Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[ GPU ] split kernel register from forwarding function in swiglu_cl @open sesame 12/02 10:40 #2809

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

[ GPU ] split kernel register from forwarding function in swiglu_cl @open sesame 12/02 10:40 #2809

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
7 changes: 4 additions & 3 deletions nntrainer/cl_context.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -43,9 +43,10 @@ static void add_default_object(ClContext &cc) {
AdditionLayerCL::type,
ml::train::LayerType::LAYER_ADDITION);

// @todo swiglulayercl also needs to be updated.
cc.registerFactory(nntrainer::createLayer<SwiGLULayerCl>, SwiGLULayerCl::type,
ml::train::LayerType::LAYER_SWIGLU);
if (SwiGLULayerCl::registerClKernels()) {
cc.registerFactory(nntrainer::createLayer<SwiGLULayerCl>,
SwiGLULayerCl::type, ml::train::LayerType::LAYER_SWIGLU);
}

if (ReshapeLayerCl::registerClKernels()) {
cc.registerFactory(nntrainer::createLayer<ReshapeLayerCl>,
Expand Down
59 changes: 46 additions & 13 deletions nntrainer/layers/cl_layers/swiglu_cl.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
* @brief Implementation of SwiGLU activation function
* @see https://github.com/nnstreamer/nntrainer
* @author Niket Agarwal <[email protected]>
* @author Eunju Yang <[email protected]>
* @bug No known bugs except for NYI items
*
*/
Expand Down Expand Up @@ -63,9 +64,6 @@ void SwiGLULayerCl::incremental_forwarding(RunLayerContext &context,
swigluProcess(in1, in2, out);
}

opencl::Kernel SwiGLULayerCl::kernel_swiglu;
opencl::Kernel SwiGLULayerCl::kernel_swiglu_fp16;

void SwiGLULayerCl::swigluProcess(Tensor const &in1, Tensor const &in2,
Tensor &result) {

Expand All @@ -90,18 +88,54 @@ void SwiGLULayerCl::swigluProcess(Tensor const &in1, Tensor const &in2,
}
}

void SwiGLULayerCl::swiglu_cl(const float *matAdata, const float *vecXdata,
float *vecYdata, unsigned int dim1,
unsigned int dim2) {
bool SwiGLULayerCl::registerClKernels() {

bool result = false;
// check if the kernels are already registered.
if (!layer_kernel_ptrs.empty()) {
ml_loge("kernels for swiglu_cl are already registered.");
return false;
}

do {
ClContext::SharedPtrClKernel kernel_swiglu_ptr =

ClContext::SharedPtrClKernel kernel_swiglu_ptr = nullptr;

kernel_swiglu_ptr =
cl_context_ref.registerClKernel(swiglu_cl_kernel_, "swiglu_cl");
if (!kernel_swiglu_ptr) {
ml_loge("OpenCL Error: Fail to register swiglu_cl kernel");
break;
}
layer_kernel_ptrs.emplace_back(kernel_swiglu_ptr);
kernel_swiglu_ptr =
cl_context_ref.registerClKernel(swiglu_cl_kernel_fp16_, "swiglu_cl_fp16");

#ifdef ENABLE_FP16
if (!kernel_swiglu_ptr) {
ml_loge("OpenCL Error: Fail to register swiglu_cl_fp16 kernel");
break;
}
layer_kernel_ptrs.emplace_back(kernel_swiglu_ptr);
#endif

return true;
} while (false);

// clear all registered kernels if any error occurs during registration
layer_kernel_ptrs.clear();

return false;
}

void SwiGLULayerCl::swiglu_cl(const float *matAdata, const float *vecXdata,
float *vecYdata, unsigned int dim1,
unsigned int dim2) {

bool result = false;

do {

auto kernel_swiglu_ptr = layer_kernel_ptrs[Kernels::SWIGLU_CL];

int dim = int(dim1 * dim2);
opencl::Buffer inputA(cl_context_ref.context_inst_,
Expand Down Expand Up @@ -160,18 +194,16 @@ void SwiGLULayerCl::swiglu_cl(const float *matAdata, const float *vecXdata,
} while (false);
}

#ifdef ENABLE_FP16
void SwiGLULayerCl::swiglu_cl_fp16(const __fp16 *matAdata,
const __fp16 *vecXdata, __fp16 *vecYdata,
unsigned int dim1, unsigned int dim2) {

bool result = false;

do {
ClContext::SharedPtrClKernel kernel_swiglu_ptr =
cl_context_ref.registerClKernel(swiglu_cl_kernel_fp16_, "swiglu_cl_fp16");
if (!kernel_swiglu_ptr) {
break;
}

auto kernel_swiglu_ptr = layer_kernel_ptrs[Kernels::SWIGLU_CL_FP16];

int dim = int(dim1 * dim2);
opencl::Buffer inputA(cl_context_ref.context_inst_,
Expand Down Expand Up @@ -229,6 +261,7 @@ void SwiGLULayerCl::swiglu_cl_fp16(const __fp16 *matAdata,

} while (false);
}
#endif

void SwiGLULayerCl::calcDerivative(nntrainer::RunLayerContext &context) {
std::throw_with_nested(std::runtime_error("Training is not supported yet."));
Expand Down
32 changes: 19 additions & 13 deletions nntrainer/layers/cl_layers/swiglu_cl.h
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,7 @@
* @brief Implementation of SwiGLU activation function
* @see https://github.com/nnstreamer/nntrainer
* @author Niket Agarwal <[email protected]>
* @author Eunju Yang <[email protected]>
* @bug No known bugs except for NYI items
*
*/
Expand All @@ -18,7 +19,7 @@
#include <common_properties.h>
#include <layer_context.h>
#include <layer_devel.h>
#include <layer_impl.h>
#include <layer_impl_cl.h>
#include <node_exporter.h>
#include <opencl_buffer.h>
#include <opencl_kernel.h>
Expand All @@ -30,17 +31,14 @@ namespace nntrainer {
* @brief A SwiGLU layer
*
*/
class SwiGLULayerCl final : public Layer {

private:
inline static ClContext cl_context_ref;
class SwiGLULayerCl final : public LayerImplCl {

public:
/**
* @brief Construct a new SwiGLU layer object
*
*/
SwiGLULayerCl() : Layer(), swiglu_props(props::Print()) {}
SwiGLULayerCl() : LayerImplCl(), swiglu_props(props::Print()) {}

/**
* @brief Destroy the SwiGLU layer object
Expand Down Expand Up @@ -79,7 +77,7 @@ class SwiGLULayerCl final : public Layer {
* @copydoc Layer::exportTo(Exporter &exporter, ExportMethods method)
*/
void exportTo(Exporter &exporter,
const ml::train::ExportMethods &method) const override {};
const ml::train::ExportMethods &method) const override{};

/**
* @copydoc Layer::getType()
Expand All @@ -93,12 +91,6 @@ class SwiGLULayerCl final : public Layer {

inline static const std::string type = "swiglu";

static opencl::Kernel kernel_swiglu;
static opencl::Kernel kernel_swiglu_fp16;

std::tuple<props::Print> swiglu_props; /**< swiglu layer properties : unit -
number of output neurons */

/**
* @brief Process data and dimensions for swiglu operation
* @param[in] input1 Tensor
Expand Down Expand Up @@ -130,6 +122,20 @@ class SwiGLULayerCl final : public Layer {
void swiglu_cl_fp16(const __fp16 *matAdata, const __fp16 *vecXdata,
__fp16 *vecYdata, unsigned int dim1, unsigned int dim2);
#endif

/**
* @brief Register OpenCL kernels for SwiGLU layer. This should be called
*/
static bool registerClKernels();

private:
std::tuple<props::Print> swiglu_props; /**< swiglu layer properties : unit -
number of output neurons */

inline static std::vector<ClContext::SharedPtrClKernel>
layer_kernel_ptrs; /** kernel list relevant with this layer */

enum Kernels { SWIGLU_CL, SWIGLU_CL_FP16 }; /** kernels enum */
};

} // namespace nntrainer
Expand Down
Loading