// this runs an entire kernel to get one value. Clearly this is going to be pretty slow, but
// at least it's more or less compatible, and comparable, to how cutorch does it
void THClStorage_set(THClState *state, THClStorage *self, long index, float value)
{
//// cout << "set size=" << self->size << " index=" << index << " value=" << value << endl;
THArgCheck((index >= 0) && (index < self->size), 2, "index out of bounds");
THArgCheck(self->wrapper != 0, 1, "storage argument not initialized, is empty");
// if( self->wrapper->isDeviceDirty() ) { // we have to do this, since we're going to copy it all back again
// // although I suppose we could set via a kernel perhaps
// // either way, this function is pretty inefficient right now :-P
// if(state->trace) cout << "wrapper->copyToHost() size " << self->size << endl;
// self->wrapper->copyToHost();
// }
// self->data[index] = value;
// if(state->trace) cout << "wrapper->copyToDevice() size " << self->size << endl;
// self->wrapper->copyToDevice();
const char *uniqueName = __FILE__ ":set";
EasyCL *cl = self->cl; // cant remember if this is a good idea or not :-P
CLKernel *kernel = 0;
if(cl->kernelExists(uniqueName)) {
kernel = cl->getKernel(uniqueName);
} else {
TemplatedKernel kernelBuilder(cl);
kernel = kernelBuilder.buildKernel( uniqueName, __FILE__, getSetKernelSource(), "THClStorageSet" );
}
kernel->inout(self->wrapper);
kernel->in((int64_t)index);
kernel->in(value);
kernel->run_1d(1, 1);
if(state->addFinish) cl->finish();
}
TEST( SLOW_testintwrapper_huge, testread ) {
Timer timer;
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
CLKernel *kernel = cl->buildKernel("testeasycl.cl", "test_read");
// const int N = 4500000;
// const int N = (4500000/512)*512;
int N = 100000;
int *out = new int[N];
CLWrapper *outwrapper = cl->wrap(N, out);
kernel->in(3)->in(7);
kernel->output( outwrapper );
int globalSize = N;
int workgroupsize = cl->getMaxWorkgroupSize();
globalSize = ( ( globalSize + workgroupsize - 1 ) / workgroupsize ) * workgroupsize;
cout << "globalsize: " << globalSize << " workgroupsize " << workgroupsize << endl;
timer.timeCheck("before kernel");
kernel->run_1d( globalSize, workgroupsize );
timer.timeCheck("after kernel");
outwrapper->copyToHost();
timer.timeCheck("after copy to host");
for( int i = 0; i < N; i++ ) {
if( out[i] != 4228 ) {
cout << "out[" << i << "] != 4228: " << out[i] << endl;
exit(-1);
}
}
delete outwrapper;
delete kernel;
delete cl;
}
TEST(test_scenario_te42kyfo, main) {
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
CLKernel *kernel = cl->buildKernelFromString(getKernel(), "test", "");
CLArrayFloat *out = cl->arrayFloat(5);
CLArrayFloat *in = cl->arrayFloat(5);
for(int i = 0; i < 5; i++) {
(*out)[i] = 0;
}
for(int i = 0; i < 100; i++) {
for(int n = 0; n < 5; n++) {
(*in)[n] = i*n;
}
kernel->in(in);
kernel->out(out);
kernel->run_1d(5, 5);
assertEquals(i*2 + 5, (*out)[2]);
assertEquals(i*4 + 5, (*out)[4]);
}
cout << "finished" << endl;
delete in;
delete out;
delete kernel;
delete cl;
}
TEST(testinout, main) {
if(!EasyCL::isOpenCLAvailable()) {
cout << "opencl library not found" << endl;
exit(-1);
}
cout << "found opencl library" << endl;
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
CLKernel *kernel = cl->buildKernelFromString(getKernel(), "test", "");
float inout[5];
for(int i = 0; i < 5; i++) {
inout[i] = i * 3;
}
kernel->inout(5, inout);
size_t global = 5;
size_t local = 5;
kernel->run(1, &global, &local);
assertEquals(inout[0] , 7);
assertEquals(inout[1] , 10);
assertEquals(inout[2] , 13);
assertEquals(inout[3] , 16);
assertEquals(inout[4] , 19);
cout << "tests completed ok" << endl;
delete kernel;
delete cl;
}
TEST( SLOW_testintwrapper_huge, testreadwrite ) {
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
CLKernel *kernel = cl->buildKernel("testeasycl.cl", "test_stress");
const int N = 1000000;
int *in = new int[N];
for( int i = 0; i < N; i++ ) {
in[i] = i * 3;
}
int *out = new int[N];
CLWrapper *inwrapper = cl->wrap(N, in);
CLWrapper *outwrapper = cl->wrap(N, out);
inwrapper->copyToDevice();
outwrapper->createOnDevice();
kernel->input( inwrapper );
kernel->output( outwrapper );
int globalSize = N;
int workgroupsize = cl->getMaxWorkgroupSize();
globalSize = ( ( globalSize + workgroupsize - 1 ) / workgroupsize ) * workgroupsize;
cout << "globalsize: " << globalSize << " workgroupsize " << workgroupsize << endl;
kernel->run_1d( globalSize, workgroupsize );
outwrapper->copyToHost();
for( int i = 0; i < N; i++ ) {
if( out[i] != 689514 ) {
cout << "out[" << i << "] != 689514: " << out[i] << endl;
exit(-1);
}
}
delete outwrapper;
delete inwrapper;
delete kernel;
delete cl;
}
// this runs an entire kernel to get one value. Clearly this is going to be pretty slow, but
// at least it's more or less compatible, and comparable, to how cutorch does it
// lgfgs expects a working implementation of this method
float THClStorage_get(THClState *state, const THClStorage *self, long index)
{
//// printf("THClStorage_get\n");
THArgCheck((index >= 0) && (index < self->size), 2, "index out of bounds");
THArgCheck(self->wrapper != 0, 1, "storage argument not initialized, is empty");
// if( self->wrapper->isDeviceDirty() ) {
// if(state->trace) cout << "wrapper->copyToHost()" << endl;
// self->wrapper->copyToHost();
// }
// return self->data[index];
const char *uniqueName = __FILE__ ":get";
EasyCL *cl = self->cl; // cant remember if this is a good idea or not :-P
CLKernel *kernel = 0;
if(cl->kernelExists(uniqueName)) {
kernel = cl->getKernel(uniqueName);
} else {
TemplatedKernel kernelBuilder(cl);
kernel = kernelBuilder.buildKernel( uniqueName, __FILE__, getGetKernelSource(), "THClStorageGet" );
}
float res;
kernel->out(1, &res);
kernel->in(self->wrapper);
kernel->in((int64_t)index);
kernel->run_1d(1, 1);
if(state->addFinish) cl->finish();
return res;
}
TEST(testCLMathWrapper, multiplyinplace) {
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
float adat[] = { 1,3,9,12.5f,2.5f };
// float bdat[] = { 4,2.1f, 5,3,9.2f };
CLWrapper *a_ = cl->wrap(5,adat);
// CLWrapper *b_ = cl->wrap(5,bdat);
a_->copyToDevice();
// b_->copyToDevice();
CLMathWrapper a(a_);
// CLMathWrapper b(b_);
// a += b;
a *= 1.5f;
a_->copyToHost();
for(int i = 0; i < 5; i++) {
cout << "a[" << i << "]=" << adat[i] << endl;
}
EXPECT_FLOAT_NEAR(1.5f, adat[0]);
EXPECT_FLOAT_NEAR(4.5f, adat[1]);
EXPECT_FLOAT_NEAR(3.75f, adat[4]);
// delete a;
// delete b;
delete a_;
// delete b_;
delete cl;
}
TEST(testfloatwrapperconst, main) {
if(!EasyCL::isOpenCLAvailable()) {
cout << "opencl library not found" << endl;
exit(-1);
}
cout << "found opencl library" << endl;
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
CLKernel *kernel = cl->buildKernelFromString(getKernel(), "test", "");
float in[5];
for(int i = 0; i < 5; i++) {
in[i] = i * 3;
}
float out[5];
CLWrapper *inwrapper = cl->wrap(5, (float const *)in);
CLWrapper *outwrapper = cl->wrap(5, out);
inwrapper->copyToDevice();
kernel->input(inwrapper);
kernel->output(outwrapper);
kernel->run_1d(5, 5);
outwrapper->copyToHost();
assertEquals(out[0] , 7);
assertEquals(out[1] , 10);
assertEquals(out[2] , 13);
assertEquals(out[3] , 16);
assertEquals(out[4] , 19);
cout << "tests completed ok" << endl;
delete inwrapper;
delete outwrapper;
delete kernel;
delete cl;
}
TEST( testforward, compare_1_n_biased_pad ) {
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
int maxWorkgroupSize = cl->getMaxWorkgroupSize();
delete cl;
LayerDimensions dim;
int batchSize = 4;
int N = 4;
string activationName = "tanh";
dim.setInputPlanes( 8 ).setInputSize(19).setNumFilters( 8 )
.setFilterSize( 5 )
.setPadZeros( true ).setBiased( true );
for( int instance = 2; instance <= 7; instance++ ) {
if( instance == 5 ) {
continue; // forwardfc, cant use for inputimagesize != filtersize
}
dim.setInputSize(19);
if(instance == 2 && maxWorkgroupSize < 19 * 19) {
dim.setInputSize(15);
}
if(instance == 3 && maxWorkgroupSize < 19 * 19) {
dim.setInputSize(15);
}
cout << "instance: " << instance << endl;
compareSpecific( false, N, batchSize, dim, 1, instance );
}
}
TEST(testcopybuffer, throwsifnotondevice) {
if(!EasyCL::isOpenCLAvailable()) {
cout << "opencl library not found" << endl;
exit(-1);
}
cout << "found opencl library" << endl;
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
//CLKernel *kernel = cl->buildKernel("testeasycl.cl", "test");
const int bufferSize = 100 * 1024 / 4;
float *in = new float[bufferSize];
float *in2 = new float[bufferSize];
for(int i = 0; i < bufferSize; i++) {
in[i] = i * 3;
in2[i] = 23 + i;
}
CLWrapper *inwrapper = cl->wrap(bufferSize, in);
CLWrapper *in2wrapper = cl->wrap(bufferSize, in2);
inwrapper->copyToDevice();
// in2wrapper->copyToDevice();
bool threw = false;
try {
inwrapper->copyTo(in2wrapper);
} catch(runtime_error &e) {
threw = true;
}
EXPECT_TRUE(threw);
delete cl;
}
TEST( testCopyLocal, basic ) {
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
float a[] = { 1,2,3,4,
5,6,7,8,
9,10,11,12 };
float b[16];
memset(b, 0, sizeof(float)*16);
CLKernel *kernel = makeKernel( cl );
kernel->in( 12, a )->out( 16, b )->in( 12 );
kernel->localFloats( 12 );
kernel->run_1d(12,12);
cl->finish();
for( int i = 0; i < 3; i++ ) {
for( int j = 0; j < 4; j++ ) {
cout << b[i*4+j] << " ";
EXPECT_EQ( i * 4 + j + 1, b[i*4+j] );
}
cout << endl;
}
cout << endl;
for( int i = 12; i < 16; i++ ) {
cout << b[i] << " ";
EXPECT_EQ( 0, b[i] );
}
cout << endl;
delete kernel;
delete cl;
}
TEST(testcopybuffer, withoffset) {
if(!EasyCL::isOpenCLAvailable()) {
cout << "opencl library not found" << endl;
exit(-1);
}
cout << "found opencl library" << endl;
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
//CLKernel *kernel = cl->buildKernel("testeasycl.cl", "test");
float in[10];
float in2[10];
for(int i = 0; i < 10; i++) {
in[i] = i * 3;
in2[i] = 23 + i;
}
float out[10];
CLWrapper *inwrapper = cl->wrap(10, in);
CLWrapper *in2wrapper = cl->wrap(10, in2);
CLWrapper *outwrapper = cl->wrap(10, out);
inwrapper->copyToDevice();
in2wrapper->copyToDevice();
EXPECT_FALSE(in2wrapper->isDeviceDirty());
inwrapper->copyTo(in2wrapper, 2, 5, 4);
EXPECT_TRUE(in2wrapper->isDeviceDirty());
// cl->finish();
// check that in2 host-side unchanged:
for(int i = 0; i < 10; i++) {
in[i] = i * 3;
EXPECT_EQ(23 + i, in2[i]);
}
in2wrapper->copyToHost();
// check that in2 is now a partial copy of in:
for(int i = 0; i < 10; i++) {
// in[i] = i * 3;
if(i >= 5 && i < 9) {
EXPECT_EQ((i-3) * 3, in2[i]);
} else {
EXPECT_EQ(23 + i, in2[i]);
}
}
// check that modifying in2 doesnt modfiy in:
in2[1] = 27;
in2wrapper->copyToDevice();
inwrapper->copyToHost();
EXPECT_EQ(1 * 3, in[1]);
in2wrapper->copyToHost();
EXPECT_EQ(1 * 3, in[1]);
EXPECT_EQ(27, in2[1]);
delete inwrapper;
delete in2wrapper;
delete outwrapper;
delete cl;
}
TEST( testCopyBlock, basic ) {
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
float a[] = { 1,2,3,4,
5,6,7,8,
9,10,11,12 };
float b[10];
memset(b, 0, sizeof(float)*10);
CLKernel *kernel = makeBasicKernel( cl );
kernel->in( 12, a )->out( 6, b )->in( ( 3<<10)|4)->in( (0<<10)|1)->in((2<<10)|3);
kernel->localFloats( 2 * 3 );
kernel->run_1d(12,4);
// kernel->run_1d(12,12);
cl->finish();
float expected[] = { 2,3,4,
6,7,8 };
for( int i = 0; i < 2; i++ ) {
for( int j = 0; j < 3; j++ ) {
cout << b[i*3+j] << " ";
EXPECT_EQ( expected[i*3+j], b[i*3+j] );
}
cout << endl;
}
cout << endl;
for( int i = 6; i < 10; i++ ) {
cout << b[i] << " ";
EXPECT_EQ( 0, b[i] );
}
cout << endl;
cout << endl;
kernel->in( 12, a )->out( 6, b )->in( ( 3<<10)|4)->in( (1<<10)|0)->in((2<<10)|3);
kernel->localFloats( 2 * 3 );
// kernel->run_1d(12,4);
kernel->run_1d(12,4);
cl->finish();
float expected2[] = { 5,6,7,
9,10,11 };
for( int i = 0; i < 2; i++ ) {
for( int j = 0; j < 3; j++ ) {
cout << b[i*3+j] << " ";
EXPECT_EQ( expected2[i*3+j], b[i*3+j] );
}
cout << endl;
}
cout << endl;
for( int i = 6; i < 10; i++ ) {
cout << b[i] << " ";
EXPECT_EQ( 0, b[i] );
}
cout << endl;
cout << endl;
delete kernel;
delete cl;
}
TEST(testlocal, reduceviascratch_multipleworkgroups_ints) {
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
CLKernel *kernel = cl->buildKernel("testlocal.cl", "reduceViaScratch_multipleworkgroups_ints");
int workgroupSize = min(512, cl->getMaxWorkgroupSize());
const int numWorkgroups = workgroupSize;
const int N = workgroupSize * numWorkgroups;
cout << "numworkgroups " << numWorkgroups << " workgroupsize " << workgroupSize << " N " << N << endl;
int *myarray = new int[N];
int sumViaCpu = 0;
int localSumViaCpu = 0;
int localSumViaCpu2 = 0;
int *localSumsViaCpu = new int[numWorkgroups];
memset(localSumsViaCpu, 0, sizeof(int)*numWorkgroups);
for(int i = 0; i < N; i++) {
myarray[i] = ((i + 7) * 3) % 50;
sumViaCpu += myarray[i];
if(i < workgroupSize) {
localSumViaCpu += myarray[i];
}
if(i >= workgroupSize && i < workgroupSize * 2) {
localSumViaCpu2 += myarray[i];
}
int workgroupId = i / workgroupSize;
localSumsViaCpu[workgroupId] += myarray[i];
}
ASSERT_EQ(localSumViaCpu, localSumsViaCpu[0]);
ASSERT_EQ(localSumViaCpu2, localSumsViaCpu[1]);
ASSERT_NE(myarray[0], sumViaCpu);
// Timer timer;
CLWrapper *a1wrapper = cl->wrap(N, myarray);
a1wrapper->copyToDevice();
int *a2 = new int[numWorkgroups];
CLWrapper *a2wrapper = cl->wrap(numWorkgroups, a2);
kernel->in(a1wrapper);
kernel->out(a2wrapper);
kernel->localInts(workgroupSize);
kernel->run_1d(N, workgroupSize);
int finalSum;
kernel->in(a2wrapper);
kernel->out(1, &finalSum);
kernel->localInts(workgroupSize);
kernel->run_1d(numWorkgroups, workgroupSize);
// timer.timeCheck("finished 2-way reduce");
EXPECT_EQ(sumViaCpu, finalSum);
delete a1wrapper;
delete a2wrapper;
delete[] a2;
delete[]myarray;
delete kernel;
delete cl;
}
TEST(testcopybuffer, main) {
if(!EasyCL::isOpenCLAvailable()) {
cout << "opencl library not found" << endl;
exit(-1);
}
cout << "found opencl library" << endl;
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
//CLKernel *kernel = cl->buildKernel("testeasycl.cl", "test");
float in[5];
float in2[5];
for(int i = 0; i < 5; i++) {
in[i] = i * 3.0f;
in2[i] = 23.0f + i;
}
float out[5];
CLWrapper *inwrapper = cl->wrap(5, in);
CLWrapper *in2wrapper = cl->wrap(5, in2);
CLWrapper *outwrapper = cl->wrap(5, out);
inwrapper->copyToDevice();
in2wrapper->copyToDevice();
EXPECT_FALSE(in2wrapper->isDeviceDirty());
inwrapper->copyTo(in2wrapper);
EXPECT_TRUE(in2wrapper->isDeviceDirty());
// cl->finish();
// check that in2 host-side unchanged:
for(int i = 0; i < 5; i++) {
in[i] = i * 3.0f;
EXPECT_EQ(23.0f + i, in2[i]);
}
in2wrapper->copyToHost();
// check that in2 is now a copy of in:
for(int i = 0; i < 5; i++) {
in[i] = i * 3.0f;
EXPECT_EQ(i * 3.0f, in2[i]);
}
// check that modifying in2 doesnt modfiy in:
in2[1] = 27;
in2wrapper->copyToDevice();
inwrapper->copyToHost();
EXPECT_EQ(1 * 3.0f, in[1]);
in2wrapper->copyToHost();
EXPECT_EQ(1 * 3.0f, in[1]);
EXPECT_EQ(27.0f, in2[1]);
delete inwrapper;
delete in2wrapper;
delete outwrapper;
delete cl;
}
TEST( testdefines, simple ) {
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
CLKernel *kernel = cl->buildKernel("testdefines.cl", "testDefines",
"-D DOUBLE -D SOME_VALUE=5" );
float out[32];
kernel->out( 32, out );
kernel->run_1d( 32, 32 );
EXPECT_EQ( 10, out[3] );
delete kernel;
delete cl;
}
TEST(SLOW_testcopybuffer, larger) {
if(!EasyCL::isOpenCLAvailable()) {
cout << "opencl library not found" << endl;
exit(-1);
}
cout << "found opencl library" << endl;
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
//CLKernel *kernel = cl->buildKernel("testeasycl.cl", "test");
const int bufferSize = 100 * 1024 * 1024 / 4; // 100MB (of floats)
float *in = new float[bufferSize];
float *in2 = new float[bufferSize];
for(int i = 0; i < bufferSize; i++) {
in[i] = i * 3;
in2[i] = 23 + i;
}
CLWrapper *inwrapper = cl->wrap(bufferSize, in);
CLWrapper *in2wrapper = cl->wrap(bufferSize, in2);
inwrapper->copyToDevice();
in2wrapper->copyToDevice();
inwrapper->copyTo(in2wrapper);
// cl->finish();
// check that in2 host-side unchanged:
for(int i = 0; i < bufferSize; i++) {
in[i] = i * 3;
EXPECT_EQ(23 + i, in2[i]);
}
in2wrapper->copyToHost();
// check that in2 is now a copy of in:
for(int i = 0; i < bufferSize; i++) {
in[i] = i * 3;
EXPECT_EQ(i * 3, in2[i]);
}
// check that modifying in2 doesnt modfiy in:
in2[1] = 27;
in2wrapper->copyToDevice();
inwrapper->copyToHost();
EXPECT_EQ(1 * 3, in[1]);
in2wrapper->copyToHost();
EXPECT_EQ(1 * 3, in[1]);
EXPECT_EQ(27, in2[1]);
delete inwrapper;
delete in2wrapper;
delete[] in;
delete[] in2;
delete cl;
}
TEST(testClBlas, colMajor2) {
EasyCL *cl = DeepCLGtestGlobals_createEasyCL();
float A[] = {1, 3,
2, 7,
9, 5,
0, -2};
float B[] = {3,2,8,
-1,0,4};
float C[4*3];
transpose(A, 4, 2);
transpose(B, 2, 3);
// for(int row=0; row < 2; row++) {
// for(int col=0; col < 1; col++) {
// cout << B[row*1 + col] << " ";
// }
// cout << endl;
// }
ClBlasInstance clblasInstance;
// ClBlasInstance::initializeIfNecessary();
CLWrapper *AWrap = cl->wrap(4*2, A);
CLWrapper *BWrap = cl->wrap(2*3, B);
CLWrapper *CWrap = cl->wrap(4*3, C);
AWrap->copyToDevice();
BWrap->copyToDevice();
ClBlasHelper::Gemm(
cl,
clblasColumnMajor,
clblasNoTrans, clblasNoTrans,
4, 2, 3,
1,
AWrap, 0,
BWrap, 0,
0,
CWrap, 0
);
// cl->finish();
CWrap->copyToHost();
transpose(C, 3, 4);
EXPECT_EQ(1*3-1*3, C[0]);
EXPECT_EQ(1*2+3*0, C[1]);
EXPECT_EQ(1*8+4*3, C[2]);
EXPECT_EQ(-8, C[11]);
delete CWrap;
delete BWrap;
delete AWrap;
delete cl;
}
TEST(SLOW_testlocal, selfdot_3levels_withoutscratch) {
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
CLKernel *kernel = cl->buildKernel("testlocal.cl", "selfdot_ints_withoutscratch");
int workgroupSize = min(512, cl->getMaxWorkgroupSize());
const int numWorkgroups = workgroupSize;
const int level3size = numWorkgroups / 4;
const int N = workgroupSize * numWorkgroups * level3size;
cout << "numworkgroups " << numWorkgroups << " workgroupsize " << workgroupSize << " N " << N << endl;
int *myarray = new int[N];
for(int i = 0; i < N; i++) {
myarray[i] = ((i + 7) * 3) % 5;
}
// Timer timer;
CLWrapper *a1wrapper = cl->wrap(N, myarray);
a1wrapper->copyToDevice();
// timer.timeCheck("copied array to device");
int *second = new int[N];
CLWrapper *secondwrapper = cl->wrap(N, second);
int *a2 = new int[numWorkgroups*level3size];
CLWrapper *a2wrapper = cl->wrap(numWorkgroups * level3size, a2);
kernel->in(a1wrapper);
kernel->out(secondwrapper);
kernel->out(a2wrapper);
kernel->run_1d(N, workgroupSize);
cl->finish();
int *a3 = new int[numWorkgroups];
CLWrapper *a3wrapper = cl->wrap(level3size, a3);
kernel->in(a2wrapper);
kernel->out(secondwrapper);
kernel->out(a3wrapper);
kernel->run_1d(workgroupSize * level3size, workgroupSize);
cl->finish();
int finalSum;
kernel->in(a3wrapper);
kernel->out(secondwrapper);
kernel->out(1, &finalSum);
kernel->run_1d(level3size, level3size);
// timer.timeCheck("finished 3-level reduce");
EXPECT_EQ(-1306309159, finalSum);
delete a1wrapper;
delete a2wrapper;
delete a3wrapper;
delete secondwrapper;
delete[] a3;
delete[] second;
delete[] a2;
delete[]myarray;
delete kernel;
delete cl;
}
TEST(testlocal, notUselocal) {
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
CLKernel *kernel = cl->buildKernel("testlocal.cl", "notUseLocal");
int workgroupSize = 64;
float *myarray = new float[workgroupSize];
kernel->in(workgroupSize);
kernel->inout(workgroupSize, myarray);
kernel->run_1d(workgroupSize, workgroupSize);
delete[]myarray;
delete kernel;
delete cl;
}
TEST(testqueues, defaultqueue) {
cout << "start" << endl;
if(!EasyCL::isOpenCLAvailable()) {
cout << "opencl library not found" << endl;
exit(-1);
}
cout << "found opencl library" << endl;
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
CLKernel *kernel = cl->buildKernelFromString(getKernel(), "test", "");
float in[5];
float out[5];
for(int i = 0; i < 5; i++) {
in[i] = i * 3;
out[i] = 0;
}
kernel->input(5, in);
kernel->output(5, out);
size_t global = 5;
size_t local = 5;
kernel->run(1, &global, &local);
cl->finish();
assertEquals(out[0] , 7);
assertEquals(out[1] , 10);
assertEquals(out[2] , 13);
assertEquals(out[3] , 16);
assertEquals(out[4] , 19);
for(int i = 0; i < 5; i++) {
in[i] = i * 3;
out[i] = 0;
}
cout << "cl->queue " << (void *)cl->queue << endl;
cout << "*cl->queue " << (void *)*cl->queue << endl;
cout << "cl->default_queue->queue" << (void *)cl->default_queue->queue << endl;
kernel->input(5, in);
kernel->output(5, out);
kernel->run(cl->default_queue, 1, &global, &local);
cl->finish();
assertEquals(out[0] , 7);
assertEquals(out[1] , 10);
assertEquals(out[2] , 13);
assertEquals(out[3] , 16);
assertEquals(out[4] , 19);
cout << "tests completed ok" << endl;
}
TEST(testClBlas, colMajorTransB) {
EasyCL *cl = DeepCLGtestGlobals_createEasyCL();
float A[] = {1, 3,
2, 7,
9, 5};
float B[] = {3,
-1};
float C[3];
transpose(A, 3, 2);
// transpose(B, 2, 1);
// for(int row=0; row < 2; row++) {
// for(int col=0; col < 1; col++) {
// cout << B[row*1 + col] << " ";
// }
// cout << endl;
// }
ClBlasInstance clblasInstance;
// ClBlasInstance::initializeIfNecessary();
CLWrapper *AWrap = cl->wrap(6, A);
CLWrapper *BWrap = cl->wrap(2, B);
CLWrapper *CWrap = cl->wrap(3, C);
AWrap->copyToDevice();
BWrap->copyToDevice();
ClBlasHelper::Gemm(
cl,
clblasColumnMajor,
clblasNoTrans, clblasTrans,
3, 2, 1,
1,
AWrap, 0,
BWrap, 0,
0,
CWrap, 0
);
// cl->finish();
CWrap->copyToHost();
transpose(C, 1, 3);
EXPECT_EQ(0, C[0]);
EXPECT_EQ(-1, C[1]);
EXPECT_EQ(22, C[2]);
delete CWrap;
delete BWrap;
delete AWrap;
delete cl;
}
void measurePerf(int instance, int batchSize, LayerDimensions dim) {
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
int inputNumElements = dim.inputCubeSize * batchSize;
int errorsSize = dim.outputCubeSize * batchSize;
int weightsSize = dim.filtersSize;
int errorsForUpstreamSize = dim.inputCubeSize * batchSize;
float *input = new float[inputNumElements];
float *errors = new float[errorsSize];
float *weights = new float[weightsSize];
WeightRandomizer::randomize(input, inputNumElements, -0.1f, 0.1f);
WeightRandomizer::randomize(errors, errorsSize, -0.1f, 0.1f);
WeightRandomizer::randomize(weights, weightsSize, -0.1f, 0.1f);
float *errorsForUpstream = new float[errorsForUpstreamSize];
CLWrapper *inputWrapper = cl->wrap(inputNumElements, input);
CLWrapper *errorsWrapper = cl->wrap(errorsSize, errors);
CLWrapper *weightsWrapper = cl->wrap(weightsSize, weights);
CLWrapper *errorsForUpstreamWrapper = cl->wrap(errorsForUpstreamSize, errorsForUpstream);
inputWrapper->copyToDevice();
errorsWrapper->copyToDevice();
weightsWrapper->copyToDevice();
errorsForUpstreamWrapper->createOnDevice();
StatefulTimer::timeCheck("after init");
Backward *backwardImpl = Backward::instanceSpecific(instance, cl, dim);
for(int it = 0; it < 40; it++) {
backwardImpl->backward(batchSize,
inputWrapper, errorsWrapper, weightsWrapper,
errorsForUpstreamWrapper);
}
StatefulTimer::timeCheck("after backprop");
StatefulTimer::dump(true);
delete errorsForUpstreamWrapper;
delete weightsWrapper;
delete inputWrapper;
delete errorsWrapper;
delete[] errors;
delete[] weights;
delete[] input;
delete[] errorsForUpstream;
delete backwardImpl;
delete cl;
}
TEST( testMemset, basic ) {
EasyCL *cl = DeepCLGtestGlobals_createEasyCL();
CLKernel *kMemset = 0;
// [[[cog
// import stringify
// stringify.write_kernel2( "kMemset", "cl/memset.cl", "cl_memset", '""' )
// ]]]
// generated using cog, from cl/memset.cl:
const char * kMemsetSource =
"// Copyright Hugh Perkins 2015 hughperkins at gmail\n"
"//\n"
"// This Source Code Form is subject to the terms of the Mozilla Public License,\n"
"// v. 2.0. If a copy of the MPL was not distributed with this file, You can\n"
"// obtain one at http://mozilla.org/MPL/2.0/.\n"
"\n"
"kernel void cl_memset(global float *target, const float value, const int N) {\n"
" #define globalId get_global_id(0)\n"
" if ((int)globalId < N) {\n"
" target[globalId] = value;\n"
" }\n"
"}\n"
"\n"
"";
kMemset = cl->buildKernelFromString(kMemsetSource, "cl_memset", "", "cl/memset.cl");
// [[[end]]]
int N = 10000;
float *myArray = new float[N];
CLWrapper *myArrayWrapper = cl->wrap( N, myArray );
myArrayWrapper->createOnDevice();
kMemset->out( myArrayWrapper )->in( 99.0f )->in( N );
int workgroupSize = 64;
kMemset->run_1d( ( N + workgroupSize - 1 ) / workgroupSize * workgroupSize, workgroupSize );
cl->finish();
myArrayWrapper->copyToHost();
for( int i = 0; i < 10; i++ ) {
// cout << "myArray[" << i << "]=" << myArray[i] << endl;
}
for( int i = 0; i < N; i++ ) {
EXPECT_EQ( 99.0f, myArray[i] );
}
delete kMemset;
delete cl;
}
void col2im(THClState *state, THClTensor* col, const int channels,
const int height, const int width, const int patch_h, const int patch_w, const int pad_h,
const int pad_w, const int stride_h, const int stride_w, THClTensor* im) {
int height_col = (height + 2 * pad_h - patch_h) / stride_h + 1;
int width_col = (width + 2 * pad_w - patch_w) / stride_w + 1;
int num_kernels = channels * height * width;
// To avoid involving atomic operations, we will launch one kernel per
// bottom dimension, and then in the kernel add up the top dimensions.
EasyCL *cl = im->storage->cl;
std::string uniqueName = "SpatialConvolutionMM::col2im";
CLKernel *kernel = 0;
if(cl->kernelExists(uniqueName)) {
kernel = cl->getKernel(uniqueName);
} else {
TemplatedKernel kernelBuilder(cl);
kernel = kernelBuilder.buildKernel(uniqueName, "SpatialConvolutionMM.cl",
SpatialConvolutionMM_getKernelTemplate(), "col2im_kernel");
}
THClKernels k(state, kernel);
k.in(num_kernels);
k.in(col);
k.in(height);
k.in(width);
k.in(channels);
k.in(patch_h);
k.in(patch_w);
k.in(pad_h);
k.in(pad_w);
k.in(stride_h);
k.in(stride_w);
k.in(height_col);
k.in(width_col);
k.out(im);
k.run(GET_BLOCKS(state, num_kernels), getNumThreads(state));
// col2im_kernel <<<GET_BLOCKS(num_kernels), CL_NUM_THREADS, 0, stream>>> (
// num_kernels, data_col, height, width, channels,
// patch_h, patch_w, pad_h, pad_w, stride_h, stride_w,
// height_col, width_col, data_im
// );
// THError("Not implemented");
}
TEST( testpoolingforward, fromwrappers ) {
int batchSize = 1;
int numPlanes = 1;
int imageSize = 4;
int poolingSize = 2;
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
PoolingForward *poolingForward = PoolingForward::instanceSpecific( 1, cl, false, numPlanes, imageSize, poolingSize );
float input[] = { 1, 2, 5, 3,
3, 8, 4, 1,
3, 33, 14,23,
-1, -3.5f,37.4f,5
};
int outputSize = poolingForward->getOutputSize( batchSize );
int *selectors = new int[outputSize];
float *output = new float[outputSize];
const int inputSize = batchSize * numPlanes * imageSize * imageSize;
CLWrapper *inputWrapper = cl->wrap( inputSize, input );
CLWrapper *selectorsWrapper = cl->wrap( outputSize, selectors );
CLWrapper *outputWrapper = cl->wrap( outputSize, output );
inputWrapper->copyToDevice();
poolingForward->forward( batchSize, inputWrapper, selectorsWrapper, outputWrapper );
selectorsWrapper->copyToHost();
outputWrapper->copyToHost();
EXPECT_EQ( selectors[0], 3 );
EXPECT_EQ( selectors[1], 0 );
EXPECT_EQ( selectors[2], 1 );
EXPECT_EQ( selectors[3], 2 );
EXPECT_EQ( output[0], 8 );
EXPECT_EQ( output[1], 5 );
EXPECT_EQ( output[2], 33 );
EXPECT_EQ( output[3], 37.4f );
delete inputWrapper;
delete selectorsWrapper;
delete outputWrapper;
delete poolingForward;
delete[] selectors;
delete[] output;
delete cl;
}
void THClStorage_resize(THClState *state, THClStorage *self, long size)
{
StatefulTimer::timeCheck("THClStorage_resize START");
if( size <= self->size ) {
return;
}
delete self->wrapper;
if(state->trace && self->size > 0) cout << "delete wrapper" << endl;
delete[] self->data;
self->data = new float[size];
EasyCL *cl = self->cl;
self->wrapper = cl->wrap( size, self->data );
self->wrapper->createOnDevice();
if(state->trace) cout << "new wrapper, size " << size << endl;
self->size = size;
StatefulTimer::timeCheck("THClStorage_resize END");
}
TEST(testlocal, reduceviascratch_multipleworkgroups) {
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
CLKernel *kernel = cl->buildKernel("testlocal.cl", "reduceViaScratch_multipleworkgroups");
int workgroupSize = min(512, cl->getMaxWorkgroupSize());
const int numWorkgroups = workgroupSize;
const int N = workgroupSize * numWorkgroups;
float *myarray = new float[N];
float sumViaCpu = 0;
float localSumViaCpu = 0;
for(int i = 0; i < N; i++) {
myarray[i] = ((i + 7) * 3) % 10;
sumViaCpu += myarray[i];
if(i < workgroupSize) {
localSumViaCpu += myarray[i];
}
}
cout << "expected sum, calc'd via cpu, : " << sumViaCpu << endl;
EXPECT_NE(myarray[0], sumViaCpu);
// Timer timer;
CLWrapper *a1wrapper = cl->wrap(N, myarray);
a1wrapper->copyToDevice();
float *a2 = new float[numWorkgroups];
CLWrapper *a2wrapper = cl->wrap(numWorkgroups, a2);
kernel->in(a1wrapper);
kernel->out(a2wrapper);
kernel->localFloats(workgroupSize);
kernel->run_1d(N, workgroupSize);
float finalSum;
kernel->in(a2wrapper);
kernel->out(1, &finalSum);
kernel->localFloats(workgroupSize);
kernel->run_1d(numWorkgroups, workgroupSize);
EXPECT_EQ(sumViaCpu, finalSum);
delete a1wrapper;
delete a2wrapper;
delete[] a2;
delete[]myarray;
delete kernel;
delete cl;
}
TEST(testfloatarray, main) {
if(!EasyCL::isOpenCLAvailable()) {
cout << "opencl library not found" << endl;
exit(-1);
}
cout << "found opencl library" << endl;
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
CLKernel *kernel = cl->buildKernelFromString(getKernel(), "test", "");
float in[5];
float inout[5];
float out[5];
for(int i = 0; i < 5; i++) {
in[i] = i * 3;
inout[i] = i * 3;
}
kernel->in(5, in);
kernel->out(5, out);
kernel->inout(5, inout);
kernel->run_1d(5, 5);
for(int i = 0; i < 5; i++) {
cout << out[i] << " ";
}
cout << endl;
for(int i = 0; i < 5; i++) {
cout << inout[i] << " ";
}
cout << endl;
assertEquals(inout[0], 7);
assertEquals(inout[1] , 10);
assertEquals(inout[2] , 34);
assertEquals(inout[3] , 16);
assertEquals(inout[4], 19);
assertEquals(out[0] , 5);
assertEquals(out[1] , 8);
assertEquals(out[2] , 26);
assertEquals(out[3] , 14);
assertEquals(out[4] , 17);
cout << "tests completed ok" << endl;
delete kernel;
delete cl;
}
void im2col(THClState *state, THClTensor* im, const int channels,
const int height, const int width, const int ksize_h, const int ksize_w, const int pad_h,
const int pad_w, const int stride_h, const int stride_w, THClTensor* col) {
// We are going to launch channels * height_col * width_col kernels, each
// kernel responsible for copying a single-channel grid.
int height_col = (height + 2 * pad_h - ksize_h) / stride_h + 1;
int width_col = (width + 2 * pad_w - ksize_w) / stride_w + 1;
int num_kernels = channels * height_col * width_col;
std::string uniqueName = "SpatialConvolutionMM::im2col";
EasyCL *cl = im->storage->cl;
CLKernel *kernel = 0;
if(cl->kernelExists(uniqueName)) {
kernel = cl->getKernel(uniqueName);
} else {
TemplatedKernel kernelBuilder(cl);
kernel = kernelBuilder.buildKernel(uniqueName, "SpatialConvolutionMM.cl",
SpatialConvolutionMM_getKernelTemplate(), "im2col_kernel");
}
THClKernels k(state, kernel);
k.in(num_kernels);
k.in(im);
k.in(height);
k.in(width);
k.in(ksize_h);
k.in(ksize_w);
k.in(pad_h);
k.in(pad_w);
k.in(stride_h);
k.in(stride_w);
k.in(height_col);
k.in(width_col);
k.out(col);
k.run(GET_BLOCKS(state, num_kernels), getNumThreads(state));
// Launch
// im2col_kernel <<<GET_BLOCKS(num_kernels), CL_NUM_THREADS, 0, stream>>> (
// num_kernels, data_im, height, width, ksize_h, ksize_w,
// pad_h, pad_w, stride_h, stride_w,
// height_col, width_col, data_col
// );
}
