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Unverified Commit 4b70d68e authored by Chao Liu's avatar Chao Liu Committed by GitHub
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Merge branch 'develop' into add_fp16_wmma_conv_instance

parents 212b9299 f82bd593
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library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f16_instance.cpp
View file @ 4b70d68e
// 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 <cstdlib>
#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_gnwc_gkxc_gnwk_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp" #include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_xdl_instance.hpp"
namespace ck { namespace ck {
namespace tensor_operation { namespace tensor_operation {
namespace device { namespace device {
namespace instance { namespace instance {
using F16 = ck::half_t; // Compilation parameters for in[g, n, hi, wi, c] * wei[g, k, y, x, c] = out[g, n, ho, wo, k]
using F32 = float;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using GNHWC = ck::tensor_layout::convolution::GNHWC;
using GKYXC = ck::tensor_layout::convolution::GKYXC;
using GNHWK = ck::tensor_layout::convolution::GNHWK;
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;
// Compilation parameters for in[n, hi, wi, c] * wei[k, y, x, c] = out[n, ho, wo, k]
using device_grouped_conv2d_bwd_weight_xdl_c_shuffle_gnhwc_gkyxc_gnhwk_f16_default_instances =
std::tuple<
// clang-format off
//#########################################| Num| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer|
//#########################################| Dim| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector|
//#########################################| Spatial| | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| |
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 64, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 64, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 16, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 32, 64, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 8>
// clang-format on
>;
using device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_1x1_s1_p0_f16_instances = std::tuple<
// clang-format off
//#########################################| Num| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer|
//#########################################| Dim| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector|
//#########################################| Spatial| | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| |
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 64, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 64, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 16, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 32, 64, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 8>
// clang-format on
>;
void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f16_instances( void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2, std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
GNHWC, GNHWC,
...@@ -91,12 +23,22 @@ void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f16_instances( ...@@ -91,12 +23,22 @@ void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f16_instances(
PassThrough, PassThrough,
PassThrough>>>& instances) PassThrough>>>& instances)
{ {
// 1. Default
add_device_operation_instances( add_device_operation_instances(
instances, instances,
device_grouped_conv2d_bwd_weight_xdl_c_shuffle_gnhwc_gkyxc_gnhwk_f16_default_instances{}); device_grouped_conv_bwd_weight_xdl_c_shuffle_f16_instances<2,
add_device_operation_instances( GNHWC,
instances, GKYXC,
device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_1x1_s1_p0_f16_instances{}); GNHWK,
ConvBwdWeightDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(instances,
device_grouped_conv_bwd_weight_xdl_c_shuffle_f16_instances<
2,
GNHWC,
GKYXC,
GNHWK,
ConvBwdWeightFilter1x1Stride1Pad0>{});
} }
} // namespace instance } // namespace instance
......
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f32_instance.cpp
View file @ 4b70d68e
// 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 <cstdlib>
#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_gnwc_gkxc_gnwk_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp" #include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_xdl_instance.hpp"
namespace ck { namespace ck {
namespace tensor_operation { namespace tensor_operation {
namespace device { namespace device {
namespace instance { namespace instance {
using F32 = float; // Compilation parameters for in[g, n, hi, wi, c] * wei[g, k, y, x, c] = out[g, n, ho, wo, k]
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using GNHWC = ck::tensor_layout::convolution::GNHWC;
using GKYXC = ck::tensor_layout::convolution::GKYXC;
using GNHWK = ck::tensor_layout::convolution::GNHWK;
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;
// Compilation parameters for in[n, hi, wi, c] * wei[k, y, x, c] = out[n, ho, wo, k]
using device_grouped_conv2d_bwd_weight_xdl_c_shuffle_gnhwc_gkyxc_gnhwk_f32_default_instances =
std::tuple<
// clang-format off
//#########################################| Num| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer|
//#########################################| Dim| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector|
//#########################################| Spatial| | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| |
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 256, 128, 4, 4, 32, 32, 4, 2, S<1, 4, 64, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 256, 4, 4, 32, 32, 2, 4, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 64, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 128, 4, 4, 32, 32, 4, 2, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 128, 4, 4, 32, 32, 2, 2, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 64, 4, 4, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 64, 128, 4, 4, 32, 32, 2, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 64, 64, 4, 4, 32, 32, 2, 2, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 64, 4, 4, 32, 32, 2, 1, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 64, 128, 4, 4, 32, 32, 1, 2, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 32, 4, 4, 32, 32, 2, 1, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 32, 128, 4, 4, 32, 32, 1, 2, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 64, 32, 4, 4, 32, 32, 2, 1, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 32, 64, 4, 4, 32, 32, 1, 2, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 16, 1, 4>, 4>
// clang-format on
>;
using device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_1x1_s1_p0_f32_instances = std::tuple<
// clang-format off
//#########################################| Num| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer|
//#########################################| Dim| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector|
//#########################################| Spatial| | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| |
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 256, 128, 4, 4, 32, 32, 4, 2, S<1, 4, 64, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 256, 4, 4, 32, 32, 2, 4, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 64, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 128, 4, 4, 32, 32, 4, 2, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 128, 4, 4, 32, 32, 2, 2, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 64, 4, 4, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 64, 128, 4, 4, 32, 32, 2, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 64, 64, 4, 4, 32, 32, 2, 2, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 64, 4, 4, 32, 32, 2, 1, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 64, 128, 4, 4, 32, 32, 1, 2, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 32, 4, 4, 32, 32, 2, 1, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 32, 128, 4, 4, 32, 32, 1, 2, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 64, 32, 4, 4, 32, 32, 2, 1, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 2, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 32, 64, 4, 4, 32, 32, 1, 2, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 16, 1, 4>, 4>
// clang-format on
>;
void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f32_instances( void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2, std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
GNHWC, GNHWC,
...@@ -90,12 +23,22 @@ void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f32_instances( ...@@ -90,12 +23,22 @@ void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f32_instances(
PassThrough, PassThrough,
PassThrough>>>& instances) PassThrough>>>& instances)
{ {
// 1. Default
add_device_operation_instances( add_device_operation_instances(
instances, instances,
device_grouped_conv2d_bwd_weight_xdl_c_shuffle_gnhwc_gkyxc_gnhwk_f32_default_instances{}); device_grouped_conv_bwd_weight_xdl_c_shuffle_f32_instances<2,
add_device_operation_instances( GNHWC,
instances, GKYXC,
device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_1x1_s1_p0_f32_instances{}); GNHWK,
ConvBwdWeightDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(instances,
device_grouped_conv_bwd_weight_xdl_c_shuffle_f32_instances<
2,
GNHWC,
GKYXC,
GNHWK,
ConvBwdWeightFilter1x1Stride1Pad0>{});
} }
} // namespace instance } // namespace instance
......
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_instance.cpp 0 → 100644
View file @ 4b70d68e
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_xdl_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_f32_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NHWGC,
GKYXC,
NHWGK,
BF16,
F32,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv_bwd_weight_xdl_c_shuffle_bf16_instances<2,
NHWGC,
GKYXC,
NHWGK,
ConvBwdWeightDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(instances,
device_grouped_conv_bwd_weight_xdl_c_shuffle_bf16_instances<
2,
NHWGC,
GKYXC,
NHWGK,
ConvBwdWeightFilter1x1Stride1Pad0>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_instance.cpp 0 → 100644
View file @ 4b70d68e
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_xdl_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NHWGC,
GKYXC,
NHWGK,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv_bwd_weight_xdl_c_shuffle_f16_instances<2,
NHWGC,
GKYXC,
NHWGK,
ConvBwdWeightDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(instances,
device_grouped_conv_bwd_weight_xdl_c_shuffle_f16_instances<
2,
NHWGC,
GKYXC,
NHWGK,
ConvBwdWeightFilter1x1Stride1Pad0>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f32_instance.cpp 0 → 100644
View file @ 4b70d68e
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_xdl_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NHWGC,
GKYXC,
NHWGK,
F32,
F32,
F32,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv_bwd_weight_xdl_c_shuffle_f32_instances<2,
NHWGC,
GKYXC,
NHWGK,
ConvBwdWeightDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(instances,
device_grouped_conv_bwd_weight_xdl_c_shuffle_f32_instances<
2,
NHWGC,
GKYXC,
NHWGK,
ConvBwdWeightFilter1x1Stride1Pad0>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv2d_fwd/device_grouped_conv2d_fwd_dl_instance.hpp
View file @ 4b70d68e
// 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/tensor_operation/gpu/device/device_grouped_conv_fwd_dl_multiple_d_nhwc_kyxc_nhwk.hpp" #include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_dl_multiple_d_nhwc_kyxc_nhwk.hpp"
#include "device_grouped_conv2d_fwd_common.hpp" #include "device_grouped_conv2d_fwd_common.hpp"
namespace ck { namespace ck {
......
library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_weight/device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_bf16_instance.cpp
View file @ 4b70d68e
...@@ -5,81 +5,17 @@ ...@@ -5,81 +5,17 @@
#include "ck/ck.hpp" #include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp" #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_gnwc_gkxc_gnwk_xdl_cshuffle.hpp" #include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp" #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp" #include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_xdl_instance.hpp"
namespace ck { namespace ck {
namespace tensor_operation { namespace tensor_operation {
namespace device { namespace device {
namespace instance { namespace instance {
using BF16 = bhalf_t;
using F32 = float;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using GNDHWC = ck::tensor_layout::convolution::GNDHWC;
using GKZYXC = ck::tensor_layout::convolution::GKZYXC;
using GNDHWK = ck::tensor_layout::convolution::GNDHWK;
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;
// Compilation parameters for in[n, di, hi, wi, c] * wei[k, z, y, x, c] = out[n, do, ho, wo, k]
using device_grouped_conv3d_bwd_weight_xdl_c_shuffle_gndhwc_gkzyxc_gndhwk_bf16_f32_bf16_instances =
std::tuple<
// clang-format off
//#########################################| Num| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer|
//#########################################| Dim| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector|
//#########################################| Spatial| | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| |
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 64, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 64, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 32, 64, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 4>
// clang-format on
>;
using device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_1x1_s1_p0_bf16_f32_bf16_instances =
std::tuple<
// clang-format off
//#########################################| Num| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer|
//#########################################| Dim| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector|
//#########################################| Spatial| | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| |
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 64, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 64, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, BF16, F32, BF16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 32, 64, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 4>
// clang-format on
>;
void add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_bf16_f32_bf16_instances( void add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_bf16_f32_bf16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3, std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
GNDHWC, GNDHWC,
...@@ -92,12 +28,22 @@ void add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_bf16_f32_bf16 ...@@ -92,12 +28,22 @@ void add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_bf16_f32_bf16
PassThrough, PassThrough,
PassThrough>>>& instances) PassThrough>>>& instances)
{ {
// 1. Default
add_device_operation_instances( add_device_operation_instances(
instances, instances,
device_grouped_conv3d_bwd_weight_xdl_c_shuffle_gndhwc_gkzyxc_gndhwk_bf16_f32_bf16_instances{}); device_grouped_conv_bwd_weight_xdl_c_shuffle_bf16_instances<3,
add_device_operation_instances( GNDHWC,
instances, GKZYXC,
device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_1x1_s1_p0_bf16_f32_bf16_instances{}); GNDHWK,
ConvBwdWeightDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(instances,
device_grouped_conv_bwd_weight_xdl_c_shuffle_bf16_instances<
3,
GNDHWC,
GKZYXC,
GNDHWK,
ConvBwdWeightFilter1x1Stride1Pad0>{});
} }
} // namespace instance } // namespace instance
......
library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_weight/device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f16_instance.cpp
View file @ 4b70d68e
...@@ -5,81 +5,17 @@ ...@@ -5,81 +5,17 @@
#include "ck/ck.hpp" #include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp" #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_gnwc_gkxc_gnwk_xdl_cshuffle.hpp" #include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp" #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp" #include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_xdl_instance.hpp"
namespace ck { namespace ck {
namespace tensor_operation { namespace tensor_operation {
namespace device { namespace device {
namespace instance { namespace instance {
using F16 = ck::half_t;
using F32 = float;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using GNDHWC = ck::tensor_layout::convolution::GNDHWC;
using GKZYXC = ck::tensor_layout::convolution::GKZYXC;
using GNDHWK = ck::tensor_layout::convolution::GNDHWK;
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;
// Compilation parameters for in[n, di, hi, wi, c] * wei[k, z, y, x, c] = out[n, do, ho, wo, k]
using device_grouped_conv3d_bwd_weight_xdl_c_shuffle_gndhwc_gkzyxc_gndhwk_f16_default_instances =
std::tuple<
// clang-format off
//#########################################| Num| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer|
//#########################################| Dim| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector|
//#########################################| Spatial| | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| |
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 64, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 64, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 16, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 32, 64, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 8>
// clang-format on
>;
using device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_1x1_s1_p0_f16_instances =
std::tuple<
// clang-format off
//#########################################| Num| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer|
//#########################################| Dim| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector|
//#########################################| Spatial| | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| |
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 64, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 8, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 8>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 1, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 64, 32, 4, 8, 32, 32, 2, 1, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, 1, 1, S<1, 16, 1, 4>, 8>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 32, 64, 4, 8, 32, 32, 1, 2, S<1, 4, 4, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 2, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 8, 4, true, 1, 1, S<1, 16, 1, 4>, 8>
// clang-format on
>;
void add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f16_instances( void add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f16_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3, std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
GNDHWC, GNDHWC,
...@@ -92,12 +28,22 @@ void add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f16_instances ...@@ -92,12 +28,22 @@ void add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f16_instances
PassThrough, PassThrough,
PassThrough>>>& instances) PassThrough>>>& instances)
{ {
// 1. Default
add_device_operation_instances( add_device_operation_instances(
instances, instances,
device_grouped_conv3d_bwd_weight_xdl_c_shuffle_gndhwc_gkzyxc_gndhwk_f16_default_instances{}); device_grouped_conv_bwd_weight_xdl_c_shuffle_f16_instances<3,
add_device_operation_instances( GNDHWC,
instances, GKZYXC,
device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_1x1_s1_p0_f16_instances{}); GNDHWK,
ConvBwdWeightDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(instances,
device_grouped_conv_bwd_weight_xdl_c_shuffle_f16_instances<
3,
GNDHWC,
GKZYXC,
GNDHWK,
ConvBwdWeightFilter1x1Stride1Pad0>{});
} }
} // namespace instance } // namespace instance
......
library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_weight/device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f32_instance.cpp
View file @ 4b70d68e
...@@ -5,80 +5,17 @@ ...@@ -5,80 +5,17 @@
#include "ck/ck.hpp" #include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp" #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_gnwc_gkxc_gnwk_xdl_cshuffle.hpp" #include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp" #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp" #include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_xdl_instance.hpp"
namespace ck { namespace ck {
namespace tensor_operation { namespace tensor_operation {
namespace device { namespace device {
namespace instance { namespace instance {
using F32 = float;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using GNDHWC = ck::tensor_layout::convolution::GNDHWC;
using GKZYXC = ck::tensor_layout::convolution::GKZYXC;
using GNDHWK = ck::tensor_layout::convolution::GNDHWK;
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;
// Compilation parameters for in[n, di, hi, wi, c] * wei[k, z, y, x, c] = out[n, do, ho, wo, k]
using device_grouped_conv3d_bwd_weight_xdl_c_shuffle_gndhwc_gkzyxc_gndhwk_f32_default_instances =
std::tuple<
// clang-format off
//#########################################| Num| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer|
//#########################################| Dim| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector|
//#########################################| Spatial| | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| |
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 256, 128, 4, 4, 32, 32, 4, 2, S<1, 4, 64, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 256, 4, 4, 32, 32, 2, 4, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 64, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 128, 4, 4, 32, 32, 4, 2, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 128, 4, 4, 32, 32, 2, 2, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 64, 4, 4, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 64, 128, 4, 4, 32, 32, 2, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 64, 64, 4, 4, 32, 32, 2, 2, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 128, 64, 4, 4, 32, 32, 2, 1, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 256, 64, 128, 4, 4, 32, 32, 1, 2, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 128, 32, 4, 4, 32, 32, 2, 1, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 128, 32, 128, 4, 4, 32, 32, 1, 2, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 64, 32, 4, 4, 32, 32, 2, 1, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightDefault, 64, 32, 64, 4, 4, 32, 32, 1, 2, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 16, 1, 4>, 4>
// clang-format on
>;
using device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_1x1_s1_p0_f32_instances =
std::tuple<
// clang-format off
//#########################################| Num| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer|
//#########################################| Dim| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector|
//#########################################| Spatial| | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| |
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 256, 128, 4, 4, 32, 32, 4, 2, S<1, 4, 64, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 256, 4, 4, 32, 32, 2, 4, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 64, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 128, 4, 4, 32, 32, 4, 2, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 128, 4, 4, 32, 32, 2, 2, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 64, 4, 4, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 64, 128, 4, 4, 32, 32, 2, 2, S<1, 4, 16, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 64, 64, 4, 4, 32, 32, 2, 2, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 128, 64, 4, 4, 32, 32, 2, 1, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 256, 64, 128, 4, 4, 32, 32, 1, 2, S<1, 4, 16, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, S<1, 4, 32, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 128, 32, 4, 4, 32, 32, 2, 1, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 128, 32, 128, 4, 4, 32, 32, 1, 2, S<1, 4, 8, 4>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 1, true, S<1, 4, 32, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 32, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 64, 32, 4, 4, 32, 32, 2, 1, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, 1, 1, S<1, 16, 1, 4>, 4>,
DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle< 3, F32, F32, F32, F32, PassThrough, PassThrough, PassThrough, ConvBwdWeightFilter1x1Stride1Pad0, 64, 32, 64, 4, 4, 32, 32, 1, 2, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 2, true, S<1, 4, 16, 1>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 4, 4, true, 1, 1, S<1, 16, 1, 4>, 4>
// clang-format on
>;
void add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f32_instances( void add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f32_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3, std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
GNDHWC, GNDHWC,
...@@ -91,12 +28,22 @@ void add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f32_instances ...@@ -91,12 +28,22 @@ void add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f32_instances
PassThrough, PassThrough,
PassThrough>>>& instances) PassThrough>>>& instances)
{ {
// 1. Default
add_device_operation_instances( add_device_operation_instances(
instances, instances,
device_grouped_conv3d_bwd_weight_xdl_c_shuffle_gndhwc_gkzyxc_gndhwk_f32_default_instances{}); device_grouped_conv_bwd_weight_xdl_c_shuffle_f32_instances<3,
add_device_operation_instances( GNDHWC,
instances, GKZYXC,
device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_1x1_s1_p0_f32_instances{}); GNDHWK,
ConvBwdWeightDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(instances,
device_grouped_conv_bwd_weight_xdl_c_shuffle_f32_instances<
3,
GNDHWC,
GKZYXC,
GNDHWK,
ConvBwdWeightFilter1x1Stride1Pad0>{});
} }
} // namespace instance } // namespace instance
......
library/src/tensor_operation_instance/gpu/quantization/CMakeLists.txt
View file @ 4b70d68e
if(DTYPES MATCHES "int8" OR NOT DEFINED DTYPES)
set(CONV2D_PERLAYER_QUANT_SRC set(CONV2D_PERLAYER_QUANT_SRC
conv2d_fwd/device_conv2d_dl_perlayer_quantization_int8_instance.cpp conv2d_fwd/device_conv2d_dl_perlayer_quantization_int8_instance.cpp
conv2d_fwd/device_conv2d_xdl_perlayer_quantization_int8_instance.cpp conv2d_fwd/device_conv2d_xdl_perlayer_quantization_int8_instance.cpp
...@@ -36,3 +37,4 @@ add_instance_library(device_quantization_instance ...@@ -36,3 +37,4 @@ add_instance_library(device_quantization_instance
${CONV2D_BIAS_PERCHANNEL_QUANT_SRC} ${CONV2D_BIAS_PERCHANNEL_QUANT_SRC}
${GEMM_QUANT_SRC} ${GEMM_QUANT_SRC}
) )
endif()
\ No newline at end of file
library/src/tensor_operation_instance/gpu/quantization/conv2d_fwd/device_conv2d_dl_int8_instance.hpp
View file @ 4b70d68e
...@@ -4,7 +4,7 @@ ...@@ -4,7 +4,7 @@
#pragma once #pragma once
#include "conv2d_quantization_common.hpp" #include "conv2d_quantization_common.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_conv_fwd_dl_multiple_d_nhwc_kyxc_nhwk.hpp" #include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_fwd_dl_multiple_d_nhwc_kyxc_nhwk.hpp"
namespace ck { namespace ck {
namespace tensor_operation { namespace tensor_operation {
......
profiler/README.md
View file @ 4b70d68e
...@@ -141,3 +141,46 @@ avg_time: 0.768321 ...@@ -141,3 +141,46 @@ avg_time: 0.768321
tflops: 86.6679 tflops: 86.6679
GB/s: 127.947 GB/s: 127.947
``` ```
## Profile grouped convolution backward weight kernels
```bash
# arg1: tensor operation (grouped_conv_bwd_data: Grouped Convolution Backward Data)
# arg2: data type (0: Input fp32, Weight fp32, Output fp32
# 1: Input fp16, Weight fp16, Output fp16
# 2: Input bf16, Weight fp32, Output bf16)
# arg3: tensor layout (0: Input[G, N, C, Hi, Wi], Weight[G, K, C, Y, X], Output[G, N, K, Ho, Wo]
# 1: Input[G, N, Hi, Wi, C], Weight[G, K, Y, X, C], Output[G, N, Ho, Wo, K]
# 2: Input[N, Hi, Wi, G, C], Weight[G, K, Y, X, C], Output[N, Ho, Wo, G, K]
# arg4: verification (0: no, 1: yes)
# arg5: initialization (0: no init, 1: integer value, 2: decimal value)
# arg6: print tensor value (0: no; 1: yes)
# arg7: time kernel (0: no, 1: yes)
# Following arguments (depending on number of spatial dims):
# Number of spatial dimensions (1=Conv1d, 2=Conv2d, 3=Conv3d)
# G, N, K, C,
# <filter spatial dimensions>, (ie Y, X for 2D)
# <input image spatial dimensions>, (ie Hi, Wi for 2D)
# <strides>, (ie Sy, Sx for 2D)
# <dilations>, (ie Dy, Dx for 2D)
# <left padding>, (ie LeftPy, LeftPx for 2D)
# <right padding>, (ie RightPy, RightPx for 2D)
# SplitK
################ op datatype layout verify init log time Ndims G N K C Y X Hi Wi Sy Sx Dy Dx LeftPy LeftPx RightPy RightPx SplitK
./bin/ckProfiler grouped_conv_bwd_data 1 0 1 1 0 1 2 32 256 256 512 3 3 28 28 1 1 1 1 1 0 0 0 1
```
Result (MI100, FP16, GNHWC_GKYXC_GNHWK)
```
input: dim 5, lengths {32, 512, 1024, 28, 28}, strides {411041792, 802816, 1, 28672, 1024}
weight: dim 5, lengths {32, 512, 1024, 3, 3}, strides {4718592, 9216, 1, 3072, 1024}
output: dim 5, lengths {32, 512, 512, 26, 26}, strides {177209344, 346112, 1, 13312, 512}
....
Best configuration parameters:
name: DeviceGroupedConvBwdWeight_Xdl_CShuffle<256, 256, 128, 4, Default, 8, 4, 2, 8, 4, 8, 2, 1, 1, 8>
avg_time: 68.5216
tflops: 95.337
GB/s: 69.2301
```
Note: This kernel use atomic add, this will cause output buffer to be accumulated multiple times, causing verification failure. To work around it, do not use CK's own timer and do verification at the same time.
profiler/include/profiler/profile_grouped_conv_bwd_weight_impl.hpp
View file @ 4b70d68e
...@@ -139,6 +139,8 @@ bool profile_grouped_conv_bwd_weight_impl(int do_verification, ...@@ -139,6 +139,8 @@ bool profile_grouped_conv_bwd_weight_impl(int do_verification,
std::array<ck::index_t, NDimSpatial> input_spatial_lengths{}; std::array<ck::index_t, NDimSpatial> input_spatial_lengths{};
std::array<ck::index_t, NDimSpatial> filter_spatial_lengths{}; std::array<ck::index_t, NDimSpatial> filter_spatial_lengths{};
std::array<ck::index_t, NDimSpatial> output_spatial_lengths{}; std::array<ck::index_t, NDimSpatial> output_spatial_lengths{};
std::array<ck::index_t, NDimSpatial + 3> input_strides{};
std::array<ck::index_t, NDimSpatial + 3> output_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_strides{}; std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_dilations{}; std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
std::array<ck::index_t, NDimSpatial> input_left_pads{}; std::array<ck::index_t, NDimSpatial> input_left_pads{};
...@@ -149,6 +151,8 @@ bool profile_grouped_conv_bwd_weight_impl(int do_verification, ...@@ -149,6 +151,8 @@ bool profile_grouped_conv_bwd_weight_impl(int do_verification,
range_copy(conv_param.input_spatial_lengths_, begin(input_spatial_lengths)); range_copy(conv_param.input_spatial_lengths_, begin(input_spatial_lengths));
range_copy(conv_param.filter_spatial_lengths_, begin(filter_spatial_lengths)); range_copy(conv_param.filter_spatial_lengths_, begin(filter_spatial_lengths));
range_copy(conv_param.output_spatial_lengths_, begin(output_spatial_lengths)); range_copy(conv_param.output_spatial_lengths_, begin(output_spatial_lengths));
range_copy(in_g_n_c_wis_desc.GetStrides(), begin(input_strides));
range_copy(out_g_n_k_wos_desc.GetStrides(), begin(output_strides));
range_copy(conv_param.conv_filter_strides_, begin(conv_filter_strides)); range_copy(conv_param.conv_filter_strides_, begin(conv_filter_strides));
range_copy(conv_param.conv_filter_dilations_, begin(conv_filter_dilations)); range_copy(conv_param.conv_filter_dilations_, begin(conv_filter_dilations));
range_copy(conv_param.input_left_pads_, begin(input_left_pads)); range_copy(conv_param.input_left_pads_, begin(input_left_pads));
...@@ -167,6 +171,8 @@ bool profile_grouped_conv_bwd_weight_impl(int do_verification, ...@@ -167,6 +171,8 @@ bool profile_grouped_conv_bwd_weight_impl(int do_verification,
input_spatial_lengths, input_spatial_lengths,
filter_spatial_lengths, filter_spatial_lengths,
output_spatial_lengths, output_spatial_lengths,
input_strides,
output_strides,
conv_filter_strides, conv_filter_strides,
conv_filter_dilations, conv_filter_dilations,
input_left_pads, input_left_pads,
......
profiler/src/profile_batched_gemm_multi_d.cpp
View file @ 4b70d68e
...@@ -70,8 +70,10 @@ int profile_batched_gemm_multi_d(int argc, char* argv[]) ...@@ -70,8 +70,10 @@ int profile_batched_gemm_multi_d(int argc, char* argv[])
const int BatchCount = std::stoi(argv[17]); const int BatchCount = std::stoi(argv[17]);
using F16 = ck::half_t; using F16 = ck::half_t;
#ifdef __int8__
using INT8 = int8_t; using INT8 = int8_t;
#endif
using Row = ck::tensor_layout::gemm::RowMajor; using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor; using Col = ck::tensor_layout::gemm::ColumnMajor;
...@@ -163,6 +165,7 @@ int profile_batched_gemm_multi_d(int argc, char* argv[]) ...@@ -163,6 +165,7 @@ int profile_batched_gemm_multi_d(int argc, char* argv[])
{ {
return profile(F16{}, F16{}, F16{}, Col{}, Col{}, Row{}); return profile(F16{}, F16{}, F16{}, Col{}, Col{}, Row{});
} }
#ifdef __int8__
else if(data_type == GemmDataType::INT8_INT8_INT8 && layout == GemmMatrixLayout::MK_KN_MN) else if(data_type == GemmDataType::INT8_INT8_INT8 && layout == GemmMatrixLayout::MK_KN_MN)
{ {
return profile(INT8{}, INT8{}, INT8{}, Row{}, Row{}, Row{}); return profile(INT8{}, INT8{}, INT8{}, Row{}, Row{}, Row{});
...@@ -179,6 +182,7 @@ int profile_batched_gemm_multi_d(int argc, char* argv[]) ...@@ -179,6 +182,7 @@ int profile_batched_gemm_multi_d(int argc, char* argv[])
{ {
return profile(INT8{}, INT8{}, INT8{}, Col{}, Col{}, Row{}); return profile(INT8{}, INT8{}, INT8{}, Col{}, Col{}, Row{});
} }
#endif
else else
{ {
std::cout << "this data_type & layout is not implemented" << std::endl; std::cout << "this data_type & layout is not implemented" << std::endl;
......
profiler/src/profile_batchnorm_fwd.cpp
View file @ 4b70d68e
...@@ -148,7 +148,7 @@ int profile_batchnorm_forward(int argc, char* argv[]) ...@@ -148,7 +148,7 @@ int profile_batchnorm_forward(int argc, char* argv[])
{ {
if(arg_parser.inLengths.size() == 4 && arg_parser.reduceDims.size() == 3) if(arg_parser.inLengths.size() == 4 && arg_parser.reduceDims.size() == 3)
{ {
profile_batchnorm_forward_impl<F16, F16, F32, F16, F16, F16, 4, 3>( profile_batchnorm_forward_impl<F16, F16, F32, F16, F16, F32, 4, 3>(
arg_parser.do_verification, arg_parser.do_verification,
arg_parser.init_method, arg_parser.init_method,
arg_parser.do_dumpout, arg_parser.do_dumpout,
......
profiler/src/profile_conv_bwd_data.cpp
View file @ 4b70d68e
...@@ -77,7 +77,9 @@ int profile_conv_bwd_data(int argc, char* argv[]) ...@@ -77,7 +77,9 @@ int profile_conv_bwd_data(int argc, char* argv[])
using F32 = float; using F32 = float;
using F16 = ck::half_t; using F16 = ck::half_t;
using BF16 = ck::bhalf_t; using BF16 = ck::bhalf_t;
#ifdef __int8__
using INT8 = int8_t; using INT8 = int8_t;
#endif
using NWC = ck::tensor_layout::convolution::NWC; using NWC = ck::tensor_layout::convolution::NWC;
using NHWC = ck::tensor_layout::convolution::NHWC; using NHWC = ck::tensor_layout::convolution::NHWC;
...@@ -138,10 +140,12 @@ int profile_conv_bwd_data(int argc, char* argv[]) ...@@ -138,10 +140,12 @@ int profile_conv_bwd_data(int argc, char* argv[])
{ {
return profile(I1, NWC{}, KXC{}, NWK{}, BF16{}, BF16{}, BF16{}); return profile(I1, NWC{}, KXC{}, NWK{}, BF16{}, BF16{}, BF16{});
} }
#ifdef __int8__
else if(data_type == ConvDataType::INT8_INT8_INT8) else if(data_type == ConvDataType::INT8_INT8_INT8)
{ {
return profile(I1, NWC{}, KXC{}, NWK{}, INT8{}, INT8{}, INT8{}); return profile(I1, NWC{}, KXC{}, NWK{}, INT8{}, INT8{}, INT8{});
} }
#endif
} }
else if(num_dim_spatial == 2 && layout == ConvLayout::NHWC_KYXC_NHWK) else if(num_dim_spatial == 2 && layout == ConvLayout::NHWC_KYXC_NHWK)
{ {
...@@ -157,10 +161,12 @@ int profile_conv_bwd_data(int argc, char* argv[]) ...@@ -157,10 +161,12 @@ int profile_conv_bwd_data(int argc, char* argv[])
{ {
return profile(I2, NHWC{}, KYXC{}, NHWK{}, BF16{}, BF16{}, BF16{}); return profile(I2, NHWC{}, KYXC{}, NHWK{}, BF16{}, BF16{}, BF16{});
} }
#ifdef __int8__
else if(data_type == ConvDataType::INT8_INT8_INT8) else if(data_type == ConvDataType::INT8_INT8_INT8)
{ {
return profile(I2, NHWC{}, KYXC{}, NHWK{}, INT8{}, INT8{}, INT8{}); return profile(I2, NHWC{}, KYXC{}, NHWK{}, INT8{}, INT8{}, INT8{});
} }
#endif
} }
else if(num_dim_spatial == 3 && layout == ConvLayout::NHWC_KYXC_NHWK) else if(num_dim_spatial == 3 && layout == ConvLayout::NHWC_KYXC_NHWK)
{ {
...@@ -176,10 +182,12 @@ int profile_conv_bwd_data(int argc, char* argv[]) ...@@ -176,10 +182,12 @@ int profile_conv_bwd_data(int argc, char* argv[])
{ {
return profile(I3, NDHWC{}, KZYXC{}, NDHWK{}, BF16{}, BF16{}, BF16{}); return profile(I3, NDHWC{}, KZYXC{}, NDHWK{}, BF16{}, BF16{}, BF16{});
} }
#ifdef __int8__
else if(data_type == ConvDataType::INT8_INT8_INT8) else if(data_type == ConvDataType::INT8_INT8_INT8)
{ {
return profile(I3, NDHWC{}, KZYXC{}, NDHWK{}, INT8{}, INT8{}, INT8{}); return profile(I3, NDHWC{}, KZYXC{}, NDHWK{}, INT8{}, INT8{}, INT8{});
} }
#endif
} }
std::cout << "this data_type & layout is not implemented" << std::endl; std::cout << "this data_type & layout is not implemented" << std::endl;
......
profiler/src/profile_gemm.cpp
View file @ 4b70d68e
...@@ -67,11 +67,15 @@ int profile_gemm(int argc, char* argv[]) ...@@ -67,11 +67,15 @@ int profile_gemm(int argc, char* argv[])
const int StrideB = std::stoi(argv[12]); const int StrideB = std::stoi(argv[12]);
const int StrideC = std::stoi(argv[13]); const int StrideC = std::stoi(argv[13]);
using F32 = float; using F32 = float;
using F16 = ck::half_t; using F16 = ck::half_t;
using BF16 = ck::bhalf_t; #ifdef __bf16__
using BF16 = ck::bhalf_t;
#endif
#ifdef __int8__
using INT8 = int8_t; using INT8 = int8_t;
using INT32 = int32_t; using INT32 = int32_t;
#endif
using Row = ck::tensor_layout::gemm::RowMajor; using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor; using Col = ck::tensor_layout::gemm::ColumnMajor;
...@@ -149,6 +153,7 @@ int profile_gemm(int argc, char* argv[]) ...@@ -149,6 +153,7 @@ int profile_gemm(int argc, char* argv[])
{ {
return profile(Col{}, Col{}, Row{}, F16{}, F16{}, F32{}, F16{}); return profile(Col{}, Col{}, Row{}, F16{}, F16{}, F32{}, F16{});
} }
#ifdef __bf16__
else if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::MK_KN_MN) else if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::MK_KN_MN)
{ {
return profile(Row{}, Row{}, Row{}, BF16{}, BF16{}, F32{}, BF16{}); return profile(Row{}, Row{}, Row{}, BF16{}, BF16{}, F32{}, BF16{});
...@@ -165,6 +170,8 @@ int profile_gemm(int argc, char* argv[]) ...@@ -165,6 +170,8 @@ int profile_gemm(int argc, char* argv[])
{ {
return profile(Col{}, Col{}, Row{}, BF16{}, BF16{}, F32{}, BF16{}); return profile(Col{}, Col{}, Row{}, BF16{}, BF16{}, F32{}, BF16{});
} }
#endif
#ifdef __int8__
else if(data_type == GemmDataType::INT8_INT8_INT8 && layout == GemmMatrixLayout::MK_KN_MN) else if(data_type == GemmDataType::INT8_INT8_INT8 && layout == GemmMatrixLayout::MK_KN_MN)
{ {
return profile(Row{}, Row{}, Row{}, INT8{}, INT8{}, INT32{}, INT8{}); return profile(Row{}, Row{}, Row{}, INT8{}, INT8{}, INT32{}, INT8{});
...@@ -181,6 +188,7 @@ int profile_gemm(int argc, char* argv[]) ...@@ -181,6 +188,7 @@ int profile_gemm(int argc, char* argv[])
{ {
return profile(Col{}, Col{}, Row{}, INT8{}, INT8{}, INT32{}, INT8{}); return profile(Col{}, Col{}, Row{}, INT8{}, INT8{}, INT32{}, INT8{});
} }
#endif
else else
{ {
std::cout << "this data_type & layout is not implemented" << std::endl; std::cout << "this data_type & layout is not implemented" << std::endl;
......
profiler/src/profile_grouped_conv_bwd_weight.cpp
View file @ 4b70d68e
...@@ -15,6 +15,7 @@ enum struct ConvLayout ...@@ -15,6 +15,7 @@ enum struct ConvLayout
{ {
GNCHW_GKCYX_GNKHW, // 0 GNCHW_GKCYX_GNKHW, // 0
GNHWC_GKYXC_GNHWK, // 1 GNHWC_GKYXC_GNHWK, // 1
NHWGC_GKYXC_NHWGK, // 2
}; };
enum struct ConvDataType enum struct ConvDataType
...@@ -37,6 +38,8 @@ static void print_helper_msg() ...@@ -37,6 +38,8 @@ static void print_helper_msg()
"N, K, Ho, Wo]\n" "N, K, Ho, Wo]\n"
<< " 1: Input[G, N, Hi, Wi, C], Weight[G, K, Y, X, C], Output[G, " << " 1: Input[G, N, Hi, Wi, C], Weight[G, K, Y, X, C], Output[G, "
"N, Ho, Wo, K]\n" "N, Ho, Wo, K]\n"
<< " 2: Input[N, Hi, Wi, G, C], Weight[G, K, Y, X, C], Output[N, "
"Ho, Wo, G, K]\n"
<< "arg4: verification (0: no, 1: yes)\n" << "arg4: verification (0: no, 1: yes)\n"
<< "arg5: initialization (0: no init, 1: integer value, 2: decimal value)\n" << "arg5: initialization (0: no init, 1: integer value, 2: decimal value)\n"
<< "arg6: print tensor value (0: no; 1: yes)\n" << "arg6: print tensor value (0: no; 1: yes)\n"
...@@ -82,6 +85,7 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[]) ...@@ -82,6 +85,7 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[])
using GNWC = ck::tensor_layout::convolution::GNWC; using GNWC = ck::tensor_layout::convolution::GNWC;
using GNHWC = ck::tensor_layout::convolution::GNHWC; using GNHWC = ck::tensor_layout::convolution::GNHWC;
using NHWGC = ck::tensor_layout::convolution::NHWGC;
using GNDHWC = ck::tensor_layout::convolution::GNDHWC; using GNDHWC = ck::tensor_layout::convolution::GNDHWC;
using GKXC = ck::tensor_layout::convolution::GKXC; using GKXC = ck::tensor_layout::convolution::GKXC;
...@@ -90,6 +94,7 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[]) ...@@ -90,6 +94,7 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[])
using GNWK = ck::tensor_layout::convolution::GNWK; using GNWK = ck::tensor_layout::convolution::GNWK;
using GNHWK = ck::tensor_layout::convolution::GNHWK; using GNHWK = ck::tensor_layout::convolution::GNHWK;
using NHWGK = ck::tensor_layout::convolution::NHWGK;
using GNDHWK = ck::tensor_layout::convolution::GNDHWK; using GNDHWK = ck::tensor_layout::convolution::GNDHWK;
constexpr auto I1 = ck::Number<1>{}; constexpr auto I1 = ck::Number<1>{};
...@@ -157,6 +162,22 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[]) ...@@ -157,6 +162,22 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[])
return profile(I2, GNHWC{}, GKYXC{}, GNHWK{}, BF16{}, F32{}, BF16{}); return profile(I2, GNHWC{}, GKYXC{}, GNHWK{}, BF16{}, F32{}, BF16{});
} }
} }
else if(num_dim_spatial == 2 && layout == ConvLayout::NHWGC_GKYXC_NHWGK)
{
if(data_type == ConvDataType::F32_F32_F32)
{
return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, F32{}, F32{}, F32{});
}
else if(data_type == ConvDataType::F16_F16_F16)
{
return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, F16{}, F16{}, F16{});
}
else if(data_type == ConvDataType::BF16_F32_BF16)
{
// fp32 atomic add is used for weight tensor in bf16 kernel
return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, BF16{}, F32{}, BF16{});
}
}
else if(num_dim_spatial == 3 && layout == ConvLayout::GNHWC_GKYXC_GNHWK) else if(num_dim_spatial == 3 && layout == ConvLayout::GNHWC_GKYXC_GNHWK)
{ {
if(data_type == ConvDataType::F32_F32_F32) if(data_type == ConvDataType::F32_F32_F32)
......
script/check_copyright_year.sh 0 → 100755
View file @ 4b70d68e
#!/bin/bash
current_year=$(date +%Y)
exit_code=0
for file in $@; do
if grep -q "Copyright (c)" $file
then
if ! grep -q "Copyright (c).*$current_year" $file
then
echo "ERROR: File $file has a copyright notice without the current year ($current_year)."
exit_code=1
fi
fi
done
exit $exit_code
script/install_precommit.sh 0 → 100755
View file @ 4b70d68e
#!/bin/bash
run_and_check() {
"$@"
status=$?
if [ $status -ne 0 ]; then
echo "Error with \"$@\": Exited with status $status"
exit $status
fi
return $status
}
echo "I: Installing tools required for pre-commit checks..."
run_and_check apt install clang-format-10
echo "I: Installing pre-commit itself..."
run_and_check pip3 install pre-commit
run_and_check pre-commit install
echo "I: Installation successful."
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