mmr/tkDNN
Archived
1
0
Fork 0
Code Issues 46 Pull Requests 12 Releases 4 Activity

fp16 implementation, TODO deallocate in LayerWgs

Browse Source
This commit is contained in:
Francesco Gatti
2017-08-30 14:37:25 +00:00
parent a26ef98d2d
commit 2cf8d8f6fc
11 changed files with 190 additions and 42 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/NetworkRT.cpp
+45 -28
View File
@@ -1,9 +1,13 @@
#include <iostream>
#include <map>
#include <errno.h>
#include <string.h> // memcpy
#include <stdlib.h>
#include "kernels.h"
#include "utils.h"
#include "NvInfer.h"
#include "NetworkRT.h"
using namespace nvinfer1;
@@ -45,7 +49,7 @@ NetworkRT::NetworkRT(Network *net, const char *name) {
builderRT->setMaxBatchSize(1);
builderRT->setMaxWorkspaceSize(1 << 30);
/*
//change datatype based on system specs
if(builderRT->platformHasFastInt8()) {
BatchStream bstream({32,dim.c, dim.h, dim.w}, 32, 1);
@@ -53,13 +57,13 @@ NetworkRT::NetworkRT(Network *net, const char *name) {
builderRT->setInt8Mode(true);
builderRT->setInt8Calibrator(&calib);
} else if(builderRT->platformHasFastFp16()) {
} else if(net->fp16 && builderRT->platformHasFastFp16()) {
dtRT = DataType::kHALF;
builderRT->setHalf2Mode(true);
}
*/
//add input layer
ITensor *input = networkRT->addInput("data", dtRT,
ITensor *input = networkRT->addInput("data", DataType::kFLOAT,
DimsCHW{ dim.c, dim.h, dim.w});
checkNULL(input);
@@ -170,22 +174,49 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Layer *l) {
ILayer* NetworkRT::convert_layer(ITensor *input, Dense *l) {
//std::cout<<"convert Dense\n";
void *data_b, *bias_b;
if(dtRT == DataType::kHALF) {
data_b = l->data16_h;
bias_b = l->bias16_h;
} else {
data_b = l->data_h;
bias_b = l->bias_h;
}
Weights w { dtRT, l->data_h, l->inputs*l->outputs};
Weights b = { dtRT, l->bias_h, l->outputs};
Weights w { dtRT, data_b, l->inputs*l->outputs};
Weights b = { dtRT, bias_b, l->outputs};
IFullyConnectedLayer *lRT = networkRT->addFullyConnected(*input, l->outputs, w, b);
checkNULL(lRT);
return lRT;
}
ILayer* NetworkRT::convert_layer(ITensor *input, Conv2d *l) {
//std::cout<<"convert conv2D\n";
Weights w { dtRT, l->data_h, l->inputs*l->outputs*l->kernelH*l->kernelW};
void *data_b, *bias_b, *power_b, *mean_b, *variance_b, *scales_b;
if(dtRT == DataType::kHALF) {
data_b = l->data16_h;
bias_b = l->bias16_h;
power_b = l->power16_h;
mean_b = l->mean16_h;
variance_b = l->variance16_h;
scales_b = l->scales16_h;
} else {
data_b = l->data_h;
bias_b = l->bias_h;
power_b = l->power_h;
mean_b = l->mean_h;
variance_b = l->variance_h;
scales_b = l->scales_h;
}
Weights w { dtRT, data_b, l->inputs*l->outputs*l->kernelH*l->kernelW};
Weights b;
if(!l->batchnorm)
b = { dtRT, l->bias_h, l->outputs};
b = { dtRT, bias_b, l->outputs};
else
b = { dtRT, nullptr, 0}; //on batchnorm bias are added later
@@ -198,29 +229,15 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Conv2d *l) {
lRT->setPadding(DimsHW{l->paddingH, l->paddingW});
if(l->batchnorm) {
float eps = CUDNN_BN_MIN_EPSILON;
//make power array of ones
dnnType *power_h = new dnnType[l->outputs];
for(int i=0; i<l->outputs; i++) power_h[i] = 1.0f;
//convert mean
for(int i=0; i<l->outputs; i++)
l->mean_h[i] = l->mean_h[i] / -sqrt(eps + l->variance_h[i]);
//convert variance
for(int i=0; i<l->outputs; i++)
l->variance_h[i] = 1.0f / sqrt(eps + l->variance_h[i]);
Weights power{dtRT, power_h, l->outputs};
Weights shift{dtRT, l->mean_h, l->outputs};
Weights scale{dtRT, l->variance_h, l->outputs};
Weights power{dtRT, power_b, l->outputs};
Weights shift{dtRT, mean_b, l->outputs};
Weights scale{dtRT, variance_b, l->outputs};
IScaleLayer *lRT2 = networkRT->addScale(*lRT->getOutput(0), ScaleMode::kCHANNEL,
shift, scale, power);
checkNULL(lRT2);
Weights shift2{dtRT, l->bias_h, l->outputs};
Weights scale2{dtRT, l->scales_h, l->outputs};
Weights shift2{dtRT, bias_b, l->outputs};
Weights scale2{dtRT, scales_b, l->outputs};
IScaleLayer *lRT3 = networkRT->addScale(*lRT2->getOutput(0), ScaleMode::kCHANNEL,
shift2, scale2, power);
checkNULL(lRT3);