Liu Yiqun
7 years ago
commit
c8fc6037fd
@ -0,0 +1,48 @@
|
||||
set -e
|
||||
|
||||
unset OMP_NUM_THREADS MKL_NUM_THREADS
|
||||
export OMP_DYNAMIC="FALSE"
|
||||
export KMP_AFFINITY="granularity=fine,compact,0,0"
|
||||
|
||||
function train() {
|
||||
topology=$1
|
||||
bs=$2
|
||||
use_mkldnn=$3
|
||||
if [ $3 == "True" ]; then
|
||||
thread=1
|
||||
log="logs/${topology}-mkldnn-${bs}.log"
|
||||
elif [ $3 == "False" ]; then
|
||||
thread=`nproc`
|
||||
log="logs/${topology}-${thread}mklml-${bs}.log"
|
||||
else
|
||||
echo "Wrong input $3, use True or False."
|
||||
fi
|
||||
args="batch_size=${bs}"
|
||||
config="${topology}.py"
|
||||
paddle train --job=time \
|
||||
--config=$config \
|
||||
--use_mkldnn=$use_mkldnn \
|
||||
--use_gpu=False \
|
||||
--trainer_count=$thread \
|
||||
--log_period=10 \
|
||||
--test_period=100 \
|
||||
--config_args=$args \
|
||||
2>&1 | tee ${log}
|
||||
}
|
||||
|
||||
if [ ! -d "train.list" ]; then
|
||||
echo " " > train.list
|
||||
fi
|
||||
if [ ! -d "logs" ]; then
|
||||
mkdir logs
|
||||
fi
|
||||
|
||||
#========== mkldnn ==========#
|
||||
train vgg 64 True
|
||||
train vgg 128 True
|
||||
train vgg 256 True
|
||||
|
||||
#========== mklml ===========#
|
||||
train vgg 64 False
|
||||
train vgg 128 False
|
||||
train vgg 256 False
|
||||
@ -0,0 +1,103 @@
|
||||
#!/usr/bin/env python
|
||||
from paddle.trainer_config_helpers import *
|
||||
|
||||
height = 224
|
||||
width = 224
|
||||
num_class = 1000
|
||||
batch_size = get_config_arg('batch_size', int, 64)
|
||||
layer_num = get_config_arg('layer_num', int, 19)
|
||||
|
||||
args = {'height': height, 'width': width, 'color': True, 'num_class': num_class}
|
||||
define_py_data_sources2(
|
||||
"train.list", None, module="provider", obj="process", args=args)
|
||||
|
||||
settings(
|
||||
batch_size=batch_size,
|
||||
learning_rate=0.01 / batch_size,
|
||||
learning_method=MomentumOptimizer(0.9),
|
||||
regularization=L2Regularization(0.0005 * batch_size))
|
||||
|
||||
img = data_layer(name='image', size=height * width * 3)
|
||||
|
||||
|
||||
def vgg_network(vgg_num=3):
|
||||
tmp = img_conv_group(
|
||||
input=img,
|
||||
num_channels=3,
|
||||
conv_padding=1,
|
||||
conv_num_filter=[64, 64],
|
||||
conv_filter_size=3,
|
||||
conv_act=ReluActivation(),
|
||||
pool_size=2,
|
||||
pool_stride=2,
|
||||
pool_type=MaxPooling())
|
||||
|
||||
tmp = img_conv_group(
|
||||
input=tmp,
|
||||
conv_num_filter=[128, 128],
|
||||
conv_padding=1,
|
||||
conv_filter_size=3,
|
||||
conv_act=ReluActivation(),
|
||||
pool_stride=2,
|
||||
pool_type=MaxPooling(),
|
||||
pool_size=2)
|
||||
|
||||
channels = []
|
||||
for i in range(vgg_num):
|
||||
channels.append(256)
|
||||
tmp = img_conv_group(
|
||||
input=tmp,
|
||||
conv_num_filter=channels,
|
||||
conv_padding=1,
|
||||
conv_filter_size=3,
|
||||
conv_act=ReluActivation(),
|
||||
pool_stride=2,
|
||||
pool_type=MaxPooling(),
|
||||
pool_size=2)
|
||||
channels = []
|
||||
for i in range(vgg_num):
|
||||
channels.append(512)
|
||||
tmp = img_conv_group(
|
||||
input=tmp,
|
||||
conv_num_filter=channels,
|
||||
conv_padding=1,
|
||||
conv_filter_size=3,
|
||||
conv_act=ReluActivation(),
|
||||
pool_stride=2,
|
||||
pool_type=MaxPooling(),
|
||||
pool_size=2)
|
||||
tmp = img_conv_group(
|
||||
input=tmp,
|
||||
conv_num_filter=channels,
|
||||
conv_padding=1,
|
||||
conv_filter_size=3,
|
||||
conv_act=ReluActivation(),
|
||||
pool_stride=2,
|
||||
pool_type=MaxPooling(),
|
||||
pool_size=2)
|
||||
|
||||
tmp = fc_layer(
|
||||
input=tmp,
|
||||
size=4096,
|
||||
act=ReluActivation(),
|
||||
layer_attr=ExtraAttr(drop_rate=0.5))
|
||||
|
||||
tmp = fc_layer(
|
||||
input=tmp,
|
||||
size=4096,
|
||||
act=ReluActivation(),
|
||||
layer_attr=ExtraAttr(drop_rate=0.5))
|
||||
|
||||
return fc_layer(input=tmp, size=num_class, act=SoftmaxActivation())
|
||||
|
||||
|
||||
if layer_num == 16:
|
||||
vgg = vgg_network(3)
|
||||
elif layer_num == 19:
|
||||
vgg = vgg_network(4)
|
||||
else:
|
||||
print("Wrong layer number.")
|
||||
|
||||
lab = data_layer('label', num_class)
|
||||
loss = cross_entropy(input=vgg, label=lab)
|
||||
outputs(loss)
|
||||
@ -0,0 +1,222 @@
|
||||
# Design Doc: Distributed Training Architecture
|
||||
|
||||
## Abstract
|
||||
|
||||
PaddlePaddle v0.10.0 uses the "trainer-parameter server"
|
||||
architecture. We run multiple replicated instances of trainers (runs
|
||||
the same code written by the user) and parameter servers for
|
||||
distributed training. This architecture served us well, but has some
|
||||
limitations:
|
||||
|
||||
1. Need to write special code to handle tasks which should only be run
|
||||
by a single trainer. E.g., initializing model and saving model.
|
||||
|
||||
2. Model parallelism is hard: need to write if-else branches conditioned
|
||||
on the trainer ID to partition model onto each trainer, and manually
|
||||
write the inter-model-shard communication code.
|
||||
|
||||
3. The user can not directly specify the parameter update rule: need
|
||||
to modify the parameter server C++ code and compile a new
|
||||
binary. This adds complication for researchers: A lot of extra
|
||||
effort is required. Besides, the training job submission program
|
||||
may not allow running arbitrary binaries.
|
||||
|
||||
This design doc discusses PaddlePaddle's new distributed training
|
||||
architecture that addresses the above limitations.
|
||||
|
||||
## Analysis
|
||||
|
||||
We will assume the user writes the trainer program by Python, the same
|
||||
analysis holds if the trainer program is written in C++.
|
||||
|
||||
### Limitation 1
|
||||
|
||||
If we look at the Python code that the user writes, there are two
|
||||
kinds of functionalities:
|
||||
|
||||
- The training logic such as load / save model and print log.
|
||||
- The neural network definition such as the definition of the data
|
||||
layer, the fully connected layer, the cost function and the
|
||||
optimizer.
|
||||
|
||||
When we training with PaddlePaddle v0.10.0 distributedly, multiple
|
||||
replicated Python instances are running on different nodes: both the
|
||||
training logic and the neural network computation is replicated.
|
||||
|
||||
The tasks that should only run once all belong to the training logic,
|
||||
if we only replicate the neural network computation, but do **not**
|
||||
replicate the training logic, the limitation could be solved.
|
||||
|
||||
### Limitation 2
|
||||
|
||||
Model parallelism means running a single model on multiple nodes by
|
||||
partitioning the model onto different nodes and managing the
|
||||
inter-model-shard communications.
|
||||
|
||||
PaddlePaddle should be able to modify the nerual network computation
|
||||
definition to support model parallelism automatically. However, the
|
||||
computation is only specified in Python code, and PaddlePaddle can not
|
||||
modify Python code.
|
||||
|
||||
Just like compiler uses a intermediate representation (IR) so that
|
||||
programmer does not need to manually optimize their code in most of
|
||||
the cases - the compiler will optimize the IR:
|
||||
|
||||
<img src="src/compiler.png"/>
|
||||
|
||||
We can have our own IR too: PaddlePaddle can support model parallel by
|
||||
converting the IR so the user no longer need to manually do it in
|
||||
Python:
|
||||
|
||||
<img src="src/paddle-compile.png"/>
|
||||
|
||||
The IR for PaddlePaddle after refactor is called `Block`, it specifies
|
||||
the computation dependency graph and the variables used in the
|
||||
computation.
|
||||
|
||||
### Limitation 3
|
||||
|
||||
The user can not directly specify the parameter update rule for the
|
||||
parameter server because the parameter server does not use the same
|
||||
computation definition as the trainer. Instead, the update rule is
|
||||
baked in the parameter server. The user can not specify the update
|
||||
rule in the same way of specifying the trainer computation.
|
||||
|
||||
This could be fixed by making the parameter server run the same
|
||||
computation definition as the trainer. For a detailed explanation,
|
||||
please
|
||||
see
|
||||
[Design Doc: Operation Graph Based Parameter Server](./dist_train.md)
|
||||
|
||||
## Distributed Training Architecture
|
||||
|
||||
The new distributed training architecture can address the above
|
||||
limitations. Below is the illustration:
|
||||
|
||||
<img src="src/distributed_architecture.png"/>
|
||||
|
||||
The architecture includes major components: *PaddlePaddle Python*,
|
||||
*PaddlePaddle converter* and *PaddlePaddle runtime*:
|
||||
|
||||
### PaddlePaddle Python
|
||||
|
||||
PaddlePaddle Python is the Python library that user's Python trainer
|
||||
invoke to build the neural network topology, start training, etc.
|
||||
|
||||
```Python
|
||||
paddle.init()
|
||||
input = paddle.op.recordIO("/home/data/mnist.recordio") # file stored on the cluster
|
||||
img, label = input[0], input[1]
|
||||
hidden = paddle.layer.fc(input=img, size=200, act=paddle.activation.Tanh())
|
||||
prediction = paddle.layer.fc(input=img, size=10, act=paddle.activation.Softmax())
|
||||
cost = paddle.layer.classification_cost(input=prediction, label=label)
|
||||
optimizer = paddle.optimizer.SGD(cost, learning_rate=0.01)
|
||||
session = paddle.session.NewRemote(num_trainer=3, num_ps=2, GPU_per_trainer=1)
|
||||
for i in range(1000):
|
||||
_, cost_val = session.eval(targets=[cost, optimizer])
|
||||
print cost_val
|
||||
```
|
||||
|
||||
The code above is a typical Python trainer code, the neural network
|
||||
topology is built using helper functions such as
|
||||
`paddle.layer.fc`. The training is done by calling `session.eval`
|
||||
iteratively.
|
||||
|
||||
#### session.eval
|
||||
|
||||
As shown in the graph, `session.eval` sends the IR and the evaluation
|
||||
inputs/targets to the PaddlePaddle cluster for evaluation. The
|
||||
targets can be any variable in the computation graph. When the target
|
||||
is the `optimizer` variable, the neural network will be optimized
|
||||
once. When the target is the `cost` variable, `session.eval` returns
|
||||
the cost value.
|
||||
|
||||
The Python `session` is a wrapper of the C++ `Session` class. For more
|
||||
information about `Session`, please
|
||||
see [Design Doc: Session](./session.md).
|
||||
|
||||
### PaddlePaddle Converter
|
||||
|
||||
PaddlePaddle converter automatically converts the IR in the request
|
||||
(IR and evaluation inputs/targets) from PaddlePaddle Python to new
|
||||
partitioned IRs and dispatch the new IRs and evaluation inputs/targets
|
||||
to different PaddlePaddle runtimes. Below are the steps:
|
||||
|
||||
1. Add `feed` OP that feeds the eval inputs, and `fetch` OP that
|
||||
fetches the eval targets to the IR.
|
||||
|
||||
1. Extract a new computation (sub)graph with `feed` and `fetch` OP as
|
||||
the boundary. The runtime does not need to run the OP that is not
|
||||
dependent by the `fetch` OP.
|
||||
|
||||
1. Optimizes the computation graph.
|
||||
|
||||
1. Place the OPs in the graph onto different devices on different
|
||||
PaddlePaddle runtime according to a placement algorithm and device
|
||||
constraint specified by the user.
|
||||
|
||||
1. Partition the graph according to runtime boundaries and add `send` /
|
||||
`recv` OP pair on the runtime boundaries.
|
||||
|
||||
1. Dispatch the partitioned graph to different PaddlePaddle runtimes.
|
||||
|
||||
1. PaddlePaddle runtimes with the `fetch` OP reports evaluation
|
||||
results back to the converter, the convert reports the evaluation
|
||||
results back to the PaddlePaddle Python.
|
||||
|
||||
The output IRs will be cached to optimize the conversion latency.
|
||||
|
||||
|
||||
#### Placement Algorithm
|
||||
|
||||
Our first implementation will only support "trainer-parameter server"
|
||||
placement: the parameters, initializers, and optimizers are placed on
|
||||
the PaddlePaddle runtimes with the parameter server role. And
|
||||
everything else will be placed on the PaddlePaddle runtimes with the
|
||||
trainer role. This has the same functionality of our
|
||||
"trainer-parameter server" architecture of PaddlePaddle v0.10.0, but
|
||||
is more general and flexible.
|
||||
|
||||
In the future, we will implement the general placement algorithm,
|
||||
which makes placements according to the input IR, and a model of
|
||||
device computation time and device communication time. Model
|
||||
parallelism requires the general placement algorithm.
|
||||
|
||||
|
||||
### PaddlePaddle Runtime
|
||||
|
||||
The PaddlePaddle runtime owns multiple devices (e.g., CPUs, GPUs) and
|
||||
runs the IR. The runtime does not need to do OP placement since it's
|
||||
already done by the converter.
|
||||
|
||||
|
||||
### Local Training Architecture
|
||||
|
||||
The local training architecture will be the same as the distributed
|
||||
training architecture, the differences are everything runs locally,
|
||||
and there is just one PaddlePaddle runtime:
|
||||
|
||||
<img src="src/local_architecture.png"/>
|
||||
|
||||
|
||||
### Training Data
|
||||
|
||||
In PaddlePaddle v0.10.0, training data is typically read
|
||||
with [data reader](../reader/README.md) from Python. This approach is
|
||||
no longer efficient when training distributedly since the Python
|
||||
process no longer runs on the same node with the trainer processes,
|
||||
the Python reader will need to read from the distributed filesystem
|
||||
(assuming it has the access) and send to the trainers, doubling the
|
||||
network traffic.
|
||||
|
||||
When doing distributed training, the user can still use Python data
|
||||
reader: the training data are sent with `session.eval`. However should
|
||||
be used for debugging purpose only. The users are encouraged to use
|
||||
the read data OPs.
|
||||
|
||||
|
||||
## References:
|
||||
|
||||
[1] [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf)
|
||||
|
||||
[2] [TensorFlow: A System for Large-Scale Machine Learning](https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf)
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 16 KiB |
|
Before Width: | Height: | Size: 222 KiB After Width: | Height: | Size: 222 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 46 KiB |
|
Before Width: | Height: | Size: 28 KiB After Width: | Height: | Size: 28 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 28 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 20 KiB |
File diff suppressed because it is too large
Load Diff
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in new issue