Caffe studying 2017

工業技術研究院機密資料禁止複製、轉載、外流 ITRI CONFIDENTIAL DOCUMENT DO NOT COPY OR DISTRIBUTE
Caffe Study
資訊與通訊研究所
劉得彥
Danny Liu

concept of view in Blob
2

Blob
3
• There are some implemented functions,
such as:
▪ assum_diff(), sumsq_data(), update(), asum_data()…
• Has both side of implementation of math
functions
▪ CPU: For example: caffe_axpy
a. Using CBLAS Library
▪ GPU: for example: caffe_gpu_axpy
a. Using cuBLAS Library
• Use SyncMemory class to do the data sync
between CPU and GPU
▪ Always use {cpu,gpu}_data() or mutable_{cpu,gpu}_data()
to get data pointer

Layer
4
• caffe::Layer is a base class
• All the layers as follows all
inherent caffe:Layer
▪ Data, Vision, Recurrent,
Common, Normalization,
Activation, Loss layers, and so
on.
▪ http://caffe.berkeleyvision.org/tut
orial/layers.html

Layers have GPU implemented code
5
• src/caffe/layers/
▪ *_layer.cu
▪ cudnn_*_layer.cu
• src/caffe/util/
▪ math_functions.cu
▪ im2col.cu
• include/caffe/util/
▪ device_alternate.hpp 
CUDA macro definition

Layer
6
• Setup()
▪ Initialize layers
• Forward()
▪ Use bottom blob’s data as input to the layer and
calculate the output/loss to top blob’s data.
• Backward()
▪ Use top blob’s diff as input to the layer and calculate
the diff/gradient to the bottom blob’s diff.
▪ For the calculation of diff/gradient, it’s about
bottom_diff - top_diff · top_data

Solver using NCCL
7

NCCL::Run()
8
• boost::barrier
▪ it is a synchronization
point between multiple
threads.
• Worker
▪ class Worker : public
InternalThread

Caffe training with NCCL
9

Broadcast and All-Reduce in Caffe
10
• Worker is an internal thread served a GPU
• The picture introduces the broadcast and all-reduce operation

The Data Layer
11

Prototxt: Define Net
12

Prototxt: Define Net
13

LeNet
14
LeNet: a layered model composed of convolution and subsampling operations followed
by a holistic representation and ultimately a classifier for handwritten digits. [ LeNet ]
• LeNet-5
▪ https://world4jason.gitbooks.io/research-
log/content/deepLearning/CNN/Model%20&%20ImgNet/lenet.html

Lenet.prototxt
15
name: "LeNet"
layer {
name: "data"
type: "Input"
top: "data"
input_param
{ shape: { dim: 64
dim: 1 dim: 28 dim:
28 } }
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param
{
num_output: 20
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param
{
num_output: 50
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "ip1"
type:
"InnerProduct"
bottom: "pool2"
top: "ip1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_para
m {
num_output: 500
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "ip1"
top: "ip1"
}
layer {
name: "ip2"
type:
"InnerProduct"
bottom: "ip1"
top: "ip2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_para
m {
num_output: 10
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "prob"
type: "Softmax"
bottom: "ip2"
top: "prob"
}

都在GPU
上做
Caffe data flow
16
name: "LogReg"
layer {
name: "mnist"
type: "Data"
top: "data"
top: "label"
data_param {
source: "input_leveldb"
batch_size: 64
}
}
layer {
name: "ip"
type: "InnerProduct"
bottom: "data"
top: "ip"
inner_product_param {
num_output: 2
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "ip"
bottom: "label"
top: "loss"
}
Forward()
Forward()
Forward()
Load_batch()
Copy data
to GPU
計算 output
計算 output
計算 loss
Backward()
Backward()
計算 gradient
計算 gradient
Step()
ApplyUpdate()
ForwardBackward()
Normalize(param_id);
Regularize(param_id);
this->net_->Update();
Prefetching
data and label
cblas_saxpy
(data, diff)
on_gradients_ready()
NCCL: All-Reduce
for diff/gradient
Learnable
params

工業技術研究院機密資料禁止複製、轉載、外流 ITRI CONFIDENTIAL DOCUMENT DO NOT COPY OR DISTRIBUTE 17
• blocking_queue.cpp:49 Waiting for data
43 template<typename T>
44 T BlockingQueue<T>::pop(const string& log_on_wait) {
45 boost::mutex::scoped_lock lock(sync_->mutex_);
46
47 while (queue_.empty()) {
48 if (!log_on_wait.empty()) {
49 LOG_EVERY_N(INFO, 1000)<< log_on_wait;
50 }
51 sync_->condition_.wait(lock);
52 }
53
54 T t = queue_.front();
55 queue_.pop();
56 return t;
57 }

Batch Data Prefetching
18
先以 InternalThread
將Image放到CPU Memory
再以cudaMemcpyAsync
Copy到 GPU Memory

Caffe studying 2017

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