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"examples/deprecated_api/imagenet/main_reducer.py" did not exist on "bc62f325384a62663901f48a09001ca9477646e9"
Commit 578bc24a authored by Harisankar Sadasivan's avatar Harisankar Sadasivan
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Merge branch 'develop' of... 

Merge branch 'develop' of https://github.com/ROCmSoftwarePlatform/composable_kernel into simple_gemm_dl
parents 84213e27 5fe687fa
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Showing with 949 additions and 38 deletions
include/ck/utility/type_convert.hpp
View file @ 578bc24a
......@@ -80,6 +80,7 @@ inline __host__ __device__ constexpr bhalf_t type_convert<bhalf_t, int8_t>(int8_
return type_convert<bhalf_t>(x_fp32);
}
#if defined CK_ENABLE_FP8
// convert fp32 to fp8
template <>
inline __host__ __device__ f8_t type_convert<f8_t, float>(float x)
......@@ -88,8 +89,9 @@ inline __host__ __device__ f8_t type_convert<f8_t, float>(float x)
constexpr bool clip = true;
constexpr f8_rounding_mode rm = f8_rounding_mode::standard;
constexpr uint32_t rng = 0;
return utils::cast_to_f8<float, negative_zero_nan, clip, (rm == f8_rounding_mode::stochastic)>(
x, rng);
return utils::
cast_to_f8<float, f8_t, negative_zero_nan, clip, (rm == f8_rounding_mode::stochastic)>(x,
rng);
}
// convert fp8 to fp32
......@@ -97,7 +99,7 @@ template <>
inline __host__ __device__ float type_convert<float, f8_t>(f8_t x)
{
constexpr bool negative_zero_nan = true;
return utils::cast_from_f8<float, negative_zero_nan>(x);
return utils::cast_from_f8<f8_t, float, negative_zero_nan>(x);
}
// convert fp16 to fp8
......@@ -108,7 +110,8 @@ inline __host__ __device__ f8_t type_convert<f8_t, half_t>(half_t x)
constexpr bool clip = true;
constexpr f8_rounding_mode rm = f8_rounding_mode::standard;
constexpr uint32_t rng = 0;
return utils::cast_to_f8<half_t, negative_zero_nan, clip, (rm == f8_rounding_mode::stochastic)>(
return utils::
cast_to_f8<half_t, f8_t, negative_zero_nan, clip, (rm == f8_rounding_mode::stochastic)>(
x, rng);
}
......@@ -117,8 +120,53 @@ template <>
inline __host__ __device__ half_t type_convert<half_t, f8_t>(f8_t x)
{
constexpr bool negative_zero_nan = true;
return utils::cast_from_f8<half_t, negative_zero_nan>(x);
return utils::cast_from_f8<f8_t, half_t, negative_zero_nan>(x);
}
#endif
#if defined CK_ENABLE_BF8
// convert fp32 to bf8
template <>
inline __host__ __device__ bf8_t type_convert<bf8_t, float>(float x)
{
constexpr bool negative_zero_nan = true;
constexpr bool clip = true;
constexpr f8_rounding_mode rm = f8_rounding_mode::standard;
constexpr uint32_t rng = 0;
return utils::
cast_to_f8<float, bf8_t, negative_zero_nan, clip, (rm == f8_rounding_mode::stochastic)>(
x, rng);
}
// convert bf8 to fp32
template <>
inline __host__ __device__ float type_convert<float, bf8_t>(bf8_t x)
{
constexpr bool negative_zero_nan = true;
return utils::cast_from_f8<bf8_t, float, negative_zero_nan>(x);
}
// convert fp16 to bf8
template <>
inline __host__ __device__ bf8_t type_convert<bf8_t, half_t>(half_t x)
{
constexpr bool negative_zero_nan = true;
constexpr bool clip = true;
constexpr f8_rounding_mode rm = f8_rounding_mode::standard;
constexpr uint32_t rng = 0;
return utils::
cast_to_f8<half_t, bf8_t, negative_zero_nan, clip, (rm == f8_rounding_mode::stochastic)>(
x, rng);
}
// convert bf8 to fp16
template <>
inline __host__ __device__ half_t type_convert<half_t, bf8_t>(bf8_t x)
{
constexpr bool negative_zero_nan = true;
return utils::cast_from_f8<bf8_t, half_t, negative_zero_nan>(x);
}
#endif
// Declare a template function for bf16 conversion using RTN
template <typename Y, typename X>
......@@ -181,6 +229,7 @@ inline __host__ __device__ constexpr bhalf_t bf16_convert_rtn<bhalf_t, half_t>(h
template <typename Y, typename X>
__host__ __device__ constexpr Y f8_convert_sr(X x);
#if defined CK_ENABLE_FP8
// convert fp32 to fp8 with stochastic rounding
template <>
inline __host__ __device__ f8_t f8_convert_sr<f8_t, float>(float x)
......@@ -191,8 +240,9 @@ inline __host__ __device__ f8_t f8_convert_sr<f8_t, float>(float x)
constexpr int seed = 42;
// as thread id is not available on host, use 0 for prn generation
uint32_t rng = prand_generator<float, seed>(reinterpret_cast<uintptr_t>(&x), x);
return utils::cast_to_f8<float, negative_zero_nan, clip, (rm == f8_rounding_mode::stochastic)>(
x, rng);
return utils::
cast_to_f8<float, f8_t, negative_zero_nan, clip, (rm == f8_rounding_mode::stochastic)>(x,
rng);
}
// convert fp16 to fp8 with stochastic rounding
......@@ -205,8 +255,42 @@ inline __host__ __device__ f8_t f8_convert_sr<f8_t, half_t>(half_t x)
constexpr int seed = 42;
// as thread id is not available on host, use 0 for prn generation
uint32_t rng = prand_generator<half_t, seed>(reinterpret_cast<uintptr_t>(&x), x);
return utils::cast_to_f8<half_t, negative_zero_nan, clip, (rm == f8_rounding_mode::stochastic)>(
return utils::
cast_to_f8<half_t, f8_t, negative_zero_nan, clip, (rm == f8_rounding_mode::stochastic)>(
x, rng);
}
#endif
#if defined CK_ENABLE_BF8
// convert fp32 to bf8 with stochastic rounding
template <>
inline __host__ __device__ bf8_t f8_convert_sr<bf8_t, float>(float x)
{
constexpr bool negative_zero_nan = true;
constexpr bool clip = true;
constexpr f8_rounding_mode rm = f8_rounding_mode::stochastic;
constexpr int seed = 42;
// as thread id is not available on host, use 0 for prn generation
uint32_t rng = prand_generator<float, seed>(reinterpret_cast<uintptr_t>(&x), x);
return utils::
cast_to_f8<float, bf8_t, negative_zero_nan, clip, (rm == f8_rounding_mode::stochastic)>(
x, rng);
}
// convert fp16 to bf8 with stochastic rounding
template <>
inline __host__ __device__ bf8_t f8_convert_sr<bf8_t, half_t>(half_t x)
{
constexpr bool negative_zero_nan = true;
constexpr bool clip = true;
constexpr f8_rounding_mode rm = f8_rounding_mode::stochastic;
constexpr int seed = 42;
// as thread id is not available on host, use 0 for prn generation
uint32_t rng = prand_generator<half_t, seed>(reinterpret_cast<uintptr_t>(&x), x);
return utils::
cast_to_f8<half_t, bf8_t, negative_zero_nan, clip, (rm == f8_rounding_mode::stochastic)>(
x, rng);
}
#endif
} // namespace ck
library/include/ck/library/reference_tensor_operation/cpu/reference_gemm.hpp
View file @ 578bc24a
......@@ -20,7 +20,8 @@ template <typename ADataType,
typename AccDataType,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CElementwiseOperation>
typename CElementwiseOperation,
typename ComputType = ADataType>
struct ReferenceGemm : public device::BaseOperator
{
// Argument
......@@ -64,8 +65,8 @@ struct ReferenceGemm : public device::BaseOperator
for(int k = 0; k < K; ++k)
{
ADataType v_a;
BDataType v_b;
ComputType v_a;
ComputType v_b;
// use PassThrough instead of ConvertBF16RTN for reference calculation
if constexpr(is_same_v<AElementwiseOperation,
......
library/include/ck/library/tensor_operation_instance/device_operation_instance_factory.hpp
View file @ 578bc24a
......@@ -17,10 +17,15 @@ namespace instance {
using F64 = double;
using F32 = float;
using F16 = ck::half_t;
using F8 = ck::f8_t;
using BF16 = ck::bhalf_t;
using I8 = int8_t;
using I32 = int32_t;
#if defined CK_ENABLE_FP8
using F8 = ck::f8_t;
#endif
#if defined CK_ENABLE_BF8
using BF8 = ck::bf8_t;
#endif
using Empty_Tuple = ck::Tuple<>;
......
library/include/ck/library/tensor_operation_instance/gpu/gemm.hpp
View file @ 578bc24a
......@@ -23,12 +23,17 @@ void add_device_gemm_dl_f16_f16_f16_km_kn_mn_instances(
DeviceGemm<Col, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dl_f16_f16_f16_km_kn_mn_irregular_instances(
std::vector<std::unique_ptr<
DeviceGemm<Col, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dpp_f16_f16_f16_km_kn_mn_instances(
std::vector<std::unique_ptr<
DeviceGemm<Col, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dl_f16_f16_f16_km_kn_mn_irregular_instances(
void add_device_gemm_dpp_f16_f16_f16_km_kn_mn_irregular_instances(
std::vector<std::unique_ptr<
DeviceGemm<Col, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
......@@ -38,12 +43,17 @@ void add_device_gemm_dl_f16_f16_f16_km_nk_mn_instances(
DeviceGemm<Col, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dl_f16_f16_f16_km_nk_mn_irregular_instances(
std::vector<std::unique_ptr<
DeviceGemm<Col, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dpp_f16_f16_f16_km_nk_mn_instances(
std::vector<std::unique_ptr<
DeviceGemm<Col, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dl_f16_f16_f16_km_nk_mn_irregular_instances(
void add_device_gemm_dpp_f16_f16_f16_km_nk_mn_irregular_instances(
std::vector<std::unique_ptr<
DeviceGemm<Col, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
......@@ -53,12 +63,17 @@ void add_device_gemm_dl_f16_f16_f16_mk_kn_mn_instances(
DeviceGemm<Row, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dl_f16_f16_f16_mk_kn_mn_irregular_instances(
std::vector<std::unique_ptr<
DeviceGemm<Row, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dpp_f16_f16_f16_mk_kn_mn_instances(
std::vector<std::unique_ptr<
DeviceGemm<Row, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dl_f16_f16_f16_mk_kn_mn_irregular_instances(
void add_device_gemm_dpp_f16_f16_f16_mk_kn_mn_irregular_instances(
std::vector<std::unique_ptr<
DeviceGemm<Row, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
......@@ -68,12 +83,17 @@ void add_device_gemm_dl_f16_f16_f16_mk_nk_mn_instances(
DeviceGemm<Row, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dl_f16_f16_f16_mk_nk_mn_irregular_instances(
std::vector<std::unique_ptr<
DeviceGemm<Row, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dpp_f16_f16_f16_mk_nk_mn_instances(
std::vector<std::unique_ptr<
DeviceGemm<Row, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dl_f16_f16_f16_mk_nk_mn_irregular_instances(
void add_device_gemm_dpp_f16_f16_f16_mk_nk_mn_irregular_instances(
std::vector<std::unique_ptr<
DeviceGemm<Row, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
......@@ -375,6 +395,7 @@ struct DeviceOperationInstanceFactory<
add_device_gemm_dl_f16_f16_f16_mk_kn_mn_instances(op_ptrs);
add_device_gemm_dl_f16_f16_f16_mk_kn_mn_irregular_instances(op_ptrs);
add_device_gemm_dpp_f16_f16_f16_mk_kn_mn_instances(op_ptrs);
add_device_gemm_dpp_f16_f16_f16_mk_kn_mn_irregular_instances(op_ptrs);
#endif
add_device_gemm_xdl_c_shuffle_f16_f16_f16_mk_kn_mn_instances(op_ptrs);
}
......@@ -386,6 +407,7 @@ struct DeviceOperationInstanceFactory<
add_device_gemm_dl_f16_f16_f16_mk_nk_mn_instances(op_ptrs);
add_device_gemm_dl_f16_f16_f16_mk_nk_mn_irregular_instances(op_ptrs);
add_device_gemm_dpp_f16_f16_f16_mk_nk_mn_instances(op_ptrs);
add_device_gemm_dpp_f16_f16_f16_mk_nk_mn_irregular_instances(op_ptrs);
#endif
add_device_gemm_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_instances(op_ptrs);
add_device_gemm_xdl_c_shuffle_2_stage_f16_f16_f16_mk_nk_mn_instances(op_ptrs);
......@@ -398,6 +420,7 @@ struct DeviceOperationInstanceFactory<
add_device_gemm_dl_f16_f16_f16_km_kn_mn_instances(op_ptrs);
add_device_gemm_dl_f16_f16_f16_km_kn_mn_irregular_instances(op_ptrs);
add_device_gemm_dpp_f16_f16_f16_km_kn_mn_instances(op_ptrs);
add_device_gemm_dpp_f16_f16_f16_km_kn_mn_irregular_instances(op_ptrs);
#endif
add_device_gemm_xdl_c_shuffle_f16_f16_f16_km_kn_mn_instances(op_ptrs);
}
......@@ -409,6 +432,7 @@ struct DeviceOperationInstanceFactory<
add_device_gemm_dl_f16_f16_f16_km_nk_mn_instances(op_ptrs);
add_device_gemm_dl_f16_f16_f16_km_nk_mn_irregular_instances(op_ptrs);
add_device_gemm_dpp_f16_f16_f16_km_nk_mn_instances(op_ptrs);
add_device_gemm_dpp_f16_f16_f16_km_nk_mn_irregular_instances(op_ptrs);
#endif
add_device_gemm_xdl_c_shuffle_f16_f16_f16_km_nk_mn_instances(op_ptrs);
}
......
library/include/ck/library/tensor_operation_instance/gpu/gemm_multiply_add.hpp
View file @ 578bc24a
......@@ -45,6 +45,7 @@ void add_device_gemm_multiply_add_xdl_c_shuffle_f16_f16_f16_f16_f16_mk_nk_mn_mn_
PassThrough,
MultiplyAdd>>>&);
#if defined CK_ENABLE_FP8
void add_device_gemm_multiply_add_xdl_c_shuffle_f16_f8_f32_f32_f16_mk_kn_mn_mn_mn_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleD<Row,
Row,
......@@ -70,6 +71,7 @@ void add_device_gemm_multiply_add_xdl_c_shuffle_f16_f8_f32_f32_f16_mk_nk_mn_mn_m
PassThrough,
PassThrough,
MultiplyAdd>>>&);
#endif
// GEMM + Multiply + Add
template <typename ALayout,
......@@ -131,6 +133,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGemmMu
}
}
#if defined CK_ENABLE_FP8
if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, f8_t> &&
is_same_v<D0DataType, float> && is_same_v<D1DataType, float> &&
is_same_v<EDataType, half_t>)
......@@ -150,6 +153,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGemmMu
op_ptrs);
}
}
#endif
return op_ptrs;
}
......
library/include/ck/library/tensor_operation_instance/gpu/gemm_splitk.hpp
View file @ 578bc24a
......@@ -57,6 +57,7 @@ void add_device_gemm_xdl_splitk_f32_f32_f32_mk_nk_mn_instances(
DeviceGemmSplitK<Row, Col, Row, F32, F32, F32, PassThrough, PassThrough, PassThrough>>>&
instances);
#if defined CK_ENABLE_FP8
void add_device_gemm_xdl_splitk_f8_f16_f16_km_kn_mn_instances(
std::vector<std::unique_ptr<
DeviceGemmSplitK<Col, Row, Row, F8, F16, F16, PassThrough, PassThrough, PassThrough>>>&
......@@ -96,6 +97,7 @@ void add_device_gemm_xdl_splitk_f16_f8_f16_mk_nk_mn_instances(
std::vector<std::unique_ptr<
DeviceGemmSplitK<Row, Col, Row, F16, F8, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
#endif
template <typename ADataType,
typename BDataType,
......@@ -176,6 +178,7 @@ struct DeviceOperationInstanceFactory<
add_device_gemm_xdl_splitk_f16_f16_f16_km_nk_mn_instances(op_ptrs);
}
}
#if defined CK_ENABLE_FP8
else if constexpr(is_same_v<ADataType, f8_t> && is_same_v<BDataType, half_t> &&
is_same_v<CDataType, half_t>)
{
......@@ -224,6 +227,7 @@ struct DeviceOperationInstanceFactory<
add_device_gemm_xdl_splitk_f16_f8_f16_km_nk_mn_instances(op_ptrs);
}
}
#endif
return op_ptrs;
}
......
library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_dl_instance.hpp 0 → 100644
View file @ 578bc24a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_dl.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using namespace ck::tensor_layout::convolution;
using BF16 = ck::bhalf_t;
using F16 = ck::half_t;
using F32 = float;
using Empty_Tuple = ck::Tuple<>;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto ConvBwdWeightDefault =
ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Default;
static constexpr auto ConvBwdWeightFilter1x1Stride1Pad0 =
ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0;
template <ck::index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename ELayout,
ConvolutionBackwardWeightSpecialization ConvSpec>
using device_grouped_conv_bwd_weight_dl_f32_instances = std::tuple<
// clang-format off
//############################| Num| InLayout| WeiLayout| OutLayout| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| M1Per| N1Per| KPer| M1N1Thread| M1N1Thread| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| CThreadTransfer| CThreadTransfer| CThreadTransfer|
//############################| Dim| | | | Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | Thread| Thread| Thread| ClusterM1Xs| ClusterN1Xs| ThreadSliceLengths| ThreadClusterLengths| ThreadCluster| SrcAccessOrder| SrcVectorTensorLengths| SrcVectorTensor| DstVectorTensorLengths| ThreadSliceLengths| ThreadClusterLengths| ThreadCluster| SrcAccessOrder| SrcVectorTensorLengths| SrcVectorTensor| DstVectorTensorLengths| SrcDstAccessOrder| SrcDstVectorDim| DstScalarPerVector|
//############################| Spatial| | | | | | | | Operation| Operation| Operation| Specialization| | | | | | | | | | | _K0_M0_M1_K1| _K0_M0_M1_K1| ArrangeOrder| | _K0_M0_M1_K1| ContiguousDimOrder| _K0_M0_M1_K1| _K0_N0_N1_K1| _K0_N0_N1_K1| ArrangeOrder| | _K0_N0_N1_K1| ContiguousDimOrder| _K0_N0_N1_K1| | | |
//############################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvBwdWeight_Dl< NDimSpatial, ALayout, BLayout, ELayout, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 256, 128, 128, 16, 1, 4, 4, 1, S<8, 2>, S<8, 2>, S<1, 8, 1, 1, 1>, S<1, 2, 1, 128, 1>, S<0, 2, 3, 1, 4>, S<0, 2, 3, 1, 4>, S<1, 1, 1, 1, 1>, S<0, 2, 3, 1, 4>, S<1, 1, 1, 1, 1>, S<1, 1, 1, 8, 1>, S<1, 16, 1, 16, 1>, S<0, 1, 4, 2, 3>, S<0, 1, 4, 2, 3>, S<1, 1, 1, 1, 1>, S<0, 1, 4, 2, 3>, S<1, 1, 1, 1, 1>, S<0, 1, 2, 3, 4, 5>, 5, 1>
// clang-format on
>;
template <ck::index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename ELayout,
ConvolutionBackwardWeightSpecialization ConvSpec>
using device_grouped_conv_bwd_weight_dl_f16_instances = std::tuple<
// clang-format off
//############################| Num| InLayout| WeiLayout| OutLayout| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| M1Per| N1Per| KPer| M1N1Thread| M1N1Thread| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| CThreadTransfer| CThreadTransfer| CThreadTransfer|
//############################| Dim| | | | Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | Thread| Thread| Thread| ClusterM1Xs| ClusterN1Xs| ThreadSliceLengths| ThreadClusterLengths| ThreadCluster| SrcAccessOrder| SrcVectorTensorLengths| SrcVectorTensor| DstVectorTensorLengths| ThreadSliceLengths| ThreadClusterLengths| ThreadCluster| SrcAccessOrder| SrcVectorTensorLengths| SrcVectorTensor| DstVectorTensorLengths| SrcDstAccessOrder| SrcDstVectorDim| DstScalarPerVector|
//############################| Spatial| | | | | | | | Operation| Operation| Operation| Specialization| | | | | | | | | | | _K0_M0_M1_K1| _K0_M0_M1_K1| ArrangeOrder| | _K0_M0_M1_K1| ContiguousDimOrder| _K0_M0_M1_K1| _K0_N0_N1_K1| _K0_N0_N1_K1| ArrangeOrder| | _K0_N0_N1_K1| ContiguousDimOrder| _K0_N0_N1_K1| | | |
//############################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvBwdWeight_Dl< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 256, 128, 128, 16, 1, 4, 4, 1, S<8, 2>, S<8, 2>, S<1, 8, 1, 1, 1>, S<1, 2, 1, 128, 1>, S<0, 2, 3, 1, 4>, S<0, 2, 3, 1, 4>, S<1, 1, 1, 1, 1>, S<0, 2, 3, 1, 4>, S<1, 1, 1, 1, 1>, S<1, 1, 1, 8, 1>, S<1, 16, 1, 16, 1>, S<0, 1, 4, 2, 3>, S<0, 1, 4, 2, 3>, S<1, 1, 1, 1, 1>, S<0, 1, 4, 2, 3>, S<1, 1, 1, 1, 1>, S<0, 1, 2, 3, 4, 5>, 5, 1>
// clang-format on
>;
template <ck::index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename ELayout,
ConvolutionBackwardWeightSpecialization ConvSpec>
using device_grouped_conv_bwd_weight_dl_bf16_instances = std::tuple<
// clang-format off
//############################| Num| InLayout| WeiLayout| OutLayout| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| M1Per| N1Per| KPer| M1N1Thread| M1N1Thread| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| CThreadTransfer| CThreadTransfer| CThreadTransfer|
//############################| Dim| | | | Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | Thread| Thread| Thread| ClusterM1Xs| ClusterN1Xs| ThreadSliceLengths| ThreadClusterLengths| ThreadCluster| SrcAccessOrder| SrcVectorTensorLengths| SrcVectorTensor| DstVectorTensorLengths| ThreadSliceLengths| ThreadClusterLengths| ThreadCluster| SrcAccessOrder| SrcVectorTensorLengths| SrcVectorTensor| DstVectorTensorLengths| SrcDstAccessOrder| SrcDstVectorDim| DstScalarPerVector|
//############################| Spatial| | | | | | | | Operation| Operation| Operation| Specialization| | | | | | | | | | | _K0_M0_M1_K1| _K0_M0_M1_K1| ArrangeOrder| | _K0_M0_M1_K1| ContiguousDimOrder| _K0_M0_M1_K1| _K0_N0_N1_K1| _K0_N0_N1_K1| ArrangeOrder| | _K0_N0_N1_K1| ContiguousDimOrder| _K0_N0_N1_K1| | | |
//############################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvBwdWeight_Dl< NDimSpatial, ALayout, BLayout, ELayout, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 256, 128, 128, 16, 1, 4, 4, 1, S<8, 2>, S<8, 2>, S<1, 8, 1, 1, 1>, S<1, 2, 1, 128, 1>, S<0, 2, 3, 1, 4>, S<0, 2, 3, 1, 4>, S<1, 1, 1, 1, 1>, S<0, 2, 3, 1, 4>, S<1, 1, 1, 1, 1>, S<1, 1, 1, 8, 1>, S<1, 16, 1, 16, 1>, S<0, 1, 4, 2, 3>, S<0, 1, 4, 2, 3>, S<1, 1, 1, 1, 1>, S<0, 1, 4, 2, 3>, S<1, 1, 1, 1, 1>, S<0, 1, 2, 3, 4, 5>, 5, 1>
// clang-format on
>;
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_dl_instance.hpp
View file @ 578bc24a
......@@ -55,8 +55,8 @@ using device_grouped_conv2d_fwd_dl_f16_instances = std::tuple<
// ########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instances
// TODO: Change to ScalarPerVector = 1 when inner_product<half_t, half_t, float> will be supported
DeviceGroupedConvFwdDlMultipleD_NHWC_KYXC_NHWK< 2, F16, F16, DsDatatype, F16, F32, InLayout, WeiLayout, DsLayout, OutLayout, PassThrough, PassThrough, CDEElementOp, ConvSpec, GemmMNKPadding, 8, 16, 4, 2, 2, 1, 2, 1, S<4, 2>, S<1, 1>, S<2, 1, 2, 2>, S<1, 1, 8, 1>, S<1, 2, 0, 3>, S<1, 2, 0, 3>, S<1, 1, 1, 2>, S<1, 2, 0, 3>, S<1, 1, 1, 2>, S<1, 1, 1, 2>, S<2, 1, 4, 1>, S<1, 2, 0, 3>, S<1, 2, 0, 3>, S<1, 1, 1, 1>, S<1, 2, 0, 3>, S<1, 1, 1, 2>, S<0, 1, 2, 3, 4, 5>, 5, 2>,
DeviceGroupedConvFwdDlMultipleD_NHWC_KYXC_NHWK< 2, F16, F16, DsDatatype, F16, F32, InLayout, WeiLayout, DsLayout, OutLayout, PassThrough, PassThrough, CDEElementOp, ConvSpec, GemmMNKPadding, 256, 128, 128, 16, 2, 4, 4, 1, S<8, 2>, S<8, 2>, S<8, 1, 1, 2>, S<2, 1, 128, 1>, S<1, 2, 0, 3>, S<1, 2, 0, 3>, S<1, 1, 1, 2>, S<1, 2, 0, 3>, S<1, 1, 1, 2>, S<8, 1, 1, 2>, S<2, 1, 128, 1>, S<1, 2, 0, 3>, S<1, 2, 0, 3>, S<1, 1, 1, 1>, S<1, 2, 0, 3>, S<1, 1, 1, 2>, S<0, 1, 2, 3, 4, 5>, 5, 2>,
DeviceGroupedConvFwdDlMultipleD_NHWC_KYXC_NHWK< 2, F16, F16, DsDatatype, F16, F32, InLayout, WeiLayout, DsLayout, OutLayout, PassThrough, PassThrough, CDEElementOp, ConvSpec, GemmMNKPadding, 8, 16, 4, 2, 1, 1, 2, 1, S<4, 2>, S<1, 1>, S<2, 1, 2, 1>, S<1, 1, 8, 1>, S<1, 2, 0, 3>, S<1, 2, 0, 3>, S<1, 1, 1, 1>, S<1, 2, 0, 3>, S<1, 1, 1, 1>, S<1, 1, 1, 1>, S<2, 1, 4, 1>, S<1, 2, 0, 3>, S<1, 2, 0, 3>, S<1, 1, 1, 1>, S<1, 2, 0, 3>, S<1, 1, 1, 1>, S<0, 1, 2, 3, 4, 5>, 5, 1>,
DeviceGroupedConvFwdDlMultipleD_NHWC_KYXC_NHWK< 2, F16, F16, DsDatatype, F16, F32, InLayout, WeiLayout, DsLayout, OutLayout, PassThrough, PassThrough, CDEElementOp, ConvSpec, GemmMNKPadding, 256, 128, 128, 16, 1, 4, 4, 1, S<8, 2>, S<8, 2>, S<8, 1, 1, 1>, S<2, 1, 128, 1>, S<1, 2, 0, 3>, S<1, 2, 0, 3>, S<1, 1, 1, 1>, S<1, 2, 0, 3>, S<1, 1, 1, 1>, S<8, 1, 1, 1>, S<2, 1, 128, 1>, S<1, 2, 0, 3>, S<1, 2, 0, 3>, S<1, 1, 1, 1>, S<1, 2, 0, 3>, S<1, 1, 1, 1>, S<0, 1, 2, 3, 4, 5>, 5, 1>,
DeviceGroupedConvFwdDlMultipleD_NHWC_KYXC_NHWK< 2, F16, F16, DsDatatype, F16, F32, InLayout, WeiLayout, DsLayout, OutLayout, PassThrough, PassThrough, CDEElementOp, ConvSpec, GemmMNKPadding, 256, 128, 128, 16, 2, 4, 4, 1, S<8, 2>, S<8, 2>, S<8, 1, 1, 2>, S<2, 1, 128, 1>, S<1, 2, 0, 3>, S<1, 2, 0, 3>, S<4, 1, 1, 2>, S<1, 2, 0, 3>, S<1, 1, 1, 2>, S<8, 1, 1, 2>, S<2, 1, 128, 1>, S<1, 2, 0, 3>, S<1, 2, 0, 3>, S<4, 1, 1, 2>, S<1, 2, 0, 3>, S<1, 1, 1, 2>, S<0, 1, 2, 3, 4, 5>, 5, 4>
// clang-format on
......
library/include/ck/library/utility/check_err.hpp
View file @ 578bc24a
......@@ -230,5 +230,99 @@ check_err(const Range& out,
return res;
}
#if defined CK_ENABLE_FP8
template <typename Range, typename RefRange>
std::enable_if_t<(std::is_same_v<ranges::range_value_t<Range>, ranges::range_value_t<RefRange>> &&
std::is_same_v<ranges::range_value_t<Range>, f8_t>),
bool>
check_err(const Range& out,
const RefRange& ref,
const std::string& msg = "Error: Incorrect results!",
double rtol = 1e-3,
double atol = 1e-3)
{
if(out.size() != ref.size())
{
std::cerr << msg << " out.size() != ref.size(), :" << out.size() << " != " << ref.size()
<< std::endl;
return false;
}
bool res{true};
int err_count = 0;
double err = 0;
double max_err = std::numeric_limits<float>::min();
for(std::size_t i = 0; i < ref.size(); ++i)
{
const double o = type_convert<float>(*std::next(std::begin(out), i));
const double r = type_convert<float>(*std::next(std::begin(ref), i));
err = std::abs(o - r);
if(err > atol + rtol * std::abs(r) || !std::isfinite(o) || !std::isfinite(r))
{
max_err = err > max_err ? err : max_err;
err_count++;
if(err_count < 5)
{
std::cerr << msg << std::setw(12) << std::setprecision(7) << " out[" << i
<< "] != ref[" << i << "]: " << o << " != " << r << std::endl;
}
res = false;
}
}
if(!res)
{
std::cerr << std::setw(12) << std::setprecision(7) << "max err: " << max_err << std::endl;
}
return res;
}
#endif
#if defined CK_ENABLE_BF8
template <typename Range, typename RefRange>
std::enable_if_t<(std::is_same_v<ranges::range_value_t<Range>, ranges::range_value_t<RefRange>> &&
std::is_same_v<ranges::range_value_t<Range>, bf8_t>),
bool>
check_err(const Range& out,
const RefRange& ref,
const std::string& msg = "Error: Incorrect results!",
double rtol = 1e-3,
double atol = 1e-3)
{
if(out.size() != ref.size())
{
std::cerr << msg << " out.size() != ref.size(), :" << out.size() << " != " << ref.size()
<< std::endl;
return false;
}
bool res{true};
int err_count = 0;
double err = 0;
double max_err = std::numeric_limits<float>::min();
for(std::size_t i = 0; i < ref.size(); ++i)
{
const double o = type_convert<float>(*std::next(std::begin(out), i));
const double r = type_convert<float>(*std::next(std::begin(ref), i));
err = std::abs(o - r);
if(err > atol + rtol * std::abs(r) || !std::isfinite(o) || !std::isfinite(r))
{
max_err = err > max_err ? err : max_err;
err_count++;
if(err_count < 5)
{
std::cerr << msg << std::setw(12) << std::setprecision(7) << " out[" << i
<< "] != ref[" << i << "]: " << o << " != " << r << std::endl;
}
res = false;
}
}
if(!res)
{
std::cerr << std::setw(12) << std::setprecision(7) << "max err: " << max_err << std::endl;
}
return res;
}
#endif
} // namespace utils
} // namespace ck
library/src/tensor_operation_instance/gpu/gemm/CMakeLists.txt
View file @ 578bc24a
......@@ -32,9 +32,13 @@ if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
list(APPEND GEMM_INSTANCES device_gemm_dl_f16_f16_f16_km_nk_mn_instance.cpp)
list(APPEND GEMM_INSTANCES device_gemm_dl_f16_f16_f16_km_nk_mn_irregular_instance.cpp)
list(APPEND GEMM_INSTANCES device_gemm_dpp_f16_f16_f16_km_kn_mn_instance.cpp)
list(APPEND GEMM_INSTANCES device_gemm_dpp_f16_f16_f16_km_kn_mn_irregular_instance.cpp)
list(APPEND GEMM_INSTANCES device_gemm_dpp_f16_f16_f16_km_nk_mn_instance.cpp)
list(APPEND GEMM_INSTANCES device_gemm_dpp_f16_f16_f16_km_nk_mn_irregular_instance.cpp)
list(APPEND GEMM_INSTANCES device_gemm_dpp_f16_f16_f16_mk_kn_mn_instance.cpp)
list(APPEND GEMM_INSTANCES device_gemm_dpp_f16_f16_f16_mk_kn_mn_irregular_instance.cpp)
list(APPEND GEMM_INSTANCES device_gemm_dpp_f16_f16_f16_mk_nk_mn_instance.cpp)
list(APPEND GEMM_INSTANCES device_gemm_dpp_f16_f16_f16_mk_nk_mn_irregular_instance.cpp)
endif()
list(APPEND GEMM_INSTANCES device_gemm_xdl_c_shuffle_f16_f16_f16_mk_kn_mn_instance.cpp)
list(APPEND GEMM_INSTANCES device_gemm_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_instance.cpp)
......
library/src/tensor_operation_instance/gpu/gemm/device_gemm_dpp_f16_f16_f16_km_kn_mn_instance.cpp
View file @ 578bc24a
......@@ -36,11 +36,21 @@ using device_gemm_dpp_f16_f16_f16_km_kn_mn_instances = std::tuple<
// ########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 128, 64, 4, 4, 16, 16, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 128, 64, 4, 4, 32, 8, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 64, 64, 64, 4, 4, 16, 16, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 64, 64, 64, 4, 4, 32, 8, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 32, 64, 64, 4, 4, 16, 16, 1, 2, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 32, 32, 32, 4, 4, 32, 8, 1, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 64, 64, 64, 4, 4, 32, 8, 2, 4, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 32, 32, 32, 32, 4, 4, 32, 8, 1, 4, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 32, 16, 16, 16, 4, 4, 16, 16, 1, 1, S<2, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<2, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, 5, 1>
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 1, 128, 64, 4, 4, 1, 32, 1, 1, S<4, 1, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 16, 32, 64, 4, 4, 8, 32, 1, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 16, 16, 64, 4, 4, 8, 16, 1, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 4, 32, 64, 4, 4, 2, 32, 1, 1, S<4, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 2, 64, 64, 4, 4, 2, 32, 1, 1, S<4, 2, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 2, 32, 64, 4, 4, 1, 32, 1, 1, S<4, 2, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 1, 64, 64, 4, 4, 1, 32, 1, 1, S<4, 1, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 32, 8, 16, 64, 4, 4, 4, 16, 2, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 32, 8, 16, 32, 4, 4, 8, 16, 1, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 32, 4, 32, 32, 4, 4, 4, 32, 1, 1, S<4, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<2, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 32, 2, 16, 32, 4, 4, 2, 16, 1, 1, S<4, 2, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<2, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, 5, 1>
>;
// clang-format on
......
library/src/tensor_operation_instance/gpu/gemm/device_gemm_dpp_f16_f16_f16_km_kn_mn_irregular_instance.cpp 0 → 100644
View file @ 578bc24a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_dpp.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto MNPadding = ck::tensor_operation::device::GemmSpecialization::MNPadding;
// Compilation parameters for a[k, m] * b[k, n] = c[m, n]
// clang-format off
using device_gemm_dpp_f16_f16_f16_km_kn_mn_irregular_instances = std::tuple<
// ########| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MDpp| NDpp| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
// ########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise| Specialization| Size| Block| Block| Block| | | Dpp| Dpp| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
// ########| | | | | | | | Operation| Operation| Operation| | | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
// ########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, MNPadding, 256, 128, 128, 64, 4, 4, 16, 16, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, MNPadding, 256, 64, 64, 64, 4, 4, 16, 16, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, MNPadding, 128, 64, 64, 64, 4, 4, 32, 8, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, MNPadding, 128, 32, 32, 64, 4, 4, 32, 8, 1, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, MNPadding, 128, 4, 32, 64, 4, 4, 1, 32, 1, 1, S<4, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, MNPadding, 64, 64, 64, 32, 4, 4, 32, 8, 2, 4, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, MNPadding, 64, 16, 32, 64, 4, 4, 8, 32, 1, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, MNPadding, 64, 16, 16, 64, 4, 4, 8, 16, 1, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, MNPadding, 64, 2, 32, 64, 4, 4, 1, 32, 1, 1, S<4, 2, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, MNPadding, 32, 32, 16, 64, 4, 4, 16, 16, 2, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, MNPadding, 32, 8, 16, 64, 4, 4, 4, 16, 2, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, MNPadding, 32, 1, 32, 64, 4, 4, 1, 32, 1, 1, S<4, 1, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, 5, 1>
>;
// clang-format on
void add_device_gemm_dpp_f16_f16_f16_km_kn_mn_irregular_instances(
std::vector<std::unique_ptr<
DeviceGemm<Col, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances)
{
add_device_operation_instances(instances,
device_gemm_dpp_f16_f16_f16_km_kn_mn_irregular_instances{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/gemm/device_gemm_dpp_f16_f16_f16_km_nk_mn_instance.cpp
View file @ 578bc24a
......@@ -36,11 +36,21 @@ using device_gemm_dpp_f16_f16_f16_km_nk_mn_instances = std::tuple<
// ########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 128, 64, 4, 8, 16, 16, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 128, 64, 4, 8, 32, 8, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 64, 64, 64, 4, 8, 16, 16, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 64, 64, 64, 4, 8, 32, 8, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 32, 64, 64, 4, 8, 16, 16, 1, 2, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 32, 32, 32, 4, 8, 32, 8, 1, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 64, 64, 64, 4, 8, 32, 8, 2, 4, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 32, 32, 32, 32, 4, 8, 32, 8, 1, 4, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, S<4, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 32, 16, 16, 16, 4, 8, 16, 16, 1, 1, S<2, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 1, 128, 64, 4, 8, 1, 32, 1, 1, S<4, 1, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 16, 32, 64, 4, 8, 8, 32, 1, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 16, 16, 64, 4, 8, 8, 16, 1, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 4, 32, 64, 4, 8, 2, 32, 1, 1, S<4, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 2, 64, 64, 4, 8, 2, 32, 1, 1, S<4, 2, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 2, 32, 64, 4, 8, 1, 32, 1, 1, S<4, 2, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 1, 64, 64, 4, 8, 1, 32, 1, 1, S<4, 1, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 32, 8, 16, 64, 4, 8, 4, 16, 2, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 32, 8, 16, 32, 4, 8, 8, 16, 1, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 32, 4, 32, 32, 4, 8, 4, 32, 1, 1, S<4, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 32, 2, 16, 32, 4, 8, 2, 16, 1, 1, S<4, 2, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>
>;
// clang-format on
......
library/src/tensor_operation_instance/gpu/gemm/device_gemm_dpp_f16_f16_f16_km_nk_mn_irregular_instance.cpp 0 → 100644
View file @ 578bc24a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_dpp.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto MNPadding = ck::tensor_operation::device::GemmSpecialization::MNPadding;
// Compilation parameters for a[k, m] * b[n, k] = c[m, n]
// clang-format off
using device_gemm_dpp_f16_f16_f16_km_nk_mn_irregular_instances = std::tuple<
// ########| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MDpp| NDpp| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
// ########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise| Specialization| Size| Block| Block| Block| | | Dpp| Dpp| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
// ########| | | | | | | | Operation| Operation| Operation| | | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
// ########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, MNPadding, 256, 128, 128, 64, 4, 8, 16, 16, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, MNPadding, 128, 64, 64, 64, 4, 8, 32, 8, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, MNPadding, 128, 32, 64, 64, 4, 8, 16, 16, 1, 2, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, MNPadding, 128, 32, 32, 64, 4, 8, 32, 8, 1, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, MNPadding, 128, 4, 32, 64, 4, 8, 1, 32, 1, 1, S<4, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, MNPadding, 64, 64, 64, 32, 4, 8, 32, 8, 2, 4, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, MNPadding, 64, 16, 32, 64, 4, 8, 8, 32, 1, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, MNPadding, 64, 16, 16, 64, 4, 8, 8, 16, 1, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, MNPadding, 64, 2, 32, 64, 4, 8, 1, 32, 1, 1, S<4, 2, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, MNPadding, 32, 32, 16, 64, 4, 8, 16, 16, 2, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, S<4, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, MNPadding, 32, 8, 16, 64, 4, 8, 4, 16, 2, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, MNPadding, 32, 1, 32, 64, 4, 8, 1, 32, 1, 1, S<4, 1, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, S<4, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>
>;
// clang-format on
void add_device_gemm_dpp_f16_f16_f16_km_nk_mn_irregular_instances(
std::vector<std::unique_ptr<
DeviceGemm<Col, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances)
{
add_device_operation_instances(instances,
device_gemm_dpp_f16_f16_f16_km_nk_mn_irregular_instances{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/gemm/device_gemm_dpp_f16_f16_f16_mk_kn_mn_instance.cpp
View file @ 578bc24a
......@@ -35,11 +35,21 @@ using device_gemm_dpp_f16_f16_f16_mk_kn_mn_instances = std::tuple<
// ########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 128, 64, 8, 4, 16, 16, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 128, 64, 8, 4, 32, 8, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 64, 64, 64, 8, 4, 16, 16, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 64, 64, 64, 8, 4, 32, 8, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 32, 64, 64, 8, 4, 16, 16, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 32, 32, 32, 8, 4, 32, 8, 1, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 64, 64, 64, 8, 4, 32, 8, 2, 4, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 32, 32, 32, 32, 8, 4, 32, 8, 1, 4, S<4, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 32, 16, 16, 16, 8, 4, 16, 16, 1, 1, S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<2, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, 5, 1>
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 1, 128, 64, 8, 4, 1, 32, 1, 1, S<4, 1, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 16, 32, 64, 8, 4, 8, 32, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 16, 16, 64, 8, 4, 8, 16, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 4, 32, 64, 8, 4, 2, 32, 1, 1, S<4, 4, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 2, 64, 64, 8, 4, 2, 32, 1, 1, S<4, 2, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 2, 32, 64, 8, 4, 1, 32, 1, 1, S<4, 2, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 1, 64, 64, 8, 4, 1, 32, 1, 1, S<4, 1, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 32, 8, 16, 64, 8, 4, 4, 16, 2, 1, S<4, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 32, 8, 16, 32, 8, 4, 8, 16, 1, 1, S<4, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 32, 4, 32, 32, 8, 4, 4, 32, 1, 1, S<4, 4, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<2, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 32, 2, 16, 32, 8, 4, 2, 16, 1, 1, S<4, 2, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<2, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, 5, 1>
>;
// clang-format on
......
library/src/tensor_operation_instance/gpu/gemm/device_gemm_dpp_f16_f16_f16_mk_kn_mn_irregular_instance.cpp 0 → 100644
View file @ 578bc24a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_dpp.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto MNPadding = ck::tensor_operation::device::GemmSpecialization::MNPadding;
// Compilation parameters for a[m, k] * b[k, n] = c[m, n]
// clang-format off
using device_gemm_dpp_f16_f16_f16_mk_kn_mn_irregular_instances = std::tuple<
// ########| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MDpp| NDpp| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
// ########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise| Specialization| Size| Block| Block| Block| | | Dpp| Dpp| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
// ########| | | | | | | | Operation| Operation| Operation| | | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
// ########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, MNPadding, 256, 128, 128, 64, 8, 4, 16, 16, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, MNPadding, 256, 64, 64, 64, 8, 4, 16, 16, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, MNPadding, 128, 64, 64, 64, 8, 4, 32, 8, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, MNPadding, 128, 32, 32, 64, 8, 4, 32, 8, 1, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, MNPadding, 128, 4, 32, 64, 8, 4, 1, 32, 1, 1, S<4, 4, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, MNPadding, 64, 64, 64, 32, 8, 4, 32, 8, 2, 4, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, MNPadding, 64, 16, 32, 64, 8, 4, 8, 32, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, MNPadding, 64, 16, 16, 64, 8, 4, 8, 16, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, MNPadding, 64, 2, 32, 64, 8, 4, 1, 32, 1, 1, S<4, 2, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, MNPadding, 32, 32, 16, 64, 8, 4, 16, 16, 2, 1, S<4, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, MNPadding, 32, 8, 16, 64, 8, 4, 4, 16, 2, 1, S<4, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, MNPadding, 32, 1, 32, 64, 8, 4, 1, 32, 1, 1, S<4, 1, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, true, 5, 1>
>;
// clang-format on
void add_device_gemm_dpp_f16_f16_f16_mk_kn_mn_irregular_instances(
std::vector<std::unique_ptr<
DeviceGemm<Row, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances)
{
add_device_operation_instances(instances,
device_gemm_dpp_f16_f16_f16_mk_kn_mn_irregular_instances{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/gemm/device_gemm_dpp_f16_f16_f16_mk_nk_mn_instance.cpp
View file @ 578bc24a
......@@ -36,11 +36,21 @@ using device_gemm_dpp_f16_f16_f16_mk_nk_mn_instances = std::tuple<
// ########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 128, 64, 8, 8, 16, 16, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 128, 64, 8, 8, 32, 8, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 64, 64, 64, 8, 8, 16, 16, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 64, 64, 64, 8, 8, 32, 8, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 32, 64, 64, 8, 8, 16, 16, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 32, 32, 32, 8, 8, 32, 8, 1, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 64, 64, 64, 8, 8, 32, 8, 2, 4, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 32, 32, 32, 32, 8, 8, 32, 8, 1, 4, S<4, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 32, 16, 16, 16, 8, 8, 16, 16, 1, 1, S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 1, 128, 64, 8, 8, 1, 32, 1, 1, S<4, 1, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 16, 32, 64, 8, 8, 8, 32, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 16, 16, 64, 8, 8, 8, 16, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 4, 32, 64, 8, 8, 2, 32, 1, 1, S<4, 4, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 2, 64, 64, 8, 8, 2, 32, 1, 1, S<4, 2, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 2, 32, 64, 8, 8, 1, 32, 1, 1, S<4, 2, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 1, 64, 64, 8, 8, 1, 32, 1, 1, S<4, 1, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 32, 8, 16, 64, 8, 8, 4, 16, 2, 1, S<4, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 32, 8, 16, 32, 8, 8, 8, 16, 1, 1, S<4, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 32, 4, 32, 32, 8, 8, 4, 32, 1, 1, S<4, 4, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 32, 2, 16, 32, 8, 8, 2, 16, 1, 1, S<4, 2, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>
>;
// clang-format on
......
library/src/tensor_operation_instance/gpu/gemm/device_gemm_dpp_f16_f16_f16_mk_nk_mn_irregular_instance.cpp 0 → 100644
View file @ 578bc24a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_dpp.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto MNPadding = ck::tensor_operation::device::GemmSpecialization::MNPadding;
// Compilation parameters for a[m, k] * b[n, k] = c[m, n]
// clang-format off
using device_gemm_dpp_f16_f16_f16_mk_nk_mn_irregular_instances = std::tuple<
// ########| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MDpp| NDpp| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
// ########| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise| Specialization| Size| Block| Block| Block| | | Dpp| Dpp| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
// ########| | | | | | | | Operation| Operation| Operation| | | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
// ########| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, MNPadding, 256, 128, 128, 64, 8, 8, 16, 16, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, MNPadding, 256, 64, 64, 64, 8, 8, 16, 16, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, MNPadding, 128, 64, 64, 64, 8, 8, 32, 8, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, MNPadding, 128, 32, 32, 64, 8, 8, 32, 8, 1, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, MNPadding, 128, 4, 32, 64, 8, 8, 1, 32, 1, 1, S<4, 4, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, MNPadding, 64, 64, 64, 32, 8, 8, 32, 8, 2, 4, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, MNPadding, 64, 16, 32, 64, 8, 8, 8, 32, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, MNPadding, 64, 16, 16, 64, 8, 8, 8, 16, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, MNPadding, 64, 2, 32, 64, 8, 8, 1, 32, 1, 1, S<4, 2, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, MNPadding, 32, 32, 16, 64, 8, 8, 16, 16, 2, 1, S<4, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, MNPadding, 32, 8, 16, 32, 8, 8, 8, 16, 1, 1, S<4, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>,
DeviceGemmDpp< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, MNPadding, 32, 1, 32, 64, 8, 8, 1, 32, 1, 1, S<4, 1, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 5, 1>
>;
// clang-format on
void add_device_gemm_dpp_f16_f16_f16_mk_nk_mn_irregular_instances(
std::vector<std::unique_ptr<
DeviceGemm<Row, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances)
{
add_device_operation_instances(instances,
device_gemm_dpp_f16_f16_f16_mk_nk_mn_irregular_instances{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/gemm_multiply_add/CMakeLists.txt
View file @ 578bc24a
add_instance_library(device_gemm_multiply_add_instance
device_gemm_multiply_add_xdl_c_shuffle_f16_f16_f16_f16_f16_mk_kn_mn_mn_mn_instance.cpp
device_gemm_multiply_add_xdl_c_shuffle_f16_f16_f16_f16_f16_mk_nk_mn_mn_mn_instance.cpp
set(GEMM_MULTIPLY_ADD_INSTANCES)
device_gemm_multiply_add_xdl_c_shuffle_f16_f8_f32_f32_f16_mk_kn_mn_mn_mn_instance.cpp
device_gemm_multiply_add_xdl_c_shuffle_f16_f8_f32_f32_f16_mk_nk_mn_mn_mn_instance.cpp
)
if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
list(APPEND GEMM_MULTIPLY_ADD_INSTANCES device_gemm_multiply_add_xdl_c_shuffle_f16_f16_f16_f16_f16_mk_kn_mn_mn_mn_instance.cpp)
list(APPEND GEMM_MULTIPLY_ADD_INSTANCES device_gemm_multiply_add_xdl_c_shuffle_f16_f16_f16_f16_f16_mk_nk_mn_mn_mn_instance.cpp)
endif()
if((DTYPES MATCHES "fp16" AND DTYPES MATCHES "fp8") OR NOT DEFINED DTYPES)
list(APPEND GEMM_MULTIPLY_ADD_INSTANCES device_gemm_multiply_add_xdl_c_shuffle_f16_f8_f32_f32_f16_mk_kn_mn_mn_mn_instance.cpp)
list(APPEND GEMM_MULTIPLY_ADD_INSTANCES device_gemm_multiply_add_xdl_c_shuffle_f16_f8_f32_f32_f16_mk_nk_mn_mn_mn_instance.cpp)
endif()
add_instance_library(device_gemm_multiply_add_instance ${GEMM_MULTIPLY_ADD_INSTANCES})
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