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Commit 3dd91688 authored by Li Peng's avatar Li Peng
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fp16 ocl support for googlenet 

Signed-off-by: 's avatarLi Peng <peng.li@intel.com>
parent 329abb5b
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Showing with 461 additions and 222 deletions
modules/dnn/src/layers/concat_layer.cpp
View file @ 3dd91688
......@@ -128,14 +128,14 @@ public:
for( i = 0; i < ninputs; i++ )
{
Mat& inp = *inputs[i];
CV_Assert( inp.isContinuous() && inp.type() == CV_32F &&
CV_Assert( inp.isContinuous() && (inp.type() == CV_32F || inp.type() == CV_16S) &&
inp.dims == 4 && inp.size[0] == output.size[0] &&
inp.size[2] == output.size[2] &&
inp.size[3] == output.size[3] );
nchannels += inp.size[1];
}
CV_Assert( nchannels == output.size[1] );
CV_Assert( output.isContinuous() && output.type() == CV_32F );
CV_Assert( output.isContinuous() && (output.type() == CV_32F || output.type() == CV_16S) );
cc.chptrs.resize(nchannels*batchsz);
......@@ -186,6 +186,7 @@ public:
std::vector<UMat> inputs;
std::vector<UMat> outputs;
bool use_half = (inps.depth() == CV_16S);
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
......@@ -199,11 +200,12 @@ public:
int num_concats = total(shape(inputs[0]), 0, cAxis);
int offset_concat_axis = 0;
UMat& outMat = outputs[0];
String buildopt = String("-DDtype=") + ocl::typeToStr(inputs[0].type()) + String(" ");
String buildopt = format(" -DDtype=%s", (use_half) ? "half" : "float");
String kname = format("concat_%s", use_half ? "half" : "float");
for (size_t i = 0; i < inputs.size(); i++)
{
ocl::Kernel kernel("concat", ocl::dnn::concat_oclsrc, buildopt);
ocl::Kernel kernel(kname.c_str(), ocl::dnn::concat_oclsrc, buildopt);
if (kernel.empty())
return false;
......@@ -235,7 +237,7 @@ public:
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
......
modules/dnn/src/layers/convolution_layer.cpp
View file @ 3dd91688
......@@ -94,7 +94,7 @@ public:
CV_Assert(blobs[0].dims == 4 && blobs[0].size[3] == kernel.width && blobs[0].size[2] == kernel.height);
const Mat &input = *inputs[0];
CV_Assert(input.dims == 4 && (input.type() == CV_32F || input.type() == CV_64F));
CV_Assert(input.dims == 4 && (input.type() == CV_32F || input.type() == CV_64F || input.type() == CV_16S));
for (size_t i = 0; i < inputs.size(); i++)
{
CV_Assert(inputs[i]->type() == input.type());
......@@ -288,7 +288,7 @@ public:
newActiv = true;
activType = OCL4DNN_CONV_FUSED_ACTIV_NONE;
if (preferableTarget == DNN_TARGET_OPENCL)
if (IS_DNN_OPENCL_TARGET(preferableTarget))
{
Ptr<PowerLayer> activ_power = activ.dynamicCast<PowerLayer>();
if (!activ_power.empty())
......@@ -842,6 +842,7 @@ public:
std::vector<UMat> inputs;
std::vector<UMat> outputs;
bool use_half = (inps.depth() == CV_16S);
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
......@@ -860,6 +861,7 @@ public:
config.dilation = dilation;
config.group = inputs[0].size[1] / umat_blobs[0].size[1];
config.bias_term = (hasBias()) ? true : false;
config.use_half = use_half;
convolutionOp = Ptr<OCL4DNNConvSpatial<float> >(new OCL4DNNConvSpatial<float>(config));
}
......@@ -964,7 +966,7 @@ public:
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
......@@ -1360,6 +1362,9 @@ public:
std::vector<UMat> outputs;
std::vector<UMat> internals;
if (inputs_.depth() == CV_16S)
return false;
inputs_.getUMatVector(inputs);
outputs_.getUMatVector(outputs);
internals_.getUMatVector(internals);
......@@ -1450,7 +1455,7 @@ public:
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
......
modules/dnn/src/layers/elementwise_layers.cpp
View file @ 3dd91688
......@@ -176,7 +176,7 @@ public:
{
CV_TRACE_FUNCTION();
CV_OCL_RUN((this->preferableTarget == DNN_TARGET_OPENCL) &&
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(this->preferableTarget) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
func.applyOCL(inputs_arr, outputs_arr, internals_arr))
......@@ -223,7 +223,12 @@ public:
#ifdef HAVE_OPENCL
static String oclGetTMacro(const UMat &m)
{
return String("-DT=") + ocl::typeToStr(m.type()) + String(" ");
String str_name = ocl::typeToStr(m.type());
if (str_name == "short")
str_name = "half";
return format("-DT=%s -Dconvert_T=convert_%s ", str_name.c_str(), str_name.c_str());
}
#endif
......@@ -516,8 +521,28 @@ struct SigmoidFunctor
#ifdef HAVE_OPENCL
bool applyOCL(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
{
// TODO: implement OCL version
return false;
std::vector<UMat> inputs;
std::vector<UMat> outputs;
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
String buildopt = oclGetTMacro(inputs[0]);
for (size_t i = 0; i < inputs.size(); i++)
{
UMat& src = inputs[i];
UMat& dst = outputs[i];
ocl::Kernel kernel("SigmoidForward", ocl::dnn::activations_oclsrc, buildopt);
kernel.set(0, (int)src.total());
kernel.set(1, ocl::KernelArg::PtrReadOnly(src));
kernel.set(2, ocl::KernelArg::PtrWriteOnly(dst));
size_t gSize = src.total();
CV_Assert(kernel.run(1, &gSize, NULL, false));
}
return true;
}
#endif
......@@ -561,8 +586,28 @@ struct ELUFunctor
#ifdef HAVE_OPENCL
bool applyOCL(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
{
// TODO: implement OCL version
return false;
std::vector<UMat> inputs;
std::vector<UMat> outputs;
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
String buildopt = oclGetTMacro(inputs[0]);
for (size_t i = 0; i < inputs.size(); i++)
{
UMat& src = inputs[i];
UMat& dst = outputs[i];
ocl::Kernel kernel("ELUForward", ocl::dnn::activations_oclsrc, buildopt);
kernel.set(0, (int)src.total());
kernel.set(1, ocl::KernelArg::PtrReadOnly(src));
kernel.set(2, ocl::KernelArg::PtrWriteOnly(dst));
size_t gSize = src.total();
CV_Assert(kernel.run(1, &gSize, NULL, false));
}
return true;
}
#endif
......@@ -604,8 +649,28 @@ struct AbsValFunctor
#ifdef HAVE_OPENCL
bool applyOCL(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
{
// TODO: implement OCL version
return false;
std::vector<UMat> inputs;
std::vector<UMat> outputs;
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
String buildopt = oclGetTMacro(inputs[0]);
for (size_t i = 0; i < inputs.size(); i++)
{
UMat& src = inputs[i];
UMat& dst = outputs[i];
ocl::Kernel kernel("AbsValForward", ocl::dnn::activations_oclsrc, buildopt);
kernel.set(0, (int)src.total());
kernel.set(1, ocl::KernelArg::PtrReadOnly(src));
kernel.set(2, ocl::KernelArg::PtrWriteOnly(dst));
size_t gSize = src.total();
CV_Assert(kernel.run(1, &gSize, NULL, false));
}
return true;
}
#endif
......
modules/dnn/src/layers/eltwise_layer.cpp
View file @ 3dd91688
......@@ -271,6 +271,9 @@ public:
std::vector<UMat> inputs;
std::vector<UMat> outputs;
if (inputs_.depth() == CV_16S && op != SUM)
return false;
inputs_.getUMatVector(inputs);
outputs_.getUMatVector(outputs);
......@@ -284,10 +287,15 @@ public:
{
size_t localsize[] = { 128 };
size_t globalsize[] = { (size_t)channels / 4 * localsize[0] };
String opts;
if (inputs_.depth() == CV_16S)
opts = " -DDtype=half -DDtype4=half4 -DDtype8=half8";
else
opts = " -DDtype=float -DDtype4=float4 -DDtype8=float8";
for (int i = 0; i < (inputs.size() - 1); ++i)
{
String buildopt = format("-DLOOP=%d", i);
String buildopt = format("-DLOOP=%d", i) + opts;
ocl::Kernel kernel("op_sum4", ocl::dnn::eltwise_oclsrc, buildopt);
int idx = 0;
UMat inpMat = (i == 0) ? inputs[0] : UMat();
......@@ -306,6 +314,9 @@ public:
}
else
{
if (inputs_.depth() == CV_16S)
return false;
float coeff1 = coeffs.empty() ? 1.f : coeffs[0];
float coeff2 = coeffs.empty() ? 1.f : coeffs[1];
UMat mul0, mul1;
......@@ -343,7 +354,7 @@ public:
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
......
modules/dnn/src/layers/flatten_layer.cpp
View file @ 3dd91688
......@@ -140,7 +140,7 @@ public:
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
outputs_arr.isUMatVector() &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
......
modules/dnn/src/layers/fully_connected_layer.cpp
View file @ 3dd91688
......@@ -64,6 +64,7 @@ public:
#ifdef HAVE_OPENCL
Ptr<OCL4DNNInnerProduct<float> > innerProductOp;
std::vector<UMat> umat_blobs;
std::vector<UMat> half_blobs;
#endif
FullyConnectedLayerImpl(const LayerParams& params)
......@@ -277,6 +278,7 @@ public:
std::vector<UMat> inputs;
std::vector<UMat> outputs;
bool use_half = (inps.depth() == CV_16S);
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
......@@ -293,6 +295,17 @@ public:
config.bias_term = bias;
config.M = outerSize;
config.K = innerSize;
config.use_half = use_half;
if (use_half)
{
half_blobs.resize(umat_blobs.size());
for (int i = 0; i < umat_blobs.size(); i++)
{
if (!umat_blobs[i].empty())
convertFp16(umat_blobs[i], half_blobs[i]);
}
}
innerProductOp = Ptr<OCL4DNNInnerProduct<float> >(new OCL4DNNInnerProduct<float>(config));
}
......@@ -309,13 +322,15 @@ public:
dstMat = outputs[i].reshape(1, outshape.size(), &outshape[0]);
dstMat.setTo(0.0f);
if (!innerProductOp->Forward(srcMat, umat_blobs[0], (bias) ? umat_blobs[1] : UMat(), dstMat))
if (!innerProductOp->Forward(srcMat, (use_half) ? half_blobs[0] : umat_blobs[0],
(bias) ? (use_half ? half_blobs[1] : umat_blobs[1]) : UMat(),
dstMat))
{
ret = false;
break;
}
if (bias && (outerSize > 1))
if (!use_half && bias && (outerSize > 1))
{
UMat& biases = umat_blobs[1];
cv::gemm(biasOnesMat, biases, 1, dstMat, 1, dstMat, 0);
......@@ -353,7 +368,7 @@ public:
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
......
modules/dnn/src/layers/lrn_layer.cpp
View file @ 3dd91688
......@@ -106,6 +106,7 @@ public:
std::vector<UMat> inputs;
std::vector<UMat> outputs;
bool use_half = (inps.depth() == CV_16S);
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
......@@ -128,6 +129,7 @@ public:
config.height = inputs[0].size[2];
config.width = inputs[0].size[3];
config.norm_by_size = normBySize;
config.use_half = use_half;
lrnOp = Ptr<OCL4DNNLRN<float> >(new OCL4DNNLRN<float>(config));
}
......@@ -146,7 +148,7 @@ public:
CV_Assert(inputs_arr.total() == outputs_arr.total());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
......
modules/dnn/src/layers/mvn_layer.cpp
View file @ 3dd91688
......@@ -102,6 +102,9 @@ public:
{
UMat bnorm_weight = scale.empty() ? UMat() : scale.getUMat(ACCESS_READ);
UMat bnorm_bias = shift.empty() ? UMat() : shift.getUMat(ACCESS_READ);
bool use_half = (inputs[0].depth() == CV_16S);
String opts = format(" -DT=%s -DT4=%s -Dconvert_T=%s", use_half ? "half" : "float",
use_half ? "half4" : "float4", use_half ? "convert_half4" : "convert_float4");
int splitDim = (acrossChannels) ? 1 : 2;
for (size_t inpIdx = 0; inpIdx < inputs.size(); inpIdx++)
......@@ -111,12 +114,11 @@ public:
int newRows = total(shape(inpMat), 0, splitDim);
MatShape s = shape(newRows, inpMat.total() / newRows);
UMat oneMat = UMat::ones(s[1], 1, CV_32F);
UMat meanMat = UMat(s[0], 1, CV_32F);
UMat meanMat = UMat(s[0], 1, (use_half) ? CV_16S : CV_32F);
UMat tmpMat = UMat(s[0], s[1], CV_32F);
float alpha = 1.0f / s[1];
String buildopt = "-DNUM=4";
String buildopt = "-DNUM=4" + opts;
ocl::Kernel k("mean_fuse4", ocl::dnn::mvn_oclsrc, buildopt);
size_t localsize[] = { 128 };
size_t globalsize[] = { (size_t)s[0] / 4 * localsize[0] };
......@@ -167,13 +169,14 @@ public:
int row_size = total(shape(inputs[0]), 0, splitDim);
int plane_size = total(shape(inputs[0]), splitDim);
if (normVariance && (row_size % 4 == 0) && (plane_size % 4 == 0))
{
bool ret = fast_forward_ocl(inputs, outputs);
return ret;
}
return fast_forward_ocl(inputs, outputs);
if (inputs[0].depth() == CV_16S)
return false;
UMat bnorm_weight = scale.empty() ? UMat() : scale.getUMat(ACCESS_READ);
UMat bnorm_bias = shift.empty() ? UMat() : shift.getUMat(ACCESS_READ);
String opts = format(" -DT=float -DT4=float4 -Dconvert_T=convert_float4");
for (size_t inpIdx = 0; inpIdx < inputs.size(); inpIdx++)
{
......@@ -195,7 +198,7 @@ public:
int number = (s[1] % 8 == 0) ? 8 : ((s[1] % 4 == 0) ? 4 : 1);
size_t global[] = { (size_t)s[0], (size_t)(s[1] / number) };
String buildopt = format("-DNUM=%d", number);
String buildopt = format("-DNUM=%d", number) + opts;
if (normVariance)
{
String kname = format("calc_mean%d", number);
......@@ -249,7 +252,7 @@ public:
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
......
modules/dnn/src/layers/pooling_layer.cpp
View file @ 3dd91688
......@@ -147,6 +147,7 @@ public:
std::vector<UMat> inputs;
std::vector<UMat> outputs;
bool use_half = (inps.depth() == CV_16S);
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
......@@ -164,6 +165,7 @@ public:
(type == AVE ? LIBDNN_POOLING_METHOD_AVE :
LIBDNN_POOLING_METHOD_STO);
config.avePoolPaddedArea = avePoolPaddedArea;
config.use_half = use_half;
poolOp = Ptr<OCL4DNNPool<float> >(new OCL4DNNPool<float>(config));
}
......@@ -189,7 +191,7 @@ public:
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
......
modules/dnn/src/layers/slice_layer.cpp
View file @ 3dd91688
......@@ -181,6 +181,7 @@ public:
std::vector<UMat> inputs;
std::vector<UMat> outputs;
bool use_half = (inputs_.depth() == CV_16S);
inputs_.getUMatVector(inputs);
outputs_.getUMatVector(outputs);
......@@ -188,6 +189,11 @@ public:
(total(shape(outputs[0]), 2) % 4 != 0))
return false;
String opts;
if (use_half)
opts = "-DDtype=half -DDtype4=half4 -DDtype8=half8";
else
opts = "-DDtype=float -DDtype4=float4 -DDtype8=float8";
const UMat& inpMat = inputs[0];
for (size_t i = 0; i < outputs.size(); i++)
{
......@@ -196,7 +202,7 @@ public:
int rows = outputs[i].size[2];
int cols = outputs[i].size[3];
ocl::Kernel kernel("slice", ocl::dnn::slice_oclsrc);
ocl::Kernel kernel("slice", ocl::dnn::slice_oclsrc, opts);
size_t local[] = { 128 };
size_t global[] = { (size_t)groups * channels / 4 * local[0] };
int idx = 0;
......@@ -222,7 +228,7 @@ public:
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
......
modules/dnn/src/layers/softmax_layer.cpp
View file @ 3dd91688
......@@ -99,15 +99,16 @@ public:
softmaxOp.release();
}
bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays itns)
bool forward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
{
std::vector<UMat> inputs;
std::vector<UMat> outputs;
std::vector<UMat> internals;
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
itns.getUMatVector(internals);
bool use_half = (inputs_.depth() == CV_16S);
inputs_.getUMatVector(inputs);
outputs_.getUMatVector(outputs);
internals_.getUMatVector(internals);
if (softmaxOp.empty())
{
......@@ -117,6 +118,7 @@ public:
config.axis = axisRaw;
config.channels = inputs[0].size[axisRaw];
config.logsoftmax = logSoftMax;
config.use_half = use_half;
softmaxOp = Ptr<OCL4DNNSoftmax<float> >(new OCL4DNNSoftmax<float>(config));
}
......@@ -128,15 +130,13 @@ public:
return true;
UMat& bufMat = internals[0];
src.copyTo(dstMat);
int axis = clamp(axisRaw, src.dims);
MatShape s = shape(src);
size_t outerSize = total(s, 0, axis);
size_t channels = src.size[axis];
size_t innerSize = total(s, axis + 1);
String buildOpts = String("-DT=") + ocl::typeToStr(src.type());
String buildOpts = format("-DT=%s", use_half ? "half" : "float");
ocl::Kernel kmax, ksub, ksum, kdiv;
if (!kmax.create("kernel_channel_max", ocl::dnn::softmax_oclsrc, buildOpts))
......@@ -152,38 +152,31 @@ public:
if (!kdiv.create("kernel_channel_div", ocl::dnn::softmax_oclsrc, buildOpts))
return false;
size_t wgSize = ocl::Device::getDefault().maxWorkGroupSize();
size_t bufSize = internals[0].total();
size_t totalSize = src.total();
// adjust local/global size
size_t internal_localSize[1] = { (bufSize == 1) ? 1 : wgSize };
size_t internal_globalSize[1] = { divUp(bufSize, (unsigned int)internal_localSize[0]) * internal_localSize[0] };
// adjust local/global size (total)
size_t total_localSize[1] = { (totalSize == 1) ? 1 : wgSize };
size_t total_globalSize[1] = { divUp(totalSize, (unsigned int)total_localSize[0]) * total_localSize[0] };
size_t internal_globalSize[1] = { bufSize };
size_t total_globalSize[1] = { totalSize };
kmax.args((int)outerSize, (int)channels, (int)innerSize,
ocl::KernelArg::PtrReadOnly(dstMat), ocl::KernelArg::PtrReadWrite(bufMat));
if (!kmax.run(1, internal_globalSize, internal_localSize, false))
ocl::KernelArg::PtrReadOnly(src), ocl::KernelArg::PtrReadWrite(bufMat));
if (!kmax.run(1, internal_globalSize, NULL, false))
return false;
ksub.args((int)totalSize, (int)outerSize, (int)channels, (int)innerSize,
ocl::KernelArg::PtrReadOnly(bufMat), ocl::KernelArg::PtrReadWrite(dstMat));
if (!ksub.run(1, total_globalSize, total_localSize, false))
ocl::KernelArg::PtrReadOnly(bufMat),
ocl::KernelArg::PtrReadOnly(src), ocl::KernelArg::PtrWriteOnly(dstMat));
if (!ksub.run(1, total_globalSize, NULL, false))
return false;
cv::exp(dstMat, dstMat);
ksum.args((int)outerSize, (int)channels, (int)innerSize,
ocl::KernelArg::PtrReadOnly(dstMat), ocl::KernelArg::PtrReadWrite(bufMat));
if (!ksum.run(1, internal_globalSize, internal_localSize, false))
if (!ksum.run(1, internal_globalSize, NULL, false))
return false;
kdiv.args((int)totalSize, (int)outerSize, (int)channels, (int)innerSize,
ocl::KernelArg::PtrReadOnly(bufMat), ocl::KernelArg::PtrReadWrite(dstMat));
if (!kdiv.run(1, total_globalSize, total_localSize, false))
if (!kdiv.run(1, total_globalSize, NULL, false))
return false;
return true;
......@@ -195,7 +188,7 @@ public:
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
......
modules/dnn/src/ocl4dnn/include/ocl4dnn.hpp
View file @ 3dd91688
......@@ -59,7 +59,8 @@ struct OCL4DNNConvConfig
stride(1, 1),
dilation(1, 1),
group(1),
bias_term(false)
bias_term(false),
use_half(false)
{}
MatShape in_shape;
MatShape out_shape;
......@@ -69,6 +70,7 @@ struct OCL4DNNConvConfig
Size dilation;
int group; // = 1;
bool bias_term; // = false;
bool use_half; // = false;
};
typedef enum {
......@@ -272,6 +274,8 @@ class OCL4DNNConvSpatial
int32_t group_;
bool bias_term_;
UMat swizzled_weights_umat;
UMat weights_half;
UMat bias_half;
UMat bottom_data2_;
int32_t bottom_index_;
......@@ -327,6 +331,7 @@ class OCL4DNNConvSpatial
ocl4dnnFusedActiv_t fused_activ_;
float power_;
bool fused_eltwise_;
bool use_half_;
};
typedef enum {
......@@ -345,7 +350,8 @@ struct OCL4DNNPoolConfig
channels(0),
pool_method(LIBDNN_POOLING_METHOD_MAX),
global_pooling(false),
avePoolPaddedArea(false)
avePoolPaddedArea(true),
use_half(false)
{}
MatShape in_shape;
MatShape out_shape;
......@@ -358,6 +364,7 @@ struct OCL4DNNPoolConfig
ocl4dnnPoolingMethod_t pool_method; // = LIBDNN_POOLING_METHOD_MAX;
bool global_pooling; // = false;
bool avePoolPaddedArea;
bool use_half;
};
template<typename Dtype>
......@@ -391,13 +398,14 @@ class OCL4DNNPool
int32_t pooled_height_;
int32_t pooled_width_;
bool avePoolPaddedArea;
bool use_half;
};
struct OCL4DNNInnerProductConfig
{
OCL4DNNInnerProductConfig() :
num_output(0), M(0), K(0),
bias_term(false), transpose(false), phase_test(true)
bias_term(false), transpose(false), phase_test(true), use_half(false)
{}
int num_output;
int M;
......@@ -405,6 +413,7 @@ struct OCL4DNNInnerProductConfig
bool bias_term;
bool transpose; // = false;
bool phase_test; // = true;
bool use_half; // = false;
};
template<typename Dtype>
......@@ -428,6 +437,7 @@ class OCL4DNNInnerProduct
bool transpose_;
bool image_copied_;
bool phase_test_;
bool use_half_;
};
typedef enum {
......@@ -441,7 +451,7 @@ struct OCL4DNNLRNConfig
lrn_type(LRNParameter_NormRegion_ACROSS_CHANNELS),
phase_test(true),
local_size(0), alpha(0.f), beta(0.f), k(0.f), norm_by_size(false),
batch_size(0), channels(0), height(0), width(0)
batch_size(0), channels(0), height(0), width(0), use_half(false)
{}
MatShape in_shape;
LRNParameter_NormRegion_WITHIN_CHANNEL_t lrn_type;
......@@ -455,6 +465,7 @@ struct OCL4DNNLRNConfig
int32_t channels;
int32_t height;
int32_t width;
bool use_half;
};
template<typename Dtype>
......@@ -477,16 +488,18 @@ class OCL4DNNLRN
int32_t height_;
int32_t width_;
bool norm_by_size_;
bool use_half_;
};
struct OCL4DNNSoftmaxConfig
{
OCL4DNNSoftmaxConfig() : axis(0), channels(0), logsoftmax(false)
OCL4DNNSoftmaxConfig() : axis(0), channels(0), logsoftmax(false), use_half(false)
{}
MatShape in_shape;
int axis;
int channels;
bool logsoftmax;
bool use_half;
};
template<typename Dtype>
......@@ -506,6 +519,7 @@ class OCL4DNNSoftmax
bool use_slm_;
bool log_softmax_;
UMat scale_data_;
bool use_half_;
};
}}} // namespace cv::dnn::ocl4dnn
......
modules/dnn/src/ocl4dnn/src/ocl4dnn_inner_product.cpp
View file @ 3dd91688
......@@ -56,6 +56,7 @@ OCL4DNNInnerProduct<Dtype>::OCL4DNNInnerProduct(OCL4DNNInnerProductConfig config
K_ = config.K;
phase_test_ = config.phase_test;
image_copied_ = false;
use_half_ = config.use_half;
}
template<typename Dtype>
......@@ -89,13 +90,24 @@ bool OCL4DNNInnerProduct<Dtype>::Forward(const UMat& bottom,
if (M_ <= max_image_size &&
N_ <= max_image_size &&
K_ <= max_image_size &&
cv::traits::Depth<Dtype>::value == CV_32F &&
ocl::Device::getDefault().intelSubgroupsSupport())
{
ret = ocl4dnnGEMMCommon<Dtype>(transpose_ ? CblasNoTrans : CblasTrans,
M_, N_, K_, bottom, weight, UMat(), top,
max_image_size);
}
if (use_half_ && bias_term_)
{
UMat biasOneMat = UMat::ones(M_, 1, CV_32F);
UMat newbias, tmpTop;
convertFp16(bias, newbias);
convertFp16(top, tmpTop);
cv::gemm(biasOneMat, newbias, 1, tmpTop, 1, tmpTop, 0);
convertFp16(tmpTop, top);
}
return ret;
}
}
......
modules/dnn/src/ocl4dnn/src/ocl4dnn_lrn.cpp
View file @ 3dd91688
......@@ -61,6 +61,7 @@ OCL4DNNLRN<Dtype>::OCL4DNNLRN(OCL4DNNLRNConfig config)
channels_ = config.channels;
height_ = config.height;
width_ = config.width;
use_half_ = config.use_half;
}
template<typename Dtype>
......@@ -97,8 +98,10 @@ bool OCL4DNNLRN<Dtype>::crossChannelForward(const UMat& bottom, UMat& top)
int32_t n_threads = num_ * height_ * width_;
size_t global_work_size_[1] = {(size_t)n_threads};
String opts = clOptionSupport("-cl-no-subgroup-ifp") ? " -cl-no-subgroup-ifp " : "";
opts += format("-D Dtype=%s", (use_half_) ? "half" : "float");
ocl::Kernel oclk_lrn_fill;
if (!oclk_lrn_fill.create(CL_KERNEL_SELECT("lrn_full_no_scale"), ocl::dnn::ocl4dnn_lrn_oclsrc, opts))
String kname = format("lrn_full_no_scale_%s", (use_half_) ? "half" : "float");
if (!oclk_lrn_fill.create(kname.c_str(), ocl::dnn::ocl4dnn_lrn_oclsrc, opts))
return false;
oclk_lrn_fill.set(argIdx++, n_threads);
......
modules/dnn/src/ocl4dnn/src/ocl4dnn_pool.cpp
View file @ 3dd91688
......@@ -56,6 +56,7 @@ OCL4DNNPool<Dtype>::OCL4DNNPool(OCL4DNNPoolConfig config)
channels_ = config.channels;
pool_method_ = config.pool_method;
avePoolPaddedArea = config.avePoolPaddedArea;
use_half = config.use_half;
for (int i = 0; i < spatial_dims; ++i)
{
......@@ -105,12 +106,15 @@ bool OCL4DNNPool<Dtype>::Forward(const UMat& bottom,
case LIBDNN_POOLING_METHOD_MAX:
{
bool haveMask = !top_mask.empty();
String kname = haveMask ? "max_pool_forward_mask" : "max_pool_forward";
kname += (use_half) ? "_half" : "_float";
ocl::Kernel oclk_max_pool_forward(
haveMask ? CL_KERNEL_SELECT("max_pool_forward_mask") : CL_KERNEL_SELECT("max_pool_forward"),
kname.c_str(),
ocl::dnn::ocl4dnn_pooling_oclsrc,
format("-D KERNEL_MAX_POOL=1 -D KERNEL_W=%d -D KERNEL_H=%d"
format(" -D Dtype=%s -D KERNEL_MAX_POOL=1 -D KERNEL_W=%d -D KERNEL_H=%d"
" -D STRIDE_W=%d -D STRIDE_H=%d"
" -D PAD_W=%d -D PAD_H=%d%s",
(use_half) ? "half" : "float",
kernel_w_, kernel_h_,
stride_w_, stride_h_,
pad_w_, pad_h_,
......@@ -139,11 +143,14 @@ bool OCL4DNNPool<Dtype>::Forward(const UMat& bottom,
{
CV_Assert(top_mask.empty());
ocl::Kernel oclk_ave_pool_forward(CL_KERNEL_SELECT("ave_pool_forward"),
String kname = format("ave_pool_forward_%s", (use_half) ? "half" : "float");
ocl::Kernel oclk_ave_pool_forward(
kname.c_str(),
ocl::dnn::ocl4dnn_pooling_oclsrc,
format("-D KERNEL_AVE_POOL=1 -D KERNEL_W=%d -D KERNEL_H=%d"
format(" -D Dtype=%s -D KERNEL_AVE_POOL=1 -D KERNEL_W=%d -D KERNEL_H=%d"
" -D STRIDE_W=%d -D STRIDE_H=%d"
" -D PAD_W=%d -D PAD_H=%d%s",
(use_half) ? "half" : "float",
kernel_w_, kernel_h_,
stride_w_, stride_h_,
pad_w_, pad_h_,
......@@ -171,7 +178,9 @@ bool OCL4DNNPool<Dtype>::Forward(const UMat& bottom,
{
CV_Assert(top_mask.empty());
ocl::Kernel oclk_sto_pool_forward(CL_KERNEL_SELECT("sto_pool_forward_test"),
String kname = format("sto_pool_forward_test_%s", (use_half) ? "half" : "float");
ocl::Kernel oclk_sto_pool_forward(
kname.c_str(),
ocl::dnn::ocl4dnn_pooling_oclsrc,
format("-D KERNEL_STO_POOL=1 -D KERNEL_W=%d -D KERNEL_H=%d"
" -D STRIDE_W=%d -D STRIDE_H=%d",
......
modules/dnn/src/ocl4dnn/src/ocl4dnn_softmax.cpp
View file @ 3dd91688
......@@ -52,6 +52,7 @@ OCL4DNNSoftmax<Dtype>::OCL4DNNSoftmax(OCL4DNNSoftmaxConfig config)
softmax_axis_ = config.axis;
channels_ = config.channels;
log_softmax_ = config.logsoftmax;
use_half_ = config.use_half;
inner_num_ = 1;
outer_num_ = 1;
......@@ -91,10 +92,13 @@ bool OCL4DNNSoftmax<Dtype>::Forward(const UMat& bottom, UMat& top)
if (log_softmax_) opts += " -DLOG_SOFTMAX ";
if (use_slm_)
kname = CL_KERNEL_SELECT("softmax_forward_slm");
kname = "softmax_forward_slm";
else
kname = CL_KERNEL_SELECT("softmax_forward");
kname = "softmax_forward";
kname += format("%s", (use_half_) ? "_half" : "_float");
opts += format(" -D Dtype=%s -D DTYPE_MAX=%s", (use_half_) ? "half" : "float",
(use_half_) ? "HALF_MAX" : "FLT_MAX");
if (!oclk_softmax_forward_kernel.create(kname.c_str(), ocl::dnn::softmax_loss_oclsrc, opts))
return false;
......
modules/dnn/src/opencl/activations.cl
View file @ 3dd91688
......@@ -40,9 +40,17 @@
//
//M*/
#define CONCAT(A,B) A##_##B
#define TEMPLATE(name,type) CONCAT(name,type)
#define KERNEL_ARG_DTYPE float
#if defined(cl_khr_fp16)
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#endif
__kernel void ReLUForward(const int count, __global const T* in, __global T* out
#ifndef RELU_NO_SLOPE
, T negative_slope
, KERNEL_ARG_DTYPE negative_slope
#endif
) {
int index = get_global_id(0);
......@@ -55,18 +63,19 @@ __kernel void ReLUForward(const int count, __global const T* in, __global T* out
}
__kernel void ReLU6Forward(const int count, __global const T* in, __global T* out,
const T minValue, const T maxValue)
const KERNEL_ARG_DTYPE minValue, const KERNEL_ARG_DTYPE maxValue)
{
int index = get_global_id(0);
if(index < count)
{
T x = in[index];
out[index] = clamp(x, minValue, maxValue);
out[index] = clamp(x, convert_T(minValue), convert_T(maxValue));
}
}
__kernel void PReLUForward(const int count, const int channels, const int plane_size,
__global const T* in, __global T* out, __global const T* slope_data)
__global const T* in, __global T* out,
__global const KERNEL_ARG_DTYPE* slope_data)
{
int index = get_global_id(0);
int c = (index / plane_size) % channels;
......@@ -99,8 +108,22 @@ __kernel void AbsValForward(const int n, __global const T* in, __global T* out)
out[index] = fabs(in[index]);
}
__kernel void PowForward(const int n, __global const T* in, __global T* out, const T power, const T scale, const T shift) {
__kernel void PowForward(const int n, __global const T* in, __global T* out,
const KERNEL_ARG_DTYPE power,
const KERNEL_ARG_DTYPE scale,
const KERNEL_ARG_DTYPE shift)
{
int index = get_global_id(0);
if (index < n)
out[index] = pow(shift + scale * in[index], power);
}
__kernel void ELUForward(const int n, __global const T* in, __global T* out)
{
int index = get_global_id(0);
if (index < n)
{
T src = in[index];
out[index] = (src >= 0.f) ? src : exp(src) - 1;
}
}
modules/dnn/src/opencl/concat.cl
View file @ 3dd91688
......@@ -39,22 +39,29 @@
//
//M*/
__kernel void concat(const int nthreads,
#if defined(cl_khr_fp16)
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#endif
#define CONCAT(A,B) A##_##B
#define TEMPLATE(name,type) CONCAT(name,type)
__kernel void TEMPLATE(concat, Dtype)(const int nthreads,
__global const Dtype* in_data,
const int num_concats,
const int concat_size,
const int top_concat_axis,
const int bottom_concat_axis,
const int offset_concat_axis,
__global Dtype* out_data) {
for (int index = get_global_id(0); index < nthreads;
index += get_global_size(0)) {
__global Dtype* out_data)
{
for (int index = get_global_id(0); index < nthreads; index += get_global_size(0))
{
const int total_concat_size = concat_size * bottom_concat_axis;
const int concat_num = index / total_concat_size;
const int concat_index = index % total_concat_size;
const int top_index = concat_index
+ (concat_num * top_concat_axis + offset_concat_axis) * concat_size;
const int top_index = concat_index +
(concat_num * top_concat_axis + offset_concat_axis) * concat_size;
out_data[top_index] = in_data[index];
}
}
modules/dnn/src/opencl/conv_layer_spatial.cl
View file @ 3dd91688
......@@ -40,27 +40,29 @@
//
//M*/
#if APPLY_BIAS
#define BIAS_KERNEL_ARG __global Dtype * biases_base,
#else
#define BIAS_KERNEL_ARG
#if defined(cl_khr_fp16)
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#endif
#define KERNEL_ARG_DTYPE float
#define TYPE_FLOAT 1
#define TYPE_HALF 2
#if defined(FUSED_CONV_RELU)
#define ACTIVATION_RELU_FUNCTION(x, c) ((Dtype)(x) > 0 ? (Dtype)(x) : ((Dtype)(x) * (Dtype)(negative_slope)))
#define FUSED_ARG Dtype negative_slope,
#define ACTIVATION_RELU_FUNCTION(x, c) ((Dtype)(x) > 0 ? (Dtype)(x) : ((Dtype)(x) * (negative_slope)))
#define FUSED_ARG KERNEL_ARG_DTYPE negative_slope,
#elif defined(FUSED_CONV_PRELU)
#define ACTIVATION_RELU_FUNCTION(x, c) ((Dtype)(x) > 0 ? (Dtype)(x) : ((Dtype)(x) * (Dtype)(negative_slope[c])))
#define FUSED_ARG __global const Dtype *negative_slope,
#define ACTIVATION_RELU_FUNCTION(x, c) ((Dtype)(x) > 0 ? (Dtype)(x) : ((Dtype)(x) * (negative_slope[c])))
#define FUSED_ARG __global const KERNEL_ARG_DTYPE* negative_slope,
#elif defined(FUSED_CONV_POWER)
#define ACTIVATION_RELU_FUNCTION(x, c) pow(x, power)
#define FUSED_ARG Dtype power,
#define ACTIVATION_RELU_FUNCTION(x, c) pow(x, (Dtype)power)
#define FUSED_ARG KERNEL_ARG_DTYPE power,
#elif defined(FUSED_CONV_TANH)
#define ACTIVATION_RELU_FUNCTION(x, c) tanh(x)
#define FUSED_ARG
#elif defined(FUSED_CONV_RELU6)
#define ACTIVATION_RELU_FUNCTION(x, c) (clamp((Dtype)(x), min_value, max_value))
#define FUSED_ARG Dtype min_value, Dtype max_value,
#define ACTIVATION_RELU_FUNCTION(x, c) (clamp((Dtype)(x), (Dtype)min_value, (Dtype)max_value))
#define FUSED_ARG KERNEL_ARG_DTYPE min_value, KERNEL_ARG_DTYPE max_value,
#else
#define ACTIVATION_RELU_FUNCTION(x, c) (x)
#define FUSED_ARG
......@@ -74,6 +76,11 @@
#define ELTWISE_DATA_ARG
#endif
#if APPLY_BIAS
#define BIAS_KERNEL_ARG __global Dtype * biases_base,
#else
#define BIAS_KERNEL_ARG
#endif
#define __CAT(x, y) x##y
#define CAT(x, y) __CAT(x, y)
......@@ -97,6 +104,16 @@
#define LOOP(N, VAR, STMT) CAT(LOOP, N)((VAR), (STMT))
#if defined(convolve_simd) || defined(Conv_Interleaved)
#if TYPE == TYPE_HALF
#define INT_TYPE ushort
#define INT_TYPE2 ushort2
#define INT_TYPE4 ushort4
#define INT_TYPE8 ushort8
#define SUB_GROUP_BLOCK_READ2 intel_sub_group_block_read_us2
#define SUB_GROUP_BLOCK_READ4 intel_sub_group_block_read_us4
#define SUB_GROUP_BLOCK_READ8 intel_sub_group_block_read_us8
#define SUB_GROUP_BLOCK_READ intel_sub_group_block_read_us
#else
#define INT_TYPE uint
#define INT_TYPE2 uint2
#define INT_TYPE4 uint4
......@@ -106,6 +123,7 @@
#define SUB_GROUP_BLOCK_READ8 intel_sub_group_block_read8
#define SUB_GROUP_BLOCK_READ intel_sub_group_block_read
#endif
#endif
#ifdef KERNEL_BASIC
......@@ -418,6 +436,25 @@ typedef struct float15 { float s0; float s1; float s2; float s3; float s4; float
float s6; float s7; float s8; float s9; float sa; float sb; float sc; float sd; float se; } float15;
typedef struct float0 { float s0; } float0; //never used but makes compiler happy.
typedef struct half1 { half s0; } half1;
typedef struct half5 { half s0; half s1; half s2; half s3; half s4; } half5;
typedef struct half6 { half s0; half s1; half s2; half s3; half s4; half s5; } half6;
typedef struct half7 { half s0; half s1; half s2; half s3; half s4; half s5; half s6; } half7;
typedef struct half9 { half s0; half s1; half s2; half s3; half s4; half s5; half s6; half s7; half s8; } half9;
typedef struct half10 { half s0; half s1; half s2; half s3; half s4; half s5;
half s6; half s7; half s8; half s9; } half10;
typedef struct half11 { half s0; half s1; half s2; half s3; half s4; half s5;
half s6; half s7; half s8; half s9; half sa; } half11;
typedef struct half12 { half s0; half s1; half s2; half s3; half s4; half s5;
half s6; half s7; half s8; half s9; half sa; half sb; } half12;
typedef struct half13 { half s0; half s1; half s2; half s3; half s4; half s5;
half s6; half s7; half s8; half s9; half sa; half sb; half sc; } half13;
typedef struct half14 { half s0; half s1; half s2; half s3; half s4; half s5;
half s6; half s7; half s8; half s9; half sa; half sb; half sc; half sd; } half14;
typedef struct half15 { half s0; half s1; half s2; half s3; half s4; half s5;
half s6; half s7; half s8; half s9; half sa; half sb; half sc; half sd; half se; } half15;
typedef struct half0 { half s0; } half0; //never used but makes compiler happy.
#define OUT_PITCH_X output_width
#define ROW_PITCH input_width
......
modules/dnn/src/opencl/eltwise.cl
View file @ 3dd91688
......@@ -40,9 +40,9 @@
//
//M*/
#define Dtype float
#define Dtype4 float4
#define Dtype8 float8
#if defined(cl_khr_fp16)
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#endif
__kernel void op_sum4(__global const Dtype * A,
__global const Dtype * B,
......@@ -73,20 +73,20 @@ __kernel void op_sum4(__global const Dtype * A,
a2 = vload4(i, src0_read + 2 * A_col_size);
a3 = vload4(i, src0_read + 3 * A_col_size);
dot0 = a0 * coeff1 + b0 * coeff2;
dot1 = a1 * coeff1 + b1 * coeff2;
dot2 = a2 * coeff1 + b2 * coeff2;
dot3 = a3 * coeff1 + b3 * coeff2;
dot0 = a0 * (Dtype4)coeff1 + b0 * (Dtype4)coeff2;
dot1 = a1 * (Dtype4)coeff1 + b1 * (Dtype4)coeff2;
dot2 = a2 * (Dtype4)coeff1 + b2 * (Dtype4)coeff2;
dot3 = a3 * (Dtype4)coeff1 + b3 * (Dtype4)coeff2;
#else
a0 = vload4(i, dst0_read);
a1 = vload4(i, dst0_read + A_col_size);
a2 = vload4(i, dst0_read + 2 * A_col_size);
a3 = vload4(i, dst0_read + 3 * A_col_size);
dot0 = a0 + b0 * coeff2;
dot1 = a1 + b1 * coeff2;
dot2 = a2 + b2 * coeff2;
dot3 = a3 + b3 * coeff2;
dot0 = a0 + b0 * (Dtype4)coeff2;
dot1 = a1 + b1 * (Dtype4)coeff2;
dot2 = a2 + b2 * (Dtype4)coeff2;
dot3 = a3 + b3 * (Dtype4)coeff2;
#endif
vstore4(dot0, i, dst0_read);
vstore4(dot1, i, dst0_read + A_col_size);
......
modules/dnn/src/opencl/gemm_buffer.cl 0 → 100644
View file @ 3dd91688
This diff is collapsed.
modules/dnn/src/opencl/gemm_image.cl
View file @ 3dd91688
This diff is collapsed.
modules/dnn/src/opencl/math.cl
View file @ 3dd91688
......@@ -40,16 +40,20 @@
//
//M*/
#if defined(cl_khr_fp16)
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#endif
#define CONCAT(A,B) A##_##B
#define TEMPLATE(name,type) CONCAT(name,type)
#define Dtype float
#define KERNEL_ARG_DTYPE float
__kernel void TEMPLATE(axpy,Dtype)(const int n, const Dtype alpha, __global const Dtype* x,
__kernel void TEMPLATE(axpy,Dtype)(const int n, const KERNEL_ARG_DTYPE alpha, __global const Dtype* x,
const int offx, __global Dtype* y,
const int offy) {
for (int index = get_global_id(0); index < n; index += get_global_size(0)) {
Dtype src = x[offx + index];
Dtype dst = y[offy + index];
y[offy + index] = alpha * src + dst;
y[offy + index] = convert_Dtype(alpha) * src + dst;
}
}
modules/dnn/src/opencl/matvec_mul.cl
View file @ 3dd91688
......@@ -39,41 +39,45 @@
//
//M*/
#if defined(cl_khr_fp16)
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#endif
#define CONCAT(A,B) A##_##B
#define TEMPLATE(name,type) CONCAT(name,type)
#define Dtype float
#define KERNEL_ARG_DTYPE float
__kernel void TEMPLATE(matvec_mul4,Dtype)(
__global const float * A,
__global const Dtype * A,
int offA,
unsigned int A_col_size,
unsigned int trail_item,
__global const float * v,
__global const Dtype * v,
int offv,
float alpha,
float beta,
__global float4 * result,
KERNEL_ARG_DTYPE alpha,
KERNEL_ARG_DTYPE beta,
__global Dtype4* result,
int offr,
__local float4 * work)
__local Dtype4* work)
{
unsigned int row_gid = get_group_id(0);
unsigned int lid = get_local_id(0);
const __global float *src0_read = A + row_gid * 4 * A_col_size + offA;
const __global float *src1_read = v + offv;
result = (__global float4*)((__global float*)result + offr);
float4 dot0 = (float4)(0.f);
float4 dot1 = (float4)(0.f);
float4 dot2 = (float4)(0.f);
float4 dot3 = (float4)(0.f);
const __global Dtype *src0_read = A + row_gid * 4 * A_col_size + offA;
const __global Dtype *src1_read = v + offv;
result = (__global Dtype4*)((__global Dtype*)result + offr);
Dtype4 dot0 = (Dtype4)(0.f);
Dtype4 dot1 = (Dtype4)(0.f);
Dtype4 dot2 = (Dtype4)(0.f);
Dtype4 dot3 = (Dtype4)(0.f);
unsigned int i = lid;
while( i < A_col_size / 4) {
const float4 a0 = vload4(i, src0_read);
const float4 a1 = vload4(i, src0_read + A_col_size);
const float4 a2 = vload4(i, src0_read + 2 * A_col_size);
const float4 a3 = vload4(i, src0_read + 3 * A_col_size);
const Dtype4 a0 = vload4(i, src0_read);
const Dtype4 a1 = vload4(i, src0_read + A_col_size);
const Dtype4 a2 = vload4(i, src0_read + 2 * A_col_size);
const Dtype4 a3 = vload4(i, src0_read + 3 * A_col_size);
const float4 b0 = vload4(i, src1_read);
const Dtype4 b0 = vload4(i, src1_read);
dot0 += a0 * b0;
dot1 += a1 * b0;
......@@ -92,15 +96,15 @@ __kernel void TEMPLATE(matvec_mul4,Dtype)(
{
if(trail_item != 0)
{
const __global float *src0_trail = src0_read + i * 4;
const __global float *src1_trail = src1_read + i * 4;
const __global Dtype *src0_trail = src0_read + i * 4;
const __global Dtype *src1_trail = src1_read + i * 4;
for(unsigned int i = 0; i < trail_item; ++i) {
const float at0 = src0_trail[i];
const float at1 = src0_trail[i + A_col_size];
const float at2 = src0_trail[i + 2 * A_col_size];
const float at3 = src0_trail[i + 3 * A_col_size];
const Dtype at0 = src0_trail[i];
const Dtype at1 = src0_trail[i + A_col_size];
const Dtype at2 = src0_trail[i + 2 * A_col_size];
const Dtype at3 = src0_trail[i + 3 * A_col_size];
const float bt = src1_trail[i];
const Dtype bt = src1_trail[i];
work[lid].s0 += at0 * bt;
work[lid].s1 += at1 * bt;
......@@ -118,40 +122,40 @@ __kernel void TEMPLATE(matvec_mul4,Dtype)(
}
if(lid == 0) {
if(beta == (Dtype)0)
result[row_gid] = alpha * work[0];
result[row_gid] = convert_Dtype(alpha) * work[0];
else
result[row_gid] = alpha * work[0] + beta * result[row_gid];
result[row_gid] = convert_Dtype(alpha) * work[0] + convert_Dtype(beta) * result[row_gid];
}
}
/* This kernel used for the trailing rows when row_of_A %4 !=0 */
__kernel void TEMPLATE(matvec_mul1,Dtype)(
__global const float * A,
__global const Dtype * A,
int offA,
unsigned int A_col_size,
unsigned int row_offset,
unsigned int trail_item,
__global const float * v,
__global const Dtype * v,
int offv,
float alpha,
float beta,
__global float * result,
KERNEL_ARG_DTYPE alpha,
KERNEL_ARG_DTYPE beta,
__global Dtype * result,
int offr,
__local float * work)
__local Dtype * work)
{
unsigned int row_gid = get_group_id(0);
unsigned int lid = get_local_id(0);
const __global float *src0_read = A + (row_offset + row_gid) * A_col_size + offA;
const __global float *src1_read = v + + offv;
const __global Dtype *src0_read = A + (row_offset + row_gid) * A_col_size + offA;
const __global Dtype *src1_read = v + + offv;
result = result + offr;
float4 dot0 = (float4)(0.f);
Dtype4 dot0 = (Dtype4)(0.f);
unsigned int i = lid;
while( i < A_col_size / 4)
{
const float4 a0 = vload4(i, src0_read);
const float4 b0 = vload4(i, src1_read);
const Dtype4 a0 = vload4(i, src0_read);
const Dtype4 b0 = vload4(i, src1_read);
dot0 += a0 * b0;
i += get_local_size(0);
......@@ -163,11 +167,11 @@ __kernel void TEMPLATE(matvec_mul1,Dtype)(
{
if(trail_item != 0)
{
const __global float *src0_trail = src0_read + i * 4;
const __global float *src1_trail = src1_read + i * 4;
const __global Dtype *src0_trail = src0_read + i * 4;
const __global Dtype *src1_trail = src1_read + i * 4;
for(unsigned int i = 0; i < trail_item; ++i) {
const float at0 = src0_trail[i];
const float bt = src1_trail[i];
const Dtype at0 = src0_trail[i];
const Dtype bt = src1_trail[i];
work[lid] += at0 * bt;
}
......@@ -182,10 +186,10 @@ __kernel void TEMPLATE(matvec_mul1,Dtype)(
if(lid == 0) {
if(beta == (Dtype)0) {
result[row_gid+row_offset] = alpha * work[0];
result[row_gid+row_offset] = convert_Dtype(alpha) * work[0];
} else {
result[row_gid+row_offset] *= beta;
result[row_gid+row_offset] += alpha * work[0];
result[row_gid+row_offset] *= convert_Dtype(beta);
result[row_gid+row_offset] += convert_Dtype(alpha) * work[0];
}
}
}
modules/dnn/src/opencl/mvn.cl
View file @ 3dd91688
......@@ -40,6 +40,10 @@
//
//M*/
#if defined(cl_khr_fp16)
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#endif
#define Dtype float
#define Dtype4 float4
#define Dtype8 float8
......@@ -135,17 +139,17 @@ __kernel void MVN(__global const Dtype* src,
store(dst_vec, dst, index);
}
__kernel void MEAN_FUSE(__global const Dtype * A,
__kernel void MEAN_FUSE(__global const T * A,
unsigned int A_col_size,
float alpha,
__global Dtype4 * result,
__global Dtype * B,
__global T4 * mean,
__global Dtype * tmp,
__local Dtype4 * work)
{
unsigned int row_gid = get_group_id(0);
unsigned int lid = get_local_id(0);
const __global Dtype *src0_read = A + row_gid * 4 * A_col_size;
__global Dtype *dst0_read = B + row_gid * 4 * A_col_size;
const __global T *src0_read = A + row_gid * 4 * A_col_size;
__global Dtype *dst0_read = tmp + row_gid * 4 * A_col_size;
Dtype4 dot0, dot1, dot2, dot3;
dot0 = dot1 = dot2 = dot3 = (Dtype4)(0.f);
......@@ -153,15 +157,15 @@ __kernel void MEAN_FUSE(__global const Dtype * A,
const Dtype4 b0 = (Dtype4)1.f;
while( i < A_col_size / 4)
{
const Dtype4 a0 = vload4(i, src0_read);
const Dtype4 a1 = vload4(i, src0_read + A_col_size);
const Dtype4 a2 = vload4(i, src0_read + 2 * A_col_size);
const Dtype4 a3 = vload4(i, src0_read + 3 * A_col_size);
const T4 a0 = vload4(i, src0_read);
const T4 a1 = vload4(i, src0_read + A_col_size);
const T4 a2 = vload4(i, src0_read + 2 * A_col_size);
const T4 a3 = vload4(i, src0_read + 3 * A_col_size);
dot0 += a0;
dot1 += a1;
dot2 += a2;
dot3 += a3;
dot0 += convert_float4(a0);
dot1 += convert_float4(a1);
dot2 += convert_float4(a2);
dot3 += convert_float4(a3);
i += get_local_size(0);
}
......@@ -181,22 +185,22 @@ __kernel void MEAN_FUSE(__global const Dtype * A,
if(lid == 0)
{
result[row_gid] = alpha * work[0];
mean[row_gid] = convert_T(alpha * work[0]);
}
Dtype4 sum = work[0] * alpha;
i = lid;
while( i < A_col_size / 4)
{
const Dtype4 a0 = vload4(i, src0_read);
const Dtype4 a1 = vload4(i, src0_read + A_col_size);
const Dtype4 a2 = vload4(i, src0_read + 2 * A_col_size);
const Dtype4 a3 = vload4(i, src0_read + 3 * A_col_size);
const T4 a0 = vload4(i, src0_read);
const T4 a1 = vload4(i, src0_read + A_col_size);
const T4 a2 = vload4(i, src0_read + 2 * A_col_size);
const T4 a3 = vload4(i, src0_read + 3 * A_col_size);
dot0 = native_powr(a0 - (Dtype4)sum.x, 2);
dot1 = native_powr(a1 - (Dtype4)sum.y, 2);
dot2 = native_powr(a2 - (Dtype4)sum.z, 2);
dot3 = native_powr(a3 - (Dtype4)sum.w, 2);
dot0 = native_powr(convert_float4(a0) - (Dtype4)sum.x, 2);
dot1 = native_powr(convert_float4(a1) - (Dtype4)sum.y, 2);
dot2 = native_powr(convert_float4(a2) - (Dtype4)sum.z, 2);
dot3 = native_powr(convert_float4(a3) - (Dtype4)sum.w, 2);
vstore4(dot0, i, dst0_read);
vstore4(dot1, i, dst0_read + A_col_size);
......@@ -208,22 +212,22 @@ __kernel void MEAN_FUSE(__global const Dtype * A,
}
__kernel void MVN_FUSE(__global const Dtype * tmp,
__global const Dtype * A,
__global const Dtype4 * mean,
__global const T * A,
__global const T4 * mean,
unsigned int A_col_size,
const float alpha_val,
const float eps,
const float relu_slope,
__global const Dtype4 * bnorm_weight,
__global const Dtype4 * bnorm_bias,
__global Dtype * B,
__global T * B,
__local Dtype4 * work)
{
unsigned int row_gid = get_group_id(0);
unsigned int lid = get_local_id(0);
const __global Dtype *src0_read = tmp + row_gid * 4 * A_col_size;
const __global Dtype *src1_read = A + row_gid * 4 * A_col_size;
__global Dtype *dst0_read = B + row_gid * 4 * A_col_size;
const __global T *src1_read = A + row_gid * 4 * A_col_size;
__global T *dst0_read = B + row_gid * 4 * A_col_size;
Dtype4 dot0, dot1, dot2, dot3;
dot0 = dot1 = dot2 = dot3 = (Dtype4)(0.f);
......@@ -257,7 +261,7 @@ __kernel void MVN_FUSE(__global const Dtype * tmp,
}
barrier(CLK_LOCAL_MEM_FENCE);
Dtype4 mean_val = mean[row_gid];
Dtype4 mean_val = convert_float4(mean[row_gid]);
Dtype4 dev_val = sqrt(work[0] * alpha_val) + (Dtype4)eps;
Dtype4 alpha = (Dtype4)1.f / dev_val;
......@@ -271,15 +275,15 @@ __kernel void MVN_FUSE(__global const Dtype * tmp,
i = lid;
while( i < A_col_size / 4)
{
const Dtype4 a0 = vload4(i, src1_read);
const Dtype4 a1 = vload4(i, src1_read + A_col_size);
const Dtype4 a2 = vload4(i, src1_read + 2 * A_col_size);
const Dtype4 a3 = vload4(i, src1_read + 3 * A_col_size);
const T4 a0 = vload4(i, src1_read);
const T4 a1 = vload4(i, src1_read + A_col_size);
const T4 a2 = vload4(i, src1_read + 2 * A_col_size);
const T4 a3 = vload4(i, src1_read + 3 * A_col_size);
dot0 = (a0 - (Dtype4)mean_val.x) * alpha.x;
dot1 = (a1 - (Dtype4)mean_val.y) * alpha.y;
dot2 = (a2 - (Dtype4)mean_val.z) * alpha.z;
dot3 = (a3 - (Dtype4)mean_val.w) * alpha.w;
dot0 = (convert_float4(a0) - (Dtype4)mean_val.x) * alpha.x;
dot1 = (convert_float4(a1) - (Dtype4)mean_val.y) * alpha.y;
dot2 = (convert_float4(a2) - (Dtype4)mean_val.z) * alpha.z;
dot3 = (convert_float4(a3) - (Dtype4)mean_val.w) * alpha.w;
dot0 = dot0 * w.x + (Dtype4)b.x;
dot1 = dot1 * w.y + (Dtype4)b.y;
......@@ -300,10 +304,10 @@ __kernel void MVN_FUSE(__global const Dtype * tmp,
dot3 = select(new3, dot3, dot3 > (Dtype4)0.f);
#endif
vstore4(dot0, i, dst0_read);
vstore4(dot1, i, dst0_read + A_col_size);
vstore4(dot2, i, dst0_read + 2 * A_col_size);
vstore4(dot3, i, dst0_read + 3 * A_col_size);
vstore4(convert_T(dot0), i, dst0_read);
vstore4(convert_T(dot1), i, dst0_read + A_col_size);
vstore4(convert_T(dot2), i, dst0_read + 2 * A_col_size);
vstore4(convert_T(dot3), i, dst0_read + 3 * A_col_size);
i += get_local_size(0);
}
......
modules/dnn/src/opencl/ocl4dnn_lrn.cl
View file @ 3dd91688
......@@ -42,14 +42,18 @@
#define CONCAT(A,B) A##_##B
#define TEMPLATE(name,type) CONCAT(name,type)
#define Dtype float
#define KERNEL_ARG_DTYPE float
#if defined(cl_khr_fp16)
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#endif
__kernel void TEMPLATE(lrn_full_no_scale,Dtype)(const int nthreads, __global const Dtype* in,
const int num, const int channels,
const int height, const int width, const int size,
const Dtype alpha_over_size, const Dtype k,
const KERNEL_ARG_DTYPE alpha_over_size, const KERNEL_ARG_DTYPE k,
__global Dtype* const out,
const Dtype negative_beta) {
const KERNEL_ARG_DTYPE negative_beta) {
for (int index = get_global_id(0); index < nthreads;
index += get_global_size(0)) {
// find out the local offset
......@@ -60,11 +64,11 @@ __kernel void TEMPLATE(lrn_full_no_scale,Dtype)(const int nthreads, __global con
const int step = height * width;
__global const Dtype* in_off = in + offset;
__global Dtype* out_off = out + offset;
Dtype scale_val;
KERNEL_ARG_DTYPE scale_val;
int head = 0;
const int pre_pad = (size - 1) / 2;
const int post_pad = size - pre_pad - 1;
Dtype accum_scale = 0;
KERNEL_ARG_DTYPE accum_scale = 0;
// fill the scale at [n, :, h, w]
// accumulate values
while (head < post_pad && head < channels) {
......@@ -79,7 +83,7 @@ __kernel void TEMPLATE(lrn_full_no_scale,Dtype)(const int nthreads, __global con
* in_off[(head - size) * step];
}
scale_val = k + accum_scale * alpha_over_size;
out_off[(head - post_pad) * step] = in_off[(head - post_pad) * step] * (Dtype)native_powr((float)scale_val, (float)negative_beta);
out_off[(head - post_pad) * step] = in_off[(head - post_pad) * step] * (Dtype)native_powr((Dtype)scale_val, (Dtype)negative_beta);
++head;
}
// subtract only
......@@ -89,7 +93,7 @@ __kernel void TEMPLATE(lrn_full_no_scale,Dtype)(const int nthreads, __global con
* in_off[(head - size) * step];
}
scale_val = k + accum_scale * alpha_over_size;
out_off[(head - post_pad) * step] = in_off[(head - post_pad) * step] * (Dtype)native_powr((float)scale_val, (float)negative_beta);
out_off[(head - post_pad) * step] = in_off[(head - post_pad) * step] * (Dtype)native_powr((Dtype)scale_val, (Dtype)negative_beta);
++head;
}
}
......
modules/dnn/src/opencl/ocl4dnn_pooling.cl
View file @ 3dd91688
......@@ -42,7 +42,10 @@
#define CONCAT(A,B) A##_##B
#define TEMPLATE(name,type) CONCAT(name,type)
#define Dtype float
#if defined(cl_khr_fp16)
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#endif
#if defined KERNEL_MAX_POOL
......
modules/dnn/src/opencl/slice.cl
View file @ 3dd91688
......@@ -40,9 +40,9 @@
//
//M*/
#define Dtype float
#define Dtype4 float4
#define Dtype8 float8
#if defined(cl_khr_fp16)
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#endif
__kernel void slice(__global const Dtype* src,
const int src_plane_size,
......
modules/dnn/src/opencl/softmax.cl
View file @ 3dd91688
......@@ -24,6 +24,10 @@
* POSSIBILITY OF SUCH DAMAGE.
**************************************************************************************/
#if defined(cl_khr_fp16)
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#endif
__kernel void kernel_channel_max(const int num, const int channels,
const int spatial_dim, __global const T* data, __global T* out) {
int index = get_global_id(0);
......@@ -40,12 +44,12 @@ __kernel void kernel_channel_max(const int num, const int channels,
__kernel void kernel_channel_subtract(const int count,
const int num, const int channels,
const int spatial_dim, __global const T* channel_max, __global T* data) {
const int spatial_dim, __global const T* channel_max, __global const T* src, __global T* data) {
int index = get_global_id(0);
if(index < count) {
int n = index / channels / spatial_dim;
int s = index % spatial_dim;
data[index] -= channel_max[n * spatial_dim + s];
data[index] = exp(src[index] - channel_max[n * spatial_dim + s]);
}
}
......
modules/dnn/src/opencl/softmax_loss.cl
View file @ 3dd91688
......@@ -42,12 +42,15 @@
#define CONCAT(A,B) A##_##B
#define TEMPLATE(name,type) CONCAT(name,type)
#define Dtype float
#if defined(cl_intel_subgroups)
#pragma OPENCL EXTENSION cl_intel_subgroups : enable
#endif
#if defined(cl_khr_fp16)
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#endif
__kernel void TEMPLATE(softmax_forward_slm,Dtype)(const int num, const int channels,
const int spatial_dim,
__global Dtype* scale,
......@@ -60,12 +63,12 @@ __kernel void TEMPLATE(softmax_forward_slm,Dtype)(const int num, const int chann
int n = get_global_id(1);
for (int index = get_global_id(0), s = 0; index < spatial_dim * get_local_size(0); index +=
get_global_size(0), ++s) {
float maxval = -FLT_MAX;
Dtype maxval = -DTYPE_MAX;
for (int c = get_global_id(0); c < channels; c += get_global_size(0)) {
Dtype tmp = data[(n * channels + c) * spatial_dim + s];
maxval = max((Dtype)tmp, (Dtype)maxval);
}
maxval = sub_group_reduce_max(maxval * 100000);
maxval = sub_group_reduce_max(maxval);
//if (get_sub_group_local_id() == 0)
group_tmp[get_sub_group_id() * spatial_dim + s] = maxval;
}
......@@ -77,7 +80,7 @@ __kernel void TEMPLATE(softmax_forward_slm,Dtype)(const int num, const int chann
int s = index / get_max_sub_group_size();
Dtype maxval = sub_group_reduce_max(group_tmp[get_sub_group_local_id() * spatial_dim + s]);
//if (get_sub_group_local_id() == 0)
scale_tmp[s] = maxval / 100000;
scale_tmp[s] = maxval;
}
barrier(CLK_LOCAL_MEM_FENCE);
......@@ -95,7 +98,7 @@ __kernel void TEMPLATE(softmax_forward_slm,Dtype)(const int num, const int chann
for (int c = get_global_id(0); c < channels; c += get_global_size(0)) {
sum += out_tmp[c * spatial_dim + s];
}
sum = sub_group_reduce_add(sum * 100000);
sum = sub_group_reduce_add(sum);
group_tmp[get_sub_group_id() * spatial_dim + s] = sum;
}
barrier(CLK_LOCAL_MEM_FENCE);
......@@ -105,7 +108,7 @@ __kernel void TEMPLATE(softmax_forward_slm,Dtype)(const int num, const int chann
int s = index / get_max_sub_group_size();
Dtype sum = sub_group_reduce_add(group_tmp[get_sub_group_local_id() * spatial_dim + s]);
//if (get_sub_group_local_id() == 0)
scale_tmp[s] = sum / 100000;
scale_tmp[s] = sum;
}
barrier(CLK_LOCAL_MEM_FENCE);
......@@ -130,12 +133,12 @@ __kernel void TEMPLATE(softmax_forward,Dtype)(const int num, const int channels,
__global Dtype *group_tmp = scale + spatial_dim * num + n * get_max_sub_group_size() * spatial_dim;
for (int index = get_global_id(0), s = 0; index < spatial_dim * get_local_size(0); index +=
get_global_size(0), ++s) {
float maxval = -FLT_MAX;
Dtype maxval = -DTYPE_MAX;
for (int c = get_global_id(0); c < channels; c += get_global_size(0)) {
Dtype tmp = data[(n * channels + c) * spatial_dim + s];
maxval = max((Dtype)tmp, (Dtype)maxval);
}
maxval = sub_group_reduce_max(maxval * 100000);
maxval = sub_group_reduce_max(maxval);
//if (get_sub_group_local_id() == 0)
group_tmp[get_sub_group_id() * spatial_dim + s] = maxval;
}
......@@ -146,7 +149,7 @@ __kernel void TEMPLATE(softmax_forward,Dtype)(const int num, const int channels,
int s = index / get_max_sub_group_size();
Dtype maxval = sub_group_reduce_max(group_tmp[get_sub_group_local_id() * spatial_dim + s]);
//if (get_sub_group_local_id() == 0)
scale[n * spatial_dim + s] = maxval / 100000;
scale[n * spatial_dim + s] = maxval;
}
barrier(CLK_GLOBAL_MEM_FENCE);
......@@ -164,7 +167,7 @@ __kernel void TEMPLATE(softmax_forward,Dtype)(const int num, const int channels,
for (int c = get_global_id(0); c < channels; c += get_global_size(0)) {
sum += out[n * channels * spatial_dim + c * spatial_dim + s];
}
sum = sub_group_reduce_add(sum * 100000);
sum = sub_group_reduce_add(sum);
group_tmp[get_sub_group_id() * spatial_dim + s] = sum;
}
barrier(CLK_GLOBAL_MEM_FENCE);
......@@ -174,7 +177,7 @@ __kernel void TEMPLATE(softmax_forward,Dtype)(const int num, const int channels,
int s = index / get_max_sub_group_size();
Dtype sum = sub_group_reduce_add(group_tmp[get_sub_group_local_id() * spatial_dim + s]);
//if (get_sub_group_local_id() == 0)
scale[n * spatial_dim + s] = sum / 100000;
scale[n * spatial_dim + s] = sum;
}
barrier(CLK_GLOBAL_MEM_FENCE);
......
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