Files
composable_kernel/composable_kernel/include/utility/math.hpp
zjing14 3835318cc3 xdlops_v4r4_fwd fp32/fp16 (#34)
* create files for xdlops

* working on blockwise_gemm_xdlops

* add KReduction

* add m/n repeats

* add 2x2 pipeline

* added 128x128 wavegemm

* use StaticBuffer of vector_type

* break vector type to blk_size

* add kpack into xldops_gemm and blockwise_gemm

* abroadcast only

* add fp32 mfma instructions

* adding fp16 mfma

* pack half4_t

* rename kperwave to kpack

* add 32x32x8fp16

* add fp16 mfma

* clean code

* clean code

* V4r4 xdlops kpack (#35)

* add kpack with incorrect results

* bug fix for make_dynamic_naive_tensor_descriptor_aligned_v2

* add 1x1 kernel

* add gridwise_gemm_v2 - single_buffer

* enabled dwordx4 for fp16

Co-authored-by: Chao Liu <chao.liu2@amd.com>

* refactor fwd-v4r4-xdlops

* add v4r4-nhwc-xdlop

* improve some perf of nhwc and nchw by tuning parameters, and change scheuduling in gridwise-gemm loop

* tweak scheduling in gridwise gemm

* add v4r3 with a single output copy

* init commit: output with slice win

* adding sliceWin

* add multiple repeats pattern

* starting adding bwd-v4r1-xdlops

* use tuple as SrcBuffer

* adding bwd-data v4r1 nhwc xdlops

* fix bug in make_dynamic_naive_tensor_descriptor_aligned_v2()

* fix bug in host bwd-data conv

* initial implementation of bwd-data v4r1 nhwc xdlops

* add launch bound flags

* enable launch bound

* add m/nrepeat=4

* tweak bwd-data v4r1 nhwc xdlops

* added bwd-data v4r1 nhwc xlops with output A and weight B

* add fwd-v4r4 nhwc xdlops, A input, B weight, C output

Co-authored-by: Chao Liu <chao.liu2@amd.com>
2021-07-01 14:33:00 -05:00

218 lines
4.2 KiB
C++

#ifndef CK_MATH_HPP
#define CK_MATH_HPP
#include "config.hpp"
#include "integral_constant.hpp"
#include "number.hpp"
#include "type.hpp"
namespace ck {
namespace math {
template <typename T, T s>
struct scales
{
__host__ __device__ constexpr T operator()(T a) const { return s * a; }
};
template <typename T>
struct plus
{
__host__ __device__ constexpr T operator()(T a, T b) const { return a + b; }
};
template <typename T>
struct minus
{
__host__ __device__ constexpr T operator()(T a, T b) const { return a - b; }
};
template <typename T>
struct multiplies
{
__host__ __device__ constexpr T operator()(T a, T b) const { return a * b; }
};
struct multiplies_v2
{
template <typename A, typename B>
__host__ __device__ constexpr auto operator()(const A& a, const B& b) const
{
return a * b;
}
};
template <typename T>
struct maximize
{
__host__ __device__ constexpr T operator()(T a, T b) const { return a >= b ? a : b; }
};
template <typename T>
struct minimize
{
__host__ __device__ constexpr T operator()(T a, T b) const { return a <= b ? a : b; }
};
template <typename T>
struct integer_divide_ceiler
{
__host__ __device__ constexpr T operator()(T a, T b) const
{
static_assert(is_same<T, index_t>{} || is_same<T, int>{}, "wrong type");
return (a + b - Number<1>{}) / b;
}
};
template <typename X, typename Y>
__host__ __device__ constexpr auto integer_divide_floor(X x, Y y)
{
return x / y;
}
template <typename X, typename Y>
__host__ __device__ constexpr auto integer_divide_ceil(X x, Y y)
{
return (x + y - Number<1>{}) / y;
}
template <typename X, typename Y>
__host__ __device__ constexpr auto integer_least_multiple(X x, Y y)
{
return y * integer_divide_ceil(x, y);
}
template <typename T>
__host__ __device__ constexpr T max(T x)
{
return x;
}
template <typename T>
__host__ __device__ constexpr T max(T x, T y)
{
return x > y ? x : y;
}
template <index_t X>
__host__ __device__ constexpr index_t max(Number<X>, index_t y)
{
return X > y ? X : y;
}
template <index_t Y>
__host__ __device__ constexpr index_t max(index_t x, Number<Y>)
{
return x > Y ? x : Y;
}
template <typename X, typename... Ys>
__host__ __device__ constexpr auto max(X x, Ys... ys)
{
static_assert(sizeof...(Ys) > 0, "not enough argument");
return max(x, max(ys...));
}
template <typename T>
__host__ __device__ constexpr T min(T x)
{
return x;
}
template <typename T>
__host__ __device__ constexpr T min(T x, T y)
{
return x < y ? x : y;
}
template <index_t X>
__host__ __device__ constexpr index_t min(Number<X>, index_t y)
{
return X < y ? X : y;
}
template <index_t Y>
__host__ __device__ constexpr index_t min(index_t x, Number<Y>)
{
return x < Y ? x : Y;
}
template <typename X, typename... Ys>
__host__ __device__ constexpr auto min(X x, Ys... ys)
{
static_assert(sizeof...(Ys) > 0, "not enough argument");
return min(x, min(ys...));
}
// greatest common divisor, aka highest common factor
__host__ __device__ constexpr index_t gcd(index_t x, index_t y)
{
if(x == y || x == 0)
{
return y;
}
else if(y == 0)
{
return x;
}
else if(x > y)
{
return gcd(x - y, y);
}
else
{
return gcd(x, y - x);
}
}
template <index_t X, index_t Y>
__host__ __device__ constexpr auto gcd(Number<X>, Number<Y>)
{
constexpr auto r = gcd(X, Y);
return Number<r>{};
}
template <typename X,
typename... Ys,
typename std::enable_if<sizeof...(Ys) >= 2, bool>::type = false>
__host__ __device__ constexpr auto gcd(X x, Ys... ys)
{
return gcd(x, ys...);
}
// least common multiple
template <typename X, typename Y>
__host__ __device__ constexpr auto lcm(X x, Y y)
{
return (x * y) / gcd(x, y);
}
template <typename X,
typename... Ys,
typename std::enable_if<sizeof...(Ys) >= 2, bool>::type = false>
__host__ __device__ constexpr auto lcm(X x, Ys... ys)
{
return lcm(x, lcm(ys...));
}
template <typename T>
struct equal
{
__host__ __device__ constexpr bool operator()(T x, T y) const { return x == y; }
};
template <typename T>
struct less
{
__host__ __device__ constexpr bool operator()(T x, T y) const { return x < y; }
};
} // namespace math
} // namespace ck
#endif