DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(concat(datasets::SmallShapes(), datasets::LargeShapes()), AbsoluteDifferenceS16Dataset),
shape, data_type0, data_type1, output_data_type)
{
// Create tensors
CLTensor ref_src1 = create_tensor<CLTensor>(shape, data_type0);
CLTensor ref_src2 = create_tensor<CLTensor>(shape, data_type1);
CLTensor dst = create_tensor<CLTensor>(shape, output_data_type);
// Create and Configure function
CLAbsoluteDifference abs_diff;
abs_diff.configure(&ref_src1, &ref_src2, &dst);
// Validate valid region
const ValidRegion valid_region = shape_to_valid_region(shape);
validate(dst.info()->valid_region(), valid_region);
// Validate padding
const PaddingSize padding = PaddingCalculator(shape.x(), 16).required_padding();
validate(ref_src1.info()->padding(), padding);
validate(ref_src2.info()->padding(), padding);
validate(dst.info()->padding(), padding);
}
int main( int argc, char *argv[] )
{
int r=0;
/*data init to be replaced with ppm images*/
unsigned int width = 16;
unsigned int height = 16;
unsigned int bsize = 2;
unsigned int seed = 12;
if (getenv("CK_WIDTH")!=NULL) width=atol(getenv("CK_WIDTH"));
if (getenv("CK_HEIGHT")!=NULL) height=atol(getenv("CK_HEIGHT"));
if (getenv("CK_BSIZE")!=NULL) bsize=atol(getenv("CK_BSIZE"));
std::cout << "WIDTH, HEIGHT, BSIZE " << width << " " << height << " " << bsize << "\n";
if (getenv("CK_SEED")!=NULL) seed=atol(getenv("CK_SEED"));
std::cout << "CK_SEED " << seed << "\n";
srand(seed);
auto *src_data = new float[width * height * bsize];
auto *dst_data = new float[width * height * bsize];
for(unsigned int b = 0; b < bsize; b++){
for(unsigned int h = 0; h < height; h++){
for(unsigned int w = 0; w < width; w++){
// float r = static_cast <float> (rand()) / (static_cast <float> (RAND_MAX/MYMAX));
src_data[b * (width * height) + h * width + w] = static_cast<float>(100 * b + 10 * h + w); //replace with random fixed seed value
dst_data[b * (width * height) + h * width + w] = 0;
}
}
}
//tuner.AddArgumentInput(src_data);
//tuner.AddArgumentInput(dst_data);
//tuner.AddArgumentScalar(static_cast<int>(width));
//tuner.SetNumRuns(2);
//tuner.Tune();
//tuner.PrintToScreen();
// OpenCL init
CLScheduler::get().default_init();
CLTensor ATensor; //NETensor for Neon
CLTensor OTensor;
TensorShape shape(width, height, bsize);
ATensor.allocator()->init(TensorInfo(shape, Format::F32));
OTensor.allocator()->init(TensorInfo(shape, Format::F32));
//FILL TENSORS... easiest way is: create an iteretor
Window input_window,output_window;
input_window.use_tensor_dimensions(ATensor.info());
output_window.use_tensor_dimensions(ATensor.info());
//Data in/out
ATensor.allocator()->allocate();
OTensor.allocator()->allocate();
ATensor.map();
Iterator input_it(&ATensor, input_window);
execute_window_loop(input_window, [&](const Coordinates & id){
*reinterpret_cast<float *>(input_it.ptr()) = src_data[id.z() * (width * height) + id.y() * width + id.x()];
},input_it);
ATensor.unmap();
const ITensorInfo *Ainfo = ATensor.info();
const ITensorInfo *Oinfo = OTensor.info();
const unsigned int num_elems_processed_per_iteration = ceil_to_multiple(ATensor.info()->dimension(0),16);
printf("[KERNEL SET-UP] num_elems_processed_per_iteration=%d\n",num_elems_processed_per_iteration); if (ATensor.info()->dimension(0) % 16 != 0){
setenv("CLTUNE_BUILD_OPTION","-DNON_MULTIPLE_OF_16", true);
printf("SET CLTUNE BUILD\n");
}
printf("READ %s\n",getenv("CLTUNE_BUILD_OPTION"));
auto kernel_file = std::vector<std::string>{"/home/flavio/CK_REPOS/ck-math/program/acl-softmax-opencl-tuner/softmax_layer2.cl"};
cltune::Tuner tuner(size_t{0}, size_t{0});
unsigned int gws_x = (input_window.x().end()-input_window.x().start())/16;
unsigned gws_y =(input_window.y().end()-input_window.y().start())/1;
const auto id = tuner.AddKernel(kernel_file, "softmax_layer_max", {1, gws_y}, {1,1});
tuner.AddParameter(id, "GROUP_SIZE", {1, 2, 4, 8, 16, 32});
tuner.MulLocalSize(id, {"GROUP_SIZE"});
const Strides &Astrides = Ainfo->strides_in_bytes();
unsigned int Aoffset_first_element = Ainfo->offset_first_element_in_bytes();
for(unsigned int n = 0; n < Ainfo->num_dimensions(); ++n){
Aoffset_first_element += input_window[n].start() * Astrides[n];
}
const Strides &Ostrides = Oinfo->strides_in_bytes();
unsigned int Ooffset_first_element = Oinfo->offset_first_element_in_bytes();
for(unsigned int n = 0; n < Oinfo->num_dimensions(); ++n){
Ooffset_first_element += output_window[n].start() * Ostrides[n];
}
std::vector<float> v(src_data, src_data + sizeof src_data / sizeof src_data[0]);
std::vector<float> vout(dst_data, dst_data + sizeof dst_data / sizeof dst_data[0]);
uint step_y = 1;
/*
tuner.AddArgumentInput(v);
tuner.AddArgumentScalar(static_cast<unsigned int>(Astrides[0]));
tuner.AddArgumentScalar(static_cast<unsigned int>(width));
tuner.AddArgumentScalar(static_cast<unsigned int>(Astrides[1]));
tuner.AddArgumentScalar(static_cast<unsigned int>(step_y));
tuner.AddArgumentScalar(static_cast<unsigned int>(Aoffset_first_element));
tuner.AddArgumentScalar(static_cast<unsigned int>(Ostrides[0]));
tuner.AddArgumentInput(vout);
tuner.AddArgumentScalar(static_cast<unsigned int>(Ostrides[1]));
tuner.AddArgumentScalar(static_cast<unsigned int>(step_y));
tuner.AddArgumentScalar(static_cast<unsigned int>(Ooffset_first_element));
tuner.AddArgumentScalar(static_cast<unsigned int>(width));
tuner.SetNumRuns(10);
tuner.Tune();
*/
tuner.PrintToScreen();
//Get output
/* Window output_window;
output_window.use_tensor_dimensions(OTensor.info());
OTensor.map();
Iterator output_it(&OTensor, output_window);
execute_window_loop(output_window, [&](const Coordinates & id){
#ifdef PRINT
std::cout << "Copying one row starting from [" << id.x() << "," << id.y() << "," << id.z() << "]\n";
#endif
// Copy one whole row:
memcpy(dst_data + id.z() * (width * height) + id.y() * width, output_it.ptr(), width * sizeof(float));
}, output_it);
OTensor.unmap();
*/
// Compute time
// secs += (after.tv_sec - before.tv_sec) + (after.tv_usec - before.tv_usec)/1000000.0;
// std::cout << "Softmax[time]= " << secs;
// double avg_time = secs;
delete[] src_data;
delete[] dst_data;
return 0;
}