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Commit 8c1ad9e6 authored by turneram's avatar turneram
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Call from global function

parent bf523dbe
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Showing with 161 additions and 30 deletions
src/targets/gpu/jit/ck_elementwise.cpp
View file @ 8c1ad9e6
...@@ -25,6 +25,9 @@ ...@@ -25,6 +25,9 @@
#include <migraphx/make_op.hpp> #include <migraphx/make_op.hpp>
#include <migraphx/gpu/context.hpp> #include <migraphx/gpu/context.hpp>
#include <migraphx/gpu/compile_gen.hpp>
#include <migraphx/gpu/compile_hip_code_object.hpp> #include <migraphx/gpu/compile_hip_code_object.hpp>
#include <migraphx/gpu/compile_hip.hpp> #include <migraphx/gpu/compile_hip.hpp>
#include <migraphx/ranges.hpp> #include <migraphx/ranges.hpp>
...@@ -39,22 +42,104 @@ namespace migraphx { ...@@ -39,22 +42,104 @@ namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS { inline namespace MIGRAPHX_INLINE_NS {
namespace gpu { namespace gpu {
// NOLINTNEXTLINE using namespace migraphx::gpu::gen; // NOLINT
static const char* const ck_elementwise_kernel = R"__migraphx__( static const char* const ck_elementwise_kernel = R"__migraphx__(
#include <migraphx/kernels/ck_elementwise.hpp> //#include <migraphx/kernels/ck_elementwise.hpp>
#include <migraphx/kernels/ops.hpp> #include <migraphx/kernels/ops.hpp>
#include <migraphx/kernels/integral_constant.hpp> #include <migraphx/kernels/integral_constant.hpp>
#include <migraphx/kernels/generic_constant.hpp> #include <migraphx/kernels/generic_constant.hpp>
#include <args.hpp> #include <args.hpp>
#include <migraphx/kernels/index.hpp>
#include <migraphx/kernels/algorithm.hpp>
#include <migraphx/kernels/integral_constant.hpp>
#include <migraphx/kernels/tensor_view.hpp>
#include "ck/device_utility/device_prop.hpp"
#include "ck/device_utility/kernel_launch.hpp"
#include "ck/tensor_operation/gpu/device/device_base.hpp"
#include "ck/tensor_operation/gpu/device/device_elementwise.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_binary_elementwise_1d.hpp"
namespace migraphx { namespace migraphx {
using ADataType = float;
using BDataType = float;
using CDataType = float;
using ElementwiseFunctor = float;
static constexpr auto I0 = ck::Number<0>{};
template <class L, class S, class N>
constexpr auto MakeDescriptor_M(const L& lengths, const S& strides, const N& ndim)
{
auto gridSize = 72;
auto blockSize = 1024;
//constexpr auto ndim = 1;
// auto idx = make_index();
auto tupleOfShape = generate_tuple([&](auto I) { return static_cast<ck::index_t>(lengths[I]); },
ck::Number<ndim>{});
auto tupleOfStride = generate_tuple(
[&](auto I) { return static_cast<ck::index_t>(strides[I]); }, ck::Number<1>{});
const auto desc = make_naive_tensor_descriptor(tupleOfShape, tupleOfStride);
auto desc_m = desc;
// merge nd to 1d desc - [s0 * s1 * ...]
if constexpr(ndim > 1)
{
desc_m = transform_tensor_descriptor(
desc,
make_tuple(make_merge_transform(tupleOfShape)),
make_tuple(generate_sequence_v2([&](auto I) { return I; }, ck::Number<ndim>{})),
make_tuple(ck::Sequence<0>{}));
}
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>{}));
return desc_m_pad;
}
struct Add
{
template <typename Y, typename X0, typename X1>
__device__ constexpr void operator()(Y& y, const X0& x0, const X1& x1) const
{
y = x0 + x1;
};
};
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)
{ {
make_tensors()(a_p, b_p, c_p)([](auto&&... xs) { make_tensors()(a_p, b_p, c_p)([](auto a_t, auto b_t, auto c_t) {
ck_elementwise(xs...); constexpr auto lengths = get_shape_c<decltype(a_t)>{}.lens;
constexpr auto strides = get_shape_c<decltype(a_t)>{}.strides;
constexpr auto ndim = _c<decltype(lengths.size()){}>[1];
constexpr auto a_desc = MakeDescriptor_M(lengths, strides, ndim);
using AGridDesc_M = decltype(a_desc);
using GridwiseBinEltwise = ck::GridwiseBinaryElementwise_1D<ADataType,
BDataType,
CDataType,
CDataType,
AGridDesc_M,
AGridDesc_M,
AGridDesc_M,
Add,
1,
1,
1,
1>;
auto op = Add{};
GridwiseBinEltwise::Run(a_t.data(), b_t.data(), c_t.data(), a_desc, a_desc, a_desc, op);
}); });
} }
...@@ -64,20 +149,68 @@ __global__ void ck_elementwise_kernel(void* a_p, void* b_p, void* c_p) ...@@ -64,20 +149,68 @@ __global__ void ck_elementwise_kernel(void* a_p, void* b_p, void* c_p)
)__migraphx__"; )__migraphx__";
// NOLINTNEXTLINE
// static const char* const ck_elementwise_kernel = R"__migraphx__(
// #include <migraphx/kernels/ck_elementwise.hpp>
// #include <migraphx/kernels/ops.hpp>
// #include <migraphx/kernels/integral_constant.hpp>
// #include <migraphx/kernels/generic_constant.hpp>
// #include <args.hpp>
// namespace migraphx {
// extern "C" {
// __global__ void ck_elementwise_kernel(void* a_p, void* b_p, void* c_p)
// {
// make_tensors()(a_p, b_p, c_p)([](auto&&... xs) {
// ck_elementwise(xs...);
// });
// }
// }
// } // namespace migraphx
// )__migraphx__";
struct ck_elementwise_compiler : compiler<ck_elementwise_compiler> struct ck_elementwise_compiler : compiler<ck_elementwise_compiler>
{ {
std::vector<std::string> names() const { return {"ck_elementwise"}; } std::vector<std::string> names() const { return {"ck_elementwise"}; }
static std::size_t oversubscribe_if(bool b)
{
if(b)
return 256;
else
return 1;
}
operation compile_op(context& ctx, const std::vector<shape>& inputs, const value& v) const operation compile_op(context& ctx, const std::vector<shape>& inputs, const value& v) const
{ {
// hip_compile_options options;
// auto out_s = inputs.back();
// options.set_launch_params(v, compute_global_for(ctx, out_s.elements()));
// options.inputs = inputs;
// options.output = out_s;
// options.kernel_name = "ck_elementwise_kernel";
// options.virtual_inputs = inputs;
// return compile_hip_code_object(ck_elementwise_kernel, options);
hip_compile_options options; hip_compile_options options;
auto out_s = inputs.back();
options.set_launch_params(v, compute_global_for(ctx, out_s.elements()));
options.inputs = inputs; options.inputs = inputs;
options.output = out_s; options.output = inputs.back();
options.virtual_inputs = reduce_dims(inputs);
options.params = "-Wno-float-equal";
auto axis = find_fast_axis(options.virtual_inputs);
auto vec = vectorize::elements(axis, options.virtual_inputs);
auto preloads = preload::broadcasts(axis, options.virtual_inputs);
options.kernel_name = "ck_elementwise_kernel"; options.kernel_name = "ck_elementwise_kernel";
options.virtual_inputs = inputs; options.set_launch_params(
v,
compute_global_for(ctx,
options.output.elements() / vec.size,
oversubscribe_if(not preloads.is_preloading())));
return compile_hip_code_object(ck_elementwise_kernel, options); return compile_hip_code_object(ck_elementwise_kernel, options);
} }
......
src/targets/gpu/kernels/include/migraphx/kernels/ck_elementwise.hpp
View file @ 8c1ad9e6
...@@ -91,28 +91,26 @@ template <class T, class U, class V> ...@@ -91,28 +91,26 @@ template <class T, class U, class V>
__device__ void ck_elementwise(const T& a_t, const U& b_t, const V& c_t) __device__ void ck_elementwise(const T& a_t, const U& b_t, const V& c_t)
{ {
auto idx = make_index(); auto idx = make_index();
if(idx.global == 0) constexpr auto lengths = get_shape_c<T>{}.lens;
{ constexpr auto strides = get_shape_c<T>{}.strides;
constexpr auto lengths = get_shape_c<T>{}.lens; constexpr auto a_desc = MakeDescriptor_M(lengths, strides, 1);
constexpr auto strides = get_shape_c<T>{}.strides;
constexpr auto a_desc = MakeDescriptor_M(lengths, strides, 1);
using AGridDesc_M = decltype(a_desc); using AGridDesc_M = decltype(a_desc);
using GridwiseBinEltwise = ck::GridwiseBinaryElementwise_1D<ADataType, using GridwiseBinEltwise = ck::GridwiseBinaryElementwise_1D<ADataType,
BDataType, BDataType,
CDataType, CDataType,
CDataType, CDataType,
AGridDesc_M, AGridDesc_M,
AGridDesc_M, AGridDesc_M,
AGridDesc_M, AGridDesc_M,
Add, Add,
1, 1,
1, 1,
1, 1,
1>; 1>;
auto op = Add{}; auto op = Add{};
GridwiseBinEltwise::Run(a_t.data(), b_t.data(), c_t.data(), a_desc, a_desc, a_desc, op); GridwiseBinEltwise::Run(a_t.data(), b_t.data(), c_t.data(), a_desc, a_desc, a_desc, op);
}
} }
} // namespace migraphx } // namespace migraphx
......
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