,
in->data<T>(), in_stride, in_stride[axis]);
output_offset += in_stride[axis];
}
}
}
};
......
paddle/fluid/operators/conv_op.cc
浏览文件 @ 2a4e7add
......@@ -54,12 +54,6 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
std::vector<int64_t> output_shape({in_dims[0], filter_dims[0]});
for (size_t i = 0; i < strides.size(); ++i) {
PADDLE_ENFORCE(in_dims[i + 2] + 2 * paddings[i] -
(dilations[i] * (filter_dims[i + 2] - 1) + 1) >
0,
"Due to the settings of paddings, filter_dims and "
"dilations, the output size is less than 0, please check "
"again.");
output_shape.push_back(ConvOutputSize(in_dims[i + 2], filter_dims[i + 2],
dilations[i], paddings[i],
strides[i]));
......
paddle/fluid/operators/conv_op.h
浏览文件 @ 2a4e7add
......@@ -31,7 +31,14 @@ using Tensor = framework::Tensor;
inline int ConvOutputSize(int input_size, int filter_size, int dilation,
int padding, int stride) {
const int dkernel = dilation * (filter_size - 1) + 1;
const int output_size = (input_size + 2 * padding - dkernel) / stride + 1;
int output_size = (input_size + 2 * padding - dkernel) / stride + 1;
PADDLE_ENFORCE(
output_size > 0,
"Due to the settings of padding(%d), filter_size(%d), dilation(%d) and "
"stride(%d), the output size is less than 0, please check "
"again. Input_size:%d",
padding, filter_size, dilation, stride, input_size);
return output_size;
}
inline bool IsExpand(std::vector<int64_t>& filter_dim,
......
paddle/fluid/operators/detail/grpc_client.cc
浏览文件 @ 2a4e7add
......@@ -177,8 +177,8 @@ std::shared_ptr<grpc::Channel> RPCClient::GetChannel(const std::string& ep) {
args.SetMaxSendMessageSize(std::numeric_limits<int>::max());
args.SetMaxReceiveMessageSize(std::numeric_limits<int>::max());
auto ch = std::shared_ptr<grpc::Channel>(
grpc::CreateCustomChannel(ep, grpc::InsecureChannelCredentials(), args));
auto ch =
grpc::CreateCustomChannel(ep, grpc::InsecureChannelCredentials(), args);
channels_[ep] = ch;
return ch;
......
paddle/fluid/operators/listen_and_serv_op.cc
浏览文件 @ 2a4e7add
......@@ -129,6 +129,8 @@ class ListenAndServOp : public framework::OperatorBase {
}
if (exit_flag) {
rpc_service_->ShutDown();
rpc_service_->SetCond(1);
break;
}
try {
executor.Run(*program, &recv_scope, block->ID(), /*global_block*/
......
paddle/fluid/operators/nccl_op.cc
浏览文件 @ 2a4e7add
......@@ -65,7 +65,7 @@ class NCCLInitOpVarTypeInference : public framework::VarTypeInference {
framework::BlockDesc *block) const override {
auto out_var_name = op_desc.Output("Communicator").front();
auto &out_var = block->FindRecursiveOrCreateVar(out_var_name);
auto var_type = framework::proto::VarType::NCCL_COM;
auto var_type = framework::proto::VarType::RAW;
out_var.SetType(var_type);
}
};
......
paddle/fluid/operators/pool_op.cc
浏览文件 @ 2a4e7add
......@@ -19,6 +19,11 @@ namespace operators {
int PoolOutputSize(int input_size, int filter_size, int padding, int stride) {
int output_size = (input_size - filter_size + 2 * padding) / stride + 1;
PADDLE_ENFORCE(output_size > 0,
"Due to the settings of padding(%d), filter_size(%d) and "
"stride(%d), the output size is less than 0, please check "
"again. Input_size:%d",
padding, filter_size, stride, input_size);
return output_size;
}
......
paddle/fluid/operators/reshape_op.cc
浏览文件 @ 2a4e7add
......@@ -121,10 +121,15 @@ class ReshapeGradOp : public framework::OperatorWithKernel {
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
using CPU = paddle::platform::CPUDeviceContext;
REGISTER_OP(reshape, ops::ReshapeOp, ops::ReshapeOpMaker, reshape_grad,
ops::ReshapeGradOp);
REGISTER_OP_CPU_KERNEL(reshape,
ops::ReshapeKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
reshape_grad, ops::ReshapeGradKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(reshape, ops::ReshapeKernel<CPU, float>,
ops::ReshapeKernel<CPU, double>,
ops::ReshapeKernel<CPU, int>,
ops::ReshapeKernel<CPU, int64_t>);
REGISTER_OP_CPU_KERNEL(reshape_grad, ops::ReshapeGradKernel<CPU, float>,
ops::ReshapeGradKernel<CPU, double>,
ops::ReshapeGradKernel<CPU, int>,
ops::ReshapeGradKernel<CPU, int64_t>);
paddle/fluid/operators/reshape_op.cu
浏览文件 @ 2a4e7add
......@@ -13,10 +13,14 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/reshape_op.h"
using CUDA = paddle::platform::CUDADeviceContext;
REGISTER_OP_CUDA_KERNEL(
reshape,
paddle::operators::ReshapeKernel<paddle::platform::CUDAPlace, float>);
REGISTER_OP_CUDA_KERNEL(
reshape_grad,
paddle::operators::ReshapeGradKernel<paddle::platform::CUDAPlace, float>);
REGISTER_OP_CUDA_KERNEL(reshape, paddle::operators::ReshapeKernel<CUDA, float>,
paddle::operators::ReshapeKernel<CUDA, double>,
paddle::operators::ReshapeKernel<CUDA, int>,
paddle::operators::ReshapeKernel<CUDA, int64_t>);
REGISTER_OP_CUDA_KERNEL(reshape_grad,
paddle::operators::ReshapeGradKernel<CUDA, float>,
paddle::operators::ReshapeGradKernel<CUDA, double>,
paddle::operators::ReshapeGradKernel<CUDA, int>,
paddle::operators::ReshapeGradKernel<CUDA, int64_t>);
paddle/fluid/operators/send_op.cc
浏览文件 @ 2a4e7add
......@@ -121,9 +121,27 @@ This operator will send tensor to recv_op at the parameter server.
}
};
class SendOpVarTypeInference : public framework::VarTypeInference {
public:
void operator()(const framework::OpDesc& op_desc,
framework::BlockDesc* block) const override {
auto out_var_name = op_desc.Output("RPCClient").front();
auto& out_var = block->FindRecursiveOrCreateVar(out_var_name);
auto var_type = framework::proto::VarType::RAW;
out_var.SetType(var_type);
}
};
class SendOpShapeInference : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext* ctx) const override {}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(send, ops::SendOp, ops::SendOpMaker);
REGISTER_OPERATOR(send, ops::SendOp, paddle::framework::EmptyGradOpMaker,
ops::SendOpMaker, ops::SendOpVarTypeInference,
ops::SendOpShapeInference);
paddle/fluid/operators/send_recv_op_test.cc
浏览文件 @ 2a4e7add
......@@ -95,7 +95,7 @@ void AddOp(const std::string &type, const f::VariableNameMap &inputs,
for (auto kv : outputs) {
for (auto v : kv.second) {
auto var = block->Var(v);
var->SetDataType(f::proto::DataType::FP32);
var->SetDataType(f::proto::VarType::FP32);
}
}
......@@ -122,33 +122,37 @@ void StartServerNet(bool is_sparse) {
// sub program run in listen_and_serv_op, for simple test we use sum
f::ProgramDesc program;
f::BlockDesc *block = program.MutableBlock(0);
f::BlockDesc *optimize_block = program.MutableBlock(0);
// X for server side tensors, RX for received tensers, must be of same shape.
AddOp("sum", {{"X", {"x0", "x1"}}}, {{"Out", {"Out"}}}, {}, block);
AddOp("sum", {{"X", {"x0", "x1"}}}, {{"Out", {"Out"}}}, {}, optimize_block);
f::AttributeMap attrs;
attrs.insert({"endpoint", std::string("127.0.0.1:6174")});
attrs.insert({"Fanin", 1});
attrs.insert({"ParamList", std::vector<std::string>({"Out"})});
attrs.insert({"GradList", std::vector<std::string>({"x1"})});
attrs.insert({"OptimizeBlock", block});
attrs.insert({"OptimizeBlock", optimize_block});
listen_and_serv_op =
f::OpRegistry::CreateOp("listen_and_serv", {}, {}, attrs);
f::OpRegistry::CreateOp("listen_and_serv", {{"X", {"x1"}}}, {}, attrs);
listen_and_serv_op->Run(scope, place);
}
TEST(SendRecvOp, CPUDense) {
std::thread server_thread(StartServerNet, false);
sleep(10); // wait server to start
sleep(5); // wait server to start
// local net
f::Scope scope;
p::CPUPlace place;
InitTensorsInScope(scope, place);
// create rpc client var
scope.Var("RPC_CLIENT_VAR");
f::AttributeMap attrs;
attrs.insert({"endpoints", std::vector<std::string>({"127.0.0.1:6174"})});
attrs.insert({"epmap", std::vector<std::string>({"127.0.0.1:6174"})});
auto send_op = f::OpRegistry::CreateOp("send", {{"X", {"x1"}}},
{{"Out", {"Out"}}}, attrs);
auto send_op = f::OpRegistry::CreateOp(
"send", {{"X", {"x1"}}},
{{"Out", {"Out"}}, {"RPCClient", {"RPC_CLIENT_VAR"}}}, attrs);
send_op->Run(scope, place);
auto in_var = scope.Var("x1");
......@@ -175,11 +179,13 @@ TEST(SendRecvOp, CPUSparse) {
p::CPUPlace place;
p::CPUDeviceContext ctx(place);
InitSelectedRowsInScope(scope, place);
scope.Var("RPC_CLIENT_VAR");
f::AttributeMap attrs;
attrs.insert({"endpoints", std::vector<std::string>({"127.0.0.1:6174"})});
attrs.insert({"epmap", std::vector<std::string>({"127.0.0.1:6174"})});
auto send_op = f::OpRegistry::CreateOp("send", {{"X", {"x1"}}},
{{"Out", {"Out"}}}, attrs);
auto send_op = f::OpRegistry::CreateOp(
"send", {{"X", {"x1"}}},
{{"Out", {"Out"}}, {"RPCClient", {"RPC_CLIENT_VAR"}}}, attrs);
send_op->Run(scope, place);
auto x0 = scope.Var("x0")->GetMutable<f::SelectedRows>();
......
paddle/fluid/platform/CMakeLists.txt
浏览文件 @ 2a4e7add
proto_library(profiler_proto SRCS profiler.proto)
if(WITH_GPU)
cc_library(enforce SRCS enforce.cc DEPS)
else()
......@@ -37,7 +39,8 @@ nv_test(cudnn_helper_test SRCS cudnn_helper_test.cc DEPS dynload_cuda)
nv_test(transform_test SRCS transform_test.cu DEPS paddle_memory place device_context)
nv_test(nccl_test SRCS nccl_test.cu DEPS dynload_cuda gpu_info device_context)
cc_library(profiler SRCS profiler.cc DEPS device_context)
cc_library(device_tracer SRCS device_tracer.cc DEPS profiler_proto ${GPU_CTX_DEPS})
cc_library(profiler SRCS profiler.cc DEPS device_context device_tracer)
cc_test(profiler_test SRCS profiler_test.cc DEPS profiler)
nv_test(float16_gpu_test SRCS float16_test.cu)
......
paddle/fluid/platform/device_tracer.cc 0 → 100644
浏览文件 @ 2a4e7add
/* 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. */
#include "paddle/fluid/platform/device_tracer.h"
#include <map>
#include <mutex>
#include "glog/logging.h"
#include "paddle/fluid/framework/block_desc.h"
#include "paddle/fluid/string/printf.h"
namespace paddle {
namespace platform {
namespace {
thread_local const char *cur_annotation = nullptr;
std::once_flag tracer_once_flag;
DeviceTracer *tracer = nullptr;
} // namespace
#ifdef PADDLE_WITH_CUPTI
namespace {
// TODO(panyx0718): Revisit the buffer size here.
uint64_t kBufSize = 32 * 1024;
uint64_t kAlignSize = 8;
#define ALIGN_BUFFER(buffer, align) \
(((uintptr_t)(buffer) & ((align)-1)) \
? ((buffer) + (align) - ((uintptr_t)(buffer) & ((align)-1))) \
: (buffer))
#define CUPTI_CALL(call) \
do { \
CUptiResult _status = call; \
if (_status != CUPTI_SUCCESS) { \
const char *errstr; \
dynload::cuptiGetResultString(_status, &errstr); \
fprintf(stderr, "%s:%d: error: function %s failed with error %s.\n", \
__FILE__, __LINE__, #call, errstr); \
exit(-1); \
} \
} while (0)
void EnableActivity() {
// Device activity record is created when CUDA initializes, so we
// want to enable it before cuInit() or any CUDA runtime call.
CUPTI_CALL(dynload::cuptiActivityEnable(CUPTI_ACTIVITY_KIND_MEMCPY));
CUPTI_CALL(dynload::cuptiActivityEnable(CUPTI_ACTIVITY_KIND_KERNEL));
CUPTI_CALL(dynload::cuptiActivityEnable(CUPTI_ACTIVITY_KIND_DEVICE));
CUPTI_CALL(dynload::cuptiActivityEnable(CUPTI_ACTIVITY_KIND_MEMSET));
CUPTI_CALL(dynload::cuptiActivityEnable(CUPTI_ACTIVITY_KIND_OVERHEAD));
// We don't track these activities for now.
// CUPTI_CALL(dynload::cuptiActivityEnable(CUPTI_ACTIVITY_KIND_CONTEXT));
// CUPTI_CALL(dynload::cuptiActivityEnable(CUPTI_ACTIVITY_KIND_DRIVER));
// CUPTI_CALL(dynload::cuptiActivityEnable(CUPTI_ACTIVITY_KIND_RUNTIME));
// CUPTI_CALL(dynload::cuptiActivityEnable(CUPTI_ACTIVITY_KIND_NAME));
// CUPTI_CALL(dynload::cuptiActivityEnable(CUPTI_ACTIVITY_KIND_MARKER));
}
void DisableActivity() {
CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_MEMCPY));
CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_KERNEL));
CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_DEVICE));
// Disable all other activity record kinds.
CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_CONTEXT));
CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_DRIVER));
CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_RUNTIME));
CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_MEMSET));
CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_NAME));
CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_MARKER));
CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_OVERHEAD));
}
void CUPTIAPI bufferRequested(uint8_t **buffer, size_t *size,
size_t *maxNumRecords) {
uint8_t *buf = (uint8_t *)malloc(kBufSize + kAlignSize);
*size = kBufSize;
*buffer = ALIGN_BUFFER(buf, kAlignSize);
*maxNumRecords = 0;
}
void CUPTIAPI bufferCompleted(CUcontext ctx, uint32_t streamId, uint8_t *buffer,
size_t size, size_t validSize) {
CUptiResult status;
CUpti_Activity *record = NULL;
if (validSize > 0) {
do {
status = dynload::cuptiActivityGetNextRecord(buffer, validSize, &record);
if (status == CUPTI_SUCCESS) {
switch (record->kind) {
case CUPTI_ACTIVITY_KIND_KERNEL:
case CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL: {
auto *kernel =
reinterpret_cast<const CUpti_ActivityKernel3 *>(record);
tracer->AddKernelRecords(kernel->start, kernel->end,
kernel->deviceId, kernel->streamId,
kernel->correlationId);
break;
}
default: { break; }
}
} else if (status == CUPTI_ERROR_MAX_LIMIT_REACHED) {
// Seems not an error in this case.
break;
} else {
CUPTI_CALL(status);
}
} while (1);
size_t dropped;
CUPTI_CALL(
dynload::cuptiActivityGetNumDroppedRecords(ctx, streamId, &dropped));
if (dropped != 0) {
fprintf(stderr, "Dropped %u activity records\n", (unsigned int)dropped);
}
}
free(buffer);
}
} // namespace
class DeviceTracerImpl : public DeviceTracer {
public:
DeviceTracerImpl() : enabled_(false) {}
void AddAnnotation(uint64_t id, const std::string &anno) {
std::lock_guard<std::mutex> l(trace_mu_);
correlations_[id] = anno;
}
void AddKernelRecords(uint64_t start, uint64_t end, uint32_t device_id,
uint32_t stream_id, uint32_t correlation_id) {
std::lock_guard<std::mutex> l(trace_mu_);
kernel_records_.push_back(
KernelRecord{start, end, device_id, stream_id, correlation_id});
}
bool IsEnabled() {
std::lock_guard<std::mutex> l(trace_mu_);
return enabled_;
}
void Enable() {
std::lock_guard<std::mutex> l(trace_mu_);
if (enabled_) {
fprintf(stderr, "DeviceTracer already enabled\n");
return;
}
EnableActivity();
// Register callbacks for buffer requests and completed by CUPTI.
CUPTI_CALL(dynload::cuptiActivityRegisterCallbacks(bufferRequested,
bufferCompleted));
CUptiResult ret;
ret = dynload::cuptiSubscribe(
&subscriber_, static_cast<CUpti_CallbackFunc>(ApiCallback), this);
if (ret == CUPTI_ERROR_MAX_LIMIT_REACHED) {
fprintf(stderr, "CUPTI subcriber limit reached.\n");
} else if (ret != CUPTI_SUCCESS) {
fprintf(stderr, "Failed to create CUPTI subscriber.\n");
}
CUPTI_CALL(
dynload::cuptiEnableCallback(1, subscriber_, CUPTI_CB_DOMAIN_DRIVER_API,
CUPTI_DRIVER_TRACE_CBID_cuLaunchKernel));
CUPTI_CALL(dynload::cuptiGetTimestamp(&start_ns_));
enabled_ = true;
}
proto::Profile GenProfile() {
std::lock_guard<std::mutex> l(trace_mu_);
proto::Profile profile_pb;
profile_pb.set_start_ns(start_ns_);
profile_pb.set_end_ns(end_ns_);
std::map<std::string, std::vector<uint64_t>> event_times;
for (const KernelRecord &r : kernel_records_) {
if (correlations_.find(r.correlation_id) == correlations_.end()) {
fprintf(stderr, "cannot relate a kernel activity\n");
continue;
}
auto *event = profile_pb.add_events();
event->set_name(correlations_.at(r.correlation_id));
event->set_start_ns(r.start_ns);
event->set_end_ns(r.end_ns);
event->set_stream_id(r.stream_id);
event->set_device_id(r.device_id);
event_times[event->name()].push_back(r.end_ns - r.start_ns);
}
for (const auto &et : event_times) {
fprintf(
stderr, "%s: total: %fms invoked cuda kernels: %lu\n",
et.first.c_str(),
std::accumulate(et.second.begin(), et.second.end(), 0) / 1000000.0,
et.second.size());
}
return profile_pb;
}
void Disable() {
// flush might cause additional calls to DeviceTracker.
dynload::cuptiActivityFlushAll(CUPTI_ACTIVITY_FLAG_FLUSH_FORCED);
std::lock_guard<std::mutex> l(trace_mu_);
DisableActivity();
dynload::cuptiUnsubscribe(subscriber_);
CUPTI_CALL(dynload::cuptiGetTimestamp(&end_ns_));
PADDLE_ENFORCE(dynload::cuptiFinalize());
enabled_ = false;
}
private:
static void CUPTIAPI ApiCallback(void *userdata, CUpti_CallbackDomain domain,
CUpti_CallbackId cbid, const void *cbdata) {
auto *cbInfo = reinterpret_cast<const CUpti_CallbackData *>(cbdata);
DeviceTracer *tracer = reinterpret_cast<DeviceTracer *>(userdata);
if ((domain == CUPTI_CB_DOMAIN_DRIVER_API) &&
(cbid == CUPTI_DRIVER_TRACE_CBID_cuLaunchKernel)) {
if (cbInfo->callbackSite == CUPTI_API_ENTER) {
const std::string anno =
cur_annotation ? cur_annotation : cbInfo->symbolName;
tracer->AddAnnotation(cbInfo->correlationId, anno);
}
} else {
VLOG(1) << "Unhandled API Callback for " << domain << " " << cbid;
}
}
std::mutex trace_mu_;
bool enabled_;
uint64_t start_ns_;
uint64_t end_ns_;
std::vector<KernelRecord> kernel_records_;
std::unordered_map<uint32_t, std::string> correlations_;
CUpti_SubscriberHandle subscriber_;
};
#endif // PADDLE_WITH_CUPTI
class DeviceTracerDummy : public DeviceTracer {
public:
DeviceTracerDummy() {}
void AddAnnotation(uint64_t id, const std::string &anno) {}
void AddKernelRecords(uint64_t start, uint64_t end, uint32_t device_id,
uint32_t stream_id, uint32_t correlation_id) {}
bool IsEnabled() { return false; }
void Enable() {}
proto::Profile GenProfile() { return proto::Profile(); }
void Disable() {}
};
void CreateTracer(DeviceTracer **t) {
#ifdef PADDLE_WITH_CUPTI
*t = new DeviceTracerImpl();
#else
*t = new DeviceTracerDummy();
#endif // PADDLE_WITH_CUPTI
}
DeviceTracer *GetDeviceTracer() {
std::call_once(tracer_once_flag, CreateTracer, &tracer);
return tracer;
}
void SetCurAnnotation(const char *anno) { cur_annotation = anno; }
void ClearCurAnnotation() { cur_annotation = nullptr; }
} // namespace platform
} // namespace paddle
paddle/fluid/platform/device_tracer.h 0 → 100644
浏览文件 @ 2a4e7add
/* 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. */
#pragma once
#include "paddle/fluid/platform/dynload/cupti.h"
#include "paddle/fluid/platform/profiler.pb.h"
namespace paddle {
namespace platform {
///////////////////////
// WARN: Under Development. Don't depend on it yet.
//////////////////////
// DeviceTracer performs the following tasks:
// 1. Register cuda callbacks for various events: kernel, memcpy, etc.
// 2. Collect cuda statistics: start/end ts, memory, etc.
// 3. Generate a protobuf for further analysis.
class DeviceTracer {
public:
struct KernelRecord {
uint64_t start_ns;
uint64_t end_ns;
uint32_t device_id;
uint32_t stream_id;
uint32_t correlation_id;
};
virtual ~DeviceTracer() {}
// Needs to be called once before use.
virtual void Enable() = 0;
// Needs to be called once after use.
virtual void Disable() = 0;
// Add a pair to correlate internal cuda id with high level
// annotation (string). So cuda statistics can be represented by
// human-readable annotations.
virtual void AddAnnotation(uint64_t id, const std::string& anno) = 0;
// Add a cuda kernel stats. `correlation_id` will be mapped to annotation
// added before for human readability.
virtual void AddKernelRecords(uint64_t start, uint64_t end,
uint32_t device_id, uint32_t stream_id,
uint32_t correlation_id) = 0;
// Generate a proto after done (Disabled).
virtual proto::Profile GenProfile() = 0;
virtual bool IsEnabled() = 0;
};
// Get a DeviceTracer.
DeviceTracer* GetDeviceTracer();
// Set a name for the cuda kernel operation being launched by the thread.
void SetCurAnnotation(const char* anno);
// Clear the name after the operation is done.
void ClearCurAnnotation();
} // namespace platform
} // namespace paddle
paddle/fluid/platform/dynload/CMakeLists.txt
浏览文件 @ 2a4e7add
cc_library(dynamic_loader SRCS dynamic_loader.cc DEPS glog gflags enforce)
nv_library(dynload_cuda SRCS cublas.cc cudnn.cc curand.cc nccl.cc
DEPS dynamic_loader)
list(APPEND CUDA_SRCS cublas.cc cudnn.cc curand.cc nccl.cc)
if (CUPTI_FOUND)
list(APPEND CUDA_SRCS cupti.cc)
endif(CUPTI_FOUND)
nv_library(dynload_cuda SRCS ${CUDA_SRCS} DEPS dynamic_loader)
cc_library(dynload_warpctc SRCS warpctc.cc DEPS dynamic_loader warpctc)
paddle/fluid/platform/dynload/cupti.cc 0 → 100644
浏览文件 @ 2a4e7add
/* 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. */
#ifdef PADDLE_WITH_CUPTI
#include "paddle/fluid/platform/dynload/cupti.h"
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace platform {
namespace dynload {
std::once_flag cupti_dso_flag;
void *cupti_dso_handle = nullptr;
#define DEFINE_WRAP(__name) DynLoad__##__name __name
CUPTI_ROUTINE_EACH(DEFINE_WRAP);
} // namespace dynload
} // namespace platform
} // namespace paddle
#endif // PADDLE_WITH_CUPTI
paddle/fluid/platform/dynload/cupti.h 0 → 100644
浏览文件 @ 2a4e7add
/* 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. */
#pragma once
#ifdef PADDLE_WITH_CUPTI
#include <cuda.h>
#include <cupti.h>
#include <dlfcn.h>
#include <mutex>
#include "paddle/fluid/platform/dynload/dynamic_loader.h"
namespace paddle {
namespace platform {
namespace dynload {
extern std::once_flag cupti_dso_flag;
extern void *cupti_dso_handle;
/**
* The following macro definition can generate structs
* (for each function) to dynamic load cupti routine
* via operator overloading.
*
* note: default dynamic linked libs
*/
#ifdef PADDLE_USE_DSO
#define DECLARE_DYNAMIC_LOAD_CUPTI_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
inline CUptiResult CUPTIAPI operator()(Args... args) { \
typedef CUptiResult CUPTIAPI (*cuptiFunc)(Args...); \
std::call_once(cupti_dso_flag, \
paddle::platform::dynload::GetCUPTIDsoHandle, \
&cupti_dso_handle); \
void *p_##__name = dlsym(cupti_dso_handle, #__name); \
return reinterpret_cast<cuptiFunc>(p_##__name)(args...); \
} \
}; \
extern DynLoad__##__name __name
#else
#define DECLARE_DYNAMIC_LOAD_CUPTI_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
inline CUptiResult CUPTIAPI operator()(Args... args) { \
return __name(args...); \
} \
}; \
extern DynLoad__##__name __name
#endif
#define CUPTI_ROUTINE_EACH(__macro) \
__macro(cuptiActivityEnable); \
__macro(cuptiActivityDisable); \
__macro(cuptiActivityRegisterCallbacks); \
__macro(cuptiActivityGetAttribute); \
__macro(cuptiActivitySetAttribute); \
__macro(cuptiGetTimestamp); \
__macro(cuptiActivityGetNextRecord); \
__macro(cuptiGetResultString); \
__macro(cuptiActivityGetNumDroppedRecords); \
__macro(cuptiActivityFlushAll); \
__macro(cuptiFinalize); \
__macro(cuptiSubscribe); \
__macro(cuptiUnsubscribe); \
__macro(cuptiEnableCallback);
CUPTI_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_CUPTI_WRAP);
#undef DECLARE_DYNAMIC_LOAD_CUPTI_WRAP
} // namespace dynload
} // namespace platform
} // namespace paddle
#endif // PADDLE_WITH_CUPTI
paddle/fluid/platform/dynload/dynamic_loader.cc
浏览文件 @ 2a4e7add
......@@ -40,10 +40,14 @@ DEFINE_string(nccl_dir, "",
"libcurand. For instance, /usr/local/cuda/lib64. If default, "
"dlopen will search cuda from LD_LIBRARY_PATH");
DEFINE_string(cupti_dir, "", "Specify path for loading cupti.so.");
namespace paddle {
namespace platform {
namespace dynload {
static const char* cupti_lib_path = CUPTI_LIB_PATH;
static inline std::string join(const std::string& part1,
const std::string& part2) {
// directory separator
......@@ -143,6 +147,18 @@ void GetCUDNNDsoHandle(void** dso_handle) {
#endif
}
void GetCUPTIDsoHandle(void** dso_handle) {
std::string cupti_path = cupti_lib_path;
if (!FLAGS_cupti_dir.empty()) {
cupti_path = FLAGS_cupti_dir;
}
#if defined(__APPLE__) || defined(__OSX__)
GetDsoHandleFromSearchPath(cupti_path, "libcupti.dylib", dso_handle, false);
#else
GetDsoHandleFromSearchPath(cupti_path, "libcupti.so", dso_handle, false);
#endif
}
void GetCurandDsoHandle(void** dso_handle) {
#if defined(__APPLE__) || defined(__OSX__)
GetDsoHandleFromSearchPath(FLAGS_cuda_dir, "libcurand.dylib", dso_handle);
......
paddle/fluid/platform/dynload/dynamic_loader.h
浏览文件 @ 2a4e7add
......@@ -34,6 +34,8 @@ void GetCublasDsoHandle(void** dso_handle);
*/
void GetCUDNNDsoHandle(void** dso_handle);
void GetCUPTIDsoHandle(void** dso_handle);
/**
* @brief load the DSO of CURAND
*
......
paddle/fluid/platform/profiler.cc
浏览文件 @ 2a4e7add
......@@ -15,7 +15,13 @@ limitations under the License. */
#include "paddle/fluid/platform/profiler.h"
#include <iomanip>
#include <map>
#ifdef PADDLE_WITH_CUDA
#include <cuda.h>
#endif // PADDLE_WITH_CUDA
#include "glog/logging.h"
#include "paddle/fluid/framework/block_desc.h"
#include "paddle/fluid/platform/device_tracer.h"
#include "paddle/fluid/string/printf.h"
namespace paddle {
namespace platform {
......@@ -126,15 +132,20 @@ void PopEvent(const std::string& name, const DeviceContext* dev_ctx) {
GetEventList().Record(EventKind::kPopRange, name, g_thread_id, dev_ctx);
}
RecordEvent::RecordEvent(const std::string& name,
const DeviceContext* dev_ctx) {
RecordEvent::RecordEvent(const std::string& name, const DeviceContext* dev_ctx,
int32_t block_id) {
if (g_state == ProfilerState::kDisabled) return;
dev_ctx_ = dev_ctx;
name_ = name;
PushEvent(name_, dev_ctx_);
full_name_ = string::Sprintf("%s_b%d", name, block_id);
// Maybe need the same push/pop behavior.
SetCurAnnotation(full_name_.c_str());
}
RecordEvent::~RecordEvent() {
ClearCurAnnotation();
if (g_state == ProfilerState::kDisabled) return;
PopEvent(name_, dev_ctx_);
}
......@@ -147,7 +158,14 @@ void EnableProfiler(ProfilerState state) {
"The profiling state should be disabled when calling ",
"EnableProfiler.");
g_state = state;
g_profiler_place = (g_state == ProfilerState::kCUDA) ? "CUDA" : "CPU";
if (g_state == ProfilerState::kCUDA) {
g_profiler_place = "CUDA";
} else if (g_state == ProfilerState::kCPU) {
g_profiler_place = "CPU";
} else {
g_profiler_place = "All";
GetDeviceTracer()->Enable();
}
#ifdef PADDLE_WITH_CUDA
if (g_state == ProfilerState::kCUDA) {
// Generate some dummy evenets first to reduce the startup overhead.
......@@ -190,6 +208,12 @@ void DisableProfiler(EventSortingKey sorted_key) {
Mark("_stop_profiler_", nullptr);
g_state = ProfilerState::kDisabled;
DeviceTracer* tracer = GetDeviceTracer();
if (g_profiler_place == "All" && tracer && tracer->IsEnabled()) {
tracer->Disable();
tracer->GenProfile();
}
std::vector<std::vector<Event>> all_events = GetAllEvents();
ParseEvents(all_events, sorted_key);
ResetProfiler();
......@@ -254,9 +278,11 @@ void ParseEvents(std::vector<std::vector<Event>>& events,
}
if (rit != pushed_events.rend()) {
double event_time = (g_profiler_place == "CUDA")
? rit->CudaElapsedMs(events[i][j])
: rit->CpuElapsedMs(events[i][j]);
double event_time =
(g_profiler_place == "CUDA" || g_profiler_place == "All")
? rit->CudaElapsedMs(events[i][j])
: rit->CpuElapsedMs(events[i][j]);
std::string event_name =
"thread" + std::to_string(rit->thread_id()) + "::" + rit->name();
max_name_width = std::max(max_name_width, event_name.size());
......
paddle/fluid/platform/profiler.h
浏览文件 @ 2a4e7add
......@@ -18,6 +18,7 @@ limitations under the License. */
#include <mutex>
#include <vector>
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/profiler.pb.h"
namespace paddle {
namespace platform {
......@@ -93,6 +94,7 @@ enum ProfilerState {
kDisabled, // disabled state
kCPU, // CPU profiling state
kCUDA, // GPU profiling state
kAll, // Profile both CPU and GPU. (Currently experimental).
};
void Mark(const std::string& name, const DeviceContext* dev_ctx);
......@@ -102,7 +104,8 @@ void PushEvent(const std::string& name, const DeviceContext* dev_ctx);
void PopEvent(const std::string& name, const DeviceContext* dev_ctx);
struct RecordEvent {
explicit RecordEvent(const std::string& name, const DeviceContext* dev_ctx);
RecordEvent(const std::string& name, const DeviceContext* dev_ctx,
int32_t block_id);
~RecordEvent();
......@@ -110,9 +113,12 @@ struct RecordEvent {
const DeviceContext* dev_ctx_;
// Event name
std::string name_;
// Need to distinguish name by op type, block_id, program_id and perhaps
// different kernel invocations within an op.
std::string full_name_;
};
// Return the event list of all threads. Asummed the returned value calls
// Return the event list of all threads. Assumed the returned value calls
// event_lists, event_lists[i][j] represents the j-th Event of i-th thread.
std::vector<std::vector<Event>> GetAllEvents();
......
paddle/fluid/platform/profiler.proto 0 → 100644
浏览文件 @ 2a4e7add
/* 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. */
syntax = "proto2";
package paddle.platform.proto;
message Event {
optional string name = 1;
optional uint64 start_ns = 2;
optional uint64 end_ns = 3;
optional uint32 device_id = 5;
optional uint32 stream_id = 6;
}
message Profile {
repeated Event events = 1;
optional uint64 start_ns = 2;
optional uint64 end_ns = 3;
}
\ No newline at end of file
paddle/fluid/platform/profiler_test.cc
浏览文件 @ 2a4e7add
......@@ -95,7 +95,7 @@ TEST(RecordEvent, RecordEvent) {
*/
for (int i = 1; i < 5; ++i) {
std::string name = "evs_op_" + std::to_string(i);
RecordEvent record_event(name, dev_ctx);
RecordEvent record_event(name, dev_ctx, 0);
int counter = 1;
while (counter != i * 1000) counter++;
}
......
paddle/fluid/pybind/protobuf.cc
浏览文件 @ 2a4e7add
......@@ -252,7 +252,7 @@ void BindVarDsec(py::module &m) {
.value("CHANNEL", proto::VarType::CHANNEL)
.value("PLACE_LIST", proto::VarType::PLACE_LIST)
.value("READER", proto::VarType::READER)
.value("NCCL_COM", proto::VarType::NCCL_COM);
.value("RAW", proto::VarType::RAW);
}
void BindOpDesc(py::module &m) {
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ 2a4e7add
......@@ -459,6 +459,7 @@ All parameter, weight, gradient are variables in Paddle.
.value("kDisabled", platform::ProfilerState::kDisabled)
.value("kCPU", platform::ProfilerState::kCPU)
.value("kCUDA", platform::ProfilerState::kCUDA)
.value("kAll", platform::ProfilerState::kAll)
.export_values();
py::enum_<platform::EventSortingKey>(m, "EventSortingKey", py::arithmetic())
......
paddle/scripts/docker/README.md
浏览文件 @ 2a4e7add
......@@ -58,7 +58,7 @@ Users can specify the following Docker build arguments with either "ON" or "OFF"
| `WITH_AVX` | OFF | Set to "ON" to enable AVX support. |
| `WITH_TESTING` | OFF | Build unit tests binaries. |
| `WITH_MKL` | ON | Build with [Intel® MKL](https://software.intel.com/en-us/mkl) and [Intel® MKL-DNN](https://github.com/01org/mkl-dnn) support. |
| `WITH_GOLANG` | ON | Build fault-tolerant parameter server written in go. |
| `WITH_GOLANG` | OFF | Build fault-tolerant parameter server written in go. |
| `WITH_SWIG_PY` | ON | Build with SWIG python API support. |
| `WITH_C_API` | OFF | Build capi libraries for inference. |
| `WITH_PYTHON` | ON | Build with python support. Turn this off if build is only for capi. |
......
paddle/scripts/docker/build.sh
浏览文件 @ 2a4e7add
......@@ -40,7 +40,7 @@ function cmake_gen() {
-DWITH_DISTRIBUTE=${WITH_DISTRIBUTE:-OFF}
-DWITH_MKL=${WITH_MKL:-ON}
-DWITH_AVX=${WITH_AVX:-OFF}
-DWITH_GOLANG=${WITH_GOLANG:-ON}
-DWITH_GOLANG=${WITH_GOLANG:-OFF}
-DCUDA_ARCH_NAME=${CUDA_ARCH_NAME:-All}
-DWITH_SWIG_PY=ON
-DWITH_C_API=${WITH_C_API:-OFF}
......@@ -49,6 +49,7 @@ function cmake_gen() {
-DCUDNN_ROOT=/usr/
-DWITH_STYLE_CHECK=${WITH_STYLE_CHECK:-ON}
-DWITH_TESTING=${WITH_TESTING:-ON}
-DWITH_FAST_BUNDLE_TEST=ON
-DCMAKE_EXPORT_COMPILE_COMMANDS=ON
========================================
EOF
......@@ -64,7 +65,7 @@ EOF
-DWITH_DISTRIBUTE=${WITH_DISTRIBUTE:-OFF} \
-DWITH_MKL=${WITH_MKL:-ON} \
-DWITH_AVX=${WITH_AVX:-OFF} \
-DWITH_GOLANG=${WITH_GOLANG:-ON} \
-DWITH_GOLANG=${WITH_GOLANG:-OFF} \
-DCUDA_ARCH_NAME=${CUDA_ARCH_NAME:-All} \
-DWITH_SWIG_PY=${WITH_SWIG_PY:-ON} \
-DWITH_C_API=${WITH_C_API:-OFF} \
......@@ -72,6 +73,7 @@ EOF
-DCUDNN_ROOT=/usr/ \
-DWITH_STYLE_CHECK=${WITH_STYLE_CHECK:-ON} \
-DWITH_TESTING=${WITH_TESTING:-ON} \
-DWITH_FAST_BUNDLE_TEST=ON \
-DCMAKE_EXPORT_COMPILE_COMMANDS=ON
}
......
paddle/testing/paddle_gtest_main.cc
浏览文件 @ 2a4e7add
......@@ -28,10 +28,9 @@ int main(int argc, char** argv) {
}
#ifdef PADDLE_WITH_CUDA
new_argv.push_back(
strdup("--tryfromenv=fraction_of_gpu_memory_to_use,use_pinned_memory,"
"warpctc_dir"));
strdup("--tryfromenv=fraction_of_gpu_memory_to_use,use_pinned_memory"));
#else
new_argv.push_back(strdup("--tryfromenv=use_pinned_memory,warpctc_dir"));
new_argv.push_back(strdup("--tryfromenv=use_pinned_memory"));
#endif
int new_argc = static_cast<int>(new_argv.size());
char** new_argv_address = new_argv.data();
......
python/paddle/fluid/distribute_transpiler.py
浏览文件 @ 2a4e7add
......@@ -226,8 +226,7 @@ class DistributeTranspiler:
rpc_client_var = program.global_block().create_var(
name="RPC_CLIENT_VAR",
persistable=True,
dtype='float32', # dtype and shape is not used in fact
shape=[0])
type=core.VarDesc.VarType.RAW)
# create send_op
program.global_block().append_op(
......
python/paddle/fluid/framework.py
浏览文件 @ 2a4e7add
......@@ -784,6 +784,7 @@ class Block(object):
elif type(v) == Variable:
var = Variable(
self,
type=v.type,
name=new_name,
error_clip=error_clip,
stop_gradient=stop_gradient)
......
python/paddle/fluid/io.py
浏览文件 @ 2a4e7add
......@@ -68,7 +68,7 @@ def save_vars(executor,
main_program=None,
vars=None,
predicate=None,
save_file_name=None):
filename=None):
"""
Save variables to directory by executor.
......@@ -80,8 +80,8 @@ def save_vars(executor,
as a bool. If it returns true, the corresponding input variable will be saved.
:param vars: variables need to be saved. If vars is specified, program & predicate
will be ignored
:param save_file_name: The name of a single file that all vars are saved to.
If it is None, save variables to separate files.
:param filename: The name of a single file that all vars are saved to.
If it is None, save variables to separate files.
:return: None
"""
......@@ -95,7 +95,7 @@ def save_vars(executor,
executor,
dirname=dirname,
vars=filter(predicate, main_program.list_vars()),
save_file_name=save_file_name)
filename=filename)
else:
save_program = Program()
save_block = save_program.global_block()
......@@ -103,7 +103,7 @@ def save_vars(executor,
save_var_map = {}
for each_var in vars:
new_var = _clone_var_in_block_(save_block, each_var)
if save_file_name is None:
if filename is None:
save_block.append_op(
type='save',
inputs={'X': [new_var]},
......@@ -112,7 +112,7 @@ def save_vars(executor,
else:
save_var_map[new_var.name] = new_var
if save_file_name is not None:
if filename is not None:
save_var_list = []
for name in sorted(save_var_map.keys()):
save_var_list.append(save_var_map[name])
......@@ -121,12 +121,12 @@ def save_vars(executor,
type='save_combine',
inputs={'X': save_var_list},
outputs={},
attrs={'file_path': os.path.join(dirname, save_file_name)})
attrs={'file_path': os.path.join(dirname, filename)})
executor.run(save_program)
def save_params(executor, dirname, main_program=None, save_file_name=None):
def save_params(executor, dirname, main_program=None, filename=None):
"""
Save all parameters to directory with executor.
"""
......@@ -136,11 +136,10 @@ def save_params(executor, dirname, main_program=None, save_file_name=None):
main_program=main_program,
vars=None,
predicate=is_parameter,
save_file_name=save_file_name)
filename=filename)
def save_persistables(executor, dirname, main_program=None,
save_file_name=None):
def save_persistables(executor, dirname, main_program=None, filename=None):
"""
Save all persistables to directory with executor.
"""
......@@ -150,7 +149,7 @@ def save_persistables(executor, dirname, main_program=None,
main_program=main_program,
vars=None,
predicate=is_persistable,
save_file_name=save_file_name)
filename=filename)
def load_vars(executor,
......@@ -158,7 +157,7 @@ def load_vars(executor,
main_program=None,
vars=None,
predicate=None,
load_file_name=None):
filename=None):
"""
Load variables from directory by executor.
......@@ -170,8 +169,8 @@ def load_vars(executor,
as a bool. If it returns true, the corresponding input variable will be loaded.
:param vars: variables need to be loaded. If vars is specified, program &
predicate will be ignored
:param load_file_name: The name of the single file that all vars are loaded from.
If it is None, load variables from separate files.
:param filename: The name of the single file that all vars are loaded from.
If it is None, load variables from separate files.
:return: None
"""
......@@ -185,7 +184,7 @@ def load_vars(executor,
executor,
dirname=dirname,
vars=filter(predicate, main_program.list_vars()),
load_file_name=load_file_name)
filename=filename)
else:
load_prog = Program()
load_block = load_prog.global_block()
......@@ -194,7 +193,7 @@ def load_vars(executor,
for each_var in vars:
assert isinstance(each_var, Variable)
new_var = _clone_var_in_block_(load_block, each_var)
if load_file_name is None:
if filename is None:
load_block.append_op(
type='load',
inputs={},
......@@ -203,7 +202,7 @@ def load_vars(executor,
else:
load_var_map[new_var.name] = new_var
if load_file_name is not None:
if filename is not None:
load_var_list = []
for name in sorted(load_var_map.keys()):
load_var_list.append(load_var_map[name])
......@@ -212,12 +211,12 @@ def load_vars(executor,
type='load_combine',
inputs={},
outputs={"Out": load_var_list},
attrs={'file_path': os.path.join(dirname, load_file_name)})
attrs={'file_path': os.path.join(dirname, filename)})
executor.run(load_prog)
def load_params(executor, dirname, main_program=None, load_file_name=None):
def load_params(executor, dirname, main_program=None, filename=None):
"""
load all parameters from directory by executor.
"""
......@@ -226,11 +225,10 @@ def load_params(executor, dirname, main_program=None, load_file_name=None):
dirname=dirname,
main_program=main_program,
predicate=is_parameter,
load_file_name=load_file_name)
filename=filename)
def load_persistables(executor, dirname, main_program=None,
load_file_name=None):
def load_persistables(executor, dirname, main_program=None, filename=None):
"""
load all persistables from directory by executor.
"""
......@@ -239,7 +237,7 @@ def load_persistables(executor, dirname, main_program=None,
dirname=dirname,
main_program=main_program,
predicate=is_persistable,
load_file_name=load_file_name)
filename=filename)
def get_inference_program(target_vars, main_program=None):
......@@ -299,7 +297,8 @@ def save_inference_model(dirname,
target_vars,
executor,
main_program=None,
save_file_name=None):
model_filename=None,
params_filename=None):
"""
Build a model especially for inference,
and save it to directory by the executor.
......@@ -310,8 +309,11 @@ def save_inference_model(dirname,
:param executor: executor that save inference model
:param main_program: original program, which will be pruned to build the inference model.
Default default_main_program().
:param save_file_name: The name of a single file that all parameters are saved to.
If it is None, save parameters to separate files.
:param model_filename: The name of file to save inference program.
If not specified, default filename `__model__` will be used.
:param params_filename: The name of file to save parameters.
It is used for the case that all parameters are saved in a single binary file.
If not specified, parameters are considered saved in separate files.
:return: None
"""
......@@ -342,15 +344,19 @@ def save_inference_model(dirname,
prepend_feed_ops(inference_program, feeded_var_names)
append_fetch_ops(inference_program, fetch_var_names)
if save_file_name == None:
model_file_name = dirname + "/__model__"
if model_filename is not None:
model_filename = os.path.basename(model_filename)
else:
model_file_name = dirname + "/__model_combined__"
model_filename = "__model__"
model_filename = os.path.join(dirname, model_filename)
with open(model_file_name, "wb") as f:
if params_filename is not None:
params_filename = os.path.basename(params_filename)
with open(model_filename, "wb") as f:
f.write(inference_program.desc.serialize_to_string())
save_persistables(executor, dirname, inference_program, save_file_name)
save_persistables(executor, dirname, inference_program, params_filename)
def get_feed_targets_names(program):
......@@ -371,15 +377,21 @@ def get_fetch_targets_names(program):
return fetch_targets_names
def load_inference_model(dirname, executor, load_file_name=None):
def load_inference_model(dirname,
executor,
model_filename=None,
params_filename=None):
"""
Load inference model from a directory
:param dirname: directory path
:param executor: executor that load inference model
:param load_file_name: The name of the single file that all parameters are loaded from.
If it is None, load parameters from separate files.
:param model_filename: The name of file to load inference program.
If not specified, default filename `__model__` will be used.
:param params_filename: The name of file to load parameters.
It is used for the case that all parameters are saved in a single binary file.
If not specified, parameters are considered saved in separate files.
:return: [program, feed_target_names, fetch_targets]
program: program especially for inference.
feed_target_names: Names of variables that need to feed data
......@@ -388,16 +400,20 @@ def load_inference_model(dirname, executor, load_file_name=None):
if not os.path.isdir(dirname):
raise ValueError("There is no directory named '%s'", dirname)
if load_file_name == None:
model_file_name = dirname + "/__model__"
if model_filename is not None:
model_filename = os.path.basename(model_filename)
else:
model_file_name = dirname + "/__model_combined__"
model_filename = "__model__"
model_filename = os.path.join(dirname, model_filename)
if params_filename is not None:
params_filename = os.path.basename(params_filename)
with open(model_file_name, "rb") as f:
with open(model_filename, "rb") as f:
program_desc_str = f.read()
program = Program.parse_from_string(program_desc_str)
load_persistables(executor, dirname, program, load_file_name)
load_persistables(executor, dirname, program, params_filename)
feed_target_names = get_feed_targets_names(program)
fetch_target_names = get_fetch_targets_names(program)
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 2a4e7add
......@@ -16,6 +16,7 @@ All layers just related to the detection neural network.
"""
from layer_function_generator import generate_layer_fn
from layer_function_generator import autodoc
from ..layer_helper import LayerHelper
import tensor
import ops
......@@ -28,6 +29,7 @@ __all__ = [
'target_assign',
'detection_output',
'ssd_loss',
'detection_map',
]
__auto__ = [
......@@ -132,7 +134,48 @@ def detection_output(scores,
return nmsed_outs
def bipartite_match(dist_matrix, name=None):
@autodoc()
def detection_map(detect_res,
label,
pos_count=None,
true_pos=None,
false_pos=None,
overlap_threshold=0.3,
evaluate_difficult=True,
ap_type='integral'):
helper = LayerHelper("detection_map", **locals())
map_out = helper.create_tmp_variable(dtype='float32')
accum_pos_count_out = helper.create_tmp_variable(dtype='int32')
accum_true_pos_out = helper.create_tmp_variable(dtype='float32')
accum_false_pos_out = helper.create_tmp_variable(dtype='float32')
helper.append_op(
type="detection_map",
inputs={
'Label': label,
'DetectRes': detect_res,
'PosCount': pos_count,
'TruePos': true_pos,
'FalsePos': false_pos
},
outputs={
'MAP': map_out,
'AccumPosCount': accum_pos_count_out,
'AccumTruePos': accum_true_pos_out,
'AccumFalsePos': accum_false_pos_out
},
attrs={
'overlap_threshold': overlap_threshold,
'evaluate_difficult': evaluate_difficult,
'ap_type': ap_type
})
return map_out, accum_pos_count_out, accum_true_pos_out, accum_false_pos_out
def bipartite_match(dist_matrix,
match_type=None,
dist_threshold=None,
name=None):
"""
**Bipartite matchint operator**
......@@ -164,6 +207,11 @@ def bipartite_match(dist_matrix, name=None):
This tensor can contain LoD information to represent a batch of
inputs. One instance of this batch can contain different numbers of
entities.
match_type(string|None): The type of matching method, should be
'bipartite' or 'per_prediction', 'bipartite' by defalut.
dist_threshold(float|None): If `match_type` is 'per_prediction',
this threshold is to determine the extra matching bboxes based
on the maximum distance, 0.5 by defalut.
Returns:
match_indices(Variable): A 2-D Tensor with shape [N, M] in int type.
N is the batch size. If match_indices[i][j] is -1, it
......@@ -183,6 +231,10 @@ def bipartite_match(dist_matrix, name=None):
helper.append_op(
type='bipartite_match',
inputs={'DistMat': dist_matrix},
attrs={
'match_type': match_type,
'dist_threshold': dist_threshold,
},
outputs={
'ColToRowMatchIndices': match_indices,
'ColToRowMatchDist': match_distance
......@@ -333,7 +385,7 @@ def ssd_loss(location,
loc_loss_weight (float): Weight for localization loss, 1.0 by default.
conf_loss_weight (float): Weight for confidence loss, 1.0 by default.
match_type (str): The type of matching method during training, should
be 'bipartite' or 'per_prediction'.
be 'bipartite' or 'per_prediction', 'per_prediction' by defalut.
mining_type (str): The hard example mining type, should be 'hard_example'
or 'max_negative', now only support `max_negative`.
......@@ -381,7 +433,8 @@ def ssd_loss(location,
# 1.1 Compute IOU similarity between ground-truth boxes and prior boxes.
iou = iou_similarity(x=gt_box, y=prior_box)
# 1.2 Compute matched boundding box by bipartite matching algorithm.
matched_indices, matched_dist = bipartite_match(iou)
matched_indices, matched_dist = bipartite_match(iou, match_type,
overlap_threshold)
# 2. Compute confidence for mining hard examples
# 2.1. Get the target label based on matched indices
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 2a4e7add
......@@ -21,6 +21,7 @@ from ..framework import Variable
from ..param_attr import ParamAttr
from layer_function_generator import autodoc
from tensor import concat
import utils
__all__ = [
'fc',
......@@ -1139,8 +1140,8 @@ def sequence_conv(input,
def conv2d(input,
num_filters,
filter_size,
stride=None,
padding=None,
stride=1,
padding=0,
groups=None,
param_attr=None,
bias_attr=None,
......@@ -1253,12 +1254,10 @@ def conv2d(input,
raise ValueError("num_channels must be divisible by groups.")
num_filter_channels = num_channels / groups
if isinstance(filter_size, int):
filter_size = [filter_size, filter_size]
if isinstance(stride, int):
stride = [stride, stride]
if isinstance(padding, int):
padding = [padding, padding]
filter_size = utils.convert_to_list(filter_size, 2, 'filter_size')
stride = utils.convert_to_list(stride, 2, 'stride')
padding = utils.convert_to_list(padding, 2, 'padding')
if not isinstance(use_cudnn, bool):
raise ValueError("use_cudnn should be True or False")
......@@ -1433,10 +1432,10 @@ def sequence_last_step(input):
def pool2d(input,
pool_size,
pool_type,
pool_stride=None,
pool_padding=None,
pool_size=-1,
pool_type="max",
pool_stride=1,
pool_padding=0,
global_pooling=False,
use_cudnn=True,
name=None):
......@@ -1444,20 +1443,20 @@ def pool2d(input,
This function adds the operator for pooling in 2 dimensions, using the
pooling configurations mentioned in input parameters.
"""
if pool_padding is None:
pool_padding = [0, 0]
if pool_stride is None:
pool_stride = [1, 1]
if pool_type not in ["max", "avg"]:
raise ValueError(
"Unknown pool_type: '%s'. It can only be 'max' or 'avg'.",
str(pool_type))
if isinstance(pool_size, int):
pool_size = [pool_size, pool_size]
if isinstance(pool_stride, int):
pool_stride = [pool_stride, pool_stride]
if isinstance(pool_padding, int):
pool_padding = [pool_padding, pool_padding]
if global_pooling is False and pool_size == -1:
raise ValueError(
"When the global_pooling is False, pool_size must be passed "
"and be a valid value. Received pool_size: " + str(pool_size))
pool_size = utils.convert_to_list(pool_size, 2, 'pool_size')
pool_padding = utils.convert_to_list(pool_padding, 2, 'pool_padding')
pool_stride = utils.convert_to_list(pool_stride, 2, 'pool_stride')
if not isinstance(use_cudnn, bool):
raise ValueError("use_cudnn should be True or False")
......@@ -1686,9 +1685,9 @@ def conv2d_transpose(input,
num_filters,
output_size=None,
filter_size=None,
padding=None,
stride=None,
dilation=None,
padding=0,
stride=1,
dilation=1,
param_attr=None,
use_cudnn=True,
name=None):
......@@ -1784,26 +1783,12 @@ def conv2d_transpose(input,
raise TypeError("Input of conv2d_transpose must be Variable")
input_channel = input.shape[1]
op_attr = dict()
if isinstance(padding, int):
op_attr['paddings'] = [padding, padding]
elif padding is not None:
op_attr['paddings'] = padding
if isinstance(stride, int):
op_attr['strides'] = [stride, stride]
elif stride is not None:
op_attr['strides'] = stride
if isinstance(dilation, int):
op_attr['dilations'] = [dilation, dilation]
elif dilation is not None:
op_attr['dilations'] = dilation
padding = utils.convert_to_list(padding, 2, 'padding')
stride = utils.convert_to_list(stride, 2, 'stride')
dilation = utils.convert_to_list(dilation, 2, 'dilation')
if not isinstance(use_cudnn, bool):
raise ValueError("use_cudnn should be True or False")
op_attr['use_cudnn'] = use_cudnn
if filter_size is None:
if output_size is None:
......@@ -1811,10 +1796,6 @@ def conv2d_transpose(input,
if isinstance(output_size, int):
output_size = [output_size, output_size]
padding = op_attr.get('paddings', [0, 0])
stride = op_attr.get('strides', [1, 1])
dilation = op_attr.get('dilations', [1, 1])
h_in = input.shape[2]
w_in = input.shape[3]
......@@ -1823,9 +1804,9 @@ def conv2d_transpose(input,
filter_size_w = (output_size[1] - (w_in - 1) * stride[1] + 2 *
padding[1] - 1) / dilation[1] + 1
filter_size = [filter_size_h, filter_size_w]
elif isinstance(filter_size, int):
filter_size = [filter_size, filter_size]
else:
filter_size = utils.convert_to_list(filter_size, 2,
'conv2d_transpose.filter_size')
filter_shape = [input_channel, num_filters] + filter_size
img_filter = helper.create_parameter(
......@@ -1837,7 +1818,12 @@ def conv2d_transpose(input,
inputs={'Input': [input],
'Filter': [img_filter]},
outputs={'Output': out},
attrs=op_attr)
attrs={
'strides': stride,
'paddings': padding,
'dilations': dilation,
'use_cudnn': use_cudnn
})
return out
......
python/paddle/fluid/layers/utils.py 0 → 100644
浏览文件 @ 2a4e7add
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
def convert_to_list(value, n, name, dtype=np.int):
"""
Converts a single numerical type or iterable of numerical
types into an numerical type list.
Arguments:
value: The value to validate and convert. Could an int, or any iterable
of ints.
n: The size of the list to be returned.
name: The name of the argument being validated, e.g. "stride" or
"filter_size". This is only used to format error messages.
dtype: the numerical type of the element of the list to be returned.
Returns:
A list of n dtypes.
Raises:
ValueError: If something else than an int/long or iterable thereof was
passed.
"""
if isinstance(value, dtype):
return [value, ] * n
else:
try:
value_list = list(value)
except TypeError:
raise ValueError("The " + name +
"'s type must be list or tuple. Received: " + str(
value))
if len(value_list) != n:
raise ValueError("The " + name + "'s length must be " + str(n) +
". Received: " + str(value))
for single_value in value_list:
try:
dtype(single_value)
except (ValueError, TypeError):
raise ValueError(
"The " + name + "'s type must be a list or tuple of " + str(
n) + " " + str(dtype) + " . Received: " + str(
value) + " "
"including element " + str(single_value) + " of type" + " "
+ str(type(single_value)))
return value_list
python/paddle/fluid/learning_rate_decay.py
浏览文件 @ 2a4e7add
......@@ -13,7 +13,6 @@
# limitations under the License.
import layers
from framework import Variable
from initializer import init_on_cpu
__all__ = [
......
python/paddle/fluid/profiler.py
浏览文件 @ 2a4e7add
......@@ -97,9 +97,14 @@ def profiler(state, sorted_key=None):
The `ave` means sorting by the average execution time.
"""
if state not in ['CPU', 'GPU']:
raise ValueError("The state must be 'CPU' or 'GPU'.")
prof_state = core.ProfilerState.kCUDA if state == "GPU" else core.ProfilerState.kCPU
if state not in ['CPU', 'GPU', "All"]:
raise ValueError("The state must be 'CPU' or 'GPU' or 'All'.")
if state == "GPU":
prof_state = core.ProfilerState.kCUDA
elif state == "CPU":
prof_state = core.ProfilerState.kCPU
else:
prof_state = core.ProfilerState.kAll
core.enable_profiler(prof_state)
yield
......
python/paddle/fluid/tests/CMakeLists.txt
浏览文件 @ 2a4e7add
......@@ -7,5 +7,4 @@ endforeach()
add_subdirectory(unittests)
add_subdirectory(book)
add_subdirectory(book_distribute)
add_subdirectory(book_memory_optimization)
python/paddle/fluid/tests/book/notest_rnn_encoder_decoer.py
浏览文件 @ 2a4e7add
......@@ -228,32 +228,34 @@ def infer(use_cuda, save_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
lod = [0, 4, 10]
word_data = create_random_lodtensor(lod, place, low=0, high=1)
trg_word = create_random_lodtensor(lod, place, low=0, high=1)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
assert feed_target_names[0] == 'source_sequence'
assert feed_target_names[1] == 'target_sequence'
results = exe.run(inference_program,
feed={
feed_target_names[0]: word_data,
feed_target_names[1]: trg_word,
},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].lod())
np_data = np.array(results[0])
print("Inference shape: ", np_data.shape)
print("Inference results: ", np_data)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
lod = [0, 4, 10]
word_data = create_random_lodtensor(lod, place, low=0, high=1)
trg_word = create_random_lodtensor(lod, place, low=0, high=1)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
assert feed_target_names[0] == 'source_sequence'
assert feed_target_names[1] == 'target_sequence'
results = exe.run(inference_program,
feed={
feed_target_names[0]: word_data,
feed_target_names[1]: trg_word,
},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].lod())
np_data = np.array(results[0])
print("Inference shape: ", np_data.shape)
print("Inference results: ", np_data)
def main(use_cuda):
......
python/paddle/fluid/tests/book/test_fit_a_line.py
浏览文件 @ 2a4e7add
......@@ -19,9 +19,10 @@ import numpy
import unittest
import math
import sys
import os
def train(use_cuda, save_dirname):
def train(use_cuda, save_dirname, is_local):
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
......@@ -32,7 +33,7 @@ def train(use_cuda, save_dirname):
avg_cost = fluid.layers.mean(cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)
optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
BATCH_SIZE = 20
......@@ -42,27 +43,57 @@ def train(use_cuda, save_dirname):
batch_size=BATCH_SIZE)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for data in train_reader():
avg_loss_value, = exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[avg_cost])
print(avg_loss_value)
if avg_loss_value[0] < 10.0:
if save_dirname is not None:
fluid.io.save_inference_model(save_dirname, ['x'],
[y_predict], exe)
return
if math.isnan(float(avg_loss_value)):
sys.exit("got NaN loss, training failed.")
raise AssertionError("Fit a line cost is too large, {0:2.2}".format(
avg_loss_value[0]))
def train_loop(main_program):
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe.run(fluid.default_startup_program())
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for data in train_reader():
avg_loss_value, = exe.run(main_program,
feed=feeder.feed(data),
fetch_list=[avg_cost])
print(avg_loss_value)
if avg_loss_value[0] < 10.0:
if save_dirname is not None:
fluid.io.save_inference_model(save_dirname, ['x'],
[y_predict], exe)
return
if math.isnan(float(avg_loss_value)):
sys.exit("got NaN loss, training failed.")
raise AssertionError("Fit a line cost is too large, {0:2.2}".format(
avg_loss_value[0]))
if is_local:
train_loop(fluid.default_main_program())
else:
port = os.getenv("PADDLE_INIT_PORT", "6174")
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("TRAINERS"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
t = fluid.DistributeTranspiler()
t.transpile(
optimize_ops,
params_grads,
trainer_id,
pservers=pserver_endpoints,
trainers=trainers)
if training_role == "PSERVER":
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint,
pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
train_loop(t.get_trainer_program())
def infer(use_cuda, save_dirname=None):
......@@ -72,33 +103,36 @@ def infer(use_cuda, save_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
# The input's dimension should be 2-D and the second dim is 13
# The input data should be >= 0
batch_size = 10
tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
assert feed_target_names[0] == 'x'
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_x},
fetch_list=fetch_targets)
print("infer shape: ", results[0].shape)
print("infer results: ", results[0])
def main(use_cuda):
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
# The input's dimension should be 2-D and the second dim is 13
# The input data should be >= 0
batch_size = 10
tensor_x = numpy.random.uniform(0, 10,
[batch_size, 13]).astype("float32")
assert feed_target_names[0] == 'x'
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_x},
fetch_list=fetch_targets)
print("infer shape: ", results[0].shape)
print("infer results: ", results[0])
def main(use_cuda, is_local=True):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
# Directory for saving the trained model
save_dirname = "fit_a_line.inference.model"
train(use_cuda, save_dirname)
train(use_cuda, save_dirname, is_local)
infer(use_cuda, save_dirname)
......
python/paddle/fluid/tests/book/test_image_classification.py
浏览文件 @ 2a4e7add
......@@ -21,6 +21,7 @@ import math
import sys
import numpy
import unittest
import os
def resnet_cifar10(input, depth=32):
......@@ -92,7 +93,7 @@ def vgg16_bn_drop(input):
return fc2
def train(net_type, use_cuda, save_dirname):
def train(net_type, use_cuda, save_dirname, is_local):
classdim = 10
data_shape = [3, 32, 32]
......@@ -117,7 +118,7 @@ def train(net_type, use_cuda, save_dirname):
test_program = fluid.default_main_program().clone()
optimizer = fluid.optimizer.Adam(learning_rate=0.001)
optimizer.minimize(avg_cost)
optimize_ops, params_grads = optimizer.minimize(avg_cost)
BATCH_SIZE = 128
PASS_NUM = 1
......@@ -133,38 +134,68 @@ def train(net_type, use_cuda, save_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
exe.run(fluid.default_startup_program())
loss = 0.0
for pass_id in range(PASS_NUM):
for batch_id, data in enumerate(train_reader()):
exe.run(feed=feeder.feed(data))
if (batch_id % 10) == 0:
acc_list = []
avg_loss_list = []
for tid, test_data in enumerate(test_reader()):
loss_t, acc_t = exe.run(program=test_program,
feed=feeder.feed(test_data),
fetch_list=[avg_cost, acc])
if math.isnan(float(loss_t)):
sys.exit("got NaN loss, training failed.")
acc_list.append(float(acc_t))
avg_loss_list.append(float(loss_t))
break # Use 1 segment for speeding up CI
acc_value = numpy.array(acc_list).mean()
avg_loss_value = numpy.array(avg_loss_list).mean()
print(
'PassID {0:1}, BatchID {1:04}, Test Loss {2:2.2}, Acc {3:2.2}'.
format(pass_id, batch_id + 1,
float(avg_loss_value), float(acc_value)))
if acc_value > 0.01: # Low threshold for speeding up CI
fluid.io.save_inference_model(save_dirname, ["pixel"],
[predict], exe)
return
def train_loop(main_program):
exe.run(fluid.default_startup_program())
loss = 0.0
for pass_id in range(PASS_NUM):
for batch_id, data in enumerate(train_reader()):
exe.run(main_program, feed=feeder.feed(data))
if (batch_id % 10) == 0:
acc_list = []
avg_loss_list = []
for tid, test_data in enumerate(test_reader()):
loss_t, acc_t = exe.run(program=test_program,
feed=feeder.feed(test_data),
fetch_list=[avg_cost, acc])
if math.isnan(float(loss_t)):
sys.exit("got NaN loss, training failed.")
acc_list.append(float(acc_t))
avg_loss_list.append(float(loss_t))
break # Use 1 segment for speeding up CI
acc_value = numpy.array(acc_list).mean()
avg_loss_value = numpy.array(avg_loss_list).mean()
print(
'PassID {0:1}, BatchID {1:04}, Test Loss {2:2.2}, Acc {3:2.2}'.
format(pass_id, batch_id + 1,
float(avg_loss_value), float(acc_value)))
if acc_value > 0.01: # Low threshold for speeding up CI
fluid.io.save_inference_model(save_dirname, ["pixel"],
[predict], exe)
return
if is_local:
train_loop(fluid.default_main_program())
else:
port = os.getenv("PADDLE_INIT_PORT", "6174")
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("TRAINERS"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
t = fluid.DistributeTranspiler()
t.transpile(
optimize_ops,
params_grads,
trainer_id,
pservers=pserver_endpoints,
trainers=trainers)
if training_role == "PSERVER":
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint,
pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
train_loop(t.get_trainer_program())
def infer(use_cuda, save_dirname=None):
......@@ -174,32 +205,36 @@ def infer(use_cuda, save_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
# The input's dimension of conv should be 4-D or 5-D.
tensor_img = numpy.random.rand(1, 3, 32, 32).astype("float32")
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_img},
fetch_list=fetch_targets)
print("infer results: ", results[0])
def main(net_type, use_cuda):
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
# The input's dimension of conv should be 4-D or 5-D.
# Use normilized image pixels as input data, which should be in the range [0, 1.0].
batch_size = 1
tensor_img = numpy.random.rand(batch_size, 3, 32, 32).astype("float32")
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_img},
fetch_list=fetch_targets)
print("infer results: ", results[0])
def main(net_type, use_cuda, is_local=True):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
# Directory for saving the trained model
save_dirname = "image_classification_" + net_type + ".inference.model"
train(net_type, use_cuda, save_dirname)
train(net_type, use_cuda, save_dirname, is_local)
infer(use_cuda, save_dirname)
......
python/paddle/fluid/tests/book/test_label_semantic_roles.py
浏览文件 @ 2a4e7add
......@@ -22,11 +22,12 @@ from paddle.fluid.initializer import init_on_cpu
import contextlib
import time
import unittest
import os
word_dict, verb_dict, label_dict = conll05.get_dict()
word_dict_len = len(word_dict)
label_dict_len = len(label_dict)
pred_len = len(verb_dict)
pred_dict_len = len(verb_dict)
mark_dict_len = 2
word_dim = 32
......@@ -53,7 +54,7 @@ def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
# 8 features
predicate_embedding = fluid.layers.embedding(
input=predicate,
size=[pred_len, word_dim],
size=[pred_dict_len, word_dim],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr='vemb')
......@@ -138,7 +139,7 @@ def create_random_lodtensor(lod, place, low, high):
return res
def train(use_cuda, save_dirname=None):
def train(use_cuda, save_dirname=None, is_local=True):
# define network topology
word = fluid.layers.data(
name='word_data', shape=[1], dtype='int64', lod_level=1)
......@@ -174,7 +175,7 @@ def train(use_cuda, save_dirname=None):
decay_steps=100000,
decay_rate=0.5,
staircase=True))
sgd_optimizer.minimize(avg_cost)
optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
# TODO(qiao)
# add dependency track and move this config before optimizer
......@@ -200,44 +201,78 @@ def train(use_cuda, save_dirname=None):
place=place)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
embedding_param = fluid.global_scope().find_var(embedding_name).get_tensor()
embedding_param.set(
load_parameter(conll05.get_embedding(), word_dict_len, word_dim), place)
start_time = time.time()
batch_id = 0
for pass_id in xrange(PASS_NUM):
chunk_evaluator.reset(exe)
for data in train_data():
cost, precision, recall, f1_score = exe.run(
fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[avg_cost] + chunk_evaluator.metrics)
pass_precision, pass_recall, pass_f1_score = chunk_evaluator.eval(
exe)
if batch_id % 10 == 0:
print("avg_cost:" + str(cost) + " precision:" + str(
precision) + " recall:" + str(recall) + " f1_score:" + str(
f1_score) + " pass_precision:" + str(
pass_precision) + " pass_recall:" + str(pass_recall)
+ " pass_f1_score:" + str(pass_f1_score))
if batch_id != 0:
print("second per batch: " + str((time.time() - start_time)
/ batch_id))
# Set the threshold low to speed up the CI test
if float(pass_precision) > 0.05:
if save_dirname is not None:
fluid.io.save_inference_model(save_dirname, [
'word_data', 'verb_data', 'ctx_n2_data',
'ctx_n1_data', 'ctx_0_data', 'ctx_p1_data',
'ctx_p2_data', 'mark_data'
], [feature_out], exe)
return
batch_id = batch_id + 1
def train_loop(main_program):
exe.run(fluid.default_startup_program())
embedding_param = fluid.global_scope().find_var(
embedding_name).get_tensor()
embedding_param.set(
load_parameter(conll05.get_embedding(), word_dict_len, word_dim),
place)
start_time = time.time()
batch_id = 0
for pass_id in xrange(PASS_NUM):
chunk_evaluator.reset(exe)
for data in train_data():
cost, precision, recall, f1_score = exe.run(
main_program,
feed=feeder.feed(data),
fetch_list=[avg_cost] + chunk_evaluator.metrics)
pass_precision, pass_recall, pass_f1_score = chunk_evaluator.eval(
exe)
if batch_id % 10 == 0:
print("avg_cost:" + str(cost) + " precision:" + str(
precision) + " recall:" + str(recall) + " f1_score:" +
str(f1_score) + " pass_precision:" + str(
pass_precision) + " pass_recall:" + str(
pass_recall) + " pass_f1_score:" + str(
pass_f1_score))
if batch_id != 0:
print("second per batch: " + str((time.time(
) - start_time) / batch_id))
# Set the threshold low to speed up the CI test
if float(pass_precision) > 0.05:
if save_dirname is not None:
# TODO(liuyiqun): Change the target to crf_decode
fluid.io.save_inference_model(save_dirname, [
'word_data', 'verb_data', 'ctx_n2_data',
'ctx_n1_data', 'ctx_0_data', 'ctx_p1_data',
'ctx_p2_data', 'mark_data'
], [feature_out], exe)
return
batch_id = batch_id + 1
if is_local:
train_loop(fluid.default_main_program())
else:
port = os.getenv("PADDLE_INIT_PORT", "6174")
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("TRAINERS"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
t = fluid.DistributeTranspiler()
t.transpile(
optimize_ops,
params_grads,
trainer_id,
pservers=pserver_endpoints,
trainers=trainers)
if training_role == "PSERVER":
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint,
pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
train_loop(t.get_trainer_program())
def infer(use_cuda, save_dirname=None):
......@@ -247,61 +282,70 @@ def infer(use_cuda, save_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
lod = [0, 4, 10]
ts_word = create_random_lodtensor(lod, place, low=0, high=1)
ts_pred = create_random_lodtensor(lod, place, low=0, high=1)
ts_ctx_n2 = create_random_lodtensor(lod, place, low=0, high=1)
ts_ctx_n1 = create_random_lodtensor(lod, place, low=0, high=1)
ts_ctx_0 = create_random_lodtensor(lod, place, low=0, high=1)
ts_ctx_p1 = create_random_lodtensor(lod, place, low=0, high=1)
ts_ctx_p2 = create_random_lodtensor(lod, place, low=0, high=1)
ts_mark = create_random_lodtensor(lod, place, low=0, high=1)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
assert feed_target_names[0] == 'word_data'
assert feed_target_names[1] == 'verb_data'
assert feed_target_names[2] == 'ctx_n2_data'
assert feed_target_names[3] == 'ctx_n1_data'
assert feed_target_names[4] == 'ctx_0_data'
assert feed_target_names[5] == 'ctx_p1_data'
assert feed_target_names[6] == 'ctx_p2_data'
assert feed_target_names[7] == 'mark_data'
results = exe.run(inference_program,
feed={
feed_target_names[0]: ts_word,
feed_target_names[1]: ts_pred,
feed_target_names[2]: ts_ctx_n2,
feed_target_names[3]: ts_ctx_n1,
feed_target_names[4]: ts_ctx_0,
feed_target_names[5]: ts_ctx_p1,
feed_target_names[6]: ts_ctx_p2,
feed_target_names[7]: ts_mark
},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].lod())
np_data = np.array(results[0])
print("Inference Shape: ", np_data.shape)
print("Inference results: ", np_data)
def main(use_cuda):
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
lod = [0, 4, 10]
word = create_random_lodtensor(
lod, place, low=0, high=word_dict_len - 1)
pred = create_random_lodtensor(
lod, place, low=0, high=pred_dict_len - 1)
ctx_n2 = create_random_lodtensor(
lod, place, low=0, high=word_dict_len - 1)
ctx_n1 = create_random_lodtensor(
lod, place, low=0, high=word_dict_len - 1)
ctx_0 = create_random_lodtensor(
lod, place, low=0, high=word_dict_len - 1)
ctx_p1 = create_random_lodtensor(
lod, place, low=0, high=word_dict_len - 1)
ctx_p2 = create_random_lodtensor(
lod, place, low=0, high=word_dict_len - 1)
mark = create_random_lodtensor(
lod, place, low=0, high=mark_dict_len - 1)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
assert feed_target_names[0] == 'word_data'
assert feed_target_names[1] == 'verb_data'
assert feed_target_names[2] == 'ctx_n2_data'
assert feed_target_names[3] == 'ctx_n1_data'
assert feed_target_names[4] == 'ctx_0_data'
assert feed_target_names[5] == 'ctx_p1_data'
assert feed_target_names[6] == 'ctx_p2_data'
assert feed_target_names[7] == 'mark_data'
results = exe.run(inference_program,
feed={
feed_target_names[0]: word,
feed_target_names[1]: pred,
feed_target_names[2]: ctx_n2,
feed_target_names[3]: ctx_n1,
feed_target_names[4]: ctx_0,
feed_target_names[5]: ctx_p1,
feed_target_names[6]: ctx_p2,
feed_target_names[7]: mark
},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].lod())
np_data = np.array(results[0])
print("Inference Shape: ", np_data.shape)
def main(use_cuda, is_local=True):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
# Directory for saving the trained model
save_dirname = "label_semantic_roles.inference.model"
train(use_cuda, save_dirname)
train(use_cuda, save_dirname, is_local)
infer(use_cuda, save_dirname)
......
python/paddle/fluid/tests/book/test_machine_translation.py
浏览文件 @ 2a4e7add
......@@ -20,6 +20,7 @@ import paddle.fluid.framework as framework
import paddle.fluid.layers as pd
from paddle.fluid.executor import Executor
import unittest
import os
dict_size = 30000
source_dict_dim = target_dict_dim = dict_size
......@@ -168,7 +169,7 @@ def to_lodtensor(data, place):
return res
def train_main(use_cuda, is_sparse):
def train_main(use_cuda, is_sparse, is_local=True):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
......@@ -181,7 +182,7 @@ def train_main(use_cuda, is_sparse):
avg_cost = pd.mean(cost)
optimizer = fluid.optimizer.Adagrad(learning_rate=1e-4)
optimizer.minimize(avg_cost)
optimize_ops, params_grads = optimizer.minimize(avg_cost)
train_data = paddle.batch(
paddle.reader.shuffle(
......@@ -190,27 +191,57 @@ def train_main(use_cuda, is_sparse):
exe = Executor(place)
exe.run(framework.default_startup_program())
batch_id = 0
for pass_id in xrange(1):
for data in train_data():
word_data = to_lodtensor(map(lambda x: x[0], data), place)
trg_word = to_lodtensor(map(lambda x: x[1], data), place)
trg_word_next = to_lodtensor(map(lambda x: x[2], data), place)
outs = exe.run(framework.default_main_program(),
feed={
'src_word_id': word_data,
'target_language_word': trg_word,
'target_language_next_word': trg_word_next
},
fetch_list=[avg_cost])
avg_cost_val = np.array(outs[0])
print('pass_id=' + str(pass_id) + ' batch=' + str(batch_id) +
" avg_cost=" + str(avg_cost_val))
if batch_id > 3:
break
batch_id += 1
def train_loop(main_program):
exe.run(framework.default_startup_program())
batch_id = 0
for pass_id in xrange(1):
for data in train_data():
word_data = to_lodtensor(map(lambda x: x[0], data), place)
trg_word = to_lodtensor(map(lambda x: x[1], data), place)
trg_word_next = to_lodtensor(map(lambda x: x[2], data), place)
outs = exe.run(main_program,
feed={
'src_word_id': word_data,
'target_language_word': trg_word,
'target_language_next_word': trg_word_next
},
fetch_list=[avg_cost])
avg_cost_val = np.array(outs[0])
print('pass_id=' + str(pass_id) + ' batch=' + str(batch_id) +
" avg_cost=" + str(avg_cost_val))
if batch_id > 3:
break
batch_id += 1
if is_local:
train_loop(framework.default_main_program())
else:
port = os.getenv("PADDLE_INIT_PORT", "6174")
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("TRAINERS"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
t = fluid.DistributeTranspiler()
t.transpile(
optimize_ops,
params_grads,
trainer_id,
pservers=pserver_endpoints,
trainers=trainers)
if training_role == "PSERVER":
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint,
pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
train_loop(t.get_trainer_program())
def decode_main(use_cuda, is_sparse):
......
python/paddle/fluid/tests/book/test_recognize_digits.py
浏览文件 @ 2a4e7add
......@@ -20,27 +20,7 @@ import numpy
import unittest
import math
import sys
def parse_arg():
parser = argparse.ArgumentParser()
parser.add_argument(
"nn_type",
help="The neural network type, in ['mlp', 'conv']",
type=str,
choices=['mlp', 'conv'])
parser.add_argument(
"--parallel",
help='Run in parallel or not',
default=False,
action="store_true")
parser.add_argument(
"--use_cuda",
help="Run the program by using CUDA",
default=False,
action="store_true")
return parser.parse_args()
import os
BATCH_SIZE = 64
......@@ -78,7 +58,13 @@ def conv_net(img, label):
return loss_net(conv_pool_2, label)
def train(nn_type, use_cuda, parallel, save_dirname, save_param_filename):
def train(nn_type,
use_cuda,
parallel,
save_dirname=None,
model_filename=None,
params_filename=None,
is_local=True):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
......@@ -109,12 +95,11 @@ def train(nn_type, use_cuda, parallel, save_dirname, save_param_filename):
test_program = fluid.default_main_program().clone()
optimizer = fluid.optimizer.Adam(learning_rate=0.001)
optimizer.minimize(avg_loss)
optimize_ops, params_grads = optimizer.minimize(avg_loss)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
train_reader = paddle.batch(
paddle.reader.shuffle(
......@@ -124,88 +109,133 @@ def train(nn_type, use_cuda, parallel, save_dirname, save_param_filename):
paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for batch_id, data in enumerate(train_reader()):
# train a mini-batch, fetch nothing
exe.run(feed=feeder.feed(data))
if (batch_id + 1) % 10 == 0:
acc_set = []
avg_loss_set = []
for test_data in test_reader():
acc_np, avg_loss_np = exe.run(program=test_program,
feed=feeder.feed(test_data),
fetch_list=[acc, avg_loss])
acc_set.append(float(acc_np))
avg_loss_set.append(float(avg_loss_np))
# get test acc and loss
acc_val = numpy.array(acc_set).mean()
avg_loss_val = numpy.array(avg_loss_set).mean()
if float(acc_val) > 0.2: # Smaller value to increase CI speed
if save_dirname is not None:
fluid.io.save_inference_model(
save_dirname, ["img"], [prediction],
exe,
save_file_name=save_param_filename)
return
else:
print(
'PassID {0:1}, BatchID {1:04}, Test Loss {2:2.2}, Acc {3:2.2}'.
format(pass_id, batch_id + 1,
float(avg_loss_val), float(acc_val)))
if math.isnan(float(avg_loss_val)):
sys.exit("got NaN loss, training failed.")
raise AssertionError("Loss of recognize digits is too large")
def infer(use_cuda, save_dirname=None, param_filename=None):
def train_loop(main_program):
exe.run(fluid.default_startup_program())
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for batch_id, data in enumerate(train_reader()):
# train a mini-batch, fetch nothing
exe.run(main_program, feed=feeder.feed(data))
if (batch_id + 1) % 10 == 0:
acc_set = []
avg_loss_set = []
for test_data in test_reader():
acc_np, avg_loss_np = exe.run(
program=test_program,
feed=feeder.feed(test_data),
fetch_list=[acc, avg_loss])
acc_set.append(float(acc_np))
avg_loss_set.append(float(avg_loss_np))
# get test acc and loss
acc_val = numpy.array(acc_set).mean()
avg_loss_val = numpy.array(avg_loss_set).mean()
if float(acc_val
) > 0.2: # Smaller value to increase CI speed
if save_dirname is not None:
fluid.io.save_inference_model(
save_dirname, ["img"], [prediction],
exe,
model_filename=model_filename,
params_filename=params_filename)
return
else:
print(
'PassID {0:1}, BatchID {1:04}, Test Loss {2:2.2}, Acc {3:2.2}'.
format(pass_id, batch_id + 1,
float(avg_loss_val), float(acc_val)))
if math.isnan(float(avg_loss_val)):
sys.exit("got NaN loss, training failed.")
raise AssertionError("Loss of recognize digits is too large")
if is_local:
train_loop(fluid.default_main_program())
else:
port = os.getenv("PADDLE_INIT_PORT", "6174")
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
pserver_endpoints = os.getenv("PSERVERS")
trainers = int(os.getenv("TRAINERS"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
t = fluid.DistributeTranspiler()
t.transpile(
optimize_ops,
params_grads,
trainer_id,
pservers=pserver_endpoints,
trainers=trainers)
if training_role == "PSERVER":
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint,
pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
train_loop(t.get_trainer_program())
def infer(use_cuda,
save_dirname=None,
model_filename=None,
params_filename=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names, fetch_targets
] = fluid.io.load_inference_model(save_dirname, exe, param_filename)
# The input's dimension of conv should be 4-D or 5-D.
# Use normilized image pixels as input data, which should be in the range [-1.0, 1.0].
batch_size = 1
tensor_img = numpy.random.uniform(-1.0, 1.0,
[batch_size, 1, 28, 28]).astype("float32")
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_img},
fetch_list=fetch_targets)
print("infer results: ", results[0])
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(
save_dirname, exe, model_filename, params_filename)
# The input's dimension of conv should be 4-D or 5-D.
# Use normilized image pixels as input data, which should be in the range [-1.0, 1.0].
batch_size = 1
tensor_img = numpy.random.uniform(
-1.0, 1.0, [batch_size, 1, 28, 28]).astype("float32")
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_img},
fetch_list=fetch_targets)
print("infer results: ", results[0])
def main(use_cuda, parallel, nn_type, combine):
save_dirname = None
model_filename = None
params_filename = None
if not use_cuda and not parallel:
save_dirname = "recognize_digits_" + nn_type + ".inference.model"
save_filename = None
if combine == True:
save_filename = "__params_combined__"
else:
save_dirname = None
save_filename = None
model_filename = "__model_combined__"
params_filename = "__params_combined__"
# call train() with is_local argument to run distributed train
train(
nn_type=nn_type,
use_cuda=use_cuda,
parallel=parallel,
save_dirname=save_dirname,
save_param_filename=save_filename)
model_filename=model_filename,
params_filename=params_filename)
infer(
use_cuda=use_cuda,
save_dirname=save_dirname,
param_filename=save_filename)
model_filename=model_filename,
params_filename=params_filename)
class TestRecognizeDigits(unittest.TestCase):
......
python/paddle/fluid/tests/book/test_recommender_system.py
浏览文件 @ 2a4e7add
......@@ -14,6 +14,7 @@
import math
import sys
import os
import numpy as np
import paddle.v2 as paddle
import paddle.fluid as fluid
......@@ -152,19 +153,18 @@ def model():
return scale_infer, avg_cost
def train(use_cuda, save_dirname):
def train(use_cuda, save_dirname, is_local=True):
scale_infer, avg_cost = model()
# test program
test_program = fluid.default_main_program().clone()
sgd_optimizer = SGDOptimizer(learning_rate=0.2)
opts = sgd_optimizer.minimize(avg_cost)
optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
train_reader = paddle.batch(
paddle.reader.shuffle(
......@@ -212,36 +212,69 @@ def train(use_cuda, save_dirname):
feed_tensors[key] = tensor
return feed_tensors
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for batch_id, data in enumerate(train_reader()):
# train a mini-batch
outs = exe.run(program=fluid.default_main_program(),
feed=func_feed(feeding, data),
fetch_list=[avg_cost])
out = np.array(outs[0])
if (batch_id + 1) % 10 == 0:
avg_cost_set = []
for test_data in test_reader():
avg_cost_np = exe.run(program=test_program,
feed=func_feed(feeding, test_data),
fetch_list=[avg_cost])
avg_cost_set.append(avg_cost_np[0])
break # test only 1 segment for speeding up CI
# get test avg_cost
test_avg_cost = np.array(avg_cost_set).mean()
if test_avg_cost < 6.0:
# if avg_cost less than 6.0, we think our code is good.
if save_dirname is not None:
fluid.io.save_inference_model(save_dirname, [
"user_id", "gender_id", "age_id", "job_id",
"movie_id", "category_id", "movie_title"
], [scale_infer], exe)
return
if math.isnan(float(out[0])):
sys.exit("got NaN loss, training failed.")
def train_loop(main_program):
exe.run(framework.default_startup_program())
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for batch_id, data in enumerate(train_reader()):
# train a mini-batch
outs = exe.run(program=main_program,
feed=func_feed(feeding, data),
fetch_list=[avg_cost])
out = np.array(outs[0])
if (batch_id + 1) % 10 == 0:
avg_cost_set = []
for test_data in test_reader():
avg_cost_np = exe.run(
program=test_program,
feed=func_feed(feeding, test_data),
fetch_list=[avg_cost])
avg_cost_set.append(avg_cost_np[0])
break # test only 1 segment for speeding up CI
# get test avg_cost
test_avg_cost = np.array(avg_cost_set).mean()
if test_avg_cost < 6.0:
# if avg_cost less than 6.0, we think our code is good.
if save_dirname is not None:
fluid.io.save_inference_model(save_dirname, [
"user_id", "gender_id", "age_id", "job_id",
"movie_id", "category_id", "movie_title"
], [scale_infer], exe)
return
if math.isnan(float(out[0])):
sys.exit("got NaN loss, training failed.")
if is_local:
train_loop(fluid.default_main_program())
else:
port = os.getenv("PADDLE_INIT_PORT", "6174")
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("TRAINERS"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
t = fluid.DistributeTranspiler()
t.transpile(
optimize_ops,
params_grads,
trainer_id,
pservers=pserver_endpoints,
trainers=trainers)
if training_role == "PSERVER":
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint,
pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
train_loop(t.get_trainer_program())
def infer(use_cuda, save_dirname=None):
......@@ -251,13 +284,6 @@ def infer(use_cuda, save_dirname=None):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
def create_lod_tensor(data, lod=None):
tensor = fluid.LoDTensor()
if lod is None:
......@@ -275,44 +301,53 @@ def infer(use_cuda, save_dirname=None):
tensor.set(flattened_data, place)
return tensor
# Use the first data from paddle.dataset.movielens.test() as input
assert feed_target_names[0] == "user_id"
user_id = create_lod_tensor([[1]])
assert feed_target_names[1] == "gender_id"
gender_id = create_lod_tensor([[1]])
assert feed_target_names[2] == "age_id"
age_id = create_lod_tensor([[0]])
assert feed_target_names[3] == "job_id"
job_id = create_lod_tensor([[10]])
assert feed_target_names[4] == "movie_id"
movie_id = create_lod_tensor([[783]])
assert feed_target_names[5] == "category_id"
category_id = create_lod_tensor([[10], [8], [9]], [[0, 3]])
assert feed_target_names[6] == "movie_title"
movie_title = create_lod_tensor([[1069], [4140], [2923], [710], [988]],
[[0, 5]])
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results = exe.run(inference_program,
feed={
feed_target_names[0]: user_id,
feed_target_names[1]: gender_id,
feed_target_names[2]: age_id,
feed_target_names[3]: job_id,
feed_target_names[4]: movie_id,
feed_target_names[5]: category_id,
feed_target_names[6]: movie_title
},
fetch_list=fetch_targets,
return_numpy=False)
print("inferred score: ", np.array(results[0]))
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
# Use the first data from paddle.dataset.movielens.test() as input
assert feed_target_names[0] == "user_id"
user_id = create_lod_tensor([[1]])
assert feed_target_names[1] == "gender_id"
gender_id = create_lod_tensor([[1]])
assert feed_target_names[2] == "age_id"
age_id = create_lod_tensor([[0]])
assert feed_target_names[3] == "job_id"
job_id = create_lod_tensor([[10]])
assert feed_target_names[4] == "movie_id"
movie_id = create_lod_tensor([[783]])
assert feed_target_names[5] == "category_id"
category_id = create_lod_tensor([[10], [8], [9]], [[0, 3]])
assert feed_target_names[6] == "movie_title"
movie_title = create_lod_tensor([[1069], [4140], [2923], [710], [988]],
[[0, 5]])
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results = exe.run(inference_program,
feed={
feed_target_names[0]: user_id,
feed_target_names[1]: gender_id,
feed_target_names[2]: age_id,
feed_target_names[3]: job_id,
feed_target_names[4]: movie_id,
feed_target_names[5]: category_id,
feed_target_names[6]: movie_title
},
fetch_list=fetch_targets,
return_numpy=False)
print("inferred score: ", np.array(results[0]))
def main(use_cuda):
......
python/paddle/fluid/tests/book/test_understand_sentiment.py
浏览文件 @ 2a4e7add
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
# 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.
......@@ -20,6 +20,7 @@ import contextlib
import math
import numpy as np
import sys
import os
def convolution_net(data, label, input_dim, class_dim=2, emb_dim=32,
......@@ -132,7 +133,12 @@ def create_random_lodtensor(lod, place, low, high):
return res
def train(word_dict, net_method, use_cuda, parallel=False, save_dirname=None):
def train(word_dict,
net_method,
use_cuda,
parallel=False,
save_dirname=None,
is_local=True):
BATCH_SIZE = 128
PASS_NUM = 5
dict_dim = len(word_dict)
......@@ -164,7 +170,7 @@ def train(word_dict, net_method, use_cuda, parallel=False, save_dirname=None):
assert save_dirname is None
adagrad = fluid.optimizer.Adagrad(learning_rate=0.002)
adagrad.minimize(cost)
optimize_ops, params_grads = adagrad.minimize(cost)
train_data = paddle.batch(
paddle.reader.shuffle(
......@@ -174,55 +180,88 @@ def train(word_dict, net_method, use_cuda, parallel=False, save_dirname=None):
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(feed_list=[data, label], place=place)
exe.run(fluid.default_startup_program())
for pass_id in xrange(PASS_NUM):
for data in train_data():
cost_val, acc_val = exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[cost, acc_out])
print("cost=" + str(cost_val) + " acc=" + str(acc_val))
if cost_val < 0.4 and acc_val > 0.8:
if save_dirname is not None:
fluid.io.save_inference_model(save_dirname, ["words"],
prediction, exe)
return
if math.isnan(float(cost_val)):
sys.exit("got NaN loss, training failed.")
raise AssertionError("Cost is too large for {0}".format(
net_method.__name__))
def infer(use_cuda, save_dirname=None):
def train_loop(main_program):
exe.run(fluid.default_startup_program())
for pass_id in xrange(PASS_NUM):
for data in train_data():
cost_val, acc_val = exe.run(main_program,
feed=feeder.feed(data),
fetch_list=[cost, acc_out])
print("cost=" + str(cost_val) + " acc=" + str(acc_val))
if cost_val < 0.4 and acc_val > 0.8:
if save_dirname is not None:
fluid.io.save_inference_model(save_dirname, ["words"],
prediction, exe)
return
if math.isnan(float(cost_val)):
sys.exit("got NaN loss, training failed.")
raise AssertionError("Cost is too large for {0}".format(
net_method.__name__))
if is_local:
train_loop(fluid.default_main_program())
else:
port = os.getenv("PADDLE_INIT_PORT", "6174")
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("TRAINERS"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
t = fluid.DistributeTranspiler()
t.transpile(
optimize_ops,
params_grads,
trainer_id,
pservers=pserver_endpoints,
trainers=trainers)
if training_role == "PSERVER":
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint,
pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
train_loop(t.get_trainer_program())
def infer(word_dict, use_cuda, save_dirname=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
lod = [0, 4, 10]
word_dict = paddle.dataset.imdb.word_dict()
tensor_words = create_random_lodtensor(
lod, place, low=0, high=len(word_dict) - 1)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
assert feed_target_names[0] == "words"
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_words},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].lod())
np_data = np.array(results[0])
print("Inference Shape: ", np_data.shape)
print("Inference results: ", np_data)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
word_dict_len = len(word_dict)
lod = [0, 4, 10]
tensor_words = create_random_lodtensor(
lod, place, low=0, high=word_dict_len - 1)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
assert feed_target_names[0] == "words"
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_words},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].lod())
np_data = np.array(results[0])
print("Inference Shape: ", np_data.shape)
print("Inference results: ", np_data)
def main(word_dict, net_method, use_cuda, parallel=False, save_dirname=None):
......@@ -258,7 +297,7 @@ class TestUnderstandSentiment(unittest.TestCase):
self.word_dict,
net_method=convolution_net,
use_cuda=False,
save_dirname="understand_sentiment.inference.model")
save_dirname="understand_sentiment_conv.inference.model")
def test_conv_cpu_parallel(self):
with self.new_program_scope():
......@@ -271,7 +310,11 @@ class TestUnderstandSentiment(unittest.TestCase):
@unittest.skip(reason="make CI faster")
def test_stacked_lstm_cpu(self):
with self.new_program_scope():
main(self.word_dict, net_method=stacked_lstm_net, use_cuda=False)
main(
self.word_dict,
net_method=stacked_lstm_net,
use_cuda=False,
save_dirname="understand_sentiment_stacked_lstm.inference.model")
def test_stacked_lstm_cpu_parallel(self):
with self.new_program_scope():
......@@ -287,7 +330,7 @@ class TestUnderstandSentiment(unittest.TestCase):
self.word_dict,
net_method=convolution_net,
use_cuda=True,
save_dirname="understand_sentiment.inference.model")
save_dirname="understand_sentiment_conv.inference.model")
def test_conv_gpu_parallel(self):
with self.new_program_scope():
......@@ -300,7 +343,11 @@ class TestUnderstandSentiment(unittest.TestCase):
@unittest.skip(reason="make CI faster")
def test_stacked_lstm_gpu(self):
with self.new_program_scope():
main(self.word_dict, net_method=stacked_lstm_net, use_cuda=True)
main(
self.word_dict,
net_method=stacked_lstm_net,
use_cuda=True,
save_dirname="understand_sentiment_stacked_lstm.inference.model")
def test_stacked_lstm_gpu_parallel(self):
with self.new_program_scope():
......
python/paddle/fluid/tests/book/test_word2vec.py
浏览文件 @ 2a4e7add
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
# # Licensed under the Apache License, Version 2.0 (the "License");
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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
#
......@@ -21,6 +22,7 @@ import sys
def create_random_lodtensor(lod, place, low, high):
# The range of data elements is [low, high]
data = np.random.random_integers(low, high, [lod[-1], 1]).astype("int64")
res = fluid.LoDTensor()
res.set(data, place)
......@@ -28,54 +30,7 @@ def create_random_lodtensor(lod, place, low, high):
return res
def infer(use_cuda, save_dirname=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
word_dict = paddle.dataset.imikolov.build_dict()
dict_size = len(word_dict) - 1
# Setup input, by creating 4 words, and setting up lod required for
# lookup_table_op
lod = [0, 1]
first_word = create_random_lodtensor(lod, place, low=0, high=dict_size)
second_word = create_random_lodtensor(lod, place, low=0, high=dict_size)
third_word = create_random_lodtensor(lod, place, low=0, high=dict_size)
fourth_word = create_random_lodtensor(lod, place, low=0, high=dict_size)
assert feed_target_names[0] == 'firstw'
assert feed_target_names[1] == 'secondw'
assert feed_target_names[2] == 'thirdw'
assert feed_target_names[3] == 'forthw'
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results = exe.run(inference_program,
feed={
feed_target_names[0]: first_word,
feed_target_names[1]: second_word,
feed_target_names[2]: third_word,
feed_target_names[3]: fourth_word
},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].lod())
np_data = np.array(results[0])
print("Inference Shape: ", np_data.shape)
print("Inference results: ", np_data)
def train(use_cuda, is_sparse, parallel, save_dirname):
def train(use_cuda, is_sparse, is_parallel, save_dirname, is_local=True):
PASS_NUM = 100
EMBED_SIZE = 32
HIDDEN_SIZE = 256
......@@ -130,7 +85,7 @@ def train(use_cuda, is_sparse, parallel, save_dirname):
forth_word = fluid.layers.data(name='forthw', shape=[1], dtype='int64')
next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64')
if not parallel:
if not is_parallel:
avg_cost, predict_word = __network__(
[first_word, second_word, third_word, forth_word, next_word])
else:
......@@ -146,7 +101,7 @@ def train(use_cuda, is_sparse, parallel, save_dirname):
avg_cost = fluid.layers.mean(pd())
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)
optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
train_reader = paddle.batch(
paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)
......@@ -157,30 +112,116 @@ def train(use_cuda, is_sparse, parallel, save_dirname):
feed_list=[first_word, second_word, third_word, forth_word, next_word],
place=place)
exe.run(fluid.default_startup_program())
def train_loop(main_program):
exe.run(fluid.default_startup_program())
for pass_id in range(PASS_NUM):
for data in train_reader():
avg_cost_np = exe.run(main_program,
feed=feeder.feed(data),
fetch_list=[avg_cost])
if avg_cost_np[0] < 5.0:
if save_dirname is not None:
fluid.io.save_inference_model(save_dirname, [
'firstw', 'secondw', 'thirdw', 'forthw'
], [predict_word], exe)
return
if math.isnan(float(avg_cost_np[0])):
sys.exit("got NaN loss, training failed.")
raise AssertionError("Cost is too large {0:2.2}".format(avg_cost_np[0]))
if is_local:
train_loop(fluid.default_main_program())
else:
port = os.getenv("PADDLE_INIT_PORT", "6174")
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("TRAINERS"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
t = fluid.DistributeTranspiler()
t.transpile(
optimize_ops,
params_grads,
trainer_id,
pservers=pserver_endpoints,
trainers=trainers)
if training_role == "PSERVER":
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint,
pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
train_loop(t.get_trainer_program())
for pass_id in range(PASS_NUM):
for data in train_reader():
avg_cost_np = exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[avg_cost])
if avg_cost_np[0] < 5.0:
if save_dirname is not None:
fluid.io.save_inference_model(save_dirname, [
'firstw', 'secondw', 'thirdw', 'forthw'
], [predict_word], exe)
return
if math.isnan(float(avg_cost_np[0])):
sys.exit("got NaN loss, training failed.")
raise AssertionError("Cost is too large {0:2.2}".format(avg_cost_np[0]))
def infer(use_cuda, save_dirname=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
def main(use_cuda, is_sparse, parallel):
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
word_dict = paddle.dataset.imikolov.build_dict()
dict_size = len(word_dict)
# Setup inputs, by creating 4 words, the lod of which should be [0, 1]
lod = [0, 1]
first_word = create_random_lodtensor(
lod, place, low=0, high=dict_size - 1)
second_word = create_random_lodtensor(
lod, place, low=0, high=dict_size - 1)
third_word = create_random_lodtensor(
lod, place, low=0, high=dict_size - 1)
fourth_word = create_random_lodtensor(
lod, place, low=0, high=dict_size - 1)
assert feed_target_names[0] == 'firstw'
assert feed_target_names[1] == 'secondw'
assert feed_target_names[2] == 'thirdw'
assert feed_target_names[3] == 'forthw'
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results = exe.run(inference_program,
feed={
feed_target_names[0]: first_word,
feed_target_names[1]: second_word,
feed_target_names[2]: third_word,
feed_target_names[3]: fourth_word
},
fetch_list=fetch_targets,
return_numpy=False)
print(results[0].lod())
np_data = np.array(results[0])
print("Inference Shape: ", np_data.shape)
def main(use_cuda, is_sparse, is_parallel):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
save_dirname = "word2vec.inference.model"
train(use_cuda, is_sparse, parallel, save_dirname)
if not is_parallel:
save_dirname = "word2vec.inference.model"
else:
save_dirname = None
train(use_cuda, is_sparse, is_parallel, save_dirname)
infer(use_cuda, save_dirname)
......@@ -193,10 +234,10 @@ class W2VTest(unittest.TestCase):
pass
def inject_test_method(use_cuda, is_sparse, parallel):
def inject_test_method(use_cuda, is_sparse, is_parallel):
fn_name = "test_{0}_{1}_{2}".format("cuda" if use_cuda else "cpu", "sparse"
if is_sparse else "dense", "parallel"
if parallel else "normal")
if is_parallel else "normal")
def __impl__(*args, **kwargs):
prog = fluid.Program()
......@@ -204,10 +245,12 @@ def inject_test_method(use_cuda, is_sparse, parallel):
scope = fluid.core.Scope()
with fluid.scope_guard(scope):
with fluid.program_guard(prog, startup_prog):
main(use_cuda=use_cuda, is_sparse=is_sparse, parallel=parallel)
main(
use_cuda=use_cuda,
is_sparse=is_sparse,
is_parallel=is_parallel)
# run only 2 cases: use_cuda is either True or False
if is_sparse == False and parallel == False:
if use_cuda and is_sparse:
fn = __impl__
else:
# skip the other test when on CI server
......@@ -219,8 +262,8 @@ def inject_test_method(use_cuda, is_sparse, parallel):
for use_cuda in (False, True):
for is_sparse in (False, True):
for parallel in (False, True):
inject_test_method(use_cuda, is_sparse, parallel)
for is_parallel in (False, True):
inject_test_method(use_cuda, is_sparse, is_parallel)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/book_distribute/CMakeLists.txt 已删除 100644 → 0
浏览文件 @ 5f70629f
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/tests/book_distribute/notest_dist_fit_a_line.py 已删除 100644 → 0
浏览文件 @ 5f70629f
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import paddle.v2 as paddle
import paddle.fluid as fluid
import os
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
BATCH_SIZE = 20
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.uci_housing.train(), buf_size=500),
batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)
t = fluid.DistributeTranspiler()
# all parameter server endpoints list for spliting parameters
pserver_endpoints = os.getenv("PSERVERS")
# server endpoint for current node
current_endpoint = os.getenv("SERVER_ENDPOINT")
# run as trainer or parameter server
training_role = os.getenv("TRAINING_ROLE",
"TRAINER") # get the training role: trainer/pserver
t.transpile(optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
if training_role == "PSERVER":
if not current_endpoint:
print("need env SERVER_ENDPOINT")
exit(1)
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
else:
trainer_prog = t.get_trainer_program()
exe.run(fluid.default_startup_program())
PASS_NUM = 100
for pass_id in range(PASS_NUM):
fluid.io.save_persistables(exe, "./fit_a_line.model/")
fluid.io.load_persistables(exe, "./fit_a_line.model/")
for data in train_reader():
avg_loss_value = exe.run(trainer_prog,
feed=feeder.feed(data),
fetch_list=[avg_cost])
print("loss:" + str(avg_loss_value))
if avg_loss_value[0] < 10.0:
exit(0)
exit(1)
python/paddle/fluid/tests/book_distribute/notest_recognize_digits_mlp_dist.py 已删除 100644 → 0
浏览文件 @ 5f70629f
# 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 numpy as np
import paddle.v2 as paddle
import paddle.fluid as fluid
import os
BATCH_SIZE = 128
PASS_NUM = 100
images = fluid.layers.data(name='x', shape=[784], dtype='float32')
# TODO(aroraabhinav) Add regularization and error clipping after
# Issue 7432(https://github.com/PaddlePaddle/Paddle/issues/7432) is resolved.
hidden1 = fluid.layers.fc(input=images, size=128, act='relu')
hidden2 = fluid.layers.fc(input=hidden1, size=64, act='relu')
predict = fluid.layers.fc(input=hidden2, size=10, act='softmax')
label = fluid.layers.data(name='y', shape=[1], dtype='int64')
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost)
optimizer = fluid.optimizer.Momentum(learning_rate=0.001, momentum=0.9)
optimize_ops, params_grads = optimizer.minimize(avg_cost)
accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=8192),
batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
t = fluid.DistributeTranspiler()
# all parameter server endpoints list for spliting parameters
pserver_endpoints = os.getenv("PSERVERS")
# server endpoint for current node
current_endpoint = os.getenv("SERVER_ENDPOINT")
# run as trainer or parameter server
training_role = os.getenv("TRAINING_ROLE",
"TRAINER") # get the training role: trainer/pserver
t.transpile(optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
if training_role == "PSERVER":
if not current_endpoint:
print("need env SERVER_ENDPOINT")
exit(1)
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
trainer_prog = t.get_trainer_program()
feeder = fluid.DataFeeder(feed_list=[images, label], place=place)
exe.run(fluid.default_startup_program())
for pass_id in range(PASS_NUM):
accuracy.reset(exe)
batch_id = 0
for data in train_reader():
loss, acc = exe.run(trainer_prog,
feed=feeder.feed(data),
fetch_list=[avg_cost] + accuracy.metrics)
pass_acc = accuracy.eval(exe)
if batch_id % 100 == 0:
print("batch_id %d, loss: %f, acc: %f" %
(batch_id, loss, pass_acc))
batch_id += 1
pass_acc = accuracy.eval(exe)
print("pass_id=" + str(pass_id) + " pass_acc=" + str(pass_acc))
else:
print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
python/paddle/fluid/tests/unittests/test_nvprof.py 0 → 100644
浏览文件 @ 2a4e7add
此差异已折叠。
python/paddle/v2/dataset/conll05.py
浏览文件 @ 2a4e7add
此差异已折叠。
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