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Commit e5ebcc41 authored by Artur Wojcik's avatar Artur Wojcik
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Merge branch 'develop' into uif2-migraphx

parents 57cdd70b abac8b07
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Showing with 3104 additions and 117 deletions
include/ck/tensor_operation/gpu/device/impl/device_splitk_contraction_multiple_d_xdl_cshuffle.hpp
View file @ e5ebcc41
......@@ -57,7 +57,7 @@ __global__ void
const Block2ETileMap block_2_etile_map)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__))
defined(__gfx94__))
__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
const index_t num_blocks_per_batch =
......
include/ck/tensor_operation/gpu/device/tensor_layout.hpp
View file @ e5ebcc41
......@@ -308,12 +308,6 @@ struct GNDHWK : public BaseTensorLayout
static constexpr const char* name = "GNDHWK";
};
// for output bias
struct GK : public BaseTensorLayout
{
static constexpr const char* name = "GK";
};
// output tensor
// packed NWGK/NHWGK/NDHWGK
struct NWGK : public BaseTensorLayout
......
include/ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp
View file @ e5ebcc41
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -36,6 +36,13 @@ struct Add
y = x0 + type_convert<half_t>(x1);
};
template <>
__host__ __device__ constexpr void
operator()<half_t>(half_t& y, const float& x0, const float& x1) const
{
y = type_convert<half_t>(x0 + x1);
};
template <>
__host__ __device__ constexpr void
operator()<half_t>(half_t& y, const float& x0, const half_t& x1) const
......@@ -68,6 +75,15 @@ struct Add
y = ck::type_convert<bhalf_t>(y_tmp);
}
template <>
__host__ __device__ constexpr void
operator()<bhalf_t>(bhalf_t& y, const float& x0, const bhalf_t& x1) const
{
const float x2_tmp = ck::type_convert<float>(x1);
const float y_tmp = x0 + x2_tmp;
y = ck::type_convert<bhalf_t>(y_tmp);
}
template <>
__host__ __device__ constexpr void
operator()<int8_t>(int8_t& y, const int8_t& x0, const int8_t& x1) const
......@@ -78,10 +94,13 @@ struct Add
struct ScaleAdd
{
__host__ __device__ ScaleAdd(float scale) : scale_(scale) {}
__host__ __device__ ScaleAdd(float scale = 1.f) : scale_(scale) {}
template <typename Y, typename X0, typename X1>
__host__ __device__ constexpr void operator()(Y& y, const X0& x0, const X1& x1) const;
__host__ __device__ constexpr void operator()(Y& y, const X0& x0, const X1& x1) const
{
y = ck::type_convert<Y>(scale_ * ck::type_convert<float>(x0) + ck::type_convert<float>(x1));
}
template <>
__host__ __device__ void
......@@ -146,7 +165,7 @@ struct Subtract
struct Bilinear
{
Bilinear(float alpha, float beta) : alpha_(alpha), beta_(beta){};
Bilinear(float alpha = 1.f, float beta = 1.f) : alpha_(alpha), beta_(beta){};
template <typename Y, typename X0, typename X1>
__host__ __device__ constexpr void operator()(Y&, const X0&, const X1&) const;
......@@ -165,6 +184,14 @@ struct Bilinear
y = alpha_ * x0 + beta_ * x1;
};
template <>
__host__ __device__ constexpr void
operator()<int8_t, int8_t, int8_t>(int8_t& y, const int8_t& x0, const int8_t& x1) const
{
y = type_convert<int8_t>(alpha_ * type_convert<float>(x0) +
beta_ * type_convert<float>(x1));
};
template <>
__host__ __device__ constexpr void
operator()<half_t, half_t, half_t>(half_t& y, const half_t& x0, const half_t& x1) const
......@@ -179,6 +206,33 @@ struct Bilinear
y = type_convert<half_t>(alpha_ * x0 + beta_ * ck::type_convert<float>(x1));
};
template <>
__host__ __device__ constexpr void
operator()<bhalf_t, bhalf_t, bhalf_t>(bhalf_t& y, const bhalf_t& x0, const bhalf_t& x1) const
{
const float x0_tmp = type_convert<float>(x0);
const float x1_tmp = type_convert<float>(x1);
const float y_tmp = alpha_ * x0_tmp + beta_ * x1_tmp;
y = type_convert<bhalf_t>(y_tmp);
};
template <>
__host__ __device__ constexpr void
operator()<bhalf_t, float, bhalf_t>(bhalf_t& y, const float& x0, const bhalf_t& x1) const
{
const float x1_tmp = ck::type_convert<float>(x1);
const float y_tmp = alpha_ * x0 + beta_ * x1_tmp;
y = y_tmp;
};
template <>
__host__ __device__ constexpr void operator()<std::int8_t, std::int32_t, std::int8_t>(
std::int8_t& y, const std::int32_t& x0, const std::int8_t& x1) const
{
y = type_convert<int8_t>(alpha_ * type_convert<float>(x0) +
beta_ * type_convert<float>(x1));
};
float alpha_;
float beta_;
};
......@@ -228,6 +282,14 @@ struct AddRelu
y = a > 0.0f ? a : 0.0f;
};
template <>
__host__ __device__ constexpr void
operator()<bhalf_t, float, bhalf_t>(bhalf_t& y, const float& x0, const bhalf_t& x1) const
{
const float a = x0 + type_convert<float>(x1);
y = a > type_convert<bhalf_t>(0.0f) ? a : type_convert<bhalf_t>(0.0f);
};
template <>
__host__ __device__ constexpr void
operator()<int, int, int8_t>(int& y, const int& x0, const int8_t& x1) const
......@@ -318,6 +380,70 @@ struct AddFastGelu
e = type_convert<half_t>(x1_f);
}
template <>
__host__ __device__ constexpr void
operator()<bhalf_t, float, bhalf_t>(bhalf_t& e, const float& c, const bhalf_t& d) const
{
const float x0_f = c + type_convert<float>(d);
float x1_f = 0;
FastGelu{}.template operator()<float, float>(x1_f, x0_f);
e = type_convert<bhalf_t>(x1_f);
}
};
// E = Silu(C + D)
struct AddSilu
{
template <typename E, typename C, typename D>
__host__ __device__ constexpr void operator()(E& e, const C& c, const D& d) const;
template <>
__host__ __device__ constexpr void
operator()<float, float, float>(float& e, const float& c, const float& d) const
{
const float x = c + d;
Silu{}.template operator()<float>(e, x);
}
template <>
__host__ __device__ constexpr void
operator()<half_t, half_t, half_t>(half_t& e, const half_t& c, const half_t& d) const
{
const half_t x = c + d;
Silu{}.template operator()<half_t>(e, x);
}
template <>
__host__ __device__ constexpr void
operator()<half_t, float, half_t>(half_t& e, const float& c, const half_t& d) const
{
const float x0_f = c + d;
float x1_f = 0;
Silu{}.template operator()<float>(x1_f, x0_f);
e = type_convert<half_t>(x1_f);
}
template <>
__host__ __device__ constexpr void
operator()<bhalf_t, float, bhalf_t>(bhalf_t& e, const float& c, const bhalf_t& d) const
{
const float x0_f = c + type_convert<float>(d);
float x1_f = 0;
Silu{}.template operator()<float>(x1_f, x0_f);
e = type_convert<bhalf_t>(x1_f);
}
};
} // namespace element_wise
......
include/ck/tensor_operation/gpu/element/element_wise_operation.hpp
View file @ e5ebcc41
......@@ -195,6 +195,51 @@ struct AddMultiply
}
};
// C = A * B
// E = C x D0 + D1
struct MultiplyAdd
{
template <typename E, typename C, typename D0, typename D1>
__host__ __device__ void operator()(E& e, const C& c, const D0& d0, const D1& d1) const;
template <>
__host__ __device__ void operator()<half_t, half_t, half_t, half_t>(half_t& e,
const half_t& c,
const half_t& d0,
const half_t& d1) const
{
const half_t y = (c * d0) + d1;
e = y;
}
template <>
__host__ __device__ void operator()<half_t, float, half_t, half_t>(half_t& e,
const float& c,
const half_t& d0,
const half_t& d1) const
{
const half_t y = type_convert<half_t>(c) * d0 + d1;
e = y;
}
template <>
__host__ __device__ void operator()<float, float, half_t, half_t>(float& e,
const float& c,
const half_t& d0,
const half_t& d1) const
{
const float y = c * d0 + d1;
e = y;
}
template <>
__host__ __device__ void operator()<half_t, float, float, float>(half_t& e,
const float& c,
const float& d0,
const float& d1) const
{
const float y = c * d0 + d1;
e = y;
}
};
// E = FastGelu(C + D0 + D1)
struct AddAddFastGelu
{
......@@ -266,6 +311,71 @@ struct AddAddFastGelu
}
};
// E = Relu(alpha1 * C + alpha2 * D0 + D1)
struct ScaleAddScaleAddRelu
{
ScaleAddScaleAddRelu(const float alpha1 = 1.f, const float alpha2 = 1.f)
: alpha1_(alpha1), alpha2_(alpha2)
{
}
template <typename E, typename C, typename D0, typename D1>
__host__ __device__ constexpr void
operator()(E& e, const C& c, const D0& d0, const D1& d1) const;
template <>
__host__ __device__ constexpr void operator()<float, float, float, float>(float& e,
const float& c,
const float& d0,
const float& d1) const
{
const float x = c * alpha1_ + alpha2_ * d0 + d1;
Relu{}.template operator()<float>(e, x);
}
template <>
__host__ __device__ constexpr void operator()<half_t, half_t, half_t, half_t>(
half_t& e, const half_t& c, const half_t& d0, const half_t& d1) const
{
const float x = type_convert<float>(c) * alpha1_ + alpha2_ * type_convert<float>(d0) +
type_convert<float>(d1);
float result = 0;
Relu{}.template operator()<float>(result, x);
e = type_convert<half_t>(result);
}
template <>
__host__ __device__ constexpr void operator()<bhalf_t, bhalf_t, bhalf_t, bhalf_t>(
bhalf_t& e, const bhalf_t& c, const bhalf_t& d0, const bhalf_t& d1) const
{
const float x = type_convert<float>(c) * alpha1_ + alpha2_ * type_convert<float>(d0) +
type_convert<float>(d1);
float result = 0;
Relu{}.template operator()<float>(result, x);
e = type_convert<bhalf_t>(result);
}
template <>
__host__ __device__ constexpr void operator()<int8_t, int8_t, float, float>(
int8_t& e, const int8_t& c, const float& d0, const float& d1) const
{
const float x = type_convert<float>(c) * alpha1_ + alpha2_ * d0 + d1;
float result = 0;
Relu{}.template operator()<float>(result, x);
e = type_convert<int8_t>(result);
}
const float alpha1_;
const float alpha2_;
};
struct Normalize
{
// FIXME: is double absolutely necessary?
......
include/ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp
View file @ e5ebcc41
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -16,6 +16,18 @@ namespace element_wise {
extern "C" __device__ float __ocml_native_recip_f32(float);
#endif
struct PassThroughPack2
{
template <typename Y, typename X>
__host__ __device__ void operator()(Y& y, const X& x) const;
__host__ __device__ constexpr void operator()(ck::half2_t& y, const ck::f8x2_t& x) const
{
auto t = type_convert<float2_t>(x);
y = type_convert<half2_t>(t);
}
};
struct PassThrough
{
template <typename Y, typename X>
......@@ -27,6 +39,18 @@ struct PassThrough
y = x;
}
template <>
__host__ __device__ void operator()<float, double>(float& y, const double& x) const
{
y = type_convert<float>(x);
}
template <>
__host__ __device__ void operator()<double, float>(double& y, const float& x) const
{
y = type_convert<double>(x);
}
template <>
__host__ __device__ void operator()<float, float>(float& y, const float& x) const
{
......@@ -39,6 +63,12 @@ struct PassThrough
y = x;
}
template <>
__host__ __device__ void operator()<half_t, float>(half_t& y, const float& x) const
{
y = type_convert<half_t>(x);
}
template <>
__host__ __device__ void operator()<bhalf_t, bhalf_t>(bhalf_t& y, const bhalf_t& x) const
{
......@@ -57,30 +87,65 @@ struct PassThrough
y = type_convert<bhalf_t>(x);
}
template <>
__host__ __device__ void operator()<float, bhalf_t>(float& y, const bhalf_t& x) const
{
y = type_convert<float>(x);
}
template <>
__host__ __device__ void operator()<bhalf_t, half_t>(bhalf_t& y, const half_t& x) const
{
y = type_convert<bhalf_t>(x);
}
template <>
__host__ __device__ void operator()<float, half_t>(float& y, const half_t& x) const
{
y = type_convert<float>(x);
}
template <>
__host__ __device__ void operator()<int8_t, int8_t>(int8_t& y, const int8_t& x) const
{
y = x;
}
template <>
__host__ __device__ void operator()<half_t, int8_t>(half_t& y, const int8_t& x) const
{
y = type_convert<half_t>(x);
}
template <>
__host__ __device__ void operator()<bhalf_t, int8_t>(bhalf_t& y, const int8_t& x) const
{
y = type_convert<bhalf_t>(x);
}
template <>
__host__ __device__ void operator()<int8_t, int32_t>(int8_t& y, const int32_t& x) const
{
y = type_convert<int8_t>(x);
}
template <>
__host__ __device__ void operator()<int8_t, float>(int8_t& y, const float& x) const
{
y = type_convert<int8_t>(x);
}
#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
template <>
__host__ __device__ void operator()<int4_t, int4_t>(int4_t& y, const int4_t& x) const
{
y = x;
}
template <>
__host__ __device__ void operator()<int4_t, int>(int4_t& y, const int& x) const
{
y = type_convert<int4_t>(x);
}
#endif
template <>
......@@ -112,6 +177,36 @@ struct PassThrough
{
y = type_convert<f8_t>(x);
}
template <>
__host__ __device__ void operator()<bf8_t, bf8_t>(bf8_t& y, const bf8_t& x) const
{
y = x;
}
template <>
__host__ __device__ void operator()<float, bf8_t>(float& y, const bf8_t& x) const
{
y = type_convert<float>(x);
}
template <>
__host__ __device__ void operator()<bf8_t, float>(bf8_t& y, const float& x) const
{
y = type_convert<bf8_t>(x);
}
template <>
__host__ __device__ void operator()<half_t, bf8_t>(half_t& y, const bf8_t& x) const
{
y = type_convert<half_t>(x);
}
template <>
__host__ __device__ void operator()<bf8_t, half_t>(bf8_t& y, const half_t& x) const
{
y = ck::type_convert<bf8_t>(x);
}
};
struct UnaryConvert
......@@ -147,7 +242,8 @@ struct ConvertF8SR
__host__ __device__ void operator()(Y& y, const X& x) const
{
// check Y datatype
static_assert(is_same<Y, f8_t>::value, "Data type is not supported by this operation!");
static_assert(is_same<Y, f8_t>::value || is_same<Y, bf8_t>::value,
"Data type is not supported by this operation!");
// check X datatype
static_assert(is_same<X, float>::value || is_same<X, half_t>::value,
......@@ -157,6 +253,24 @@ struct ConvertF8SR
}
};
struct ConvertF8RNE
{
// convert to fp8 using rounding to nearest even
template <typename Y, typename X>
__host__ __device__ void operator()(Y& y, const X& x) const
{
// check Y datatype
static_assert(is_same<Y, f8_t>::value || is_same<Y, bf8_t>::value,
"Data type is not supported by this operation!");
// check X datatype
static_assert(is_same<X, float>::value || is_same<X, half_t>::value,
"Data type is not supported by this operation!");
y = f8_convert_rne<Y>(x);
}
};
struct Scale
{
__host__ __device__ Scale(float scale) : scale_(scale) {}
......@@ -164,6 +278,20 @@ struct Scale
template <typename Y, typename X>
__host__ __device__ void operator()(Y& y, const X& x) const;
template <>
__host__ __device__ void operator()<half_t, half_t>(half_t& y, const half_t& x) const
{
y = ck::type_convert<half_t>(scale_) * x;
};
template <>
__host__ __device__ void operator()<bhalf_t, bhalf_t>(bhalf_t& y, const bhalf_t& x) const
{
const float x_tmp = ck::type_convert<float>(x);
const float y_tmp = scale_ * x_tmp;
y = ck::type_convert<bhalf_t>(y_tmp);
};
template <>
__host__ __device__ void operator()<float, float>(float& y, const float& x) const
{
......@@ -217,8 +345,8 @@ struct UnarySquare
template <typename T>
__host__ __device__ void operator()(T& y, const T& x) const
{
static_assert(is_same_v<T, float> || is_same_v<T, double> || is_same_v<T, int32_t> ||
is_same_v<T, int8_t>
static_assert(is_same_v<T, float> || is_same_v<T, half_t> || is_same_v<T, double> ||
is_same_v<T, int32_t> || is_same_v<T, int8_t>
#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
|| is_same_v<T, int4_t>
#endif
......@@ -292,27 +420,29 @@ struct FastGelu
template <>
__host__ void operator()<float, float>(float& y, const float& x) const
{
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);
y = x * cdf;
// const float u = -2.f * x * (0.035677f * x * x + 0.797885f);
const float c1 = -2.0 * 0.035677f;
const float c2 = -2.0 * 0.797885f;
const float u = x * (c1 * x * x + c2);
const float emu = exp(u);
y = x / (1.f + emu);
}
#endif
// device code, use lower precision "__expf" and "rcp"
template <>
__device__ void operator()<float, float>(float& y, const float& x) const
{
const float u = 2.f * x * (0.035677f * x * x + 0.797885f);
const float emu = __expf(-u);
// const float u = 2.f * x * (0.035677f * x * x + 0.797885f);
const float c1 = -2.0 * 0.035677f;
const float c2 = -2.0 * 0.797885f;
const float u = x * (c1 * x * x + c2);
const float emu = __expf(u);
#if !CK_WORKAROUND_SWDEV_383542
const float cdf = 0.5f + 0.5f * (2.f * __frcp_rn(1.f + emu) - 1.f);
y = x * __frcp_rn(1.f + emu);
#else
const float cdf = 0.5f + 0.5f * (2.f * __ocml_native_recip_f32(1.f + emu) - 1.f);
y = x * __ocml_native_recip_f32(1.f + emu);
#endif
y = x * cdf;
}
template <>
......@@ -383,10 +513,24 @@ struct Sigmoid
__host__ __device__ void operator()(T& y, const T& x) const
{
static_assert(is_same<T, float>::value || is_same<T, double>::value ||
is_same<T, ck::half_t>::value,
is_same<T, ck::half_t>::value || is_same<T, int8_t>::value ||
is_same<T, int32_t>::value,
"Data type is not supported by this operation!");
constexpr T one = type_convert<T>(1);
y = one / (one + ck::math::exp(-x));
};
};
y = 1 / (ck::type_convert<T>(1) + exp(-x));
struct Silu
{
template <typename T>
__host__ __device__ void operator()(T& y, const T& x) const
{
static_assert(is_same_v<T, float> || is_same_v<T, double> || is_same_v<T, ck::half_t> ||
is_same_v<T, int8_t> || is_same_v<T, int32_t>,
"Data type is not supported by this operation!");
constexpr T one = type_convert<T>(1);
y = x * (one / (one + ck::math::exp(-x)));
};
};
......@@ -396,7 +540,8 @@ struct TanH
__host__ __device__ void operator()(T& y, const T& x) const
{
static_assert(is_same<T, float>::value || is_same<T, double>::value ||
is_same<T, ck::half_t>::value,
is_same<T, ck::half_t>::value || is_same<T, int8_t>::value ||
is_same<T, int32_t>::value,
"Data type is not supported by this operation!");
y = ck::math::tanh(x);
......@@ -407,17 +552,116 @@ struct Swish
{
Swish(float beta = 1.0f) : beta_(beta) {}
template <typename Y, typename X>
__host__ __device__ void operator()(Y& y, const X& x) const
{
static_assert(is_same<X, float>::value || is_same<X, double>::value ||
is_same<X, ck::half_t>::value,
"Data type is not supported by this operation!");
static_assert(is_same<Y, float>::value || is_same<Y, double>::value ||
is_same<Y, ck::half_t>::value,
"Data type is not supported by this operation!");
float bx = -beta_ * type_convert<float>(x);
y = type_convert<Y>(x / (1.f + ck::math::exp(bx)));
};
const float beta_;
};
struct SoftRelu
{
SoftRelu(float alpha = 1.f) : alpha_(alpha){};
template <typename T>
__host__ __device__ void operator()(T& y, const T& x) const
{
static_assert(is_same<T, float>::value || is_same<T, double>::value ||
is_same<T, ck::half_t>::value,
is_same<T, half_t>::value || is_same<T, int32_t>::value ||
is_same<T, int8_t>::value,
"Data type is not supported by this operation!");
T casted_alpha = type_convert<T>(alpha_);
constexpr T one = type_convert<T>(1);
y = ck::math::log(one + ck::math::exp(x * casted_alpha)) / casted_alpha;
}
const float alpha_;
};
y = x / (ck::type_convert<T>(1) + ck::math::exp(-beta_ * x));
};
struct Power
{
Power(float alpha = 0.f, float beta = 1.f, float gamma = 2.f)
: alpha_(alpha), beta_(beta), gamma_(gamma){};
template <typename T>
__host__ __device__ void operator()(T& y, const T& x) const
{
static_assert(is_same<T, float>::value || is_same<T, double>::value ||
is_same<T, half_t>::value || is_same<T, int32_t>::value ||
is_same<T, int8_t>::value,
"Data type is not supported by this operation!");
T casted_alpha = type_convert<T>(alpha_);
T casted_beta = type_convert<T>(beta_);
T casted_gamma = type_convert<T>(gamma_);
T shifted_scaled_x = casted_alpha + casted_beta * x;
y = ck::math::pow(shifted_scaled_x, casted_gamma);
}
const float alpha_;
const float beta_;
const float gamma_;
};
struct ClippedRelu
{
ClippedRelu(float alpha = 0.f, float beta = 1.f) : alpha_(alpha), beta_(beta){};
float beta_ = 1.0f;
template <typename T>
__host__ __device__ void operator()(T& y, const T& x) const
{
static_assert(is_same<T, float>::value || is_same<T, double>::value ||
is_same<T, half_t>::value || is_same<T, int32_t>::value ||
is_same<T, int8_t>::value,
"Data type is not supported by this operation!");
T casted_alpha = type_convert<T>(alpha_);
T casted_beta = type_convert<T>(beta_);
y = ck::math::min(casted_beta, ck::math::max(casted_alpha, x));
}
const float alpha_;
const float beta_;
};
struct LeakyRelu
{
LeakyRelu(float alpha = 0.01f) : alpha_(alpha){};
template <typename T>
__host__ __device__ void operator()(T& y, const T& x) const
{
static_assert(is_same<T, float>::value || is_same<T, double>::value ||
is_same<T, half_t>::value || is_same<T, int32_t>::value ||
is_same<T, int8_t>::value,
"Data type is not supported by this operation!");
T casted_alpha = type_convert<T>(alpha_);
y = x >= 0 ? x : x * casted_alpha;
}
const float alpha_;
};
struct Elu
{
Elu(float alpha = 1.f) : alpha_(alpha){};
template <typename T>
__host__ __device__ void operator()(T& y, const T& x) const
{
static_assert(is_same<T, float>::value || is_same<T, double>::value ||
is_same<T, half_t>::value || is_same<T, int32_t>::value ||
is_same<T, int8_t>::value,
"Data type is not supported by this operation!");
T casted_alpha = type_convert<T>(alpha_);
y = x > 0 ? x : casted_alpha * ck::math::expm1(x);
}
const float alpha_;
};
} // namespace element_wise
......
include/ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp
View file @ e5ebcc41
......@@ -138,6 +138,11 @@ struct BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, NPerBlock, void>
__device__ constexpr BlockToCTileMap_M00_N0_M01Adapt(index_t M, index_t N, index_t M01 = 8)
: M_(M), N_(N), M01_(M01)
{
#if 0
if(get_thread_global_1d_id()==0){
printf("Ctor called, M= %d, N= %d, M01 = %d\n", M_, N_, M01_);
}
#endif
}
template <typename CGridDesc_M_N>
......@@ -259,6 +264,302 @@ struct BlockToCTileMap_M00_N0_M01Adapt : BlockToCTileMap_M00_N0_M01Adapt<MPerBlo
BlockToCTileMap_M00_N0_M01Adapt;
};
// Rows of column-vectors
// This C-tile map dynamically adjusts M01 when C-tile index is out of range
template <index_t GroupNum, index_t MPerBlock, index_t NPerBlock, typename CGridDesc_M_N = void>
struct BlockToCTileMap_Grouped_M00_N0_M01Adapt;
template <index_t GroupNum, index_t MPerBlock, index_t NPerBlock>
struct BlockToCTileMap_Grouped_M00_N0_M01Adapt<GroupNum, MPerBlock, NPerBlock, void>
{
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
__host__ __device__ BlockToCTileMap_Grouped_M00_N0_M01Adapt() = default;
__host__ __device__ BlockToCTileMap_Grouped_M00_N0_M01Adapt(
const BlockToCTileMap_Grouped_M00_N0_M01Adapt&) = default;
__host__ __device__
BlockToCTileMap_Grouped_M00_N0_M01Adapt(BlockToCTileMap_Grouped_M00_N0_M01Adapt&&) = default;
__host__ __device__ BlockToCTileMap_Grouped_M00_N0_M01Adapt&
operator=(const BlockToCTileMap_Grouped_M00_N0_M01Adapt&) = default;
__host__ __device__ BlockToCTileMap_Grouped_M00_N0_M01Adapt&
operator=(BlockToCTileMap_Grouped_M00_N0_M01Adapt&&) = default;
__host__ __device__ BlockToCTileMap_Grouped_M00_N0_M01Adapt(index_t M,
index_t N,
index_t M01 = 8)
: M_(M), N_(N), M01_(M01)
{
#if 0
if(get_thread_global_1d_id()==0){
printf("Ctor called, M= %d, N= %d, M01 = %d\n", M_, N_, M01_);
}
#endif
}
template <typename CGridDesc_M_N>
__host__ __device__
BlockToCTileMap_Grouped_M00_N0_M01Adapt(const CGridDesc_M_N& c_grid_desc_m_n, index_t M01 = 8)
: BlockToCTileMap_Grouped_M00_N0_M01Adapt(
c_grid_desc_m_n.GetLength(I0), c_grid_desc_m_n.GetLength(I1), M01)
{
}
__host__ static constexpr index_t CalculateGridSize(index_t M, index_t N)
{
const auto M0 = math::integer_divide_ceil(M, MPerBlock);
const auto N0 = math::integer_divide_ceil(N, NPerBlock);
return M0 * N0;
}
template <typename CGridDesc_M_N>
__host__ static constexpr index_t CalculateGridSize(const CGridDesc_M_N& c_grid_desc_m_n)
{
return CalculateGridSize(c_grid_desc_m_n.GetLength(I0), c_grid_desc_m_n.GetLength(I1));
}
template <typename CGridDesc_M_N>
__host__ bool CheckValidity(const CGridDesc_M_N& /* c_grid_desc_m_n */) const
{
return true;
}
template <typename TopIdx>
__host__ __device__ constexpr auto CalculateBottomIndex(const TopIdx& idx_top) const
{
auto block_1d_id = idx_top[I0];
const auto M0 = math::integer_divide_ceil(M_, MPerBlock);
const auto N0 = math::integer_divide_ceil(N_, NPerBlock);
block_1d_id = block_1d_id % (M0 * N0); // swallow batch index
const auto group_size = math::integer_divide_ceil(M0 * N0, GroupNum);
auto group_id = block_1d_id % GroupNum;
auto remap_block_1d_id = group_id * group_size + block_1d_id / GroupNum;
index_t idx_N0 = remap_block_1d_id % N0;
index_t idx_M0 = remap_block_1d_id / N0;
const auto M01_adapt = (idx_M0 < M0 - M0 % M01_) ? M01_ : M0 % M01_;
index_t idx_M00 = idx_M0 / M01_;
index_t idx_M01 = idx_M0 % M01_;
index_t idx_N0_M01_local = idx_N0 + idx_M01 * N0;
/**
* idxN0
*
* |< mtx N >|
*
* NPerBlock NPerBlock NPerBlock NPerBlock
* N_0 N_1 N_2 N_3
* - |-----------|-----------|-----------|-----|-----|-
* ^ | - - 0 |/----> 2 | | | |
* | | | / | | | | | M_0 MPerBlock
* | M | /| | | | | |
* |-0---|---/-|-----|-----|-----------|-----|-----|-
* | 1 | / | | | blockid | | |
* idxM0 | | | / | V | 5 | | | M_1 MPerBlock
* | - V 1 | - 3 | | | |
* |-----------|-----------|-----------|-----|-----|-
* mtx M | | | | | |
* | | | | | | M_2 MPerBlock
* | | | | | |
* |-----------|-----------|-----------|-----|-----|-
* | | | | | |
* | | | | | | M_3 MPerBlock
* | | | | | |
* |-----------|-----------|-----------|-----|-----|-
* V | | | | | |
* - |-----------|-----------|-----------|-----|-----|- M_4 MPerBlock
* | | | | | |
* |-----------|-----------|-----------|-----|-----|-
* Example:
* assume:
* M0 = 5
* N0 = 4
* block_1d_id = 5
* M01 = 2
*
* idx_N0 = 1
* idx_M0 = 1
* M01_adapt = 2
* idx_M00 = 0
* idx_M01 = 1
* idx_N0_M01_local = 5
* output {1, 2}
*/
return make_tuple(idx_N0_M01_local % M01_adapt + idx_M00 * M01_,
idx_N0_M01_local / M01_adapt);
}
template <typename CTileIdx, typename CTileDim>
__host__ __device__ bool ValidCTileIndex(const CTileIdx& /* c_tile_idx */,
const CTileDim& /* c_tile_dim */) const
{
return true; // always valid provided that user gets grid size from CalculateGridSize()
}
private:
index_t M_;
index_t N_;
index_t M01_;
};
// keep the redundant type argument for backward compatibility
template <index_t GroupNum, index_t MPerBlock, index_t NPerBlock, typename CGridDesc_M_N>
struct BlockToCTileMap_Grouped_M00_N0_M01Adapt
: BlockToCTileMap_Grouped_M00_N0_M01Adapt<GroupNum, MPerBlock, NPerBlock, void>
{
using BlockToCTileMap_Grouped_M00_N0_M01Adapt<GroupNum, MPerBlock, NPerBlock, void>::
BlockToCTileMap_Grouped_M00_N0_M01Adapt;
};
// columns of row-vectors
// This C-tile map dynamically adjusts N01 when C-tile index is out of range
template <index_t MPerBlock, index_t NPerBlock, typename CGridDesc_M_N = void>
struct BlockToCTileMap_N00_M0_N01Adapt;
template <index_t MPerBlock, index_t NPerBlock>
struct BlockToCTileMap_N00_M0_N01Adapt<MPerBlock, NPerBlock, void>
{
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
__host__ __device__ BlockToCTileMap_N00_M0_N01Adapt() = default;
__host__ __device__ BlockToCTileMap_N00_M0_N01Adapt(const BlockToCTileMap_N00_M0_N01Adapt&) =
default;
__host__ __device__ BlockToCTileMap_N00_M0_N01Adapt(BlockToCTileMap_N00_M0_N01Adapt&&) =
default;
__host__ __device__ BlockToCTileMap_N00_M0_N01Adapt&
operator=(const BlockToCTileMap_N00_M0_N01Adapt&) = default;
__host__ __device__ BlockToCTileMap_N00_M0_N01Adapt&
operator=(BlockToCTileMap_N00_M0_N01Adapt&&) = default;
__host__ __device__ BlockToCTileMap_N00_M0_N01Adapt(index_t M, index_t N, index_t N01 = 8)
: M_(M), N_(N), N01_(N01)
{
#if 0
if(get_thread_global_1d_id()==0){
printf("Ctor called, M= %d, N= %d, N01 = %d\n", M_, N_, N01_);
}
#endif
}
template <typename CGridDesc_M_N>
__host__ __device__ BlockToCTileMap_N00_M0_N01Adapt(const CGridDesc_M_N& c_grid_desc_m_n,
index_t N01 = 8)
: BlockToCTileMap_N00_M0_N01Adapt(
c_grid_desc_m_n.GetLength(I0), c_grid_desc_m_n.GetLength(I1), N01)
{
}
__host__ static constexpr index_t CalculateGridSize(index_t M, index_t N)
{
const auto M0 = math::integer_divide_ceil(M, MPerBlock);
const auto N0 = math::integer_divide_ceil(N, NPerBlock);
return M0 * N0;
}
template <typename CGridDesc_M_N>
__host__ static constexpr index_t CalculateGridSize(const CGridDesc_M_N& c_grid_desc_m_n)
{
return CalculateGridSize(c_grid_desc_m_n.GetLength(I0), c_grid_desc_m_n.GetLength(I1));
}
template <typename CGridDesc_M_N>
__host__ bool CheckValidity(const CGridDesc_M_N& /* c_grid_desc_m_n */) const
{
return true;
}
template <typename TopIdx>
__host__ __device__ constexpr auto CalculateBottomIndex(const TopIdx& idx_top) const
{
auto block_1d_id = idx_top[I0];
const auto M0 = math::integer_divide_ceil(M_, MPerBlock);
const auto N0 = math::integer_divide_ceil(N_, NPerBlock);
block_1d_id = block_1d_id % (M0 * N0); // swallow batch index
index_t idx_M0 = block_1d_id % M0;
index_t idx_N0 = block_1d_id / M0;
const auto N01_adapt = (idx_N0 < N0 - N0 % N01_) ? N01_ : N0 % N01_;
index_t idx_N00 = idx_N0 / N01_;
index_t idx_N01 = idx_N0 % N01_;
index_t idx_M0_N01_local = idx_M0 + idx_N01 * M0;
/**
* idxN0
*
* |< mtx N >|
*
* |<---N01--->|
* - |-----------|-----------|-----------|-----|-----|-
* ^ | 0 ----------> 1 | | | |
* | | / | | | | M_0 MPerBlock
* | / | | | |
* |------/----------------|-----------|-----|-----|-
* | | | | | | |
* idxM0 | V | | | | | M_1 MPerBlock
* | 2 ----------> 3 | | | |
* |-----------|-----------|-----------|-----|-----|-
* mtx M | | blockid | | | |
* | | 5 | | | | M_2 MPerBlock
* | | | | | |
* |-----------|-----------|-----------|-----|-----|-
* | | | | | |
* | | | | | | M_3 MPerBlock
* | | | | | |
* |-----------|-----------|-----------|-----|-----|-
* V | | | | | |
* - |-----------|-----------|-----------|-----|-----|- M_4 MPerBlock
* | | | | | |
* |-----------|-----------|-----------|-----|-----|-
* NPerBlock NPerBlock NPerBlock NPerBlock
* N_0 N_1 N_2 N_3
* Example:
* assume:
* N0 = 5
* M0 = 4
* block_1d_id = 5
* N01 = 2
*
* idx_M0 = 1
* idx_N0 = 1
* N01_adapt = 2
* idx_N00 = 0
* idx_N01 = 1
* idx_M0_N01_local = 5
* output {2, 1}
*/
return make_tuple(idx_M0_N01_local / N01_adapt,
idx_M0_N01_local % N01_adapt + idx_N00 * N01_);
}
template <typename CTileIdx, typename CTileDim>
__host__ __device__ bool ValidCTileIndex(const CTileIdx& /* c_tile_idx */,
const CTileDim& /* c_tile_dim */) const
{
return true; // always valid provided that user gets grid size from CalculateGridSize()
}
private:
index_t M_;
index_t N_;
index_t N01_;
};
// 2D slices of column-vectors in 3D space
// This C-tile map dynamically adjusts M01 when C-tile index is out of range
template <index_t MPerBlock, index_t NPerBlock, typename CGridDesc_M_N>
......@@ -594,7 +895,8 @@ struct OffsettedBlockToCTileMap
{
using underlying_type = UnderlyingBlockToCTileMap;
OffsettedBlockToCTileMap(UnderlyingBlockToCTileMap block_to_ctile_map, index_t block_start)
__host__ __device__ OffsettedBlockToCTileMap(UnderlyingBlockToCTileMap block_to_ctile_map,
index_t block_start)
{
block_to_ctile_map_ = block_to_ctile_map;
block_start_ = block_start;
......
include/ck/tensor_operation/gpu/grid/gemm_layernorm/gridwise_gemm_multiple_d_welford_first_half_xdl_cshuffle.hpp
View file @ e5ebcc41
......@@ -522,6 +522,7 @@ struct GridwiseGemmMultipleDWelfordFirstHalf_xdl_cshuffle
auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_Selector<
BlockSize,
ABDataType,
ABDataType,
AccDataType,
decltype(a_block_desc_ak0_m_ak1),
decltype(b_block_desc_bk0_n_bk1),
......
include/ck/tensor_operation/gpu/grid/gridwise_batched_gemm_gemm_xdl_cshuffle_v1.hpp
View file @ e5ebcc41
......@@ -628,7 +628,8 @@ struct GridwiseBatchedGemmGemm_Xdl_CShuffle
Gemm1KPack,
false, // TransposeC
Gemm1KPack, // AMmaKStride
Gemm1KPack * XdlopsGemm<FloatAB, MPerXdl, NPerXdl, Gemm1KPack, false>{}.K0PerXdlops>{
Gemm1KPack *
XdlopsGemm<FloatAB, MPerXdl, NPerXdl, Gemm1KPack, FloatAB, false>{}.K0PerXdlops>{
// BMmaKStride
make_tuple(0, 0, 0, 0)}; // A_origin
......
include/ck/tensor_operation/gpu/grid/gridwise_batched_gemm_multiple_d_gemm_multiple_d_xdl_cshuffle_v1.hpp
View file @ e5ebcc41
......@@ -880,7 +880,12 @@ struct GridwiseBatchedGemmMultipleDGemmMultipleD_Xdl_CShuffle
Gemm1KPack,
false, // TransposeC
Gemm1KPack, // AMmaKStride
Gemm1KPack * XdlopsGemm<A0B0B1DataType, Gemm0MPerXdl, Gemm0NPerXdl, Gemm1KPack, false>{}
Gemm1KPack * XdlopsGemm<A0B0B1DataType,
Gemm0MPerXdl,
Gemm0NPerXdl,
Gemm1KPack,
A0B0B1DataType,
false>{}
.K0PerXdlops>{ // BMmaKStride
make_tuple(0, 0, 0, 0)}; // A_origin
......
include/ck/tensor_operation/gpu/grid/gridwise_batched_gemm_multiple_d_softmax_gemm_xdl_cshuffle_v1.hpp
View file @ e5ebcc41
......@@ -794,7 +794,8 @@ struct GridwiseBatchedGemmMultipleDSoftmaxGemm_Xdl_CShuffle
Gemm1KPack,
true, // TransposeC
Gemm1KPack, // AMmaKStride
Gemm1KPack * XdlopsGemm<FloatAB, MPerXdl, NPerXdl, Gemm1KPack, false>{}.K0PerXdlops>{
Gemm1KPack *
XdlopsGemm<FloatAB, MPerXdl, NPerXdl, Gemm1KPack, FloatAB, false>{}.K0PerXdlops>{
// BMmaKStride
make_tuple(0, 0, 0, 0)}; // A_origin
......
include/ck/tensor_operation/gpu/grid/gridwise_batched_gemm_softmax_gemm_xdl_cshuffle_v1.hpp
View file @ e5ebcc41
......@@ -649,7 +649,8 @@ struct GridwiseBatchedGemmSoftmaxGemm_Xdl_CShuffle
Gemm1KPack,
true, // TransposeC
Gemm1KPack, // AMmaKStride
Gemm1KPack * XdlopsGemm<FloatAB, MPerXdl, NPerXdl, Gemm1KPack, false>{}.K0PerXdlops>{
Gemm1KPack *
XdlopsGemm<FloatAB, MPerXdl, NPerXdl, Gemm1KPack, FloatAB, false>{}.K0PerXdlops>{
// BMmaKStride
make_tuple(0, 0, 0, 0)}; // A_origin
......
include/ck/tensor_operation/gpu/grid/gridwise_elementwise_1d_scale.hpp 0 → 100644
View file @ e5ebcc41
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck/tensor_description/cluster_descriptor.hpp"
#include "ck/utility/data_type.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
namespace ck {
template <typename GridwiseElementwise1dFunctor,
typename InGrid1dDescTuple,
typename OutGrid1dDescTuple,
typename InDataTypePointerTuple,
typename OutDataTypePointerTuple,
typename ElementwiseOperation,
typename UnaryOperation,
typename Scale>
__global__ void kernel_elementwise_1d(const InGrid1dDescTuple in_grid_1d_desc_tuple,
const OutGrid1dDescTuple out_grid_1d_desc_tuple,
const InDataTypePointerTuple p_in_global_tuple,
const OutDataTypePointerTuple p_out_global_tuple,
const ElementwiseOperation elementwise_op,
const UnaryOperation unary_op,
const Scale scale_op)
{
GridwiseElementwise1dFunctor::Run(in_grid_1d_desc_tuple,
out_grid_1d_desc_tuple,
p_in_global_tuple,
p_out_global_tuple,
elementwise_op,
unary_op,
scale_op);
}
template <typename InGrid1dDescTuple,
typename OutGrid1dDescTuple,
typename InDataTypePointerTuple,
typename OutDataTypePointerTuple,
typename ElementwiseOperation,
typename UnaryOperation,
typename Scale,
index_t MPerThread,
typename InScalarPerVectorSeq,
typename OutScalarPerVectorSeq>
struct GridwiseElementwise_1D
{
static constexpr index_t NumInput = InDataTypePointerTuple::Size();
static constexpr index_t NumOutput = OutDataTypePointerTuple::Size();
static_assert(NumInput == InScalarPerVectorSeq::Size() &&
NumOutput == OutScalarPerVectorSeq::Size() &&
NumInput == InGrid1dDescTuple::Size() &&
NumOutput == OutGrid1dDescTuple::Size(),
"Tuple size is inconsistent with the number of in/out!");
static constexpr auto I0 = Number<0>{};
static constexpr auto thread_buffer_desc_m =
make_naive_tensor_descriptor_packed(make_tuple(Number<MPerThread>{}));
using PassThroughOp = tensor_operation::element_wise::PassThrough;
__device__ static void Run(const InGrid1dDescTuple in_grid_1d_desc_tuple,
const OutGrid1dDescTuple out_grid_1d_desc_tuple,
const InDataTypePointerTuple p_in_global_tuple,
const OutDataTypePointerTuple p_out_global_tuple,
const ElementwiseOperation elementwise_op,
const UnaryOperation unary_op,
const Scale scale_op)
{
const index_t thread_global_id = get_thread_global_1d_id();
auto in_thread_buf_tuple = generate_tuple(
[&](auto I) {
using DataTypePointer = remove_cvref_t<decltype(InDataTypePointerTuple{}[I])>;
using DataType = remove_cv_t<remove_pointer_t<DataTypePointer>>;
return StaticBuffer<AddressSpaceEnum::Vgpr, DataType, MPerThread, true>{};
},
Number<NumInput>{});
auto out_thread_buf_tuple = generate_tuple(
[&](auto I) {
using DataTypePointer = remove_cvref_t<decltype(OutDataTypePointerTuple{}[I])>;
using DataType = remove_pointer_t<DataTypePointer>;
return StaticBuffer<AddressSpaceEnum::Vgpr, DataType, MPerThread, true>{};
},
Number<NumOutput>{});
auto in_global_buf_tuple = generate_tuple(
[&](auto I) {
static_assert(in_grid_1d_desc_tuple[I].GetNumOfDimension() == 1);
return make_dynamic_buffer<AddressSpaceEnum::Global>(
p_in_global_tuple[I], in_grid_1d_desc_tuple[I].GetElementSpaceSize());
},
Number<NumInput>{});
auto out_global_buf_tuple = generate_tuple(
[&](auto I) {
static_assert(out_grid_1d_desc_tuple[I].GetNumOfDimension() == 1);
return make_dynamic_buffer<AddressSpaceEnum::Global>(
p_out_global_tuple[I], out_grid_1d_desc_tuple[I].GetElementSpaceSize());
},
Number<NumOutput>{});
const auto thread_global_offset = make_multi_index(thread_global_id * MPerThread);
const index_t blockSize = get_block_size();
const index_t blockPerGrid = get_grid_size();
const auto M = in_grid_1d_desc_tuple[I0].GetLength(I0);
const index_t loop_step = blockPerGrid * blockSize * MPerThread;
const auto loop_step_index = make_multi_index(loop_step);
auto in_global_load_tuple = generate_tuple(
[&](auto I) {
using DataTypePointer = remove_cvref_t<decltype(InDataTypePointerTuple{}[I])>;
using DataType = remove_cv_t<remove_pointer_t<DataTypePointer>>;
return ThreadwiseTensorSliceTransfer_v2<DataType,
DataType,
decltype(in_grid_1d_desc_tuple[I]),
decltype(thread_buffer_desc_m),
Sequence<MPerThread>, // SliceLengths
Sequence<0>, // DimAccessOrder
0, // SrcVectorDim
InScalarPerVectorSeq::At(
I), // ScalarPerVector
1, // SrcScalarStrideInVector
false>{in_grid_1d_desc_tuple[I],
thread_global_offset};
},
Number<NumInput>{});
auto out_global_store_tuple = generate_tuple(
[&](auto I) {
using DataTypePointer = remove_cvref_t<decltype(OutDataTypePointerTuple{}[I])>;
using DataType = remove_pointer_t<DataTypePointer>;
return ThreadwiseTensorSliceTransfer_v1r3<DataType,
DataType,
decltype(thread_buffer_desc_m),
decltype(out_grid_1d_desc_tuple[I]),
PassThroughOp,
Sequence<MPerThread>, // SliceLengths
Sequence<0>, // DimAccessOrder
0, // SrcVectorDim
OutScalarPerVectorSeq::At(I),
InMemoryDataOperationEnum::Set,
1,
false>(
out_grid_1d_desc_tuple[I], thread_global_offset, PassThroughOp{});
},
Number<NumOutput>{});
index_t num_iter = M / (loop_step);
do
{
static_for<0, NumInput, 1>{}([&](auto I) {
in_global_load_tuple(I).Run(in_grid_1d_desc_tuple[I],
in_global_buf_tuple[I],
thread_buffer_desc_m,
make_tuple(I0),
in_thread_buf_tuple(I));
in_global_load_tuple(I).MoveSrcSliceWindow(in_grid_1d_desc_tuple[I],
loop_step_index);
});
static_for<0, MPerThread, 1>{}([&](auto iM) {
// get reference to in data
auto uop_data_refs = generate_tie(
// return type should be lvalue
[&](auto I) -> auto& { return in_thread_buf_tuple(I)(iM); },
Number<NumInput>{});
// get reference to dst data
auto out_data_refs = generate_tie(
// return type should be lvalue
[&](auto I) -> auto& { return out_thread_buf_tuple(I)(iM); },
Number<NumOutput>{});
unpack2(unary_op, uop_data_refs, uop_data_refs);
auto sop_in_data_refs = generate_tie(
// return type should be lvalue
[&](auto I) -> auto& { return in_thread_buf_tuple(I)(iM); },
Number<NumInput>{});
auto sop_out_data_refs = generate_tie(
// return type should be lvalue
[&](auto I) -> auto& { return in_thread_buf_tuple(I)(iM); },
Number<NumInput>{});
unpack2(scale_op, sop_out_data_refs, sop_in_data_refs);
const auto in_data_refs = generate_tie(
// return type should be lvalue
[&](auto I) -> const auto& { return in_thread_buf_tuple(I)(iM); },
Number<NumInput>{});
unpack2(elementwise_op, out_data_refs, in_data_refs);
});
static_for<0, NumOutput, 1>{}([&](auto I) {
out_global_store_tuple(I).Run(thread_buffer_desc_m,
make_tuple(I0),
out_thread_buf_tuple[I],
out_grid_1d_desc_tuple[I],
out_global_buf_tuple(I));
out_global_store_tuple(I).MoveDstSliceWindow(out_grid_1d_desc_tuple[I],
loop_step_index);
});
} while(--num_iter);
}
};
} // namespace ck
include/ck/tensor_operation/gpu/grid/gridwise_elementwise_3d.hpp 0 → 100644
View file @ e5ebcc41
// SPDX-License-Identifier: MIT
// // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
//
#pragma once
#include "ck/tensor_description/cluster_descriptor.hpp"
#include "ck/utility/data_type.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
namespace ck {
template <typename GridwiseElementwise3dFunctor,
typename InGrid3dDescTuple,
typename OutGrid3dDescTuple,
typename InDataTypePointerTuple,
typename OutDataTypePointerTuple,
typename ElementwiseOperation>
__global__ void kernel_elementwise_3d(const InGrid3dDescTuple in_grid_3d_desc_tuple,
const OutGrid3dDescTuple out_grid_3d_desc_tuple,
const InDataTypePointerTuple p_in_global_tuple,
const OutDataTypePointerTuple p_out_global_tuple,
const ElementwiseOperation elementwise_op,
const index_t num_threads_m,
const index_t num_threads_n,
const index_t num_threads_k)
{
GridwiseElementwise3dFunctor::Run(in_grid_3d_desc_tuple,
out_grid_3d_desc_tuple,
p_in_global_tuple,
p_out_global_tuple,
elementwise_op,
num_threads_m,
num_threads_n,
num_threads_k);
}
template <typename InGrid3dDescTuple,
typename OutGrid3dDescTuple,
typename InDataTypePointerTuple,
typename OutDataTypePointerTuple,
typename ElementwiseOperation,
index_t MPerThread,
index_t NPerThread,
index_t KPerThread,
typename InScalarPerVectorSeq,
typename OutScalarPerVectorSeq>
struct GridwiseElementwise_3D
{
static constexpr index_t NumInput = InDataTypePointerTuple::Size();
static constexpr index_t NumOutput = OutDataTypePointerTuple::Size();
static_assert(NumInput == InScalarPerVectorSeq::Size() &&
NumOutput == OutScalarPerVectorSeq::Size() &&
NumInput == InGrid3dDescTuple::Size() &&
NumOutput == OutGrid3dDescTuple::Size(),
"Tuple size is inconsistent with the number of in/out!");
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
static constexpr auto I2 = Number<2>{};
static constexpr auto thread_buffer_desc_mnk = make_naive_tensor_descriptor_packed(
make_tuple(Number<MPerThread>{}, Number<NPerThread>{}, Number<KPerThread>{}));
using PassThroughOp = tensor_operation::element_wise::PassThrough;
__device__ static void Run(const InGrid3dDescTuple in_grid_3d_desc_tuple,
const OutGrid3dDescTuple out_grid_3d_desc_tuple,
const InDataTypePointerTuple p_in_global_tuple,
const OutDataTypePointerTuple p_out_global_tuple,
const ElementwiseOperation elementwise_op,
const index_t num_threads_m,
const index_t num_threads_n,
const index_t num_threads_k)
{
auto in_thread_buf_tuple = generate_tuple(
[&](auto I) {
using DataTypePointer = remove_cvref_t<decltype(InDataTypePointerTuple{}[I])>;
using DataType = remove_cv_t<remove_pointer_t<DataTypePointer>>;
return StaticBuffer<AddressSpaceEnum::Vgpr,
DataType,
MPerThread * NPerThread * KPerThread,
true>{};
},
Number<NumInput>{});
auto out_thread_buf_tuple = generate_tuple(
[&](auto I) {
using DataTypePointer = remove_cvref_t<decltype(OutDataTypePointerTuple{}[I])>;
using DataType = remove_pointer_t<DataTypePointer>;
return StaticBuffer<AddressSpaceEnum::Vgpr,
DataType,
MPerThread * NPerThread * KPerThread,
true>{};
},
Number<NumOutput>{});
auto in_global_buf_tuple = generate_tuple(
[&](auto I) {
return make_dynamic_buffer<AddressSpaceEnum::Global>(
p_in_global_tuple[I], in_grid_3d_desc_tuple[I].GetElementSpaceSize());
},
Number<NumInput>{});
auto out_global_buf_tuple = generate_tuple(
[&](auto I) {
return make_dynamic_buffer<AddressSpaceEnum::Global>(
p_out_global_tuple[I], out_grid_3d_desc_tuple[I].GetElementSpaceSize());
},
Number<NumOutput>{});
const auto M = in_grid_3d_desc_tuple[I0].GetLength(I0);
const auto N = in_grid_3d_desc_tuple[I0].GetLength(I1);
const auto K = in_grid_3d_desc_tuple[I0].GetLength(I2);
const index_t loop_step_m = num_threads_m * MPerThread;
const index_t loop_step_n = num_threads_n * NPerThread;
const index_t loop_step_k = num_threads_k * KPerThread;
const index_t thread_1d_id = get_thread_global_1d_id();
const index_t tid_m = thread_1d_id / (num_threads_n * num_threads_k);
const index_t tid_nk = thread_1d_id % (num_threads_n * num_threads_k);
const index_t tid_n = tid_nk / num_threads_k;
const index_t tid_k = tid_nk % num_threads_k;
const auto thread_global_offset =
make_multi_index(tid_m * MPerThread, tid_n * NPerThread, tid_k * KPerThread);
auto in_global_load_tuple = generate_tuple(
[&](auto I) {
using DataTypePointer = remove_cvref_t<decltype(InDataTypePointerTuple{}[I])>;
using DataType = remove_cv_t<remove_pointer_t<DataTypePointer>>;
return ThreadwiseTensorSliceTransfer_v2<
DataType,
DataType,
decltype(in_grid_3d_desc_tuple[I]),
decltype(thread_buffer_desc_mnk),
Sequence<MPerThread, NPerThread, KPerThread>, // SliceLengths
Sequence<0, 1, 2>, // DimAccessOrder
01, // SrcVectorDim
InScalarPerVectorSeq::At(I), // InScalarPerVectorSeq::At(I), //
// ScalarPerVector
1, // SrcScalarStrideInVector
true>{in_grid_3d_desc_tuple[I], thread_global_offset};
},
Number<NumInput>{});
auto out_global_store_tuple = generate_tuple(
[&](auto I) {
using DataTypePointer = remove_cvref_t<decltype(OutDataTypePointerTuple{}[I])>;
using DataType = remove_pointer_t<DataTypePointer>;
return ThreadwiseTensorSliceTransfer_v1r3<
DataType,
DataType,
decltype(thread_buffer_desc_mnk),
decltype(out_grid_3d_desc_tuple[I]),
PassThroughOp,
Sequence<MPerThread, NPerThread, KPerThread>, // SliceLengths
Sequence<0, 1, 2>, // DimAccessOrder
2, // SrcVectorDim
OutScalarPerVectorSeq::At(I), // OutScalarPerVectorSeq::At(I),
InMemoryDataOperationEnum::Set,
1,
true>(out_grid_3d_desc_tuple[I], thread_global_offset, PassThroughOp{});
},
Number<NumOutput>{});
index_t num_iter_m = M / (loop_step_m);
do
{
index_t num_iter_n = N / (loop_step_n);
do
{
index_t num_iter_k = K / (loop_step_k);
do
{
static_for<0, NumInput, 1>{}([&](auto I) {
in_global_load_tuple(I).Run(in_grid_3d_desc_tuple[I],
in_global_buf_tuple[I],
thread_buffer_desc_mnk,
make_tuple(I0, I0, I0),
in_thread_buf_tuple(I));
in_global_load_tuple(I).MoveSrcSliceWindow(
in_grid_3d_desc_tuple[I], make_multi_index(0, 0, loop_step_k));
});
static_for<0, MPerThread, 1>{}([&](auto iM) {
static_for<0, NPerThread, 1>{}([&](auto iN) {
static_for<0, KPerThread, 1>{}([&](auto iK) {
constexpr auto offset =
thread_buffer_desc_mnk.CalculateOffset(make_tuple(iM, iN, iK));
// get reference to in data
const auto in_data_refs = generate_tie(
// return type should be lvalue
[&](auto I) -> const auto& {
return in_thread_buf_tuple(I)(Number<offset>{});
},
Number<NumInput>{});
// get referenec to dst data
auto out_data_refs = generate_tie(
// return type should be lvalue
[&](auto I) -> auto& {
return out_thread_buf_tuple(I)(Number<offset>{});
},
Number<NumOutput>{});
unpack2(elementwise_op, out_data_refs, in_data_refs);
});
});
});
static_for<0, NumOutput, 1>{}([&](auto I) {
out_global_store_tuple(I).Run(thread_buffer_desc_mnk,
make_tuple(I0, I0, I0),
out_thread_buf_tuple[I],
out_grid_3d_desc_tuple[I],
out_global_buf_tuple(I));
out_global_store_tuple(I).MoveDstSliceWindow(
out_grid_3d_desc_tuple[I], make_multi_index(0, 0, loop_step_k));
});
} while(--num_iter_k);
static_for<0, NumInput, 1>{}([&](auto I) {
in_global_load_tuple(I).MoveSrcSliceWindow(
in_grid_3d_desc_tuple[I],
make_multi_index(0, loop_step_n, -(K / loop_step_k) * loop_step_k));
});
static_for<0, NumOutput, 1>{}([&](auto I) {
out_global_store_tuple(I).MoveDstSliceWindow(
out_grid_3d_desc_tuple[I],
make_multi_index(0, loop_step_n, -(K / loop_step_k) * loop_step_k));
});
} while(--num_iter_n);
static_for<0, NumInput, 1>{}([&](auto I) {
in_global_load_tuple(I).MoveSrcSliceWindow(
in_grid_3d_desc_tuple[I],
make_multi_index(loop_step_m,
-(N / loop_step_n) * loop_step_n,
-(K / loop_step_k) * loop_step_k));
});
static_for<0, NumOutput, 1>{}([&](auto I) {
out_global_store_tuple(I).MoveDstSliceWindow(
out_grid_3d_desc_tuple[I],
make_multi_index(loop_step_m,
-(N / loop_step_n) * loop_step_n,
-(K / loop_step_k) * loop_step_k));
});
} while(--num_iter_m);
}
};
} // namespace ck
include/ck/tensor_operation/gpu/grid/gridwise_elementwise_layernorm_welford_variance.hpp
View file @ e5ebcc41
......@@ -119,7 +119,7 @@ struct GridwiseElementwiseLayernormWelfordVariance_mk_to_mk
index_t num_k_block_tile_iteration,
AccDataType epsilon,
const InDataTypePointerTuple p_in_global_tuple,
XDataType* const __restrict__ p_x_lds,
XDataType* const __restrict__ p_x_lds_,
const GammaDataType* const __restrict__ p_gamma_global,
const BetaDataType* const __restrict__ p_beta_global,
YDataType* const __restrict__ p_y_global,
......@@ -149,7 +149,7 @@ struct GridwiseElementwiseLayernormWelfordVariance_mk_to_mk
p_y_global, y_grid_desc_m_k.GetElementSpaceSize());
auto x_lds_val_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
p_x_lds, x_grid_desc_m_k.GetElementSpaceSize() / grid_size);
p_x_lds_, x_grid_desc_m_k.GetElementSpaceSize() / grid_size);
auto in_thread_buf_tuple = generate_tuple(
[&](auto) {
......
include/ck/tensor_operation/gpu/grid/gridwise_gemm_bias_add_reduce_xdl_cshuffle_v1.hpp
View file @ e5ebcc41
......@@ -67,7 +67,7 @@ __global__ void
const Block2CTileMap block_2_ctile_map)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__))
defined(__gfx94__))
__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,
......@@ -504,6 +504,7 @@ struct GridwiseGemmBiasAddReduce_k0mk1_k0nk1_mn_xdl_cshuffle_v1
auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_Selector<
BlockSize,
FloatAB,
FloatAB,
FloatGemmAcc,
decltype(a_block_desc_ak0_m_ak1),
decltype(b_block_desc_bk0_n_bk1),
......
include/ck/tensor_operation/gpu/grid/gridwise_gemm_dl_v1r3.hpp
View file @ e5ebcc41
......@@ -7,11 +7,9 @@
#include "ck/tensor_description/multi_index_transform_helper.hpp"
#include "ck/tensor_description/tensor_descriptor.hpp"
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
#include "ck/tensor_operation/gpu/device/gemm_dl_algorithm.hpp"
#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v1.hpp"
#include "ck/tensor_operation/gpu/block/blockwise_gemm_dl_v2r3.hpp"
#include "ck/tensor_operation/gpu/block/blockwise_gemm_dl_dpp8.hpp"
#include "ck/tensor_operation/gpu/block/blockwise_tensor_slice_transfer_v5r1.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_set.hpp"
......@@ -19,8 +17,6 @@
namespace ck {
using GemmDlAlgorithm = tensor_operation::device::GemmDlAlgorithm;
template <typename GridwiseGemm,
typename FloatAB,
typename FloatC,
......@@ -29,8 +25,7 @@ template <typename GridwiseGemm,
typename CGridDesc_M0_M10_M11_N0_N10_N11,
typename Block2CTileMap,
bool HasMainKBlockLoop,
bool HasDoubleTailKBlockLoop,
GemmDlAlgorithm GemmDlAlg = GemmDlAlgorithm::Default>
bool HasDoubleTailKBlockLoop>
__global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
......@@ -43,13 +38,6 @@ __global__ void
const CGridDesc_M0_M10_M11_N0_N10_N11 c_grid_desc_m0_m10_m11_n0_n10_n11,
const Block2CTileMap block_2_ctile_map)
{
// DPP8 is currently only supported on gfx1030
#if !defined(__gfx1030__)
if(GemmDlAlg == GemmDlAlgorithm::Dpp8)
{
return;
}
#endif
constexpr index_t shared_block_size =
GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(FloatAB);
......@@ -100,8 +88,7 @@ template <index_t BlockSize,
typename BBlockTransferDstVectorTensorLengths_K0_N0_N1_K1,
typename CThreadTransferSrcDstAccessOrder,
index_t CThreadTransferSrcDstVectorDim,
index_t CThreadTransferDstScalarPerVector,
GemmDlAlgorithm GemmDlAlg = GemmDlAlgorithm::Default>
index_t CThreadTransferDstScalarPerVector>
struct GridwiseGemmDl_km_kn_mn_v1r3
{
static constexpr auto I0 = Number<0>{};
......@@ -257,45 +244,6 @@ struct GridwiseGemmDl_km_kn_mn_v1r3
c_grid_desc_m_n);
}
template <typename ABlockDesc_BK0_BM_BK1, typename BBlockDesc_BK0_BN_BK1>
__host__ __device__ static constexpr auto GetBlockwiseGemm()
{
if constexpr(GemmDlAlg == GemmDlAlgorithm::Dpp8)
{
return BlockwiseGemmDlDpp8_A_BK0_BM_BK1_B_BK0_BN_BK1_C_BM0_BM1_BN0_BN1_loop_BM0_BN0<
BlockSize,
FloatAB,
FloatAB,
FloatAcc,
ABlockDesc_BK0_BM_BK1,
BBlockDesc_BK0_BN_BK1,
M1PerThreadM111,
N1PerThreadN111,
KPerThread,
M11N11ThreadClusterM110Xs,
M11N11ThreadClusterN110Xs,
M1PerThreadM111,
N1PerThreadN111>{};
}
else
{
return BlockwiseGemmDl_A_BK0_BM_BK1_B_BK0_BN_BK1_C_BM0_BM1_BN0_BN1_pipeline_BM0_2_BN0_2<
BlockSize,
FloatAB,
FloatAB,
FloatAcc,
ABlockDesc_BK0_BM_BK1,
BBlockDesc_BK0_BN_BK1,
M1PerThreadM111,
N1PerThreadN111,
KPerThread,
M11N11ThreadClusterM110Xs,
M11N11ThreadClusterN110Xs,
M1PerThreadM111,
N1PerThreadN111>{};
}
}
using AGridDesc_K0_M0_M1_K1 = decltype(MakeAGridDescriptor_K0_M0_M1_K1(AGridDesc_K0_M_K1{}));
using BGridDesc_K0_N0_N1_K1 = decltype(MakeBGridDescriptor_K0_N0_N1_K1(BGridDesc_K0_N_K1{}));
using CGridDesc_M0_M10_M11_N0_N10_N11 =
......@@ -424,7 +372,20 @@ struct GridwiseGemmDl_km_kn_mn_v1r3
// c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
// register
const auto blockwise_gemm =
GetBlockwiseGemm<decltype(a_k0_m_k1_block_desc), decltype(b_k0_n_k1_block_desc)>();
BlockwiseGemmDl_A_BK0_BM_BK1_B_BK0_BN_BK1_C_BM0_BM1_BN0_BN1_pipeline_BM0_2_BN0_2<
BlockSize,
FloatAB,
FloatAB,
FloatAcc,
decltype(a_k0_m_k1_block_desc),
decltype(b_k0_n_k1_block_desc),
M1PerThreadM111,
N1PerThreadN111,
KPerThread,
M11N11ThreadClusterM110Xs,
M11N11ThreadClusterN110Xs,
M1PerThreadM111,
N1PerThreadN111>{};
constexpr auto c_m10_m11_n10_n11_thread_tensor_lengths =
decltype(blockwise_gemm)::GetCThreadTensorLengths_BM0_BM1_BN0_BN1();
......
include/ck/tensor_operation/gpu/grid/gridwise_gemm_dpp.hpp 0 → 100644
View file @ e5ebcc41
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck/utility/common_header.hpp"
#include "ck/tensor_description/multi_index_transform_helper.hpp"
#include "ck/tensor_description/tensor_descriptor.hpp"
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_selector.hpp"
#include "ck/tensor_operation/gpu/block/blockwise_gemm_dpp.hpp"
#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
namespace ck {
template <typename GridwiseGemm, bool HasMainKBlockLoop>
__global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
#if CK_USE_WAVES_PER_EU
__attribute__((amdgpu_waves_per_eu(CK_MIN_WAVES_PER_EU, CK_MAX_WAVES_PER_EU)))
#endif
kernel_gemm_dpp(const typename GridwiseGemm::Argument karg)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx103__) || defined(__gfx11__))
__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
const auto a_grid_desc_ak0_m_ak1 = amd_wave_read_first_lane(
GridwiseGemm::MakeAGridDescriptor_AK0_M_AK1(karg.M, karg.K, karg.AK0, karg.StrideA));
const auto b_grid_desc_bk0_n_bk1 = amd_wave_read_first_lane(
GridwiseGemm::MakeBGridDescriptor_BK0_N_BK1(karg.K, karg.N, karg.BK0, karg.StrideB));
const auto c_grid_desc_m_n = amd_wave_read_first_lane(
GridwiseGemm::MakeCGridDescriptor_M_N(karg.M, karg.N, karg.StrideC));
GridwiseGemm::template Run<HasMainKBlockLoop>(karg.p_a_grid,
karg.p_b_grid,
karg.p_c_grid,
p_shared,
a_grid_desc_ak0_m_ak1,
b_grid_desc_bk0_n_bk1,
c_grid_desc_m_n);
#else
ignore = karg;
#endif
}
template <index_t BlockSize,
typename ABDataType,
typename AccDataType,
typename CDataType,
InMemoryDataOperationEnum CGlobalMemoryDataOperation,
typename ALayout,
typename BLayout,
typename CLayout,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CElementwiseOperation,
tensor_operation::device::GemmSpecialization GemmSpec,
index_t MPerBlock,
index_t NPerBlock,
index_t KPerBlock,
index_t MPerDpp,
index_t NPerDpp,
index_t AK1Value,
index_t BK1Value,
index_t MDppPerWave,
index_t NDppPerWave,
typename ABlockTransferThreadClusterLengths_K0_M_K1,
typename ABlockTransferThreadClusterArrangeOrder,
typename ABlockTransferSrcAccessOrder,
index_t ABlockTransferSrcVectorDim,
index_t ABlockTransferSrcScalarPerVector,
index_t ABlockTransferDstScalarPerVector_K1,
bool AThreadTransferSrcResetCoordinateAfterRun,
bool ABlockLdsExtraM,
typename BBlockTransferThreadClusterLengths_K0_N_K1,
typename BBlockTransferThreadClusterArrangeOrder,
typename BBlockTransferSrcAccessOrder,
index_t BBlockTransferSrcVectorDim,
index_t BBlockTransferSrcScalarPerVector,
index_t BBlockTransferDstScalarPerVector_K1,
bool BThreadTransferSrcResetCoordinateAfterRun,
bool BBlockLdsExtraN,
typename CThreadTransferSrcDstAccessOrder,
index_t CThreadTransferSrcDstVectorDim,
index_t CThreadTransferDstScalarPerVector,
index_t NumGemmKPrefetchStage = 1,
PipelineVersion PipelineVer = PipelineVersion::v1>
struct GridwiseGemm_ak0mak1_bk0nbk1_mn_dpp
{
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
static constexpr auto I2 = Number<2>{};
static constexpr auto I3 = Number<3>{};
static constexpr auto I4 = Number<4>{};
static constexpr auto I5 = Number<5>{};
static constexpr auto AK1 = Number<AK1Value>{};
static constexpr auto BK1 = Number<BK1Value>{};
static constexpr auto AK0PerBlock = Number<KPerBlock / AK1Value>{};
static constexpr auto BK0PerBlock = Number<KPerBlock / BK1Value>{};
static constexpr auto max_lds_align = math::lcm(AK1, BK1);
using ThisThreadBlock = ThisThreadBlock<BlockSize>;
// return block_id to C matrix tile idx (m0, n0) mapping
using Block2CTileMap = BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, NPerBlock>;
__host__ static auto CalculateGridSize(index_t M, index_t N)
{
return std::make_tuple(Block2CTileMap::CalculateGridSize(M, N), 1, 1);
}
__host__ static auto CalculateMPadded(index_t M)
{
return math::integer_divide_ceil(M, MPerBlock) * MPerBlock;
}
__host__ static auto CalculateNPadded(index_t N)
{
return math::integer_divide_ceil(N, NPerBlock) * NPerBlock;
}
__host__ static auto CalculateAK0(index_t K) { return math::integer_divide_floor(K, AK1Value); }
__host__ static auto CalculateBK0(index_t K) { return math::integer_divide_floor(K, BK1Value); }
// Argument
struct Problem
{
__host__ Problem(index_t M_,
index_t N_,
index_t K_,
index_t StrideA_,
index_t StrideB_,
index_t StrideC_)
: M{M_},
N{N_},
K{K_},
StrideA{StrideA_},
StrideB{StrideB_},
StrideC{StrideC_},
MPadded{CalculateMPadded(M_)},
NPadded{CalculateNPadded(N_)},
AK0{CalculateAK0(K)},
BK0{CalculateBK0(K)}
{
}
__host__ void Print() const
{
std::cout << "problem {"
<< "M:" << M << ", "
<< "N:" << N << ", "
<< "K:" << K << ", "
<< "SA:" << StrideA << ", "
<< "SB:" << StrideB << ", "
<< "SC:" << StrideC << ", "
<< "MP:" << MPadded << ", "
<< "NP:" << NPadded << ", "
<< "AK0:" << AK0 << ", "
<< "BK0:" << BK0 << "}" << std::endl;
}
index_t M;
index_t N;
index_t K;
index_t StrideA;
index_t StrideB;
index_t StrideC;
index_t MPadded;
index_t NPadded;
index_t AK0;
index_t BK0;
};
// Argument
struct Argument : public Problem, public tensor_operation::device::BaseArgument
{
__host__ Argument(const ABDataType* p_a_grid_,
const ABDataType* p_b_grid_,
CDataType* p_c_grid_,
index_t M_,
index_t N_,
index_t K_,
index_t StrideA_,
index_t StrideB_,
index_t StrideC_)
: Problem{M_, N_, K_, StrideA_, StrideB_, StrideC_},
p_a_grid{p_a_grid_},
p_b_grid{p_b_grid_},
p_c_grid{p_c_grid_}
{
}
const ABDataType* p_a_grid;
const ABDataType* p_b_grid;
CDataType* p_c_grid;
};
using GridwiseGemmPipe = remove_cvref_t<
decltype(GridwiseGemmPipeline_Selector<PipelineVer, NumGemmKPrefetchStage>())>;
__host__ __device__ static constexpr auto GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1()
{
// A matrix in LDS memory, dst of blockwise copy
constexpr auto a_block_desc_ak0_m_ak1 = [&]() {
if constexpr(ABlockLdsExtraM)
{
return make_naive_tensor_descriptor(
make_tuple(Number<AK0PerBlock>{}, Number<MPerBlock>{}, AK1),
make_tuple(Number<MPerBlock + 1>{} * AK1, AK1, I1));
}
else
{
return make_naive_tensor_descriptor_aligned(
make_tuple(Number<AK0PerBlock>{}, Number<MPerBlock>{}, AK1), max_lds_align);
}
}();
return a_block_desc_ak0_m_ak1;
}
__host__ __device__ static constexpr auto GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1()
{
// B matrix in LDS memory, dst of blockwise copy
constexpr auto b_block_desc_bk0_n_bk1 = [&]() {
if constexpr(BBlockLdsExtraN)
{
return make_naive_tensor_descriptor(
make_tuple(Number<BK0PerBlock>{}, Number<NPerBlock>{}, BK1),
make_tuple(Number<NPerBlock + 1>{} * BK1, BK1, I1));
}
else
{
return make_naive_tensor_descriptor_aligned(
make_tuple(Number<BK0PerBlock>{}, Number<NPerBlock>{}, BK1), max_lds_align);
}
}();
return b_block_desc_bk0_n_bk1;
}
__host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
{
// LDS allocation for A and B: be careful of alignment
constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
constexpr auto b_block_space_size_aligned = math::integer_least_multiple(
b_block_desc_bk0_n_bk1.GetElementSpaceSize(), max_lds_align);
return (a_block_space_size_aligned + b_block_space_size_aligned) * sizeof(ABDataType);
}
__host__ static constexpr bool CheckValidity(const Problem& problem)
{
static_assert(is_known_at_compile_time<remove_cv_t<decltype(AK1)>>::value,
"Wrong! AK1 must be known at the time of compilation.");
static_assert(is_known_at_compile_time<remove_cv_t<decltype(BK1)>>::value,
"Wrong! BK1 must be known at the time of compilation.");
static_assert(
MPerBlock % (MPerDpp * MDppPerWave) == 0,
"Invalid tuning parameters! MPerBlock must be divisible by MPerDpp * MDppPerWave.");
static_assert(
NPerBlock % (NPerDpp * NDppPerWave) == 0,
"Invalid tuning parameters! NPerBlock must be divisible by NPerDpp * NDppPerWave.");
static_assert(
KPerBlock % AK1Value == 0 && KPerBlock % BK1Value == 0,
"Invalid tuning parameters! KPerBlock must be divisible by both AK1 and BK1.");
static_assert(AK1Value % ABlockTransferDstScalarPerVector_K1 == 0,
"Invalid tuning parameters! AK1Value must be divisible by "
"ABlockTransferDstScalarPerVector_K1");
static_assert(BK1Value % BBlockTransferDstScalarPerVector_K1 == 0,
"Invalid tuning parameters! BK1Value must be divisible by "
"BBlockTransferDstScalarPerVector_K1");
if constexpr(!(GemmSpec == tensor_operation::device::GemmSpecialization::MPadding ||
GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding ||
GemmSpec == tensor_operation::device::GemmSpecialization::MKPadding ||
GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding))
{
if(!(problem.M % MPerBlock == 0))
{
return false;
}
}
if constexpr(!(GemmSpec == tensor_operation::device::GemmSpecialization::NPadding ||
GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding ||
GemmSpec == tensor_operation::device::GemmSpecialization::NKPadding ||
GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding))
{
if(!(problem.N % NPerBlock == 0))
{
return false;
}
}
if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)
{
if(problem.K % ABlockTransferSrcScalarPerVector != 0)
{
return false;
}
}
else
{
if(problem.M % ABlockTransferSrcScalarPerVector != 0)
{
return false;
}
}
if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
{
if(problem.N % BBlockTransferSrcScalarPerVector != 0)
{
return false;
}
}
else
{
if(problem.K % BBlockTransferSrcScalarPerVector != 0)
{
return false;
}
}
if(problem.K % KPerBlock != 0)
{
return false;
}
// check gridwise gemm pipeline
const auto num_k_loop = problem.K / KPerBlock;
if(!GridwiseGemmPipe::IsSupported(num_k_loop))
{
return false;
}
return true;
}
__host__ static constexpr bool CalculateHasMainKBlockLoop(index_t K)
{
const auto num_loop = K / KPerBlock;
return GridwiseGemmPipe::CalculateHasMainLoop(num_loop);
}
template <typename CGridDesc>
__host__ __device__ static constexpr auto
MakeCGridDescriptor_M0_N0_M1_N1_M2_N2(const CGridDesc& c_grid_desc_m_n)
{
constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
constexpr index_t KPack = math::max(
math::lcm(AK1, BK1), DppSelector<ABDataType, MPerDpp, NPerDpp>::selected_dpp.k_per_dpp);
using BlockwiseGemm =
BlockwiseGemmDpp_ak0mak1_bk0nbk1_m0n0m1n1m2n2<BlockSize,
ABDataType,
AccDataType,
decltype(a_block_desc_ak0_m_ak1),
decltype(b_block_desc_bk0_n_bk1),
MPerDpp,
NPerDpp,
MDppPerWave,
NDppPerWave,
KPack>;
return BlockwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_N2(c_grid_desc_m_n);
}
static constexpr auto matrix_padder =
ck::tensor_operation::device::MatrixPadder<GemmSpec, index_t, index_t, index_t>{
MPerBlock, NPerBlock, KPerBlock};
__device__ static auto
MakeAGridDescriptor_AK0_M_AK1(index_t M, index_t K, index_t AK0, index_t StrideA)
{
const auto a_grid_desc_mraw_kraw = [&]() {
if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)
{
return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(StrideA, I1));
}
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ALayout>::value)
{
return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(I1, StrideA));
}
}();
const auto a_grid_desc_m_k = matrix_padder.PadADescriptor_M_K(a_grid_desc_mraw_kraw);
return transform_tensor_descriptor(
a_grid_desc_m_k,
make_tuple(make_unmerge_transform(make_tuple(AK0, AK1Value)),
make_pass_through_transform(M)),
make_tuple(Sequence<1>{}, Sequence<0>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
}
__device__ static auto
MakeBGridDescriptor_BK0_N_BK1(index_t K, index_t N, index_t BK0, index_t StrideB)
{
const auto b_grid_desc_nraw_kraw = [&]() {
if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
{
return make_naive_tensor_descriptor(make_tuple(N, K), make_tuple(I1, StrideB));
}
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
{
return make_naive_tensor_descriptor(make_tuple(N, K), make_tuple(StrideB, I1));
}
}();
const auto b_grid_desc_n_k = matrix_padder.PadBDescriptor_N_K(b_grid_desc_nraw_kraw);
return transform_tensor_descriptor(
b_grid_desc_n_k,
make_tuple(make_pass_through_transform(N),
make_unmerge_transform(make_tuple(BK0, BK1Value))),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<1>{}, Sequence<0, 2>{}));
}
__device__ static auto MakeCGridDescriptor_M_N(index_t M, index_t N, index_t StrideC)
{
const auto c_grid_desc_mraw_nraw = [&]() {
if constexpr(is_same<tensor_layout::gemm::RowMajor, CLayout>::value)
{
return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(StrideC, I1));
}
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, CLayout>::value)
{
return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(I1, StrideC));
}
}();
return matrix_padder.PadCDescriptor_M_N(c_grid_desc_mraw_nraw);
}
template <bool HasMainKBlockLoop,
typename AGridDesc_AK0_M_AK1,
typename BGridDesc_BK0_N_BK1,
typename CGridDesc_M_N>
__device__ static void Run(const ABDataType* __restrict__ p_a_grid,
const ABDataType* __restrict__ p_b_grid,
CDataType* __restrict__ p_c_grid,
void* __restrict__ p_shared,
const AGridDesc_AK0_M_AK1& a_grid_desc_ak0_m_ak1,
const BGridDesc_BK0_N_BK1& b_grid_desc_bk0_n_bk1,
const CGridDesc_M_N& c_grid_desc_m_n)
{
const auto c_grid_desc_m0_n0_m1_n1_m2_n2 =
MakeCGridDescriptor_M0_N0_M1_N1_M2_N2(c_grid_desc_m_n);
const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_a_grid, a_grid_desc_ak0_m_ak1.GetElementSpaceSize());
const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_b_grid, b_grid_desc_bk0_n_bk1.GetElementSpaceSize());
auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_c_grid, c_grid_desc_m0_n0_m1_n1_m2_n2.GetElementSpaceSize());
const AElementwiseOperation a_element_op{};
const BElementwiseOperation b_element_op{};
const CElementwiseOperation c_element_op{};
const auto block_2_ctile_map =
Block2CTileMap{c_grid_desc_m_n.GetLength(I0), c_grid_desc_m_n.GetLength(I1)};
// divide block work by [M, N]
const auto block_work_idx =
block_2_ctile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
if(!block_2_ctile_map.ValidCTileIndex(
block_work_idx,
make_tuple(c_grid_desc_m0_n0_m1_n1_m2_n2.GetLength(I0),
c_grid_desc_m0_n0_m1_n1_m2_n2.GetLength(I1))))
{
return;
}
// HACK: this force m/n_block_data_idx_on_grid into SGPR
const index_t m_block_data_idx_on_grid =
__builtin_amdgcn_readfirstlane(block_work_idx[I0] * MPerBlock);
const index_t n_block_data_idx_on_grid =
__builtin_amdgcn_readfirstlane(block_work_idx[I1] * NPerBlock);
// A matrix in LDS memory, dst of blockwise copy
constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
// B matrix in LDS memory, dst of blockwise copy
constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
auto a_blockwise_copy =
ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
AElementwiseOperation,
ck::tensor_operation::element_wise::PassThrough,
InMemoryDataOperationEnum::Set,
Sequence<AK0PerBlock, MPerBlock, AK1>,
ABlockTransferThreadClusterLengths_K0_M_K1,
ABlockTransferThreadClusterArrangeOrder,
ABDataType,
ABDataType,
decltype(a_grid_desc_ak0_m_ak1),
decltype(a_block_desc_ak0_m_ak1),
ABlockTransferSrcAccessOrder,
Sequence<1, 0, 2>,
ABlockTransferSrcVectorDim,
2,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_K1,
1,
1,
AThreadTransferSrcResetCoordinateAfterRun,
true,
NumGemmKPrefetchStage>(
a_grid_desc_ak0_m_ak1,
make_multi_index(0, m_block_data_idx_on_grid, 0),
a_element_op,
a_block_desc_ak0_m_ak1,
make_multi_index(0, 0, 0),
ck::tensor_operation::element_wise::PassThrough{});
auto b_blockwise_copy =
ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
BElementwiseOperation,
ck::tensor_operation::element_wise::PassThrough,
InMemoryDataOperationEnum::Set,
Sequence<BK0PerBlock, NPerBlock, BK1>,
BBlockTransferThreadClusterLengths_K0_N_K1,
BBlockTransferThreadClusterArrangeOrder,
ABDataType,
ABDataType,
decltype(b_grid_desc_bk0_n_bk1),
decltype(b_block_desc_bk0_n_bk1),
BBlockTransferSrcAccessOrder,
Sequence<1, 0, 2>,
BBlockTransferSrcVectorDim,
2,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_K1,
1,
1,
BThreadTransferSrcResetCoordinateAfterRun,
true,
NumGemmKPrefetchStage>(
b_grid_desc_bk0_n_bk1,
make_multi_index(0, n_block_data_idx_on_grid, 0),
b_element_op,
b_block_desc_bk0_n_bk1,
make_multi_index(0, 0, 0),
ck::tensor_operation::element_wise::PassThrough{});
// GEMM definition
// c_mtx += transpose(a_mtx) * b_mtx
// a_mtx[AK0PerBlock, MPerBlock] is in LDS
// b_mtx[BK0PerBlock, NPerBlock] is in LDS
// c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
// register
constexpr index_t KPack = math::max(
math::lcm(AK1, BK1), DppSelector<ABDataType, MPerDpp, NPerDpp>::selected_dpp.k_per_dpp);
auto blockwise_gemm =
BlockwiseGemmDpp_ak0mak1_bk0nbk1_m0n0m1n1m2n2<BlockSize,
ABDataType,
AccDataType,
decltype(a_block_desc_ak0_m_ak1),
decltype(b_block_desc_bk0_n_bk1),
MPerDpp,
NPerDpp,
MDppPerWave,
NDppPerWave,
KPack>();
auto c_thread_buf = blockwise_gemm.GetCThreadBuffer();
// LDS allocation for A and B: be careful of alignment
constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
static_cast<ABDataType*>(p_shared), a_block_desc_ak0_m_ak1.GetElementSpaceSize());
auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
static_cast<ABDataType*>(p_shared) + a_block_space_size_aligned,
b_block_desc_bk0_n_bk1.GetElementSpaceSize());
constexpr auto a_block_slice_copy_step = make_multi_index(AK0PerBlock, 0, 0);
constexpr auto b_block_slice_copy_step = make_multi_index(BK0PerBlock, 0, 0);
// gridwise GEMM pipeline
const auto AK0 = a_grid_desc_ak0_m_ak1.GetLength(I0);
// (AK0 / AK0PerBlock) is always equal to (BK0 / BK0PerBlock)
const index_t num_k_block_main_loop = __builtin_amdgcn_readfirstlane(AK0 / AK0PerBlock);
GridwiseGemmPipe::template Run<HasMainKBlockLoop>(a_grid_desc_ak0_m_ak1,
a_block_desc_ak0_m_ak1,
a_blockwise_copy,
a_grid_buf,
a_block_buf,
a_block_slice_copy_step,
b_grid_desc_bk0_n_bk1,
b_block_desc_bk0_n_bk1,
b_blockwise_copy,
b_grid_buf,
b_block_buf,
b_block_slice_copy_step,
blockwise_gemm,
c_thread_buf,
num_k_block_main_loop);
// output: register to global memory
{
constexpr auto c_thread_desc_m0_n0_m1_n1_m2_n2 =
blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_N2();
constexpr auto c_block_desc_m0_n0_m1_n1_m2_n2 =
blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_N2();
constexpr auto M0 = c_block_desc_m0_n0_m1_n1_m2_n2.GetLength(I0);
constexpr auto N0 = c_block_desc_m0_n0_m1_n1_m2_n2.GetLength(I1);
constexpr auto M1 = c_block_desc_m0_n0_m1_n1_m2_n2.GetLength(I2);
constexpr auto N1 = c_block_desc_m0_n0_m1_n1_m2_n2.GetLength(I3);
constexpr auto M2 = c_block_desc_m0_n0_m1_n1_m2_n2.GetLength(I4);
constexpr auto N2 = c_block_desc_m0_n0_m1_n1_m2_n2.GetLength(I5);
constexpr auto MPerThread = c_thread_desc_m0_n0_m1_n1_m2_n2.GetLength(I4);
constexpr auto NPerThread = c_thread_desc_m0_n0_m1_n1_m2_n2.GetLength(I5);
// calculate origin of thread output tensor on global memory
// blockwise GEMM c matrix starting index
const auto c_thread_mtx_on_block =
blockwise_gemm.CalculateCThreadOriginDataIndex(I0, I0);
const index_t m_thread_data_on_grid =
m_block_data_idx_on_grid + c_thread_mtx_on_block[I0];
const index_t n_thread_data_on_grid =
n_block_data_idx_on_grid + c_thread_mtx_on_block[I1];
const auto m_thread_data_on_grid_to_m0_m1_m2_adaptor = make_single_stage_tensor_adaptor(
make_tuple(make_merge_transform(make_tuple(M0, M1, M2))),
make_tuple(Sequence<0, 1, 2>{}),
make_tuple(Sequence<0>{}));
const auto m_thread_data_on_grid_idx =
m_thread_data_on_grid_to_m0_m1_m2_adaptor.CalculateBottomIndex(
make_multi_index(m_thread_data_on_grid));
const auto n_thread_data_on_grid_to_n0_n1_n2_adaptor = make_single_stage_tensor_adaptor(
make_tuple(make_merge_transform(make_tuple(N0, N1, N2))),
make_tuple(Sequence<0, 1, 2>{}),
make_tuple(Sequence<0>{}));
const auto n_thread_data_on_grid_idx =
n_thread_data_on_grid_to_n0_n1_n2_adaptor.CalculateBottomIndex(
make_multi_index(n_thread_data_on_grid));
auto c_thread_copy =
ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
CDataType,
decltype(c_thread_desc_m0_n0_m1_n1_m2_n2),
decltype(c_grid_desc_m0_n0_m1_n1_m2_n2),
CElementwiseOperation,
Sequence<M0, N0, I1, I1, MPerThread, NPerThread>,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
CGlobalMemoryDataOperation,
1,
true>{
c_grid_desc_m0_n0_m1_n1_m2_n2,
make_multi_index(m_thread_data_on_grid_idx[I0],
n_thread_data_on_grid_idx[I0],
m_thread_data_on_grid_idx[I1],
n_thread_data_on_grid_idx[I1],
m_thread_data_on_grid_idx[I2],
n_thread_data_on_grid_idx[I2]),
c_element_op};
c_thread_copy.Run(c_thread_desc_m0_n0_m1_n1_m2_n2,
make_tuple(I0, I0, I0, I0, I0, I0),
c_thread_buf,
c_grid_desc_m0_n0_m1_n1_m2_n2,
c_grid_buf);
}
}
};
} // namespace ck
include/ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_abd_xdl_cshuffle.hpp 0 → 100644
View file @ e5ebcc41
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck/utility/common_header.hpp"
#include "ck/tensor_description/multi_index_transform_helper.hpp"
#include "ck/tensor_description/tensor_descriptor.hpp"
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_selector.hpp"
#include "ck/tensor_operation/gpu/block/blockwise_gemm_xdlops.hpp"
#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v7r2.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
namespace ck {
// GEMM:
// input : A0[M, K], A1[M, K]
// input : B0[N, K], B1[N, K]
// input : D0[M, N], D1[M, N], ...
// output : E[M, N]
// C = a_op(A) * b_op(B)
// E = cde_op(C, D0, D1, ...)
// Assume:
// D0, D1, ... and E have the same layout
template <typename AsDataType,
typename BsDataType,
typename ComputeDataType_,
typename AccDataType,
typename CShuffleDataType,
typename DsDataType,
typename EDataType,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CDEElementwiseOperation,
InMemoryDataOperationEnum EGlobalMemoryDataOperation,
index_t NumGemmKPrefetchStage,
index_t BlockSize,
index_t MPerBlock,
index_t NPerBlock,
index_t KPerBlock,
index_t AK1Value,
index_t BK1Value,
index_t MPerXdl,
index_t NPerXdl,
index_t MXdlPerWave,
index_t NXdlPerWave,
typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
typename ABlockTransferThreadClusterArrangeOrder,
typename ABlockTransferSrcAccessOrder,
index_t ABlockTransferSrcVectorDim,
index_t ABlockTransferSrcScalarPerVector,
index_t ABlockTransferDstScalarPerVector_AK1,
bool AThreadTransferSrcResetCoordinateAfterRun,
index_t ABlockLdsExtraM,
typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
typename BBlockTransferThreadClusterArrangeOrder,
typename BBlockTransferSrcAccessOrder,
index_t BBlockTransferSrcVectorDim,
index_t BBlockTransferSrcScalarPerVector,
index_t BBlockTransferDstScalarPerVector_BK1,
bool BThreadTransferSrcResetCoordinateAfterRun,
index_t BBlockLdsExtraN,
index_t CShuffleMXdlPerWavePerShuffle,
index_t CShuffleNXdlPerWavePerShuffle,
typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
index_t CDEShuffleBlockTransferScalarPerVector_NPerBlock,
LoopScheduler LoopSched,
PipelineVersion PipelineVer = PipelineVersion::v1>
struct GridwiseGemmMultipleABD_xdl_cshuffle
{
static constexpr index_t NumATensor = AsDataType::Size();
static constexpr index_t NumBTensor = BsDataType::Size();
static constexpr index_t NumDTensor = DsDataType::Size();
using GemmSpecialization = ck::tensor_operation::device::GemmSpecialization;
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
static constexpr auto I2 = Number<2>{};
static constexpr auto I3 = Number<3>{};
static constexpr auto I4 = Number<4>{};
static constexpr auto I5 = Number<5>{};
static constexpr auto I6 = Number<6>{};
static constexpr auto I7 = Number<7>{};
// K1 should be Number<...>
static constexpr auto AK1 = Number<AK1Value>{};
static constexpr auto BK1 = Number<BK1Value>{};
static constexpr auto AK0PerBlock = Number<KPerBlock / AK1Value>{};
static constexpr auto BK0PerBlock = Number<KPerBlock / BK1Value>{};
using ThisThreadBlock = ThisThreadBlock<BlockSize>;
using GridwiseGemmPipe = remove_cvref_t<
decltype(GridwiseGemmPipeline_Selector<PipelineVer, NumGemmKPrefetchStage, LoopSched>())>;
#if CK_WORKAROUND_DENORM_FIX
using ComputeDataType =
conditional_t<is_same_v<ComputeDataType_, ck::half_t>, ck::bhalf_t, ComputeDataType_>;
#else
using ComputeDataType = ComputeDataType_;
#endif
__host__ __device__ static constexpr auto GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1()
{
// A matrix in LDS memory, dst of blockwise copy
return make_naive_tensor_descriptor(
make_tuple(AK0PerBlock, Number<MPerBlock>{}, AK1),
make_tuple(Number<MPerBlock + ABlockLdsExtraM>{} * AK1, AK1, I1));
}
__host__ __device__ static constexpr auto GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1()
{
// B matrix in LDS memory, dst of blockwise copy
return make_naive_tensor_descriptor(
make_tuple(BK0PerBlock, Number<NPerBlock>{}, BK1),
make_tuple(Number<NPerBlock + BBlockLdsExtraN>{} * BK1, BK1, I1));
}
__host__ __device__ static constexpr auto
GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock()
{
constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);
constexpr index_t NWave = NPerBlock / (NXdlPerWave * NPerXdl);
constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
make_naive_tensor_descriptor_packed(
make_tuple(I1,
Number<CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl>{},
I1,
Number<CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>{}));
return c_shuffle_block_desc_mblock_mperblock_nblock_nperblock;
}
static constexpr auto MakeAsGridPointer()
{
return generate_tuple(
[&](auto i) {
using ADataType = remove_cvref_t<tuple_element_t<i.value, AsDataType>>;
return static_cast<const ADataType*>(nullptr);
},
Number<NumATensor>{});
}
static constexpr auto MakeBsGridPointer()
{
return generate_tuple(
[&](auto i) {
using BDataType = remove_cvref_t<tuple_element_t<i.value, BsDataType>>;
return static_cast<const BDataType*>(nullptr);
},
Number<NumBTensor>{});
}
// ck::Tuple<const D0DataType*, const D1DataType*, ...>
static constexpr auto MakeDsGridPointer()
{
return generate_tuple(
[&](auto i) {
using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
return static_cast<const DDataType*>(nullptr);
},
Number<NumDTensor>{});
}
__host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
{
// LDS allocation for A and B: be careful of alignment
constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
// lds max alignment
constexpr auto max_lds_align = math::lcm(AK1, BK1);
constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
constexpr auto b_block_space_size_aligned = math::integer_least_multiple(
b_block_desc_bk0_n_bk1.GetElementSpaceSize(), max_lds_align);
// LDS allocation for C shuffle in LDS
constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
constexpr auto c_block_size =
c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize();
return math::max((a_block_space_size_aligned + b_block_space_size_aligned) *
sizeof(ComputeDataType),
c_block_size * sizeof(CShuffleDataType));
}
// A desc for source in blockwise copy
template <typename AGridDesc_M_K>
__host__ __device__ static constexpr auto
MakeDefaultAGridDescriptor_AK0_M_AK1(const AGridDesc_M_K& a_grid_desc_m_k)
{
const auto M = a_grid_desc_m_k.GetLength(I0);
const auto K = a_grid_desc_m_k.GetLength(I1);
const auto AK0 = K / AK1;
return transform_tensor_descriptor(a_grid_desc_m_k,
make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
make_pass_through_transform(M)),
make_tuple(Sequence<1>{}, Sequence<0>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
}
template <typename AsGridDesc_M_K>
__host__ __device__ static constexpr auto
MakeDefaultAsGridDescriptor_AK0_M_AK1(const AsGridDesc_M_K& as_grid_desc_m_k)
{
return generate_tuple(
[&](auto i) { return MakeDefaultAGridDescriptor_AK0_M_AK1(as_grid_desc_m_k[i]); },
Number<NumATensor>{});
}
// B desc for source in blockwise copy
template <typename BGridDesc_N_K>
__host__ __device__ static constexpr auto
MakeDefaultBGridDescriptor_BK0_N_BK1(const BGridDesc_N_K& b_grid_desc_n_k)
{
const auto N = b_grid_desc_n_k.GetLength(I0);
const auto K = b_grid_desc_n_k.GetLength(I1);
const auto BK0 = K / BK1;
return transform_tensor_descriptor(b_grid_desc_n_k,
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
make_pass_through_transform(N)),
make_tuple(Sequence<1>{}, Sequence<0>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
}
template <typename BsGridDesc_N_K>
__host__ __device__ static constexpr auto
MakeDefaultBsGridDescriptor_BK0_N_BK1(const BsGridDesc_N_K& bs_grid_desc_n_k)
{
return generate_tuple(
[&](auto i) { return MakeDefaultBGridDescriptor_BK0_N_BK1(bs_grid_desc_n_k[i]); },
Number<NumBTensor>{});
}
// E desc for destination in blockwise copy
template <typename EGridDesc_M_N>
__host__ __device__ static constexpr auto
MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(const EGridDesc_M_N& e_grid_desc_m_n)
{
const auto M = e_grid_desc_m_n.GetLength(I0);
const auto N = e_grid_desc_m_n.GetLength(I1);
const auto MBlock = M / MPerBlock;
const auto NBlock = N / NPerBlock;
const auto e_grid_desc_mblock_mperblock_nblock_nperblock = transform_tensor_descriptor(
e_grid_desc_m_n,
make_tuple(make_unmerge_transform(make_tuple(MBlock, Number<MPerBlock>{})),
make_unmerge_transform(make_tuple(NBlock, Number<NPerBlock>{}))),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));
return e_grid_desc_mblock_mperblock_nblock_nperblock;
}
// Ds desc for source in blockwise copy
template <typename DsGridDesc_M_N>
__host__ __device__ static constexpr auto
MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(const DsGridDesc_M_N& ds_grid_desc_m_n)
{
return generate_tuple(
[&](auto i) {
return MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(ds_grid_desc_m_n[i]);
},
Number<NumDTensor>{});
}
// return block_id to E matrix tile idx (m0, n0) mapping
template <typename EGridDesc_M_N>
__host__ __device__ static constexpr auto
MakeDefaultBlock2ETileMap(const EGridDesc_M_N& e_grid_desc_m_n)
{
return BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, NPerBlock, EGridDesc_M_N>(
e_grid_desc_m_n);
}
// block_id to matrix tile idx (m0, n0) mapping are controlled by {M01, N01}
template <typename AsGridDesc_M_K,
typename BsGridDesc_N_K,
typename DsGridDesc_M_N,
typename EGridDesc_M_N,
typename Block2ETileMap>
__host__ __device__ static constexpr bool CheckValidity(const AsGridDesc_M_K& as_grid_desc_m_k,
const BsGridDesc_N_K& bs_grid_desc_n_k,
const DsGridDesc_M_N& ds_grid_desc_m_n,
const EGridDesc_M_N& e_grid_desc_m_n,
const Block2ETileMap& block_2_etile_map)
{
static_assert((MPerBlock % (MPerXdl * MXdlPerWave) == 0) &&
(NPerBlock % (NXdlPerWave * NPerXdl)) == 0,
"Invalid tuning param!");
static_assert(KPerBlock % AK1Value == 0 && KPerBlock % BK1Value == 0,
"KPerBlock must be divisible by AK1Value and BK1Value!");
const auto M = as_grid_desc_m_k[I0].GetLength(I0);
const auto N = bs_grid_desc_n_k[I0].GetLength(I0);
const auto AK = as_grid_desc_m_k[I0].GetLength(I1);
const auto BK = bs_grid_desc_n_k[I0].GetLength(I1);
// check consistency of desc
if(!(M == e_grid_desc_m_n.GetLength(I0) && N == e_grid_desc_m_n.GetLength(I1) && AK == BK))
{
return false;
}
constexpr long_index_t TwoGB = (long_index_t{1} << 31);
bool valid = true;
static_for<0, NumATensor, 1>{}([&](auto i) {
using ADataType = remove_cvref_t<tuple_element_t<i.value, AsDataType>>;
valid =
valid && (as_grid_desc_m_k[i].GetElementSpaceSize() * sizeof(ADataType) <= TwoGB);
valid = valid && (M == as_grid_desc_m_k[i].GetLength(I0) &&
AK == as_grid_desc_m_k[i].GetLength(I1));
});
static_for<0, NumBTensor, 1>{}([&](auto i) {
using BDataType = remove_cvref_t<tuple_element_t<i.value, BsDataType>>;
valid =
valid && (bs_grid_desc_n_k[i].GetElementSpaceSize() * sizeof(BDataType) <= TwoGB);
valid = valid && (N == bs_grid_desc_n_k[i].GetLength(I0) &&
BK == bs_grid_desc_n_k[i].GetLength(I1));
});
static_for<0, NumDTensor, 1>{}([&](auto i) {
valid = valid && (M == ds_grid_desc_m_n[i].GetLength(I0) &&
N == ds_grid_desc_m_n[i].GetLength(I1));
});
if(!valid)
{
return false;
}
// check tile size
if(!(M % MPerBlock == 0 && N % NPerBlock == 0 && AK % KPerBlock == 0))
{
return false;
}
// check gridwise gemm pipeline
const auto num_k_loop = AK / KPerBlock;
if(!GridwiseGemmPipe::IsSupported(num_k_loop))
{
return false;
}
// check block-to-E-tile
if(!block_2_etile_map.CheckValidity(e_grid_desc_m_n))
{
return false;
}
// TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
// check tensor size: cannot be larger than 2GB each
if(!(e_grid_desc_m_n.GetElementSpaceSize() * sizeof(EDataType) <= TwoGB))
{
return false;
}
return true;
}
__host__ __device__ static constexpr bool CalculateHasMainKBlockLoop(index_t K)
{
const index_t num_loop = K / KPerBlock;
return GridwiseGemmPipe::CalculateHasMainLoop(num_loop);
}
using AsGridPointer = decltype(MakeAsGridPointer());
using BsGridPointer = decltype(MakeBsGridPointer());
using DsGridPointer = decltype(MakeDsGridPointer());
template <typename ALayout, GemmSpecialization GemmSpec>
__host__ __device__ static auto
MakeAGridDescriptor_M_K(index_t MRaw, index_t KRaw, index_t StrideA)
{
constexpr auto matrix_padder =
ck::tensor_operation::device::MatrixPadder<GemmSpec, index_t, index_t, index_t>{
MPerBlock, NPerBlock, KPerBlock};
const auto a_grid_desc_mraw_kraw = [&]() {
if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
{
return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
make_tuple(StrideA, I1));
}
else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
{
return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
make_tuple(I1, StrideA));
}
}();
return matrix_padder.PadADescriptor_M_K(a_grid_desc_mraw_kraw);
}
template <typename AsLayout, GemmSpecialization GemmSpec>
__host__ __device__ static auto
MakeAsGridDescriptor_M_K(const std::array<index_t, NumATensor>& MRaws,
const std::array<index_t, NumATensor>& KRaws,
const std::array<index_t, NumATensor>& AsStride)
{
return generate_tuple(
[&](auto i) {
using ALayout = remove_cvref_t<tuple_element_t<i.value, AsLayout>>;
return MakeAGridDescriptor_M_K<ALayout, GemmSpec>(MRaws[i], KRaws[i], AsStride[i]);
},
Number<NumATensor>{});
}
template <typename BLayout, GemmSpecialization GemmSpec>
__host__ __device__ static auto
MakeBGridDescriptor_N_K(index_t KRaw, index_t NRaw, index_t StrideB)
{
constexpr auto matrix_padder =
ck::tensor_operation::device::MatrixPadder<GemmSpec, index_t, index_t, index_t>{
MPerBlock, NPerBlock, KPerBlock};
const auto b_grid_desc_nraw_kraw = [&]() {
if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
{
return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
make_tuple(I1, StrideB));
}
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
{
return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
make_tuple(StrideB, I1));
}
}();
return matrix_padder.PadBDescriptor_N_K(b_grid_desc_nraw_kraw);
}
template <typename BsLayout, GemmSpecialization GemmSpec>
__host__ __device__ static auto
MakeBsGridDescriptor_N_K(const std::array<index_t, NumBTensor>& KRaws,
const std::array<index_t, NumBTensor>& NRaws,
const std::array<index_t, NumBTensor>& BsStride)
{
return generate_tuple(
[&](auto i) {
using BLayout = remove_cvref_t<tuple_element_t<i.value, BsLayout>>;
return MakeBGridDescriptor_N_K<BLayout, GemmSpec>(KRaws[i], NRaws[i], BsStride[i]);
},
Number<NumBTensor>{});
}
template <typename ELayout, GemmSpecialization GemmSpec>
__host__ __device__ static auto
MakeEGridDescriptor_M_N(index_t MRaw, index_t NRaw, index_t StrideE)
{
constexpr auto matrix_padder =
ck::tensor_operation::device::MatrixPadder<GemmSpec, index_t, index_t, index_t>{
MPerBlock, NPerBlock, KPerBlock};
const auto e_grid_desc_mraw_nraw = [&]() {
if constexpr(is_same<tensor_layout::gemm::RowMajor, ELayout>::value)
{
return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
make_tuple(StrideE, I1));
}
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ELayout>::value)
{
return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
make_tuple(I1, StrideE));
}
}();
return matrix_padder.PadCDescriptor_M_N(e_grid_desc_mraw_nraw);
}
template <typename DsLayout, GemmSpecialization GemmSpec>
__host__ __device__ static auto
MakeDsGridDescriptor_M_N(const std::array<index_t, NumDTensor>& MRaws,
const std::array<index_t, NumDTensor>& NRaws,
const std::array<index_t, NumDTensor>& DsStride)
{
return generate_tuple(
[&](auto i) {
using DLayout = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;
return MakeEGridDescriptor_M_N<DLayout, GemmSpec>(MRaws[i], NRaws[i], DsStride[i]);
},
Number<NumDTensor>{});
}
__device__ __host__ static constexpr auto GetMPerBlock() { return MPerBlock; }
template <bool HasMainKBlockLoop,
typename AsGridDesc_AK0_M_AK1,
typename BsGridDesc_BK0_N_BK1,
typename DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
typename EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
typename Block2ETileMap>
__device__ static void Run(AsGridPointer p_as_grid,
BsGridPointer p_bs_grid,
DsGridPointer p_ds_grid,
EDataType* __restrict__ p_e_grid,
void* __restrict__ p_shared,
const AElementwiseOperation& a_element_op,
const BElementwiseOperation& b_element_op,
const CDEElementwiseOperation& cde_element_op,
const AsGridDesc_AK0_M_AK1 as_grid_desc_ak0_m_ak1,
const BsGridDesc_BK0_N_BK1 bs_grid_desc_bk0_n_bk1,
const DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock&
ds_grid_desc_mblock_mperblock_nblock_nperblock,
const EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock&
e_grid_desc_mblock_mperblock_nblock_nperblock,
const Block2ETileMap& block_2_etile_map)
{
const auto as_grid_buf = generate_tuple(
[&](auto i) {
return make_dynamic_buffer<AddressSpaceEnum::Global>(
p_as_grid[i], as_grid_desc_ak0_m_ak1[i].GetElementSpaceSize());
},
Number<NumATensor>{});
const auto bs_grid_buf = generate_tuple(
[&](auto i) {
return make_dynamic_buffer<AddressSpaceEnum::Global>(
p_bs_grid[i], bs_grid_desc_bk0_n_bk1[i].GetElementSpaceSize());
},
Number<NumBTensor>{});
const auto ds_grid_buf = generate_tuple(
[&](auto i) {
return make_dynamic_buffer<AddressSpaceEnum::Global>(
p_ds_grid[i],
ds_grid_desc_mblock_mperblock_nblock_nperblock[i].GetElementSpaceSize());
},
Number<NumDTensor>{});
auto e_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
p_e_grid, e_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
// divide block work by [M, N]
const auto block_work_idx =
block_2_etile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
if(!block_2_etile_map.ValidCTileIndex(
block_work_idx,
make_tuple(e_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I0),
e_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I2))))
{
return;
}
// HACK: this force m/n_block_data_idx_on_grid into SGPR
const index_t m_block_data_idx_on_grid =
__builtin_amdgcn_readfirstlane(block_work_idx[I0] * MPerBlock);
const index_t n_block_data_idx_on_grid =
__builtin_amdgcn_readfirstlane(block_work_idx[I1] * NPerBlock);
// lds max alignment
constexpr auto max_lds_align = math::lcm(AK1, BK1);
// A matrix in LDS memory, dst of blockwise copy
constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
// B matrix in LDS memory, dst of blockwise copy
constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
const auto idx_as_block_begin =
generate_tuple([&](auto) { return make_multi_index(0, m_block_data_idx_on_grid, 0); },
Number<NumATensor>{});
static_assert(ABlockTransferSrcScalarPerVector == ABlockTransferDstScalarPerVector_AK1,
"Src and Dst ScalarPerVector must be the same");
auto a_blockwise_copy = ThreadGroupTensorSliceTransfer_v7r2<
ThisThreadBlock,
AsDataType,
Tuple<ComputeDataType>,
decltype(as_grid_desc_ak0_m_ak1),
decltype(tie(a_block_desc_ak0_m_ak1)),
AElementwiseOperation,
Sequence<static_cast<index_t>(InMemoryDataOperationEnum::Set)>,
Sequence<AK0PerBlock, MPerBlock, AK1>,
ABlockTransferThreadClusterLengths_AK0_M_AK1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
Sequence<1, 0, 2>,
ABlockTransferSrcVectorDim,
2,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_AK1,
uniform_sequence_gen_t<NumATensor, false>,
Sequence<true>>{as_grid_desc_ak0_m_ak1,
idx_as_block_begin,
tie(a_block_desc_ak0_m_ak1),
make_tuple(make_multi_index(0, 0, 0)),
a_element_op};
const auto idx_bs_block_begin =
generate_tuple([&](auto) { return make_multi_index(0, n_block_data_idx_on_grid, 0); },
Number<NumBTensor>{});
static_assert(BBlockTransferSrcScalarPerVector == BBlockTransferDstScalarPerVector_BK1,
"Src and Dst ScalarPerVector must be the same");
auto b_blockwise_copy = ThreadGroupTensorSliceTransfer_v7r2<
ThisThreadBlock,
BsDataType,
Tuple<ComputeDataType>,
decltype(bs_grid_desc_bk0_n_bk1),
decltype(tie(b_block_desc_bk0_n_bk1)),
BElementwiseOperation,
Sequence<static_cast<index_t>(InMemoryDataOperationEnum::Set)>,
Sequence<BK0PerBlock, NPerBlock, BK1>,
BBlockTransferThreadClusterLengths_BK0_N_BK1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
Sequence<1, 0, 2>,
BBlockTransferSrcVectorDim,
2,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_BK1,
uniform_sequence_gen_t<NumBTensor, false>,
Sequence<true>>{bs_grid_desc_bk0_n_bk1,
idx_bs_block_begin,
tie(b_block_desc_bk0_n_bk1),
make_tuple(make_multi_index(0, 0, 0)),
b_element_op};
// GEMM definition
// c_mtx += transpose(a_mtx) * b_mtx
// a_mtx[K0PerBlock, MPerBlock] is in LDS
// b_mtx[K0PerBlock, NPerBlock] is in LDS
// c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
// register
// sanity check
constexpr index_t KPack =
math::max(math::lcm(AK1, BK1),
MfmaSelector<ComputeDataType, MPerXdl, NPerXdl>::selected_mfma.k_per_blk);
auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_Selector<
BlockSize,
ComputeDataType, // ComputeDataType for A
ComputeDataType, // ComputeDataType for B
AccDataType,
decltype(a_block_desc_ak0_m_ak1),
decltype(b_block_desc_bk0_n_bk1),
MPerXdl,
NPerXdl,
MXdlPerWave,
NXdlPerWave,
KPack,
LoopSched>();
auto c_thread_buf = blockwise_gemm.GetCThreadBuffer();
// LDS allocation for A and B: be careful of alignment
constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
static_cast<ComputeDataType*>(p_shared), a_block_desc_ak0_m_ak1.GetElementSpaceSize());
auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
static_cast<ComputeDataType*>(p_shared) + a_block_space_size_aligned,
b_block_desc_bk0_n_bk1.GetElementSpaceSize());
constexpr auto a_block_slice_copy_step = make_multi_index(KPerBlock / AK1, 0, 0);
constexpr auto b_block_slice_copy_step = make_multi_index(KPerBlock / BK1, 0, 0);
const index_t num_k_block_main_loop = __builtin_amdgcn_readfirstlane(
(as_grid_desc_ak0_m_ak1[I0].GetLength(I0) * as_grid_desc_ak0_m_ak1[I0].GetLength(I2)) /
KPerBlock);
// gridwise GEMM pipeline
const auto gridwise_gemm_pipeline =
GridwiseGemmPipeline_Selector<PipelineVer, NumGemmKPrefetchStage, LoopSched>();
gridwise_gemm_pipeline.template Run<HasMainKBlockLoop>(as_grid_desc_ak0_m_ak1,
a_block_desc_ak0_m_ak1,
a_blockwise_copy,
as_grid_buf,
a_block_buf,
a_block_slice_copy_step,
bs_grid_desc_bk0_n_bk1,
b_block_desc_bk0_n_bk1,
b_blockwise_copy,
bs_grid_buf,
b_block_buf,
b_block_slice_copy_step,
blockwise_gemm,
c_thread_buf,
num_k_block_main_loop);
// shuffle C and write out
{
static_assert(MXdlPerWave % CShuffleMXdlPerWavePerShuffle == 0 &&
NXdlPerWave % CShuffleNXdlPerWavePerShuffle == 0,
"wrong!");
constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);
constexpr index_t NWave = NPerBlock / (NXdlPerWave * NPerXdl);
// TODO: hacky, fix it!
constexpr auto c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2 =
blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
// TODO: hacky, fix it!
// c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp is only used to get lengths
constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp =
blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
constexpr auto M0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I0);
constexpr auto N0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I1);
constexpr auto M1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I2);
constexpr auto N1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I3);
constexpr auto M2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I4);
constexpr auto M3 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I5);
constexpr auto M4 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I6);
constexpr auto N2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I7);
constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
auto c_shuffle_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
static_cast<CShuffleDataType*>(p_shared),
c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2 = transform_tensor_descriptor(
c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
make_tuple(
make_freeze_transform(I0),
make_unmerge_transform(make_tuple(
Number<CShuffleMXdlPerWavePerShuffle>{}, // M0 (MXdlPerWave) per shuffle
M1, // M1 = MWave
M2, // M2 * M3 * M4 = MPerXdl
M3,
M4)),
make_freeze_transform(I0),
make_unmerge_transform(make_tuple(
Number<CShuffleNXdlPerWavePerShuffle>{}, // N0 (NXdlPerWave) per shuffle
N1, // N1 = NWave
N2))), // N2 = NPerXdl
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(
Sequence<>{}, Sequence<0, 2, 4, 5, 6>{}, Sequence<>{}, Sequence<1, 3, 7>{}));
// calculate origin of thread output tensor on global memory
// blockwise GEMM c matrix starting index
const auto c_thread_mtx_on_block =
blockwise_gemm.CalculateCThreadOriginDataIndex(I0, I0, I0, I0);
const index_t m_thread_data_on_block = c_thread_mtx_on_block[I0];
const index_t n_thread_data_on_block = c_thread_mtx_on_block[I1];
const auto m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor =
make_single_stage_tensor_adaptor(
make_tuple(make_merge_transform(make_tuple(M0, M1, M2, M3, M4))),
make_tuple(Sequence<0, 1, 2, 3, 4>{}),
make_tuple(Sequence<0>{}));
const auto m_thread_data_on_block_idx =
m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor.CalculateBottomIndex(
make_multi_index(m_thread_data_on_block));
const auto n_thread_data_on_block_to_n0_n1_n2_adaptor =
make_single_stage_tensor_adaptor(
make_tuple(make_merge_transform(make_tuple(N0, N1, N2))),
make_tuple(Sequence<0, 1, 2>{}),
make_tuple(Sequence<0>{}));
const auto n_thread_data_on_block_idx =
n_thread_data_on_block_to_n0_n1_n2_adaptor.CalculateBottomIndex(
make_multi_index(n_thread_data_on_block));
// shuffle: threadwise copy C from VGPR to LDS
auto c_thread_copy_vgpr_to_lds =
ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
CShuffleDataType,
decltype(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2),
decltype(c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2),
ck::tensor_operation::element_wise::PassThrough,
Sequence<CShuffleMXdlPerWavePerShuffle,
CShuffleNXdlPerWavePerShuffle,
I1,
I1,
M2,
I1,
M4,
I1>,
Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
7,
1,
InMemoryDataOperationEnum::Set,
1,
true>{
c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
make_multi_index(0,
0,
m_thread_data_on_block_idx[I1],
n_thread_data_on_block_idx[I1],
m_thread_data_on_block_idx[I2],
m_thread_data_on_block_idx[I3],
m_thread_data_on_block_idx[I4],
n_thread_data_on_block_idx[I2]),
ck::tensor_operation::element_wise::PassThrough{}};
// tuple of reference to C/Ds tensor descriptors
const auto c_ds_desc_refs = concat_tuple_of_reference(
tie(c_shuffle_block_desc_mblock_mperblock_nblock_nperblock),
generate_tie(
[&](auto i) -> const auto& // return type should be reference
{ return ds_grid_desc_mblock_mperblock_nblock_nperblock[i]; },
Number<NumDTensor>{}));
// tuple of reference to C/Ds tensor descriptors
const auto c_ds_buf_refs = concat_tuple_of_reference(
tie(c_shuffle_block_buf),
generate_tie(
[&](auto i) -> const auto& // return type should be reference
{ return ds_grid_buf[i]; },
Number<NumDTensor>{}));
// tuple of starting index of C/Ds blockwise copy
const auto idx_c_ds_block_begin = container_concat(
make_tuple(make_multi_index(0, 0, 0, 0)),
generate_tuple(
[&](auto) {
return make_multi_index(block_work_idx[I0], 0, block_work_idx[I1], 0);
},
Number<NumDTensor>{}));
// blockwise copy C/D/E between LDS and global
auto cde_block_copy_lds_and_global = ThreadGroupTensorSliceTransfer_v7r2<
ThisThreadBlock,
decltype(container_concat(make_tuple(CShuffleDataType{}), DsDataType{})),
Tuple<EDataType>,
decltype(c_ds_desc_refs),
decltype(tie(e_grid_desc_mblock_mperblock_nblock_nperblock)),
CDEElementwiseOperation,
Sequence<static_cast<index_t>(EGlobalMemoryDataOperation)>, // FIXME: make Sequence
// support arbitray type
Sequence<1,
CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
1,
CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>, // BlockSliceLengths,
CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
Sequence<0, 1, 2, 3>, // typename ThreadClusterArrangeOrder,
Sequence<0, 1, 2, 3>, // typename SrcDimAccessOrder,
Sequence<0, 1, 2, 3>, // typename DstDimAccessOrder,
3, // index_t SrcVectorDim,
3, // index_t DstVectorDim,
CDEShuffleBlockTransferScalarPerVector_NPerBlock,
CDEShuffleBlockTransferScalarPerVector_NPerBlock,
sequence_merge_t<
Sequence<true>,
uniform_sequence_gen_t<NumDTensor,
false>>, // ThreadTransferSrcResetCoordinateAfterRunFlags
Sequence<false>> // ThreadTransferDstResetCoordinateAfterRunFlags
{c_ds_desc_refs,
idx_c_ds_block_begin,
tie(e_grid_desc_mblock_mperblock_nblock_nperblock),
make_tuple(make_multi_index(block_work_idx[I0], 0, block_work_idx[I1], 0)),
cde_element_op};
// space filling curve for threadwise C in VGPR before shuffle
constexpr auto sfc_c_vgpr =
SpaceFillingCurve<Sequence<MXdlPerWave, NXdlPerWave, 1, 1, M2, 1, M4, 1>,
Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
Sequence<CShuffleMXdlPerWavePerShuffle,
CShuffleNXdlPerWavePerShuffle,
1,
1,
M2,
1,
M4,
1>>{};
// space filling curve for shuffled blockwise C/D/E
constexpr auto sfc_cde_block =
SpaceFillingCurve<Sequence<1, MPerBlock, 1, NPerBlock>,
Sequence<0, 2, 1, 3>,
Sequence<1,
CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
1,
CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>>{};
constexpr index_t num_access = sfc_c_vgpr.GetNumOfAccess();
static_assert(num_access == sfc_cde_block.GetNumOfAccess(), "wrong!");
static_for<0, num_access, 1>{}([&](auto access_id) {
// make sure it's safe to write to LDS
block_sync_lds();
// each thread write its data from VGPR to LDS
c_thread_copy_vgpr_to_lds.Run(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2,
sfc_c_vgpr.GetIndexTupleOfNumber(access_id),
c_thread_buf,
c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
c_shuffle_block_buf);
// make sure it's safe to read from LDS
block_sync_lds();
// each block copy its data from LDS to global
cde_block_copy_lds_and_global.Run(
c_ds_desc_refs,
c_ds_buf_refs,
tie(e_grid_desc_mblock_mperblock_nblock_nperblock),
tie(e_grid_buf));
if constexpr(access_id < num_access - 1)
{
constexpr auto cde_lds_and_global_step =
sfc_cde_block.GetForwardStep(access_id);
// move on Ds
static_for<0, NumDTensor, 1>{}([&](auto i) {
cde_block_copy_lds_and_global.MoveSrcSliceWindow(
c_ds_desc_refs, i + I1, cde_lds_and_global_step);
});
// move on E
cde_block_copy_lds_and_global.MoveDstSliceWindow(
tie(e_grid_desc_mblock_mperblock_nblock_nperblock),
I0,
cde_lds_and_global_step);
}
});
}
}
template <bool HasMainKBlockLoop,
GemmSpecialization GemmSpec,
typename AsLayout,
typename BsLayout,
typename DsLayout,
typename ELayout,
typename Block2ETileMap>
__device__ static void Run(AsGridPointer p_as_grid,
BsGridPointer p_bs_grid,
DsGridPointer p_ds_grid,
void* __restrict__ p_e_grid_,
void* __restrict__ p_shared,
const AElementwiseOperation& a_element_op,
const BElementwiseOperation& b_element_op,
const CDEElementwiseOperation& cde_element_op,
const index_t M,
const index_t N,
const index_t K,
const std::array<index_t, NumATensor> StrideAs,
const std::array<index_t, NumBTensor> StrideBs,
const std::array<index_t, NumDTensor> StrideDs,
const index_t StrideE,
const Block2ETileMap& block_2_etile_map)
{
using AsGridDesc_M_K =
remove_cvref_t<decltype(MakeAsGridDescriptor_M_K<AsLayout, GemmSpec>({}, {}, {}))>;
using BsGridDesc_N_K =
remove_cvref_t<decltype(MakeBsGridDescriptor_N_K<BsLayout, GemmSpec>({}, {}, {}))>;
using DsGridDesc_M_N =
remove_cvref_t<decltype(MakeDsGridDescriptor_M_N<DsLayout, GemmSpec>({}, {}, {}))>;
const auto p_e_grid = reinterpret_cast<EDataType*>(p_e_grid_);
AsGridDesc_M_K as_grid_desc_m_k;
BsGridDesc_N_K bs_grid_desc_n_k;
DsGridDesc_M_N ds_grid_desc_m_n;
static_for<0, NumATensor, 1>{}([&](auto j) {
using ALayout = remove_cvref_t<tuple_element_t<j.value, AsLayout>>;
as_grid_desc_m_k(j) = MakeAGridDescriptor_M_K<ALayout, GemmSpec>(M, K, StrideAs[j]);
});
static_for<0, NumBTensor, 1>{}([&](auto j) {
using BLayout = remove_cvref_t<tuple_element_t<j.value, BsLayout>>;
bs_grid_desc_n_k(j) = MakeBGridDescriptor_N_K<BLayout, GemmSpec>(N, K, StrideBs[j]);
});
static_for<0, NumDTensor, 1>{}([&](auto j) {
using DLayout = remove_cvref_t<tuple_element_t<j.value, DsLayout>>;
ds_grid_desc_m_n(j) = MakeEGridDescriptor_M_N<DLayout, GemmSpec>(M, N, StrideDs[j]);
});
const auto e_grid_desc_m_n = MakeEGridDescriptor_M_N<ELayout, GemmSpec>(M, N, StrideE);
// tensor descriptors for block/thread-wise copy
const auto as_grid_desc_ak0_m_ak1 = MakeDefaultAsGridDescriptor_AK0_M_AK1(as_grid_desc_m_k);
const auto bs_grid_desc_bk0_n_bk1 = MakeDefaultBsGridDescriptor_BK0_N_BK1(bs_grid_desc_n_k);
const auto ds_grid_desc_mblock_mperblock_nblock_nperblock =
MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(ds_grid_desc_m_n);
const auto e_grid_desc_mblock_mperblock_nblock_nperblock =
MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(e_grid_desc_m_n);
Run<HasMainKBlockLoop>(p_as_grid,
p_bs_grid,
p_ds_grid,
p_e_grid,
p_shared,
a_element_op,
b_element_op,
cde_element_op,
as_grid_desc_ak0_m_ak1,
bs_grid_desc_bk0_n_bk1,
ds_grid_desc_mblock_mperblock_nblock_nperblock,
e_grid_desc_mblock_mperblock_nblock_nperblock,
block_2_etile_map);
}
};
} // namespace ck
include/ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_multiple_r_xdl_cshuffle.hpp
View file @ e5ebcc41
......@@ -470,6 +470,7 @@ struct GridwiseGemmMultipleDMultipleR_k0mk1_k0nk1_mn_xdl_cshuffle_v1
auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_Selector<
BlockSize,
FloatAB,
FloatAB,
FloatGemmAcc,
decltype(a_block_desc_ak0_m_ak1),
decltype(b_block_desc_bk0_n_bk1),
......
include/ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_wmma_cshuffle.hpp
View file @ e5ebcc41
......@@ -36,7 +36,7 @@ __global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
kernel_grouped_conv_fwd_multiple_d_wmma_cshuffle(
kernel_grouped_conv_multiple_d_wmma_cshuffle(
const ADataType* __restrict__ p_a_grid,
const BDataType* __restrict__ p_b_grid,
DsPointer p_ds_grid,
......@@ -54,8 +54,7 @@ __global__ void
const Block2CTileMap block_2_ctile_map,
const ComputePtrOffsetOfBatch compute_ptr_offset_of_batch)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx1100__) || defined(__gfx1101__) || \
defined(__gfx1102__))
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx11__))
// offset base pointer for each work-group
const index_t num_blocks_per_batch =
__builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
......@@ -148,8 +147,7 @@ __global__ void
const ComputePtrOffsetOfBatch compute_ptr_offset_of_batch,
const Block2CTileMap block_2_etile_map)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx1100__) || defined(__gfx1101__) || \
defined(__gfx1102__))
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx11__))
// printf("entry kernel launch");
__shared__ char p_shared[GridwiseOp::GetSharedMemoryNumberOfByte()];
......@@ -244,8 +242,7 @@ __global__ void
const CDEElementwiseOperation cde_element_op,
const Block2CTileMap block_2_ctile_map)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx1100__) || defined(__gfx1101__) || \
defined(__gfx1102__))
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx11__))
__shared__ char p_shared[GridwiseOp::GetSharedMemoryNumberOfByte()];
GridwiseOp::template Run<HasMainKBlockLoop>(p_a_grid,
......@@ -274,7 +271,7 @@ __global__ void
ignore = b_element_op;
ignore = cde_element_op;
ignore = block_2_ctile_map;
#endif // end of if (defined(__gfx1100__ ))
#endif // end of if (defined(__gfx11__ ))
}
template < // DataType Family
......@@ -452,11 +449,11 @@ struct GridwiseGemmMultipleD_k0mk1_k0nk1_mn_wmma_cshuffle
}
// block_id to matrix tile idx (m0, n0) mapping are controlled by {M01, N01}
// CheckValidity for kernels without multi D
template <typename Block2CTileMap>
__host__ __device__ static constexpr bool
CheckValidity(const AGridDesc_K0_M_K1& a_grid_desc_k0_m_k1,
const BGridDesc_K0_N_K1& b_grid_desc_k0_n_k1,
const DsGridDesc_M_N& ds_grid_desc_m_n,
const EGridDesc_M_N& e_grid_desc_m_n,
const Block2CTileMap& block_2_ctile_map)
{
......@@ -471,18 +468,6 @@ struct GridwiseGemmMultipleD_k0mk1_k0nk1_mn_wmma_cshuffle
const auto N = b_grid_desc_k0_n_k1.GetLength(I1);
const auto K0 = a_grid_desc_k0_m_k1.GetLength(I0);
bool valid = true;
static_for<0, NumDTensor, 1>{}([&](auto i) {
valid = valid && (M == ds_grid_desc_m_n[i].GetLength(I0) &&
N == ds_grid_desc_m_n[i].GetLength(I1));
});
if(!valid)
{
return false;
}
if(!(M == e_grid_desc_m_n.GetLength(I0) && N == e_grid_desc_m_n.GetLength(I1) &&
K0 == b_grid_desc_k0_n_k1.GetLength(I0) && K1 == a_grid_desc_k0_m_k1.GetLength(I2) &&
K1 == b_grid_desc_k0_n_k1.GetLength(I2)))
......@@ -517,6 +502,31 @@ struct GridwiseGemmMultipleD_k0mk1_k0nk1_mn_wmma_cshuffle
return true;
}
template <typename Block2CTileMap>
__host__ __device__ static constexpr bool
CheckValidity(const AGridDesc_K0_M_K1& a_grid_desc_k0_m_k1,
const BGridDesc_K0_N_K1& b_grid_desc_k0_n_k1,
const DsGridDesc_M_N& ds_grid_desc_m_n,
const EGridDesc_M_N& e_grid_desc_m_n,
const Block2CTileMap& block_2_ctile_map)
{
const auto M = a_grid_desc_k0_m_k1.GetLength(I1);
const auto N = b_grid_desc_k0_n_k1.GetLength(I1);
bool valid = true;
static_for<0, NumDTensor, 1>{}([&](auto i) {
valid = valid && (M == ds_grid_desc_m_n[i].GetLength(I0) &&
N == ds_grid_desc_m_n[i].GetLength(I1));
});
if(!valid)
{
return false;
}
return CheckValidity(
a_grid_desc_k0_m_k1, b_grid_desc_k0_n_k1, e_grid_desc_m_n, block_2_ctile_map);
}
__host__ __device__ static constexpr bool CalculateHasMainKBlockLoop(index_t K)
{
const index_t num_loop = K / (K0PerBlock * K1);
......
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