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batched_gemm + multiple_d + gemm + multiple_d (#394)

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* refactor

* start

* add device gemm file

* add BatchStrideD0

* add stridd0

* add gridwise file

* add d0 parameters to gridwise gemm

* add c layout transformer

* add d0 threadwise copy

* init kernel

* init kernel

* regular code

* nm desc put to out

* kernel parameter can not use reference

* host add bias+gelu

* run right for bias+gelu

* change AddFastGelu into another file

* interface add d1 bias parameters

* add d1 parameter to argument

* add d1 parameter to gridwise

* first all code,not verify

* gelu change to relu and GetElementSpaceSize bug

* add instance

* start add to ckprofiler

* ckprofiler finish code

* change input parameter for ckProfiler

* fix host bias+gelu bug

* show help for ckProfiler

* fix bug for lunch kernel ignore parametes

* add pad and fix about bug

* mutiple d0

* add dynamic d0_element_op

* change profiler and  instance to mutiple d0

* example have 2 d0

* remove some comments not using

* change 2 d0 have self  parameters

* change d element_op name

* change class name(multiple_d)

* fix bug

* fix bug that don't find file

* update profiler

* refactor

* update profiler

* clean

* revert example change

* add gon layout

* optimize parameter for gno

* add gon to gemm+gemm

* change helping input parameters

* change to GemmPadder_v2

* using ForEach

* fix gb_per_sec

Co-authored-by: Chao Liu <lc.roy86@gmail.com>
Co-authored-by: ltqin <letaoqin@amd.com>
This commit is contained in:
ltqin
2022-09-15 06:54:18 +08:00
committed by GitHub
parent b22ebd4485
commit 370efa6c08
19 changed files with 4119 additions and 0 deletions
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55
include/ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp
View File

@@ -28,6 +28,13 @@ struct Add
y = x0 + x1;
};
template <>
__host__ __device__ constexpr void
operator()<float>(float& y, const float& x0, const half_t& x1) const
{
y = x0 + type_convert<half_t>(x1);
};
template <>
__host__ __device__ constexpr void
operator()<half_t>(half_t& y, const float& x0, const half_t& x1) const
@@ -172,6 +179,14 @@ struct AddRelu
const float a = x0 + x1;
y = a > type_convert<half_t>(0.0f) ? a : type_convert<half_t>(0.0f);
};
template <>
__host__ __device__ constexpr void
operator()<float, float, half_t>(float& y, const float& x0, const half_t& x1) const
{
const float a = x0 + type_convert<float>(x1);
y = a > 0.0f ? a : 0.0f;
};
};
struct AddHardswish
@@ -210,6 +225,46 @@ struct AddHardswish
};
};
// C = A * B
// E = FastGelu(C + D)
struct AddFastGelu
{
// Fast GeLU
// https://paperswithcode.com/method/gelu
// y = 0.5*x*(1+tanh(sqrt(2/pi)*(x+0.044715*x^3)))
__host__ __device__ static constexpr float GetFastGeLU(float x)
{
const float u = 2.f * x * (0.035677f * x * x + 0.797885f);
const float emu = exp(-u);
const float cdf = 0.5f + 0.5f * (2.f / (1.f + emu) - 1.f);
return x * cdf;
}
template <typename T>
static inline constexpr bool is_valid_param_type_v =
std::is_same_v<T, float> || std::is_same_v<T, half_t> || std::is_same_v<T, bhalf_t> ||
std::is_same_v<T, int32_t> || std::is_same_v<T, int8_t>;
template <typename E, typename C, typename D>
__host__ __device__ constexpr void operator()(E& e, const C& c, const D& d) const
{
static_assert(is_valid_param_type_v<E> && is_valid_param_type_v<C> &&
is_valid_param_type_v<D>);
const float y = GetFastGeLU(type_convert<float>(c) + type_convert<float>(d));
e = type_convert<E>(y);
}
template <typename D>
__host__ __device__ constexpr void operator()(float& e, const float& c, const D& d) const
{
static_assert(is_valid_param_type_v<D>);
e = GetFastGeLU(c + type_convert<float>(d));
}
};
} // namespace element_wise
} // namespace tensor_operation
} // namespace ck

21
include/ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp
View File

@@ -211,6 +211,27 @@ struct FastGelu
}
};
// https://paperswithcode.com/method/gelu
// y = 0.5*x*(1+erf(x/sqrt(2)))
struct Gelu
{
template <typename Y, typename X>
__host__ __device__ void operator()(Y& y, const X& x) const;
template <>
__host__ __device__ void operator()<float, float>(float& y, const float& x) const
{
y = 0.5f * x * (1.f + erf(float(0.70710678118f * x)));
}
template <>
__host__ __device__ void operator()<ck::half_t, ck::half_t>(ck::half_t& y,
const ck::half_t& x) const
{
y = ck::half_t(0.5) * x * (ck::half_t(1) + ck::half_t(erf(float(0.70710678118f * x))));
}
};
} // namespace element_wise
} // namespace tensor_operation
} // namespace ck