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Commit 9a7bb6d2 authored by turneram's avatar turneram
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Create half_t test

parent f7838bc8
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Showing with 161 additions and 116 deletions
src/targets/gpu/jit/ck_elementwise.cpp
View file @ 9a7bb6d2
......@@ -43,135 +43,135 @@ namespace gpu {
using namespace migraphx::gpu::gen; // NOLINT
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>
// 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>
#include <migraphx/kernels/index.hpp>
#include <migraphx/kernels/algorithm.hpp>
#include <migraphx/kernels/integral_constant.hpp>
#include <migraphx/kernels/tensor_view.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"
// #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;
// using ADataType = float;
// using BDataType = float;
// using CDataType = float;
// using ElementwiseFunctor = float;
static constexpr auto I0 = ck::Number<0>{};
// 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;
}
// 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;
};
};
// 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)
{
make_tensors()(a_p, b_p, c_p)([](auto a_t, auto b_t, auto c_t) {
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);
});
}
// __global__ void ck_elementwise_kernel(void* a_p, void* b_p, void* c_p)
// {
// make_tensors()(a_p, b_p, c_p)([](auto a_t, auto b_t, auto c_t) {
// 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);
// });
// }
}
// }
} // namespace migraphx
// } // namespace migraphx
)__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>
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 {
namespace migraphx {
// extern "C" {
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...);
// });
// }
__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
} // namespace migraphx
// )__migraphx__";
)__migraphx__";
struct ck_elementwise_compiler : compiler<ck_elementwise_compiler>
{
......
src/targets/gpu/kernels/include/migraphx/kernels/ck_elementwise.hpp
View file @ 9a7bb6d2
......@@ -37,9 +37,9 @@
namespace migraphx {
using ADataType = float;
using BDataType = float;
using CDataType = float;
using ADataType = ck::half_t; //float;
using BDataType = ck::half_t; //float;
using CDataType = ck::half_t; //float;
using ElementwiseFunctor = float;
static constexpr auto I0 = ck::Number<0>{};
......@@ -90,7 +90,7 @@ struct Add
template <class T, class U, class V>
__device__ void ck_elementwise(const T& a_t, const U& b_t, const V& c_t)
{
auto idx = make_index();
//auto idx = make_index();
constexpr auto lengths = get_shape_c<T>{}.lens;
constexpr auto strides = get_shape_c<T>{}.strides;
constexpr auto a_desc = MakeDescriptor_M(lengths, strides, 1);
......@@ -104,10 +104,10 @@ __device__ void ck_elementwise(const T& a_t, const U& b_t, const V& c_t)
AGridDesc_M,
AGridDesc_M,
Add,
1,
1,
1,
1>;
4,
4,
4,
4>;
auto op = Add{};
GridwiseBinEltwise::Run(a_t.data(), b_t.data(), c_t.data(), a_desc, a_desc, a_desc, op);
}
......
test/verify/0ck_elementwise_half_test.cpp 0 → 100644
View file @ 9a7bb6d2
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#include "verify_program.hpp"
#include <migraphx/program.hpp>
#include <migraphx/generate.hpp>
#include <migraphx/make_op.hpp>
struct ck_elementwise_half : verify_program<ck_elementwise_half>
{
migraphx::program create_program() const
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape m1_shape{migraphx::shape::half_type, {2000}};
// migraphx::shape m2_shape{migraphx::shape::float_type, {20, 10}};
auto l1 = mm->add_parameter("1", m1_shape);
auto l2 = mm->add_parameter("2", m1_shape);
mm->add_instruction(migraphx::make_op("ck_elementwise"), l1, l2);
return p;
}
};
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