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Commit ae20247a authored by Adam Osewski's avatar Adam Osewski
Browse files

Merge remote-tracking branch 'origin' into aosewski/ggemm_multi_d2

parents d1f7a3cf a776978c
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Showing with 1144 additions and 76 deletions
library/include/ck/library/tensor_operation_instance/gpu/gemm_streamk.hpp
View file @ ae20247a
...@@ -83,12 +83,22 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGemmSt ...@@ -83,12 +83,22 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGemmSt
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> && if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
is_same_v<CLayout, Row>) is_same_v<CLayout, Row>)
{ {
add_device_gemm_xdl_splitk_f16_f16_f16_mk_kn_mn_instances(op_ptrs); add_device_gemm_xdl_splitk_f16_f16_f16_mk_kn_mn_v1_instances(op_ptrs);
add_device_gemm_xdl_splitk_f16_f16_f16_mk_kn_mn_v1_irregular_instances(op_ptrs);
add_device_gemm_xdl_splitk_f16_f16_f16_mk_kn_mn_v1_interwave_instances(op_ptrs);
add_device_gemm_xdl_splitk_f16_f16_f16_mk_kn_mn_v1_interwave_irregular_instances(op_ptrs);
add_device_gemm_xdl_splitk_f16_f16_f16_mk_kn_mn_v2_instances(op_ptrs);
add_device_gemm_xdl_splitk_f16_f16_f16_mk_kn_mn_v2_irregular_instances(op_ptrs);
} }
else if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> && else if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
is_same_v<CLayout, Row>) is_same_v<CLayout, Row>)
{ {
add_device_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_instances(op_ptrs); add_device_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_v1_instances(op_ptrs);
add_device_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_v1_irregular__instances(op_ptrs);
add_device_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_v1_interwave_instances(op_ptrs);
add_device_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_v1_interwave_irregular_instances(op_ptrs);
add_device_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_v2_instances(op_ptrs);
add_device_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_v2_irregular_instances(op_ptrs);
} }
else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Row> && else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Row> &&
is_same_v<CLayout, Row>) is_same_v<CLayout, Row>)
......
library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_data/device_grouped_conv_bwd_data_wmma_i8_instance.hpp 0 → 100644
View file @ ae20247a
// SPDX-License-Identifier: MIT
// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/convolution_backward_data_specialization.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_data_multiple_d_wmma_cshuffle.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using F16 = ck::half_t;
using F32 = float;
using I8 = int8_t;
using I32 = int32_t;
using Empty_Tuple = ck::Tuple<>;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using namespace ck::tensor_layout::convolution;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto ConvBwdDataDefault =
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization::Default;
static constexpr auto ConvBwdData1x1S1P0 =
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization::Filter1x1Stride1Pad0;
template <index_t NDSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
typename DsDatatype,
typename CDEElementOp,
ConvolutionBackwardDataSpecialization ConvSpec>
using device_grouped_conv_bwd_data_wmma_f16_instances = std::tuple<
// clang-format off
//########################################| NumDim| A| B| Ds| E| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| ConvForward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MRepeat| NRepeat| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Specialization| Size| Block| Block| Block| | WMMA| WMMA| | | ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, CDEElementOp, ConvSpec, 128, 64, 64, 4, 8, 16, 16, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 4>, 1>,
// blocksize=256
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, CDEElementOp, ConvSpec, 256, 128, 256, 8, 8, 16, 16, 4, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, CDEElementOp, ConvSpec, 256, 64, 256, 8, 8, 16, 16, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, CDEElementOp, ConvSpec, 256, 128, 256, 8, 8, 16, 16, 4, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, CDEElementOp, ConvSpec, 256, 128, 64, 8, 8, 16, 16, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// blocksize=128
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, CDEElementOp, ConvSpec, 128, 64, 128, 8, 8, 16, 16, 2, 4, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, CDEElementOp, ConvSpec, 128, 64, 128, 8, 8, 16, 16, 2, 4, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, CDEElementOp, ConvSpec, 128, 128, 64, 8, 8, 16, 16, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, CDEElementOp, ConvSpec, 128, 128, 128, 8, 8, 16, 16, 4, 4, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, CDEElementOp, ConvSpec, 128, 32, 256, 8, 8, 16, 16, 1, 8, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
// blocksize=64
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, CDEElementOp, ConvSpec, 64, 32, 64, 8, 8, 16, 16, 1, 4, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 2>, 8>,
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, CDEElementOp, ConvSpec, 64, 64, 64, 8, 8, 16, 16, 2, 4, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 2>, 8>,
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, CDEElementOp, ConvSpec, 64, 32, 64, 8, 8, 16, 16, 1, 4, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 2>, 8>,
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, CDEElementOp, ConvSpec, 64, 32, 128, 8, 8, 16, 16, 1, 8, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 2>, 8>,
// blocksize=32
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, CDEElementOp, ConvSpec, 32, 16, 64, 8, 8, 16, 16, 1, 4, S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 2>, 8>,
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, CDEElementOp, ConvSpec, 32, 64, 32, 8, 8, 16, 16, 4, 2, S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 2>, 8>,
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, CDEElementOp, ConvSpec, 32, 32, 32, 8, 8, 16, 16, 2, 2, S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 2>, 8>,
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, CDEElementOp, ConvSpec, 32, 16, 32, 8, 8, 16, 16, 1, 2, S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 16, 1, 2>, 8>
// clang-format on
>;
template <index_t NDSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
typename DsDatatype,
typename CDEElementOp,
ConvolutionBackwardDataSpecialization ConvSpec>
using device_grouped_conv_bwd_data_wmma_i8_instances = std::tuple<
// clang-format off
//########################################| NumDim| A| B| Ds| E| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| ConvForward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MRepeat| NRepeat| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Specialization| Size| Block| Block| Block| | WMMA| WMMA| | | ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, I8, I8, I32, I8, Empty_Tuple, I8, PassThrough, PassThrough, CDEElementOp, ConvSpec, 128, 64, 64, 4, 16, 16, 16, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 16, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 16, 1, 1, 1, S<1, 32, 1, 4>, 1>,
// blocksize=256
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, I8, I8, I32, I8, Empty_Tuple, I8, PassThrough, PassThrough, CDEElementOp, ConvSpec, 256, 64, 256, 8, 16, 16, 16, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 16, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// blocksize=128
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, I8, I8, I32, I8, Empty_Tuple, I8, PassThrough, PassThrough, CDEElementOp, ConvSpec, 128, 64, 256, 8, 16, 16, 16, 2, 8, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, I8, I8, I32, I8, Empty_Tuple, I8, PassThrough, PassThrough, CDEElementOp, ConvSpec, 128, 64, 128, 8, 16, 16, 16, 2, 4, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 16, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, I8, I8, I32, I8, Empty_Tuple, I8, PassThrough, PassThrough, CDEElementOp, ConvSpec, 128, 128, 256, 8, 16, 16, 16, 4, 8, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, I8, I8, I32, I8, Empty_Tuple, I8, PassThrough, PassThrough, CDEElementOp, ConvSpec, 128, 32, 256, 8, 16, 16, 16, 1, 8, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, I8, I8, I32, I8, Empty_Tuple, I8, PassThrough, PassThrough, CDEElementOp, ConvSpec, 128, 256, 128, 8, 16, 16, 16, 8, 4, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 16, 1, 1, 1, S<1, 32, 1, 4>, 8>,
// blocksize=64
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, I8, I8, I32, I8, Empty_Tuple, I8, PassThrough, PassThrough, CDEElementOp, ConvSpec, 64, 32, 128, 8, 16, 16, 16, 1, 8, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<8, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, 1, 1, 1, S<1, 32, 1, 2>, 8>,
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, I8, I8, I32, I8, Empty_Tuple, I8, PassThrough, PassThrough, CDEElementOp, ConvSpec, 64, 64, 128, 8, 16, 16, 16, 2, 8, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<8, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, 1, 1, 1, S<1, 32, 1, 2>, 8>,
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, I8, I8, I32, I8, Empty_Tuple, I8, PassThrough, PassThrough, CDEElementOp, ConvSpec, 64, 32, 128, 8, 16, 16, 16, 1, 8, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<8, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, 1, 1, 1, S<1, 32, 1, 2>, 8>,
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, I8, I8, I32, I8, Empty_Tuple, I8, PassThrough, PassThrough, CDEElementOp, ConvSpec, 64, 32, 64, 8, 16, 16, 16, 1, 4, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<8, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 16, 1, 1, 1, S<1, 32, 1, 2>, 8>,
// blocksize=32
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, I8, I8, I32, I8, Empty_Tuple, I8, PassThrough, PassThrough, CDEElementOp, ConvSpec, 32, 16, 64, 8, 16, 16, 16, 1, 4, S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<8, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, 1, 1, 1, S<1, 16, 1, 2>, 8>,
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, I8, I8, I32, I8, Empty_Tuple, I8, PassThrough, PassThrough, CDEElementOp, ConvSpec, 32, 64, 64, 8, 16, 16, 16, 4, 4, S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<8, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, 1, 1, 1, S<1, 16, 1, 2>, 8>,
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, I8, I8, I32, I8, Empty_Tuple, I8, PassThrough, PassThrough, CDEElementOp, ConvSpec, 32, 32, 32, 8, 16, 16, 16, 2, 2, S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<8, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 16, 1, 1, 1, S<1, 16, 1, 2>, 8>,
DeviceGroupedConvBwdDataMultipleD_Wmma_CShuffle<NDSpatial, ALayout, BLayout, DsLayout, ELayout, I8, I8, I32, I8, Empty_Tuple, I8, PassThrough, PassThrough, CDEElementOp, ConvSpec, 32, 16, 64, 8, 16, 16, 16, 1, 4, S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<8, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 16, 16, 1, 1, 1, S<1, 16, 1, 2>, 8>
// clang-format on
>;
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_data/device_grouped_conv_bwd_data_xdl_bilinear_instance.hpp 0 → 100644
View file @ ae20247a
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_data_multiple_d_xdl_cshuffle_v1.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 BF16 = ck::bhalf_t;
using F16 = ck::half_t;
using F32 = float;
using BF8 = ck::bf8_t;
using F8 = ck::f8_t;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using namespace ck::tensor_layout::convolution;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using Bilinear = ck::tensor_operation::element_wise::Bilinear;
static constexpr auto ConvBwdDataDefault = ConvolutionBackwardDataSpecialization::Default;
static constexpr auto ConvBwdDataFilter1x1Stride1Pad0 =
ConvolutionBackwardDataSpecialization::Filter1x1Stride1Pad0;
// f16_f16_f32_f16
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionBackwardDataSpecialization ConvSpec>
using device_grouped_conv_bwd_data_xdl_bilinear_f16_instances = std::tuple<
// clang-format off
// ##############################################| NDim| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffleMXdl| CShuffleNXdl| CDEBlockTransfer| CDEBlockTransfer|
// ##############################################| Spatial| | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| PrefetchStage| Size| Block| Block| Block| | | XDL| XDL| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| PerWave| PerWave| _MBlock_MPerBlock| ScalarPerVector|
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, ck::Tuple<ELayout>, ELayout, F16, F16, F32, F16, Tuple<F16>, F16, PassThrough, PassThrough, Bilinear, ConvSpec, true, true, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
// instances for small conv.K and conv.C
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, ck::Tuple<ELayout>, ELayout, F16, F16, F32, F16, Tuple<F16>, F16, PassThrough, PassThrough, Bilinear, ConvSpec, true, true, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, ck::Tuple<ELayout>, ELayout, F16, F16, F32, F16, Tuple<F16>, F16, PassThrough, PassThrough, Bilinear, ConvSpec, true, true, 1, 128, 128, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 4>, 1>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, ck::Tuple<ELayout>, ELayout, F16, F16, F32, F16, Tuple<F16>, F16, PassThrough, PassThrough, Bilinear, ConvSpec, true, true, 1, 256, 128, 256, 32, 8, 2, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>
// clang-format on
>;
// bf16_bf16_f32_bf16
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionBackwardDataSpecialization ConvSpec>
using device_grouped_conv_bwd_data_xdl_bilinear_bf16_instances = std::tuple<
// clang-format off
// ##############################################| NDim| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffleMXdl| CShuffleNXdl| CDEBlockTransfer| CDEBlockTransfer|
// ##############################################| Spatial| | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| PrefetchStage| Size| Block| Block| Block| | | XDL| XDL| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| PerWave| PerWave| _MBlock_MPerBlock| ScalarPerVector|
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, ck::Tuple<ELayout>, ELayout, BF16, BF16, F32, BF16, Tuple<BF16>, BF16, PassThrough, PassThrough, Bilinear, ConvSpec, true, true, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
// instances for small conv.K and conv.C
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, ck::Tuple<ELayout>, ELayout, BF16, BF16, F32, BF16, Tuple<BF16>, BF16, PassThrough, PassThrough, Bilinear, ConvSpec, true, true, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, ck::Tuple<ELayout>, ELayout, BF16, BF16, F32, BF16, Tuple<BF16>, BF16, PassThrough, PassThrough, Bilinear, ConvSpec, true, true, 1, 128, 128, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 4, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 4>, 1>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<NDimSpatial, ALayout, BLayout, ck::Tuple<ELayout>, ELayout, BF16, BF16, F32, BF16, Tuple<BF16>, BF16, PassThrough, PassThrough, Bilinear, ConvSpec, true, true, 1, 256, 128, 256, 32, 8, 2, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>
// clang-format on
>;
// f32_f32_f32_f32
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionBackwardDataSpecialization ConvSpec>
using device_grouped_conv_bwd_data_xdl_bilinear_f32_instances = std::tuple<
// clang-format off
// ##############################################| NDim| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffleMXdl| CShuffleNXdl| CDEBlockTransfer| CDEBlockTransfer|
// ##############################################| Spatial| | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| PrefetchStage| Size| Block| Block| Block| | | XDL| XDL| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| PerWave| PerWave| _MBlock_MPerBlock| ScalarPerVector|
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< NDimSpatial, ALayout, BLayout, ck::Tuple<ELayout>, ELayout, F32, F32, F32, F32, Tuple<F32>, F32, PassThrough, PassThrough, Bilinear, ConvSpec, true, true, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, 1, 1, 1, S<1, 16, 1, 4>, 1>,
// instances for small conv.K and conv.C
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< NDimSpatial, ALayout, BLayout, ck::Tuple<ELayout>, ELayout, F32, F32, F32, F32, Tuple<F32>, F32, PassThrough, PassThrough, Bilinear, ConvSpec, true, true, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< NDimSpatial, ALayout, BLayout, ck::Tuple<ELayout>, ELayout, F32, F32, F32, F32, Tuple<F32>, F32, PassThrough, PassThrough, Bilinear, ConvSpec, true, true, 1, 128, 128, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, 1, 1, 1, S<1, 32, 1, 4>, 1>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< NDimSpatial, ALayout, BLayout, ck::Tuple<ELayout>, ELayout, F32, F32, F32, F32, Tuple<F32>, F32, PassThrough, PassThrough, Bilinear, ConvSpec, true, true, 1, 256, 128, 256, 32, 8, 2, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 4>
// clang-format on
>;
// f16_f16_f16_comp_f8
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionBackwardDataSpecialization ConvSpec>
using device_grouped_conv_bwd_data_xdl_bilinear_input_fp16_comp_bf8f8_instances = std::tuple<
// clang-format off
// ##############################################| NDim| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| AElementwise| BElementwise| CDEElementwise| ConvolutionBackward| DoPad| DoPad| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffleMXdl| CShuffleNXdl| CDEBlockTransfer| CDEBlockTransfer|
// ##############################################| Spatial| | | | | Type| Type| Type| DataType| Type| Type| Operation| Operation| Operation| DataSpecialization| GemmM| GemmN| PrefetchStage| Size| Block| Block| Block| | | XDL| XDL| PerWave| PerWave| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| PerWave| PerWave| _MBlock_MPerBlock| ScalarPerVector|
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Lengths_AK0_M_AK1| ArrangeOrder| | | PerVector| PerVector_AK1| | Lengths_BK0_N_BK1| ArrangeOrder| | | PerVector| PerVector_BK1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
// ##############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< NDimSpatial, ALayout, BLayout, ck::Tuple<ELayout>, ELayout, F16, F16, F32, F32, Tuple<F16>, F16, PassThrough, PassThrough, Bilinear, ConvSpec, true, true, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, 1, 1, 1, S<1, 16, 1, 4>, 1, LoopScheduler::Default, BF8, F8>,
// instances for small conv.K and conv.C
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< NDimSpatial, ALayout, BLayout, ck::Tuple<ELayout>, ELayout, F16, F16, F32, F32, Tuple<F16>, F16, PassThrough, PassThrough, Bilinear, ConvSpec, true, true, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 1, 1, 1, S<1, 32, 1, 8>, 4, LoopScheduler::Default, BF8, F8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< NDimSpatial, ALayout, BLayout, ck::Tuple<ELayout>, ELayout, F16, F16, F32, F32, Tuple<F16>, F16, PassThrough, PassThrough, Bilinear, ConvSpec, true, true, 1, 128, 128, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 8, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 4, 1, 1, 1, S<1, 32, 1, 4>, 1, LoopScheduler::Default, BF8, F8>,
DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1< NDimSpatial, ALayout, BLayout, ck::Tuple<ELayout>, ELayout, F16, F16, F32, F32, Tuple<F16>, F16, PassThrough, PassThrough, Bilinear, ConvSpec, true, true, 1, 256, 128, 256, 32, 8, 2, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 4, LoopScheduler::Default, BF8, F8>
// 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_xdl_bilinear_instance.hpp 0 → 100644
View file @ ae20247a
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, 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_fwd_multiple_abd_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/convolution_forward_specialization.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using BF16 = ck::bhalf_t;
using F16 = ck::half_t;
using F32 = float;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using namespace ck::tensor_layout::convolution;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using Bilinear = ck::tensor_operation::element_wise::Bilinear;
static constexpr auto ConvFwdDefault =
ck::tensor_operation::device::ConvolutionForwardSpecialization::Default;
static constexpr auto ConvFwd1x1P0 = ConvolutionForwardSpecialization::Filter1x1Pad0;
static constexpr auto ConvFwd1x1S1P0 = ConvolutionForwardSpecialization::Filter1x1Stride1Pad0;
static constexpr auto ConvFwdOddC =
ck::tensor_operation::device::ConvolutionForwardSpecialization::OddC;
static constexpr auto GemmMNKPadding = GemmSpecialization::MNKPadding;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionForwardSpecialization ConvSpec>
using device_grouped_conv_fwd_xdl_bilinear_bf16_instances = std::tuple<
// clang-format off
//########################################| NumDim| A| B| Ds| E| AData| BData| AccData| CShuffle| Ds| EData| A| B| CDE| ConvForward| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Specialization| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, BF16, BF16, F32, BF16, ck::Tuple<BF16>, BF16, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
// instances for small conv.K and conv.C
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, BF16, BF16, F32, BF16, ck::Tuple<BF16>, BF16, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 64, 64, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, BF16, BF16, F32, BF16, ck::Tuple<BF16>, BF16, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, BF16, BF16, F32, BF16, ck::Tuple<BF16>, BF16, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionForwardSpecialization ConvSpec>
using device_grouped_conv_fwd_xdl_bilinear_f16_instances = std::tuple<
// clang-format off
//########################################| NumDim| A| B| Ds| E| AData| BData| AccData| CShuffle| Ds| EData| A| B| CDE| ConvForward| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Specialization| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F16, F16, F32, F16, ck::Tuple<F16>, F16, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
// instances for small conv.K and conv.C
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F16, F16, F32, F16, ck::Tuple<F16>, F16, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 64, 64, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F16, F16, F32, F16, ck::Tuple<F16>, F16, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F16, F16, F32, F16, ck::Tuple<F16>, F16, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionForwardSpecialization ConvSpec>
using device_grouped_conv_fwd_xdl_bilinear_f32_instances = std::tuple<
// clang-format off
//########################################| NumDim| A| B| Ds| E| AData| BData| AccData| CShuffle| Ds| EData| A| B| CDE| ConvForward| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Specialization| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, ck::Tuple<F32>, F32, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 64, 64, 64, 16, 4, 4, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, 1, 1, S<1, 8, 1, 8>, 1>,
// instances for small conv.K and conv.C
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, ck::Tuple<F32>, F32, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 64, 64, 32, 16, 4, 4, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 8, 1, 8>, 1>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, ck::Tuple<F32>, F32, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 256, 128, 128, 16, 4, 4, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 4, 1, 1, 1, S<1, 16, 1, 16>, 4>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, F32, F32, F32, F32, ck::Tuple<F32>, F32, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 256, 256, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4>
// clang-format on
>;
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
ConvolutionForwardSpecialization ConvSpec>
using device_grouped_conv_fwd_xdl_bilinear_int8_instances = std::tuple<
// clang-format off
//########################################| NumDim| A| B| Ds| E| AData| BData| AccData| CShuffle| Ds| EData| A| B| CDE| ConvForward| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//########################################| Spatial| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| DataType| Type| Elementwise| Elementwise| Elementwise| Specialization| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//########################################| | | | | | | | | | | | Operation| Operation| Operation| | | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// generic instance
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, int8_t, int8_t, int32_t, int8_t, ck::Tuple<int8_t>, int8_t, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
// instances for small conv.K and conv.C
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, int8_t, int8_t, int32_t, int8_t, ck::Tuple<int8_t>, int8_t, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 64, 64, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, int8_t, int8_t, int32_t, int8_t, ck::Tuple<int8_t>, int8_t, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<NDimSpatial,ALayout,BLayout, DsLayout,ELayout, int8_t, int8_t, int32_t, int8_t, ck::Tuple<int8_t>, int8_t, PassThrough, PassThrough, Bilinear, ConvSpec, GemmMNKPadding, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>
// clang-format on
>;
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_backward_data_bilinear.hpp 0 → 100644
View file @ ae20247a
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_conv_bwd_data_multiple_d.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
#ifdef CK_ENABLE_FP16
void add_device_grouped_conv3d_bwd_data_xdl_bilinear_ndhwgk_gkzyxc_ndhwgc_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<3,
NDHWGK,
GKZYXC,
Tuple<NDHWGC>,
NDHWGC,
F16,
F16,
Tuple<F16>,
F16,
PassThrough,
PassThrough,
Bilinear>>>& instances);
#endif
#ifdef CK_ENABLE_FP32
void add_device_grouped_conv3d_bwd_data_xdl_bilinear_ndhwgk_gkzyxc_ndhwgc_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<3,
NDHWGK,
GKZYXC,
Tuple<NDHWGC>,
NDHWGC,
F32,
F32,
Tuple<F32>,
F32,
PassThrough,
PassThrough,
Bilinear>>>& instances);
#endif
#ifdef CK_ENABLE_BF16
void add_device_grouped_conv3d_bwd_data_xdl_bilinear_ndhwgk_gkzyxc_ndhwgc_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<3,
NDHWGK,
GKZYXC,
Tuple<NDHWGC>,
NDHWGC,
BF16,
BF16,
Tuple<BF16>,
BF16,
PassThrough,
PassThrough,
Bilinear>>>& instances);
#endif
template <ck::index_t NumDimSpatial,
typename OutLayout,
typename WeiLayout,
typename InLayout,
typename OutDataType,
typename WeiDataType,
typename InDataType,
typename ComputeTypeA,
typename ComputeTypeB>
struct DeviceOperationInstanceFactory<
ck::tensor_operation::device::DeviceGroupedConvBwdDataMultipleD<
NumDimSpatial,
OutLayout,
WeiLayout,
Tuple<InLayout>,
InLayout,
OutDataType,
WeiDataType,
Tuple<InDataType>,
InDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::Bilinear,
ComputeTypeA,
ComputeTypeB>>
{
using DeviceOp =
DeviceGroupedConvBwdDataMultipleD<NumDimSpatial,
OutLayout,
WeiLayout,
Tuple<InLayout>,
InLayout,
OutDataType,
WeiDataType,
Tuple<InDataType>,
InDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::Bilinear,
ComputeTypeA,
ComputeTypeB>;
static auto GetInstances()
{
std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
if constexpr(NumDimSpatial == 3)
{
if constexpr(is_same_v<InLayout, NDHWGC> && is_same_v<WeiLayout, GKZYXC> &&
is_same_v<OutLayout, NDHWGK>)
{
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<InDataType, F16> && is_same_v<WeiDataType, F16> &&
is_same_v<OutDataType, F16> && is_same_v<ComputeTypeA, F16> &&
is_same_v<ComputeTypeB, F16>)
{
add_device_grouped_conv3d_bwd_data_xdl_bilinear_ndhwgk_gkzyxc_ndhwgc_f16_instances(
op_ptrs);
}
#endif
#ifdef CK_ENABLE_FP32
else if constexpr(is_same_v<InDataType, F32> && is_same_v<WeiDataType, F32> &&
is_same_v<OutDataType, F32> && is_same_v<ComputeTypeA, F32> &&
is_same_v<ComputeTypeB, F32>)
{
add_device_grouped_conv3d_bwd_data_xdl_bilinear_ndhwgk_gkzyxc_ndhwgc_f32_instances(
op_ptrs);
}
#endif
#ifdef CK_ENABLE_BF16
else if constexpr(is_same_v<InDataType, BF16> && is_same_v<WeiDataType, BF16> &&
is_same_v<OutDataType, BF16> && is_same_v<ComputeTypeA, BF16> &&
is_same_v<ComputeTypeB, BF16>)
{
add_device_grouped_conv3d_bwd_data_xdl_bilinear_ndhwgk_gkzyxc_ndhwgc_bf16_instances(
op_ptrs);
}
#endif
}
}
return op_ptrs;
}
};
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_bilinear.hpp 0 → 100644
View file @ ae20247a
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <vector>
#include <memory>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_conv_fwd_multiple_abd.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using Bilinear = ck::tensor_operation::element_wise::Bilinear;
#ifdef CK_ENABLE_BF16
// grouped conv3d forward, NDHWGC/GKZYXC/NDHWGK
void add_device_grouped_conv3d_fwd_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
ck::Tuple<NDHWGK>,
NDHWGK,
BF16,
BF16,
ck::Tuple<BF16>,
BF16,
PassThrough,
PassThrough,
Bilinear>>>& instances);
#endif
#ifdef CK_ENABLE_FP16
void add_device_grouped_conv3d_fwd_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
ck::Tuple<NDHWGK>,
NDHWGK,
F16,
F16,
ck::Tuple<F16>,
F16,
PassThrough,
PassThrough,
Bilinear>>>& instances);
#endif
#ifdef CK_ENABLE_FP32
void add_device_grouped_conv3d_fwd_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
ck::Tuple<NDHWGK>,
NDHWGK,
F32,
F32,
ck::Tuple<F32>,
F32,
PassThrough,
PassThrough,
Bilinear>>>& instances);
#endif
#ifdef CK_ENABLE_INT8
void add_device_grouped_conv3d_fwd_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_int8_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleABD<3,
NDHWGC,
GKZYXC,
ck::Tuple<NDHWGK>,
NDHWGK,
int8_t,
int8_t,
ck::Tuple<int8_t>,
int8_t,
PassThrough,
PassThrough,
Bilinear>>>& instances);
#endif
template <ck::index_t NumDimSpatial,
typename InLayout,
typename WeiLayout,
typename DLayouts,
typename OutLayout,
typename InDataType,
typename WeiDataType,
typename DDataTypes,
typename OutDataType,
typename ComputeType>
struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD<
NumDimSpatial,
InLayout,
WeiLayout,
DLayouts,
OutLayout,
InDataType,
WeiDataType,
DDataTypes,
OutDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::Bilinear,
ComputeType>>
{
using DeviceOp =
DeviceGroupedConvFwdMultipleABD<NumDimSpatial,
InLayout,
WeiLayout,
DLayouts,
OutLayout,
InDataType,
WeiDataType,
DDataTypes,
OutDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::Bilinear,
ComputeType>;
static auto GetInstances()
{
std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
if constexpr(NumDimSpatial == 3 && is_same_v<InLayout, NDHWGC> &&
is_same_v<WeiLayout, GKZYXC> && is_same_v<OutLayout, NDHWGK> &&
DLayouts::Size() == 1 && is_same_v<tuple_element_t<0, DLayouts>, NDHWGK>)
{
#ifdef CK_ENABLE_FP32
if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
is_same_v<OutDataType, float>)
{
add_device_grouped_conv3d_fwd_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_f32_instances(
op_ptrs);
}
#endif
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
is_same_v<OutDataType, half_t> && is_same_v<ComputeType, half_t>)
{
add_device_grouped_conv3d_fwd_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_f16_instances(
op_ptrs);
}
#endif
#ifdef CK_ENABLE_BF16
if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
is_same_v<WeiDataType, ck::bhalf_t> && is_same_v<OutDataType, ck::bhalf_t>)
{
add_device_grouped_conv3d_fwd_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_bf16_instances(
op_ptrs);
}
#endif
#ifdef CK_ENABLE_INT8
if constexpr(is_same_v<InDataType, int8_t> && is_same_v<WeiDataType, int8_t> &&
is_same_v<OutDataType, int8_t>)
{
add_device_grouped_conv3d_fwd_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_int8_instances(
op_ptrs);
}
#endif
}
return op_ptrs;
}
};
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/include/ck/library/tensor_operation_instance/gpu/grouped_gemm_fixed_nk.hpp
View file @ ae20247a
// SPDX-License-Identifier: MIT // 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 #pragma once
...@@ -97,6 +97,35 @@ void add_device_grouped_gemm_xdl_fixed_nk_f16_i8_f16_mk_nk_mn_instances( ...@@ -97,6 +97,35 @@ void add_device_grouped_gemm_xdl_fixed_nk_f16_i8_f16_mk_nk_mn_instances(
PassThrough, PassThrough,
PassThrough>>>& instances); PassThrough>>>& instances);
// bf16_inputA i8_inputB
#if defined(CK_ENABLE_BF16) && defined(CK_ENABLE_INT8)
void add_device_grouped_gemm_xdl_fixed_nk_bf16_i8_bf16_mk_kn_mn_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmFixedNK<Row,
Row,
Empty_Tuple,
Row,
BF16,
I8,
Empty_Tuple,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_gemm_xdl_fixed_nk_bf16_i8_bf16_mk_nk_mn_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmFixedNK<Row,
Col,
Empty_Tuple,
Row,
BF16,
I8,
Empty_Tuple,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
#endif
template <typename ALayout, template <typename ALayout,
typename BLayout, typename BLayout,
typename ELayout, typename ELayout,
...@@ -180,6 +209,24 @@ struct DeviceOperationInstanceFactory< ...@@ -180,6 +209,24 @@ struct DeviceOperationInstanceFactory<
} }
} }
// bf16_i8_input
#if defined(CK_ENABLE_BF16) && defined(CK_ENABLE_INT8)
if constexpr(is_same_v<ADataType, bhalf_t> && is_same_v<BDataType, int8_t> &&
is_same_v<EDataType, bhalf_t>)
{
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
is_same_v<ELayout, Row>)
{
add_device_grouped_gemm_xdl_fixed_nk_bf16_i8_bf16_mk_kn_mn_instances(op_ptrs);
}
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
is_same_v<ELayout, Row>)
{
add_device_grouped_gemm_xdl_fixed_nk_bf16_i8_bf16_mk_nk_mn_instances(op_ptrs);
}
}
#endif
return op_ptrs; return op_ptrs;
} }
}; };
......
library/include/ck/library/tensor_operation_instance/gpu/permute_scale.hpp
View file @ ae20247a
// SPDX-License-Identifier: MIT // 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 #pragma once
...@@ -17,7 +17,32 @@ namespace tensor_operation { ...@@ -17,7 +17,32 @@ namespace tensor_operation {
namespace device { namespace device {
namespace instance { namespace instance {
void add_device_permute_scale_f16_instances( #ifdef CK_ENABLE_FP16
void add_device_permute_scale_1d_f16_instances(
std::vector<std::unique_ptr<DeviceElementwise<ck::Tuple<F16>,
ck::Tuple<F16>,
PassThrough,
element_wise::UnarySquare,
Scale,
1>>>&);
void add_device_permute_scale_2d_f16_instances(
std::vector<std::unique_ptr<DeviceElementwise<ck::Tuple<F16>,
ck::Tuple<F16>,
PassThrough,
element_wise::UnarySquare,
Scale,
2>>>&);
void add_device_permute_scale_3d_f16_instances(
std::vector<std::unique_ptr<DeviceElementwise<ck::Tuple<F16>,
ck::Tuple<F16>,
PassThrough,
element_wise::UnarySquare,
Scale,
3>>>&);
void add_device_permute_scale_4d_f16_instances(
std::vector<std::unique_ptr<DeviceElementwise<ck::Tuple<F16>, std::vector<std::unique_ptr<DeviceElementwise<ck::Tuple<F16>,
ck::Tuple<F16>, ck::Tuple<F16>,
PassThrough, PassThrough,
...@@ -25,7 +50,50 @@ void add_device_permute_scale_f16_instances( ...@@ -25,7 +50,50 @@ void add_device_permute_scale_f16_instances(
Scale, Scale,
4>>>&); 4>>>&);
void add_device_permute_scale_f32_instances( void add_device_permute_scale_5d_f16_instances(
std::vector<std::unique_ptr<DeviceElementwise<ck::Tuple<F16>,
ck::Tuple<F16>,
PassThrough,
element_wise::UnarySquare,
Scale,
5>>>&);
void add_device_permute_scale_6d_f16_instances(
std::vector<std::unique_ptr<DeviceElementwise<ck::Tuple<F16>,
ck::Tuple<F16>,
PassThrough,
element_wise::UnarySquare,
Scale,
6>>>&);
#endif
#ifdef CK_ENABLE_FP32
void add_device_permute_scale_1d_f32_instances(
std::vector<std::unique_ptr<DeviceElementwise<ck::Tuple<F32>,
ck::Tuple<F32>,
PassThrough,
element_wise::UnarySquare,
Scale,
1>>>&);
void add_device_permute_scale_2d_f32_instances(
std::vector<std::unique_ptr<DeviceElementwise<ck::Tuple<F32>,
ck::Tuple<F32>,
PassThrough,
element_wise::UnarySquare,
Scale,
2>>>&);
void add_device_permute_scale_3d_f32_instances(
std::vector<std::unique_ptr<DeviceElementwise<ck::Tuple<F32>,
ck::Tuple<F32>,
PassThrough,
element_wise::UnarySquare,
Scale,
3>>>&);
void add_device_permute_scale_4d_f32_instances(
std::vector<std::unique_ptr<DeviceElementwise<ck::Tuple<F32>, std::vector<std::unique_ptr<DeviceElementwise<ck::Tuple<F32>,
ck::Tuple<F32>, ck::Tuple<F32>,
PassThrough, PassThrough,
...@@ -33,6 +101,23 @@ void add_device_permute_scale_f32_instances( ...@@ -33,6 +101,23 @@ void add_device_permute_scale_f32_instances(
Scale, Scale,
4>>>&); 4>>>&);
void add_device_permute_scale_5d_f32_instances(
std::vector<std::unique_ptr<DeviceElementwise<ck::Tuple<F32>,
ck::Tuple<F32>,
PassThrough,
element_wise::UnarySquare,
Scale,
5>>>&);
void add_device_permute_scale_6d_f32_instances(
std::vector<std::unique_ptr<DeviceElementwise<ck::Tuple<F32>,
ck::Tuple<F32>,
PassThrough,
element_wise::UnarySquare,
Scale,
6>>>&);
#endif
template <typename InDataTypeTuple, template <typename InDataTypeTuple,
typename OutDataTypeTuple, typename OutDataTypeTuple,
typename ElementwiseOperation, typename ElementwiseOperation,
...@@ -57,15 +142,107 @@ struct DeviceOperationInstanceFactory< ...@@ -57,15 +142,107 @@ struct DeviceOperationInstanceFactory<
static auto GetInstances() static auto GetInstances()
{ {
std::vector<std::unique_ptr<DeviceOp>> op_ptrs; std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
if constexpr(is_same_v<InDataTypeTuple, ck::Tuple<F32>> && if constexpr(NumDim == 1)
is_same_v<OutDataTypeTuple, ck::Tuple<F32>>) {
#ifdef CK_ENABLE_FP32
if constexpr(is_same_v<InDataTypeTuple, ck::Tuple<F32>> &&
is_same_v<OutDataTypeTuple, ck::Tuple<F32>>)
{
add_device_permute_scale_1d_f32_instances(op_ptrs);
}
#endif
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<InDataTypeTuple, ck::Tuple<F16>> &&
is_same_v<OutDataTypeTuple, ck::Tuple<F16>>)
{
add_device_permute_scale_1d_f16_instances(op_ptrs);
}
#endif
}
else if constexpr(NumDim == 2)
{
#ifdef CK_ENABLE_FP32
if constexpr(is_same_v<InDataTypeTuple, ck::Tuple<F32>> &&
is_same_v<OutDataTypeTuple, ck::Tuple<F32>>)
{
add_device_permute_scale_2d_f32_instances(op_ptrs);
}
#endif
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<InDataTypeTuple, ck::Tuple<F16>> &&
is_same_v<OutDataTypeTuple, ck::Tuple<F16>>)
{
add_device_permute_scale_2d_f16_instances(op_ptrs);
}
#endif
}
else if constexpr(NumDim == 3)
{
#ifdef CK_ENABLE_FP32
if constexpr(is_same_v<InDataTypeTuple, ck::Tuple<F32>> &&
is_same_v<OutDataTypeTuple, ck::Tuple<F32>>)
{
add_device_permute_scale_3d_f32_instances(op_ptrs);
}
#endif
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<InDataTypeTuple, ck::Tuple<F16>> &&
is_same_v<OutDataTypeTuple, ck::Tuple<F16>>)
{
add_device_permute_scale_3d_f16_instances(op_ptrs);
}
#endif
}
else if constexpr(NumDim == 4)
{
#ifdef CK_ENABLE_FP32
if constexpr(is_same_v<InDataTypeTuple, ck::Tuple<F32>> &&
is_same_v<OutDataTypeTuple, ck::Tuple<F32>>)
{
add_device_permute_scale_4d_f32_instances(op_ptrs);
}
#endif
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<InDataTypeTuple, ck::Tuple<F16>> &&
is_same_v<OutDataTypeTuple, ck::Tuple<F16>>)
{
add_device_permute_scale_4d_f16_instances(op_ptrs);
}
#endif
}
else if constexpr(NumDim == 5)
{ {
add_device_permute_scale_f32_instances(op_ptrs); #ifdef CK_ENABLE_FP32
if constexpr(is_same_v<InDataTypeTuple, ck::Tuple<F32>> &&
is_same_v<OutDataTypeTuple, ck::Tuple<F32>>)
{
add_device_permute_scale_5d_f32_instances(op_ptrs);
}
#endif
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<InDataTypeTuple, ck::Tuple<F16>> &&
is_same_v<OutDataTypeTuple, ck::Tuple<F16>>)
{
add_device_permute_scale_5d_f16_instances(op_ptrs);
}
#endif
} }
else if constexpr(is_same_v<InDataTypeTuple, ck::Tuple<F16>> && else if constexpr(NumDim == 6)
is_same_v<OutDataTypeTuple, ck::Tuple<F16>>)
{ {
add_device_permute_scale_f16_instances(op_ptrs); #ifdef CK_ENABLE_FP32
if constexpr(is_same_v<InDataTypeTuple, ck::Tuple<F32>> &&
is_same_v<OutDataTypeTuple, ck::Tuple<F32>>)
{
add_device_permute_scale_6d_f32_instances(op_ptrs);
}
#endif
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<InDataTypeTuple, ck::Tuple<F16>> &&
is_same_v<OutDataTypeTuple, ck::Tuple<F16>>)
{
add_device_permute_scale_6d_f16_instances(op_ptrs);
}
#endif
} }
return op_ptrs; return op_ptrs;
} }
......
library/src/tensor_operation_instance/gpu/permute_scale/device_permute_scale_instances.cpp → library/include/ck/library/tensor_operation_instance/gpu/permute_scale/device_permute_scale_instances.hpp
View file @ ae20247a
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/ck.hpp" #include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise_scale_impl.hpp" #include "ck/tensor_operation/gpu/device/impl/device_elementwise_scale_impl.hpp"
#include "ck/utility/data_type.hpp" #include "ck/utility/data_type.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp" namespace ck {
namespace tensor_operation {
namespace ck { namespace device {
namespace tensor_operation { namespace instance {
namespace device {
namespace instance { using F16 = ck::half_t;
using F32 = float;
using F16 = ck::half_t;
using F32 = float; using Pass = ck::tensor_operation::element_wise::PassThrough;
using UnaryOp = ck::tensor_operation::element_wise::UnarySquare;
using Pass = ck::tensor_operation::element_wise::PassThrough; using Scale = ck::tensor_operation::element_wise::Scale;
using UnaryOp = ck::tensor_operation::element_wise::UnarySquare;
using Scale = ck::tensor_operation::element_wise::Scale; // clang-format off
template <index_t NDims>
// clang-format off using device_permute_scale_f16_instances =
using device_permute_scale_f16_instances = std::tuple <
std::tuple < DeviceElementwiseImpl<ck::Tuple<F16>, ck::Tuple<F16>, Pass, UnaryOp, Scale, NDims, 1, ck::Sequence<1>, ck::Sequence<1>>,
DeviceElementwiseImpl<ck::Tuple<F16>, ck::Tuple<F16>, Pass, UnaryOp, Scale, 4, 1, ck::Sequence<1>, ck::Sequence<1>>, DeviceElementwiseImpl<ck::Tuple<F16>, ck::Tuple<F16>, Pass, UnaryOp, Scale, NDims, 8, ck::Sequence<8>, ck::Sequence<1>>,
DeviceElementwiseImpl<ck::Tuple<F16>, ck::Tuple<F16>, Pass, UnaryOp, Scale, 4, 8, ck::Sequence<1>, ck::Sequence<1>>, DeviceElementwiseImpl<ck::Tuple<F16>, ck::Tuple<F16>, Pass, UnaryOp, Scale, NDims, 4, ck::Sequence<4>, ck::Sequence<1>>,
DeviceElementwiseImpl<ck::Tuple<F16>, ck::Tuple<F16>, Pass, UnaryOp, Scale, 4, 4, ck::Sequence<1>, ck::Sequence<1>>, DeviceElementwiseImpl<ck::Tuple<F16>, ck::Tuple<F16>, Pass, UnaryOp, Scale, NDims, 2, ck::Sequence<2>, ck::Sequence<1>>
DeviceElementwiseImpl<ck::Tuple<F16>, ck::Tuple<F16>, Pass, UnaryOp, Scale, 4, 2, ck::Sequence<1>, ck::Sequence<1>> >;
>;
template <index_t NDims>
using device_permute_scale_f32_instances = std::tuple< using device_permute_scale_f32_instances = std::tuple<
DeviceElementwiseImpl<ck::Tuple<F32>, ck::Tuple<F32>, Pass, UnaryOp, Scale, 4, 1, ck::Sequence<1>, ck::Sequence<1>>, DeviceElementwiseImpl<ck::Tuple<F32>, ck::Tuple<F32>, Pass, UnaryOp, Scale, NDims, 1, ck::Sequence<1>, ck::Sequence<1>>,
DeviceElementwiseImpl<ck::Tuple<F32>, ck::Tuple<F32>, Pass, UnaryOp, Scale, 4, 8, ck::Sequence<1>, ck::Sequence<1>>, DeviceElementwiseImpl<ck::Tuple<F32>, ck::Tuple<F32>, Pass, UnaryOp, Scale, NDims, 8, ck::Sequence<8>, ck::Sequence<1>>,
DeviceElementwiseImpl<ck::Tuple<F32>, ck::Tuple<F32>, Pass, UnaryOp, Scale, 4, 4, ck::Sequence<1>, ck::Sequence<1>>, DeviceElementwiseImpl<ck::Tuple<F32>, ck::Tuple<F32>, Pass, UnaryOp, Scale, NDims, 4, ck::Sequence<4>, ck::Sequence<1>>,
DeviceElementwiseImpl<ck::Tuple<F32>, ck::Tuple<F32>, Pass, UnaryOp, Scale, 4, 2, ck::Sequence<1>, ck::Sequence<1>> DeviceElementwiseImpl<ck::Tuple<F32>, ck::Tuple<F32>, Pass, UnaryOp, Scale, NDims, 2, ck::Sequence<2>, ck::Sequence<1>>
>; >;
// clang-format on // clang-format on
void add_device_permute_scale_f16_instances( } // namespace instance
std::vector<std::unique_ptr< } // namespace device
DeviceElementwise<ck::Tuple<F16>, ck::Tuple<F16>, Pass, UnaryOp, Scale, 4>>>& instances) } // namespace tensor_operation
{ } // namespace ck
add_device_operation_instances(instances, device_permute_scale_f16_instances{});
}
void add_device_permute_scale_f32_instances(
std::vector<std::unique_ptr<
DeviceElementwise<ck::Tuple<F32>, ck::Tuple<F32>, Pass, UnaryOp, Scale, 4>>>& instances)
{
add_device_operation_instances(instances, device_permute_scale_f32_instances{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/gemm/CMakeLists.txt
View file @ ae20247a
...@@ -101,8 +101,12 @@ list(APPEND GEMM_INSTANCES ...@@ -101,8 +101,12 @@ list(APPEND GEMM_INSTANCES
device_gemm_xdl_c_shuffle_bf16_bf16_bf16_km_nk_mn_instance.cpp) device_gemm_xdl_c_shuffle_bf16_bf16_bf16_km_nk_mn_instance.cpp)
list(APPEND GEMM_INSTANCES list(APPEND GEMM_INSTANCES
device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_kn_mn_default_instance.cpp device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_kn_mn_v1_default_instance.cpp
device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_kn_mn_padded_instance.cpp device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_kn_mn_v1_interwave_default_instance.cpp
device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_kn_mn_v2_default_instance.cpp
device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_kn_mn_v1_padded_instance.cpp
device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_kn_mn_v1_interwave_padded_instance.cpp
device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_kn_mn_v2_padded_instance.cpp
device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_nk_mn_instance.cpp device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_nk_mn_instance.cpp
device_gemm_xdl_c_shuffle_fp8_fp8_fp8_km_kn_mn_instance.cpp device_gemm_xdl_c_shuffle_fp8_fp8_fp8_km_kn_mn_instance.cpp
device_gemm_xdl_c_shuffle_fp8_fp8_fp8_km_nk_mn_instance.cpp) device_gemm_xdl_c_shuffle_fp8_fp8_fp8_km_nk_mn_instance.cpp)
......
library/src/tensor_operation_instance/gpu/gemm/device_gemm_xdl_c_shuffle_f16_f16_f16_mk_kn_mn_instance.cpp
View file @ ae20247a
...@@ -34,6 +34,15 @@ static constexpr auto MNPadding = ck::tensor_operation::device::GemmSpecializati ...@@ -34,6 +34,15 @@ static constexpr auto MNPadding = ck::tensor_operation::device::GemmSpecializati
static constexpr auto MNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding; static constexpr auto MNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
using device_gemm_xdl_c_shuffle_f16_f16_f16_mk_kn_mn_generic_instances = std::tuple<
// clang-format off
//#####################| ALayout| BLayout| CLayout| AData| BData| CData| AccData| CShuffle| A| B| C| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| LoopScheduler| Pipeline|
//#####################| | | | Type| Type| Type| Type| DataType| Elementwise| Elementwise| Elementwise| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector| | |
//#####################| | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl| | |
DeviceGemm_Xdl_CShuffle< Row, Row, Row, F16, F16, F16, F32, F16, PassThrough, PassThrough, PassThrough, MNKPadding, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 16, 1, 8>, 1, LoopScheduler::Default, PipelineVersion::v1>
// clang-format on
>;
// Compilation parameters for a[m, k] * b[k, n] = c[m, n] // Compilation parameters for a[m, k] * b[k, n] = c[m, n]
template <ck::tensor_operation::device::GemmSpecialization GemmSpec> template <ck::tensor_operation::device::GemmSpecialization GemmSpec>
using device_gemm_xdl_c_shuffle_f16_f16_f16_mk_kn_mn_instances = std::tuple< using device_gemm_xdl_c_shuffle_f16_f16_f16_mk_kn_mn_instances = std::tuple<
...@@ -108,6 +117,9 @@ void add_device_gemm_xdl_c_shuffle_f16_f16_f16_mk_kn_mn_instances( ...@@ -108,6 +117,9 @@ void add_device_gemm_xdl_c_shuffle_f16_f16_f16_mk_kn_mn_instances(
DeviceGemm<Row, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>& DeviceGemm<Row, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances) instances)
{ {
add_device_operation_instances(
instances, device_gemm_xdl_c_shuffle_f16_f16_f16_mk_kn_mn_generic_instances{});
add_device_operation_instances( add_device_operation_instances(
instances, device_gemm_xdl_c_shuffle_f16_f16_f16_mk_kn_mn_instances<GemmDefault>{}); instances, device_gemm_xdl_c_shuffle_f16_f16_f16_mk_kn_mn_instances<GemmDefault>{});
......
library/src/tensor_operation_instance/gpu/gemm/device_gemm_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_instance.cpp
View file @ ae20247a
...@@ -32,6 +32,17 @@ static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecializa ...@@ -32,6 +32,17 @@ static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecializa
static constexpr auto MNPadding = ck::tensor_operation::device::GemmSpecialization::MNPadding; static constexpr auto MNPadding = ck::tensor_operation::device::GemmSpecialization::MNPadding;
static constexpr auto MNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding; static constexpr auto MNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
// Compilation parameters for a[m, k] * b[n, k] = c[m, n]
using device_gemm_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_generic_instances = std::tuple<
// clang-format off
//#####################| ALayout| BLayout| CLayout| AData| BData| CData| AccData| CShuffle| A| B| C| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| LoopScheduler| Pipeline|
//#####################| | | | Type| Type| Type| Type| DataType| Elementwise| Elementwise| Elementwise| Specialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector| | |
//#####################| | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl| | |
//#####################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemm_Xdl_CShuffle< Row, Col, Row, F16, F16, F16, F32, F16, PassThrough, PassThrough, PassThrough, MNKPadding, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, 1, 1, S<1, 16, 1, 8>, 1, LoopScheduler::Default, PipelineVersion::v1>
// clang-format on
>;
template <ck::tensor_operation::device::GemmSpecialization GemmSpec> template <ck::tensor_operation::device::GemmSpecialization GemmSpec>
// Compilation parameters for a[m, k] * b[n, k] = c[m, n] // Compilation parameters for a[m, k] * b[n, k] = c[m, n]
using device_gemm_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_instances = std::tuple< using device_gemm_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_instances = std::tuple<
...@@ -97,6 +108,9 @@ void add_device_gemm_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_instances( ...@@ -97,6 +108,9 @@ void add_device_gemm_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_instances(
DeviceGemm<Row, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>& DeviceGemm<Row, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances) instances)
{ {
add_device_operation_instances(
instances, device_gemm_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_generic_instances{});
add_device_operation_instances( add_device_operation_instances(
instances, device_gemm_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_instances<GemmDefault>{}); instances, device_gemm_xdl_c_shuffle_f16_f16_f16_mk_nk_mn_instances<GemmDefault>{});
......
library/src/tensor_operation_instance/gpu/gemm/device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_kn_mn_default_instance.cpp → library/src/tensor_operation_instance/gpu/gemm/device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_kn_mn_v1_default_instance.cpp
View file @ ae20247a
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_kn_mn_instance.hpp" #include "ck/library/tensor_operation_instance/gpu/device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_kn_mn_v1_instance.hpp"
#ifdef CK_ENABLE_FP8 #ifdef CK_ENABLE_FP8
namespace ck { namespace ck {
...@@ -11,12 +11,12 @@ namespace instance { ...@@ -11,12 +11,12 @@ namespace instance {
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default; static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
void add_device_gemm_xdl_c_shuffle_f8_f8_f8_mk_kn_mn_default_instances( void add_device_gemm_xdl_c_shuffle_f8_f8_f8_mk_kn_mn_v1_default_instances(
std::vector<std::unique_ptr< std::vector<std::unique_ptr<
DeviceGemm<Row, Row, Row, F8, F8, F8, PassThrough, PassThrough, PassThrough>>>& instances) DeviceGemm<Row, Row, Row, F8, F8, F8, PassThrough, PassThrough, PassThrough>>>& instances)
{ {
add_device_operation_instances( add_device_operation_instances(
instances, device_gemm_xdl_c_shuffle_f8_f8_f8_mk_kn_mn_instances<GemmDefault>{}); instances, device_gemm_xdl_c_shuffle_f8_f8_f8_mk_kn_mn_v1_instances<GemmDefault>{});
} }
} // namespace instance } // namespace instance
......
library/src/tensor_operation_instance/gpu/gemm/device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_kn_mn_v1_interwave_default_instance.cpp 0 → 100644
View file @ ae20247a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_kn_mn_v1_interwave_instance.hpp"
#ifdef CK_ENABLE_FP8
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
void add_device_gemm_xdl_c_shuffle_f8_f8_f8_mk_kn_mn_v1_interwave_default_instances(
std::vector<std::unique_ptr<
DeviceGemm<Row, Row, Row, F8, F8, F8, PassThrough, PassThrough, PassThrough>>>& instances)
{
add_device_operation_instances(
instances,
device_gemm_xdl_c_shuffle_f8_f8_f8_mk_kn_mn_v1_interwave_instances<GemmDefault>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
#endif
library/src/tensor_operation_instance/gpu/gemm/device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_kn_mn_v1_interwave_padded_instance.cpp 0 → 100644
View file @ ae20247a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_kn_mn_v1_interwave_instance.hpp"
#ifdef CK_ENABLE_FP8
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
static constexpr auto MNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
void add_device_gemm_xdl_c_shuffle_f8_f8_f8_mk_kn_mn_v1_interwave_padded_instances(
std::vector<std::unique_ptr<
DeviceGemm<Row, Row, Row, F8, F8, F8, PassThrough, PassThrough, PassThrough>>>& instances)
{
add_device_operation_instances(
instances,
device_gemm_xdl_c_shuffle_f8_f8_f8_mk_kn_mn_v1_interwave_instances<MNKPadding>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
#endif
library/src/tensor_operation_instance/gpu/gemm/device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_kn_mn_padded_instance.cpp → library/src/tensor_operation_instance/gpu/gemm/device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_kn_mn_v1_padded_instance.cpp
View file @ ae20247a
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_kn_mn_instance.hpp" #include "ck/library/tensor_operation_instance/gpu/device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_kn_mn_v1_instance.hpp"
#ifdef CK_ENABLE_FP8 #ifdef CK_ENABLE_FP8
namespace ck { namespace ck {
...@@ -11,12 +11,12 @@ namespace instance { ...@@ -11,12 +11,12 @@ namespace instance {
static constexpr auto MNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding; static constexpr auto MNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
void add_device_gemm_xdl_c_shuffle_f8_f8_f8_mk_kn_mn_padded_instances( void add_device_gemm_xdl_c_shuffle_f8_f8_f8_mk_kn_mn_v1_padded_instances(
std::vector<std::unique_ptr< std::vector<std::unique_ptr<
DeviceGemm<Row, Row, Row, F8, F8, F8, PassThrough, PassThrough, PassThrough>>>& instances) DeviceGemm<Row, Row, Row, F8, F8, F8, PassThrough, PassThrough, PassThrough>>>& instances)
{ {
add_device_operation_instances( add_device_operation_instances(
instances, device_gemm_xdl_c_shuffle_f8_f8_f8_mk_kn_mn_instances<MNKPadding>{}); instances, device_gemm_xdl_c_shuffle_f8_f8_f8_mk_kn_mn_v1_instances<MNKPadding>{});
} }
} // namespace instance } // namespace instance
......
library/src/tensor_operation_instance/gpu/gemm/device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_kn_mn_v2_default_instance.cpp 0 → 100644
View file @ ae20247a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_kn_mn_v2_instance.hpp"
#ifdef CK_ENABLE_FP8
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
void add_device_gemm_xdl_c_shuffle_f8_f8_f8_mk_kn_mn_v2_default_instances(
std::vector<std::unique_ptr<
DeviceGemm<Row, Row, Row, F8, F8, F8, PassThrough, PassThrough, PassThrough>>>& instances)
{
add_device_operation_instances(
instances, device_gemm_xdl_c_shuffle_f8_f8_f8_mk_kn_mn_v2_instances<GemmDefault>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
#endif
library/src/tensor_operation_instance/gpu/gemm/device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_kn_mn_v2_padded_instance.cpp 0 → 100644
View file @ ae20247a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/device_gemm_xdl_c_shuffle_fp8_fp8_fp8_mk_kn_mn_v2_instance.hpp"
#ifdef CK_ENABLE_FP8
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
static constexpr auto MNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
void add_device_gemm_xdl_c_shuffle_f8_f8_f8_mk_kn_mn_v2_padded_instances(
std::vector<std::unique_ptr<
DeviceGemm<Row, Row, Row, F8, F8, F8, PassThrough, PassThrough, PassThrough>>>& instances)
{
add_device_operation_instances(
instances, device_gemm_xdl_c_shuffle_f8_f8_f8_mk_kn_mn_v2_instances<MNKPadding>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
#endif
library/src/tensor_operation_instance/gpu/gemm_add/CMakeLists.txt 0 → 100644
View file @ ae20247a
add_instance_library(device_gemm_add_instance
device_gemm_add_xdl_c_shuffle_f16_i8_f16_f16_mk_kn_mn_mn_instance.cpp
device_gemm_add_xdl_c_shuffle_bf16_i8_bf16_bf16_mk_kn_mn_mn_instance.cpp
)
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