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Commit e4737e2f authored by turneram's avatar turneram
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Almost working

parent 27c6f5d3
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src/targets/gpu/jit/ck_elementwise.cpp
View file @ e4737e2f
...@@ -49,13 +49,13 @@ static const char* const ck_elementwise_kernel = R"__migraphx__( ...@@ -49,13 +49,13 @@ static const char* const ck_elementwise_kernel = R"__migraphx__(
namespace migraphx { namespace migraphx {
extern "C" { extern "C" {
__global__ void ck_elementwise_kernel(void* a_p, void* b_p, void* c_p) __global__ void ck_elementwise_kernel(void* a_p, void* b_p, void* c_p)
{ {
ck_elementwise(a_p, b_p, c_p); make_tensors()(a_p, b_p, c_p)([](auto&&... xs) {
ck_elementwise(xs...);
});
} }
} }
......
src/targets/gpu/kernels/include/migraphx/kernels/ck_elementwise.hpp
View file @ e4737e2f
...@@ -26,51 +26,119 @@ ...@@ -26,51 +26,119 @@
#include <migraphx/kernels/index.hpp> #include <migraphx/kernels/index.hpp>
#include <migraphx/kernels/algorithm.hpp> #include <migraphx/kernels/algorithm.hpp>
#include <migraphx/kernels/integral_constant.hpp>
#include <iostream> #include "ck/device_utility/device_prop.hpp"
#include <cstdlib> #include "ck/device_utility/kernel_launch.hpp"
#include "ck/ck.hpp" #include "ck/tensor_operation/gpu/device/device_base.hpp"
#include "ck/tensor_operation/gpu/device/device_elementwise.hpp" #include "ck/tensor_operation/gpu/device/device_elementwise.hpp"
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp" #include "ck/tensor_operation/gpu/grid/gridwise_binary_elementwise_1d.hpp"
namespace migraphx { namespace migraphx {
// using F16 = ck::half_t; using ADataType = float;
// using F32 = float; using BDataType = float;
using CDataType = float;
using ElementwiseFunctor = float;
// using ABDataType = F16; static constexpr auto I0 = ck::Number<0>{};
// using CDataType = F16; using index_t = index_int;
// using Add = ck::tensor_operation::element_wise::Add; template<class L, class S>
__host__ __device__ constexpr auto MakeDescriptor_M(const L& lengths, const S& strides)
{
auto idx = make_index();
auto tupleOfShape = generate_tuple([&](auto I) { return static_cast<ck::index_t>(lengths[I]); }, ck::Number<1>{});
auto tupleOfStride = generate_tuple([&](auto I) { return static_cast<ck::index_t>(strides[I]); }, ck::Number<1>{});
const auto desc_m = make_naive_tensor_descriptor(tupleOfShape, tupleOfStride);
// using DeviceElementwiseAddInstance = const auto M = desc_m.GetLength(I0);
// ck::tensor_operation::device::DeviceElementwise<ck::Tuple<ABDataType, ABDataType>, const index_t loop_step = idx.nglobal();//gridSize * blockSize * MPerThread;
// ck::Tuple<CDataType>, const auto pad = ck::math::integer_least_multiple(M, loop_step) - M;
// Add, const auto desc_m_pad =
// 1, transform_tensor_descriptor(desc_m,
// 8, make_tuple(ck::make_right_pad_transform(M, pad)),
// ck::Sequence<8, 8>, make_tuple(ck::Sequence<0>{}),
// ck::Sequence<8>>; make_tuple(ck::Sequence<0>{}));
return desc_m_pad;
}
__host__ __device__ void ck_elementwise(void* /* a_p */, void* /* b_p */, void* /* c_p */) struct Add
{ {
// ck::index_t M = 1024; template <typename Y, typename X0, typename X1>
// std::array<const void*, 2> input = {a_p, __host__ __device__ constexpr void operator()(Y& y, const X0& x0, const X1& x1) const;
// b_p};
// std::array<void*, 1> output = {c_p}; template <>
__host__ __device__ constexpr void
operator()<float>(float& y, const float& x0, const float& x1) const
{
y = x0 + x1;
};
// std::array<ck::index_t, 1> abc_lengths = {M}; template <>
// std::array<ck::index_t, 1> a_strides = {1}; __host__ __device__ constexpr void
// std::array<ck::index_t, 1> b_strides = {1}; operator()<double>(double& y, const double& x0, const double& x1) const
// std::array<ck::index_t, 1> c_strides = {1}; {
y = x0 + x1;
};
};
// auto broadcastAdd = DeviceElementwiseAddInstance{};
// auto argument = broadcastAdd.MakeArgumentPointer(
// abc_lengths, {a_strides, b_strides}, {c_strides}, input, output, Add{});
// broadcastAdd_invoker_ptr->Run(argument.get(), StreamConfig{nullptr, false}); template <class T, class U, class V>
__device__ void ck_elementwise(const T& a_t, const U& b_t, const V& c_t)
{
//auto add = [](auto a, auto b) { return a + b; };
auto lengths = a_t.get_shape().lens;
auto strides = a_t.get_shape().strides;
auto a_desc = MakeDescriptor_M(lengths, strides);
using AGridDesc_M = decltype(a_desc);
//using Add = ck::tensor_operation::element_wise::Add;
using GridwiseBinEltwise = ck::GridwiseBinaryElementwise_1D<ADataType,
BDataType,
CDataType,
CDataType,
AGridDesc_M,
AGridDesc_M,
AGridDesc_M,
Add,
8,
8,
8,
8>;
auto op = Add{};
GridwiseBinEltwise::Run(a_t.data(), b_t.data(), c_t.data(), a_desc, a_desc, a_desc, op);
// auto kernel = ck::kernel_binary_elementwise_1d<GridwiseBinEltwise,
// ADataType,
// BDataType,
// CDataType,
// AGridDesc_M,
// AGridDesc_M,
// AGridDesc_M,
// Add>;
// kernel(a_t.data(), b_t.data(), c_t.data(), a_desc, a_desc, a_desc, Add);
// Argument arg{a_t.data(), b_t.data(), c_t.data(), c_t.get_shape().lens, a_t.get_shape().strides, b_t.get_shape().strides, c_t.get_shape().strides,
// add};
// auto lengths = a_t.get_shape().lens;
// auto strides = a_t.get_shape().strides;
// auto idx = make_index();
// b_t.get_shape();
// c_t.get_shape();
// auto tupleOfShape = generate_tuple([&](auto I) { return lengths[I]; }, ck::Number<1>{});
// auto tupleOfStride = generate_tuple([&](auto I) { return strides[I]; }, ck::Number<1>{});
// const auto desc_m = make_naive_tensor_descriptor(tupleOfShape, tupleOfStride);
// const auto M = desc_m.GetLength(I0);
// const ck::index_t loop_step = idx.nglobal();//gridSize * blockSize * MPerThread;
// const auto pad = ck::math::integer_least_multiple(M, loop_step) - M;
// const auto desc_m_pad =
// transform_tensor_descriptor(desc_m,
// make_tuple(ck::make_right_pad_transform(M, pad)),
// make_tuple(ck::Sequence<0>{}),
// make_tuple(ck::Sequence<0>{}));
} }
} // namespace migraphx } // namespace migraphx
#endif #endif
test/verify/0ck_elementwise_test.cpp
View file @ e4737e2f
...@@ -33,7 +33,7 @@ struct ck_elementwise : verify_program<ck_elementwise> ...@@ -33,7 +33,7 @@ struct ck_elementwise : verify_program<ck_elementwise>
{ {
migraphx::program p; migraphx::program p;
auto* mm = p.get_main_module(); auto* mm = p.get_main_module();
migraphx::shape m1_shape{migraphx::shape::float_type, {10, 20}}; migraphx::shape m1_shape{migraphx::shape::float_type, {20}};
// migraphx::shape m2_shape{migraphx::shape::float_type, {20, 10}}; // migraphx::shape m2_shape{migraphx::shape::float_type, {20, 10}};
auto l1 = mm->add_parameter("1", m1_shape); auto l1 = mm->add_parameter("1", m1_shape);
auto l2 = mm->add_parameter("2", m1_shape); auto l2 = mm->add_parameter("2", m1_shape);
......
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