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Add support for Windows #5

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Add support for Windows #5

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d513ef6
Get initial version of Marvin working on Windows
danielsuo Nov 18, 2015
1b98c3f
Update README with Windows instructions and instructions for using pr…
danielsuo Nov 18, 2015
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Get initial version of Marvin working on Windows 
Very hacky; just doing the minimal things to compile and run MNIST
- Replace sys/time.h with chrono
- Remove async calls
- Remove threading
@danielsuo
danielsuo committed Nov 18, 2015
commit d513ef6b5950616dea945095d73e7bde76754de1
38 changes: 19 additions & 19 deletions marvin.hpp
View file
Original file line number Diff line number Diff line change
@@ -80,7 +80,7 @@
#include <cublas_v2.h>
#include <curand.h>
#include <cudnn.h>
#include <sys/time.h>
#include <chrono>

namespace marvin {

@@ -168,9 +168,7 @@ void checkCUBLAS(const int lineNumber, cublasStatus_t status) {
}

unsigned long long get_timestamp() {
struct timeval now;
gettimeofday (&now, NULL);
return now.tv_usec + (unsigned long long)now.tv_sec * 1000000;
return (unsigned long long)std::chrono::duration_cast<std::chrono::microseconds>(std::chrono::high_resolution_clock::now().time_since_epoch()).count();
}

unsigned long long ticBegin;
@@ -2850,7 +2848,7 @@ class MemoryDataLayer : public DataLayer {

template <class T>
class DiskDataLayer : public DataLayer {
std::future<void> lock;
//std::future<void (&)()> lock;
FILE* dataFILE;
Tensor<StorageT>* labelCPUall;
std::vector<size_t> ordering;
@@ -2964,7 +2962,7 @@ class DiskDataLayer : public DataLayer {
};

~DiskDataLayer(){
if (lock.valid()) lock.wait();
// if (lock.valid()) lock.wait();
delete distribution_bernoulli;
for (int i=0;i<distribution_uniform.size();++i) delete distribution_uniform[i];
if (dataFILE!=NULL) fclose(dataFILE);
@@ -3070,11 +3068,12 @@ class DiskDataLayer : public DataLayer {
};

void forward(Phase phase_){
lock.wait();
// lock.wait();
epoch = epoch_prefetch;
Kernel_convert_to_StorageT_subtract<<<CUDA_GET_BLOCKS(numel_batch_all_channel_crop), CUDA_NUM_THREADS>>>(CUDA_GET_LOOPS(numel_batch_all_channel_crop), numel_batch_all_channel_crop, numel_all_channel_crop, dataGPU, (in.size()==0? NULL: in[0]->dataGPU), out[0]->dataGPU);
std::swap(out[1]->dataGPU,labelGPU);
lock = std::async(std::launch::async,&DiskDataLayer<T>::prefetch,this);
// lock = std::async(std::launch::async,&DiskDataLayer<T>::prefetch,this);
prefetch();
};


@@ -3111,8 +3110,8 @@ class DiskDataLayer : public DataLayer {
checkCUDA(__LINE__, cudaMalloc(&dataGPU, numel_batch_all_channel_crop * sizeof(T)) );
memoryBytes += numel_batch_all_channel_crop * sizeof(T);

lock = std::async(std::launch::async,&DiskDataLayer<T>::prefetch,this);

// lock = std::async(std::launch::async,&DiskDataLayer<T>::prefetch,this);
prefetch();
return memoryBytes;
};
};
@@ -3547,7 +3546,7 @@ class InnerProductLayer : public Layer {
class DropoutLayer: public Layer{
ComputeT scale;
std::bernoulli_distribution* distribution;
std::future<void> lock;
// std::future<void (&)()> lock;
bool current_mask;
std::vector< StorageT* > GPUmask[2];
std::vector< StorageT* > CPUmask;
@@ -3619,13 +3618,13 @@ class DropoutLayer: public Layer{
memoryBytes += out[i]->Malloc(in[i]->dim);
}

lock = std::async(std::launch::async,&DropoutLayer::generateMask,this);

// lock = std::async(std::launch::async,&DropoutLayer::generateMask,this);
generateMask();
return memoryBytes;
};

~DropoutLayer(){
if (lock.valid()) lock.wait();
// if (lock.valid()) lock.wait();
for (int i=0;i<GPUmask[0].size();++i){
checkCUDA(__LINE__, cudaFree(GPUmask[0][i]) );
checkCUDA(__LINE__, cudaFree(GPUmask[1][i]) );
@@ -3636,9 +3635,10 @@ class DropoutLayer: public Layer{

void forward(Phase phase_){
if ( phase_==Training ){
lock.wait();
// lock.wait();
current_mask = !current_mask;
lock = std::async(std::launch::async,&DropoutLayer::generateMask,this);
// lock = std::async(std::launch::async,&DropoutLayer::generateMask,this);
generateMask();
for (int i=0;i<in.size();++i){
// zeros out some elements
GPU_elementwise_multiplication(SIZEmask[i], out[i]->dataGPU, GPUmask[current_mask][i], in[i]->dataGPU);
@@ -5565,7 +5565,7 @@ class Solver{
}else{
for (int t=0; t<threads.size(); ++t){
nets[t]->phase = Testing;
threads[t] = std::thread(&Net::stepTest, nets[t], true); //nets[t]->stepTest();
// threads[t] = std::thread(&Net::stepTest, nets[t], true); //nets[t]->stepTest();
}
for (int t=0; t<threads.size(); ++t){
threads[t].join();
@@ -5591,7 +5591,7 @@ class Solver{
nets[0]->stepTrain(false);
}else{
for (int t=0; t<threads.size(); ++t){
threads[t] = std::thread(&Net::stepTrain, nets[t], true); //nets[t]->stepTrain();
// threads[t] = std::thread(&Net::stepTrain, nets[t], true); //nets[t]->stepTrain();
}
for (int t=0; t<threads.size(); ++t){
threads[t].join();
@@ -5614,7 +5614,7 @@ class Solver{
nets[0]->eval(false);
}else{
for (int t=0; t<threads.size(); ++t){
threads[t] = std::thread(&Net::eval, nets[t], true); //nets[t]->eval();
// threads[t] = std::thread(&Net::eval, nets[t], true); //nets[t]->eval();
}
for (int t=0; t<threads.size(); ++t){
threads[t].join();