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Commit b79df771 authored by carlushuang's avatar carlushuang
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Merge remote-tracking branch 'origin/develop' into cpu_avx2

parents 05d38218 63914743
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library/include/ck/library/obselete_driver_offline/device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw.hpp deleted 100644 → 0
View file @ 05d38218
#include <unistd.h>
#include "device.hpp"
#include "host_tensor.hpp"
#include "transform_forward_convolution_into_gemm_v4r4r2_nchw_kcyx_nkhw.hpp"
#include "driver_gemm_xdlops_v2r3.hpp"
template <typename TInWei,
typename TAcc,
typename TOut,
typename InLengths,
typename WeiLengths,
typename OutLengths,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw(
const InLengths& in_n_c_hi_wi_lengths,
const WeiLengths& wei_k_c_y_x_lengths,
const OutLengths& out_n_k_ho_wo_lengths,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const Tensor<TInWei>& in_n_c_hi_wi,
const Tensor<TInWei>& wei_k_c_y_x,
Tensor<TOut>& out_n_k_ho_wo,
ck::index_t nrepeat)
{
using namespace ck;
std::cout << __func__ << std::endl;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
DeviceMem in_n_c_hi_wi_device_buf(sizeof(TInWei) * in_n_c_hi_wi.mDesc.GetElementSpace());
DeviceMem wei_k_c_y_x_device_buf(sizeof(TInWei) * wei_k_c_y_x.mDesc.GetElementSpace());
DeviceMem out_n_k_ho_wo_device_buf(sizeof(TOut) * out_n_k_ho_wo.mDesc.GetElementSpace());
in_n_c_hi_wi_device_buf.ToDevice(in_n_c_hi_wi.mData.data());
wei_k_c_y_x_device_buf.ToDevice(wei_k_c_y_x.mData.data());
out_n_k_ho_wo_device_buf.ToDevice(out_n_k_ho_wo.mData.data());
const auto in_n_c_hi_wi_desc = make_naive_tensor_descriptor_packed(in_n_c_hi_wi_lengths);
const auto wei_k_c_y_x_desc = make_naive_tensor_descriptor_packed(wei_k_c_y_x_lengths);
const auto out_n_k_ho_wo_desc = make_naive_tensor_descriptor_packed(out_n_k_ho_wo_lengths);
#if 0
// [M, N, K0, K1] = [128, 128, 4, 8] for fp16
constexpr index_t BlockSize = 256;
constexpr index_t GemmMPerBlock = 128;
constexpr index_t GemmNPerBlock = 128;
constexpr index_t GemmKPerBlock = 4;
constexpr index_t GemmMPerWave = 32;
constexpr index_t GemmNPerWave = 32;
constexpr index_t GemmK1 = 8;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 2;
using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1 = Sequence<1, 2, 8>;
using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 8;
constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 8;
using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1 = Sequence<1, 2, 8>;
using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmN = 1;
constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 8;
constexpr index_t GemmCThreadTransferDstScalarPerVector = 1;
#elif 1
// [M, N, K0, K1] = [256, 128, 4, 8] for fp16
constexpr index_t BlockSize = 256;
constexpr index_t GemmMPerBlock = 256;
constexpr index_t GemmNPerBlock = 128;
constexpr index_t GemmKPerBlock = 4;
constexpr index_t GemmMPerWave = 32;
constexpr index_t GemmNPerWave = 32;
constexpr index_t GemmK1 = 8;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1 = Sequence<1, 4, 8>;
using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 8;
constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 8;
using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1 = Sequence<1, 2, 8>;
using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmN = 1;
constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 8;
constexpr index_t GemmCThreadTransferDstScalarPerVector = 1;
#endif
const auto descs =
transform_forward_convolution_into_gemm_v4r4r2_nchw_kcyx_nkhw_pad(wei_k_c_y_x_desc,
in_n_c_hi_wi_desc,
out_n_k_ho_wo_desc,
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads,
Number<GemmK1>{});
const auto wei_gemmk0_gemmm_gemmk1_grid_desc = descs[I0];
const auto in_gemmk0_gemmn_gemmk1_grid_desc = descs[I1];
const auto out_gemmm_gemmn_grid_desc = descs[I2];
// HACK: hacks that control index calculation when iterating over A, B, C matrix
constexpr auto wei_gemmk0_gemmm_gemmk1_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{}, // 0+: GemmK0
Sequence<0, 0, 0, 0, 0>{}, // 1+: GemmM
Sequence<0, 0, 0, 0, 0>{}), // 2+: GemmK1
make_tuple(Sequence<0, 0, 0, 0, 0>{}, // 0-: GemmK0
Sequence<0, 0, 0, 0, 0>{}, // 1-: GemmM
Sequence<0, 0, 0, 0, 0>{})); // 2-: GemmK1
constexpr auto in_gemmk0_gemmn_gemmk1_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}, // 0+: GemmK0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{}, // 1+: GemmN
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}), // 2+: GemmK1
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{}, // 0-: GemmK0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{}, // 1-: GemmN
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{})); // 2-: GemmK1
constexpr auto out_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: M0
Sequence<0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1+: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2+: M1
Sequence<0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3+: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4+: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5+: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6+: M4
Sequence<0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 7+: N2
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0-: M0
Sequence<0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1-: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2-: M1
Sequence<0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3-: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4-: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5-: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6-: M4
Sequence<0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
constexpr auto wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0>{};
constexpr auto in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>{};
for(index_t i = 0; i < 5; ++i)
{
float ave_time = driver_gemm_xdlops_v2r3<
BlockSize,
TInWei,
TAcc,
TOut,
InMemoryDataOperationEnum::Set,
decltype(wei_gemmk0_gemmm_gemmk1_grid_desc),
decltype(in_gemmk0_gemmn_gemmk1_grid_desc),
decltype(out_gemmm_gemmn_grid_desc),
GemmMPerBlock,
GemmNPerBlock,
GemmKPerBlock,
GemmMPerWave,
GemmNPerWave,
GemmK1,
MRepeat,
NRepeat,
GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1,
GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1,
Sequence<1, 0, 2>,
Sequence<1, 0, 2>,
2,
GemmABlockTransferSrcScalarPerVector_GemmK1,
GemmABlockTransferDstScalarPerVector_GemmK1,
false, // don't move back src coordinate after threadwise copy
GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1,
GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1,
Sequence<0, 2, 1>,
Sequence<1, 0, 2>,
1,
GemmBBlockTransferSrcScalarPerVector_GemmN,
GemmBBlockTransferDstScalarPerVector_GemmK1,
false, // don't move back src coordinate after threadwise copy
Sequence<3, 0, 1, 2, 7, 5, 4, 6>,
7,
GemmCThreadTransferDstScalarPerVector,
decltype(wei_gemmk0_gemmm_gemmk1_grid_step_hacks),
decltype(in_gemmk0_gemmn_gemmk1_grid_step_hacks),
decltype(out_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks),
decltype(wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks),
decltype(in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks),
false>(static_cast<TInWei*>(wei_k_c_y_x_device_buf.GetDeviceBuffer()),
static_cast<TInWei*>(in_n_c_hi_wi_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(out_n_k_ho_wo_device_buf.GetDeviceBuffer()),
wei_gemmk0_gemmm_gemmk1_grid_desc,
in_gemmk0_gemmn_gemmk1_grid_desc,
out_gemmm_gemmn_grid_desc,
wei_gemmk0_gemmm_gemmk1_grid_step_hacks,
in_gemmk0_gemmn_gemmk1_grid_step_hacks,
out_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks,
wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks,
in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks,
nrepeat);
float perf = static_cast<float>(calculate_convolution_flops(
in_n_c_hi_wi_desc, wei_k_c_y_x_desc, out_n_k_ho_wo_desc)) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s" << std::endl;
}
// copy result back to host
out_n_k_ho_wo_device_buf.FromDevice(out_n_k_ho_wo.mData.data());
}
library/include/ck/library/obselete_driver_offline/device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk.hpp deleted 100644 → 0
View file @ 05d38218
#include <unistd.h>
#include "device.hpp"
#include "host_tensor.hpp"
#include "transform_forward_convolution_into_gemm_v4r4r4_nhwc_kyxc_nhwk.hpp"
#include "driver_gemm_xdlops_v2r3.hpp"
#if 0
__host__ __device__ static constexpr auto
MakePaddedGridDescriptors(const AGridDesc_K0Raw_MRaw_K1& a_grid_desc_k0raw_mraw_k1,
const BGridDesc_K0Raw_NRaw_K1& b_grid_desc_k0raw_nraw_k1,
const CGridDesc_MRaw_NRaw& c_grid_desc_mraw_nraw)
{
const auto K0Raw = a_grid_desc_k0raw_mraw_k1.GetLength(I0);
const auto K1 = a_grid_desc_k0raw_mraw_k1.GetLength(I2);
const auto MRaw = c_grid_desc_mraw_nraw.GetLength(I0);
const auto NRaw = c_grid_desc_mraw_nraw.GetLength(I1);
const auto K0Pad = math::integer_least_multiple(K0Raw, K0PerBlock) - K0Raw;
const auto MPad = math::integer_least_multiple(MRaw, MPerBlock) - MRaw;
const auto NPad = math::integer_least_multiple(NRaw, NPerBlock) - NRaw;
// A
const auto a_grid_desc_k0_m_k1 = [&]() {
if constexpr(DoPad_K0 && DoPad_M)
{
return transform_tensor_descriptor(
a_grid_desc_k0_m_k1,
make_tuple(make_right_pad_transform(K0Raw, K0Pad),
make_right_pad_transform(MRaw, MPad),
make_pass_through_transform(K1)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
}
else if constexpr(DoPad_K0 && !DoPad_M)
{
return transform_tensor_descriptor(
a_grid_desc_k0_m_k1,
make_tuple(make_right_pad_transform(K0Raw, K0Pad),
make_pass_through_transform(MRaw),
make_pass_through_transform(K1)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
}
else if constexpr(!DoPad_K0 && DoPad_M)
{
return transform_tensor_descriptor(
a_grid_desc_k0_m_k1,
make_tuple(make_pass_through_transform(K0Raw),
make_right_pad_transform(MRaw, MPad),
make_pass_through_transform(K1)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
}
else
{
return a_grid_desc_k0raw_mraw_k1;
}
}();
// B
const auto b_grid_desc_k0_n_k1 = [&]() {
if constexpr(DoPad_K0 && DoPad_N)
{
return transform_tensor_descriptor(
b_grid_desc_k0_n_k1,
make_tuple(make_right_pad_transform(K0Raw, K0Pad),
make_right_pad_transform(NRaw, NPad),
make_pass_through_transform(K1)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
}
else if constexpr(DoPad_K0 && !DoPad_N)
{
return transform_tensor_descriptor(
b_grid_desc_k0_n_k1,
make_tuple(make_right_pad_transform(K0Raw, K0Pad),
make_pass_through_transform(NRaw),
make_pass_through_transform(K1)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
}
else if constexpr(!DoPad_K0 && DoPad_N)
{
return transform_tensor_descriptor(
b_grid_desc_k0_n_k1,
make_tuple(make_pass_through_transform(K0Raw),
make_right_pad_transform(NRaw, NPad),
make_pass_through_transform(K1)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
}
else
{
return b_grid_desc_k0raw_nraw_k1;
}
}();
// C
const auto c_grid_desc_m_n = [&]() {
if constexpr(DoPad_M && DoPad_N)
{
return transform_tensor_descriptor(c_grid_desc_m_n,
make_tuple(make_right_pad_transform(MRaw, MPad),
make_right_pad_transform(NRaw, NPad)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
}
else if constexpr(DoPad_M && !DoPad_N)
{
return transform_tensor_descriptor(
c_grid_desc_m_n,
make_tuple(make_right_pad_transform(MRaw, MPad), make_pass_through_transform(NRaw)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
}
else if constexpr(!DoPad_M && DoPad_N)
{
return transform_tensor_descriptor(
c_grid_desc_m_n,
make_tuple(make_pass_through_transform(MRaw), make_right_pad_transform(NRaw, NPad)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
}
else
{
reutnr c_grid_desc_m_n;
}
}();
}
#endif
template <typename TInWei,
typename TAcc,
typename TOut,
typename InLengths,
typename WeiLengths,
typename OutLengths,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk(
const InLengths& in_n_hi_wi_c_lengths,
const WeiLengths& wei_k_y_x_c_lengths,
const OutLengths& out_n_ho_wo_k_lengths,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const Tensor<TInWei>& in_n_hi_wi_c,
const Tensor<TInWei>& wei_k_y_x_c,
Tensor<TOut>& out_n_ho_wo_k,
ck::index_t nrepeat)
{
using namespace ck;
std::cout << __func__ << std::endl;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
DeviceMem in_n_hi_wi_c_device_buf(sizeof(TInWei) * in_n_hi_wi_c.mDesc.GetElementSpace());
DeviceMem wei_k_y_x_c_device_buf(sizeof(TInWei) * wei_k_y_x_c.mDesc.GetElementSpace());
DeviceMem out_n_ho_wo_k_device_buf(sizeof(TOut) * out_n_ho_wo_k.mDesc.GetElementSpace());
in_n_hi_wi_c_device_buf.ToDevice(in_n_hi_wi_c.mData.data());
wei_k_y_x_c_device_buf.ToDevice(wei_k_y_x_c.mData.data());
out_n_ho_wo_k_device_buf.ToDevice(out_n_ho_wo_k.mData.data());
const auto in_n_hi_wi_c_desc = make_naive_tensor_descriptor_packed(in_n_hi_wi_c_lengths);
const auto wei_k_y_x_c_desc = make_naive_tensor_descriptor_packed(wei_k_y_x_c_lengths);
const auto out_n_ho_wo_k_desc = make_naive_tensor_descriptor_packed(out_n_ho_wo_k_lengths);
#if 0
// [M, N, K0, K1] = [256, 128, 4, 4], C = 128, for fp32
constexpr index_t BlockSize = 256;
constexpr index_t GemmMPerBlock = 256;
constexpr index_t GemmNPerBlock = 128;
constexpr index_t GemmKPerBlock = 4;
constexpr index_t GemmMPerXDL = 32;
constexpr index_t GemmNPerXDL = 32;
constexpr index_t GemmK1 = 4;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1 = Sequence<1, 4, 4>;
using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 4;
constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 4;
using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1 = Sequence<1, 2, 4>;
using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmK1 = 4;
constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 4;
constexpr index_t GemmCThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 128, 4, 4], C = 128, for fp32
constexpr index_t BlockSize = 256;
constexpr index_t GemmMPerBlock = 128;
constexpr index_t GemmNPerBlock = 128;
constexpr index_t GemmKPerBlock = 4;
constexpr index_t GemmMPerXDL = 32;
constexpr index_t GemmNPerXDL = 32;
constexpr index_t GemmK1 = 4;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 2;
using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1 = Sequence<1, 2, 4>;
using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 4;
constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 4;
using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1 = Sequence<1, 2, 4>;
using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmK1 = 4;
constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 4;
constexpr index_t GemmCThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [256, 256, 4, 8], C = 256, for fp16
constexpr index_t BlockSize = 256;
constexpr index_t GemmMPerBlock = 256;
constexpr index_t GemmNPerBlock = 256;
constexpr index_t GemmKPerBlock = 4;
constexpr index_t GemmMPerXDL = 32;
constexpr index_t GemmNPerXDL = 32;
constexpr index_t GemmK1 = 8;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 4;
using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1 = Sequence<1, 4, 8>;
using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 8;
constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 8;
using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1 = Sequence<1, 4, 8>;
using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmK1 = 8;
constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 8;
constexpr index_t GemmCThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [256, 128, 4, 8], C = 128, for fp16
constexpr index_t BlockSize = 256;
constexpr index_t GemmMPerBlock = 256;
constexpr index_t GemmNPerBlock = 128;
constexpr index_t GemmKPerBlock = 4;
constexpr index_t GemmMPerXDL = 32;
constexpr index_t GemmNPerXDL = 32;
constexpr index_t GemmK1 = 8;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1 = Sequence<1, 4, 8>;
using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 8;
constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 8;
using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1 = Sequence<1, 2, 8>;
using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmK1 = 8;
constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 8;
constexpr index_t GemmCThreadTransferDstScalarPerVector = 1;
#elif 1
// [M, N, K0, K1] = [128, 256, 4, 8], C = 128, for fp16
constexpr index_t BlockSize = 256;
constexpr index_t GemmMPerBlock = 128;
constexpr index_t GemmNPerBlock = 256;
constexpr index_t GemmKPerBlock = 4;
constexpr index_t GemmMPerXDL = 32;
constexpr index_t GemmNPerXDL = 32;
constexpr index_t GemmK1 = 8;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 4;
using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1 = Sequence<1, 2, 8>;
using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 8;
constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 8;
using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1 = Sequence<1, 4, 8>;
using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmK1 = 8;
constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 8;
constexpr index_t GemmCThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 128, 4, 8], C = 64, for fp16
constexpr index_t BlockSize = 256;
constexpr index_t GemmMPerBlock = 128;
constexpr index_t GemmNPerBlock = 128;
constexpr index_t GemmKPerBlock = 4;
constexpr index_t GemmMPerXDL = 32;
constexpr index_t GemmNPerXDL = 32;
constexpr index_t GemmK1 = 8;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 2;
using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1 = Sequence<1, 2, 8>;
using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 8;
constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 8;
using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1 = Sequence<1, 2, 8>;
using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmK1 = 8;
constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 8;
constexpr index_t GemmCThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 64, 4, 8], C = 64, for fp16
constexpr index_t BlockSize = 128;
constexpr index_t GemmMPerBlock = 128;
constexpr index_t GemmNPerBlock = 64;
constexpr index_t GemmKPerBlock = 4;
constexpr index_t GemmMPerXDL = 32;
constexpr index_t GemmNPerXDL = 32;
constexpr index_t GemmK1 = 8;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 2;
using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1 = Sequence<1, 4, 8>;
using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 32, 1>;
constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 8;
constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 8;
using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1 = Sequence<1, 2, 8>;
using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 32, 1>;
constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmK1 = 8;
constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 8;
constexpr index_t GemmCThreadTransferDstScalarPerVector = 1;
#elif 1
// [M, N, K0, K1] = [128, 64, 4, 8], C = 32, for fp16
constexpr index_t BlockSize = 256;
constexpr index_t GemmMPerBlock = 128;
constexpr index_t GemmNPerBlock = 64;
constexpr index_t GemmKPerBlock = 4;
constexpr index_t GemmMPerXDL = 32;
constexpr index_t GemmNPerXDL = 32;
constexpr index_t GemmK1 = 8;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 1;
using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1 = Sequence<1, 2, 8>;
using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 8;
constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 8;
using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1 = Sequence<1, 1, 8>;
using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmK1 = 8;
constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 8;
constexpr index_t GemmCThreadTransferDstScalarPerVector = 1;
#endif
const auto descs =
transform_forward_convolution_into_gemm_v4r4r4_nhwc_kyxc_nhwk(in_n_hi_wi_c_desc,
wei_k_y_x_c_desc,
out_n_ho_wo_k_desc,
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads,
Number<GemmK1>{});
#if 0 // debug
const auto in_gemmk0_gemmm_gemmk1_grid_desc = descs[I0];
// HACK: hacks that control index calculation when iterating over A matrix
constexpr auto in_gemmk0_gemmm_gemmk1_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}, // 0+: GemmK0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{}, // 1+: GemmM
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}), // 2+: GemmK1
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{}, // 0-: GemmK0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{}, // 1-: GemmM
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{})); // 2-: GemmK1
constexpr auto in_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>{};
#else
const auto in_gemmk0_gemmmraw_gemmk1_grid_desc = descs[I0];
const auto GemmK0 = in_gemmk0_gemmmraw_gemmk1_grid_desc.GetLength(I0);
const auto GemmMRaw = in_gemmk0_gemmmraw_gemmk1_grid_desc.GetLength(I1);
const auto GemmMPad = math::integer_least_multiple(GemmMRaw, GemmMPerBlock) - GemmMRaw;
const auto in_gemmk0_gemmm_gemmk1_grid_desc =
transform_tensor_descriptor(in_gemmk0_gemmmraw_gemmk1_grid_desc,
make_tuple(make_pass_through_transform(GemmK0),
make_right_pad_transform(GemmMRaw, GemmMPad),
make_pass_through_transform(GemmK1)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
// HACK: hacks that control index calculation when iterating over A matrix
constexpr auto in_gemmk0_gemmm_gemmk1_grid_step_hacks = make_tuple(
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0>{}, // 0+: GemmK0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0>{}, // 1+: GemmM
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0>{}), // 2+: GemmK1
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0>{}, // 0-: GemmK0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0>{}, // 1-: GemmM
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0>{})); // 2-: GemmK1
constexpr auto in_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0>{};
#endif
const auto wei_gemmk0_gemmn_gemmk1_grid_desc = descs[I1];
const auto wei_gemmk0_gemmn_gemmk1_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{}, // 0+: GemmK0
Sequence<0, 0, 0, 0, 0>{}, // 1+: GemmN
Sequence<0, 0, 0, 0, 0>{}), // 2+: GemmK1
make_tuple(Sequence<0, 0, 0, 0, 0>{}, // 0-: GemmK0
Sequence<0, 0, 0, 0, 0>{}, // 1-: GemmN
Sequence<0, 0, 0, 0, 0>{})); // 2-: GemmK1
constexpr auto wei_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0>{};
#if 0
const auto out_gemmm_gemmn_grid_desc = descs[I2];
constexpr auto out_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1+: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2+: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3+: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4+: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5+: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6+: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 7+: N2
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0-: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1-: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2-: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3-: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4-: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5-: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6-: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
#else
const auto out_gemmmraw_gemmn_grid_desc = descs[I2];
const auto GemmN = out_gemmmraw_gemmn_grid_desc.GetLength(I1);
const auto out_gemmm_gemmn_grid_desc =
transform_tensor_descriptor(out_gemmmraw_gemmn_grid_desc,
make_tuple(make_right_pad_transform(GemmMRaw, GemmMPad),
make_pass_through_transform(GemmN)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
constexpr auto out_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1+: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2+: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3+: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4+: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5+: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6+: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 7+: N2
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0-: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1-: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2-: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3-: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4-: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5-: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6-: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
#endif
for(index_t i = 0; i < 5; ++i)
{
float ave_time = driver_gemm_xdlops_v2r3<
BlockSize,
TInWei,
TAcc,
TOut,
InMemoryDataOperationEnum::Set,
decltype(in_gemmk0_gemmm_gemmk1_grid_desc),
decltype(wei_gemmk0_gemmn_gemmk1_grid_desc),
decltype(out_gemmm_gemmn_grid_desc),
GemmMPerBlock,
GemmNPerBlock,
GemmKPerBlock,
GemmMPerXDL,
GemmNPerXDL,
GemmK1,
MRepeat,
NRepeat,
GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1,
GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1,
Sequence<1, 0, 2>,
Sequence<1, 0, 2>,
2,
GemmABlockTransferSrcScalarPerVector_GemmK1,
GemmABlockTransferDstScalarPerVector_GemmK1,
false, // don't move back src coordinate after threadwise copy
GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1,
GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1,
Sequence<1, 0, 2>,
Sequence<1, 0, 2>,
2,
GemmBBlockTransferSrcScalarPerVector_GemmK1,
GemmBBlockTransferDstScalarPerVector_GemmK1,
false, // don't move back src coordinate after threadwise copy
Sequence<2, 3, 0, 1, 7, 5, 4, 6>,
7,
GemmCThreadTransferDstScalarPerVector,
decltype(in_gemmk0_gemmm_gemmk1_grid_step_hacks),
decltype(wei_gemmk0_gemmn_gemmk1_grid_step_hacks),
decltype(out_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks),
decltype(in_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks),
decltype(wei_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks),
false, // CAccessOrderMRepeatNRepeat
true, // ABlockLdsExtraM
true // BBlockLdsExtraN
>(static_cast<TInWei*>(in_n_hi_wi_c_device_buf.GetDeviceBuffer()),
static_cast<TInWei*>(wei_k_y_x_c_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(out_n_ho_wo_k_device_buf.GetDeviceBuffer()),
in_gemmk0_gemmm_gemmk1_grid_desc,
wei_gemmk0_gemmn_gemmk1_grid_desc,
out_gemmm_gemmn_grid_desc,
debug::debug_driver_gemm_xdlops_v2r3::M01,
debug::debug_driver_gemm_xdlops_v2r3::N01,
in_gemmk0_gemmm_gemmk1_grid_step_hacks,
wei_gemmk0_gemmn_gemmk1_grid_step_hacks,
out_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks,
in_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks,
wei_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks,
nrepeat);
{
const auto N = out_n_ho_wo_k_lengths[I0];
const auto K = out_n_ho_wo_k_lengths[I3];
const auto C = wei_k_y_x_c_lengths[I3];
const auto Ho = out_n_ho_wo_k_lengths[I1];
const auto Wo = out_n_ho_wo_k_lengths[I2];
const auto Y = wei_k_y_x_c_lengths[I1];
const auto X = wei_k_y_x_c_lengths[I2];
float perf = static_cast<float>((std::size_t(2) * N * K * Ho * Wo * C * Y * X)) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s"
<< std::endl;
}
}
// copy result back to host
out_n_ho_wo_k_device_buf.FromDevice(out_n_ho_wo_k.mData.data());
}
library/include/ck/library/obselete_driver_offline/device_convolution_forward_implicit_gemm_v5r1_dlops_nc0hwc1_kc0yxc1_nk0hwk1.hpp deleted 100644 → 0
View file @ 05d38218
#include <unistd.h>
#include "device.hpp"
#include "host_tensor.hpp"
#include "driver_convolution_forward_implicit_gemm_v5r1_dlops_nc0hwc1_kc0yxc1_nk0hwk1.hpp"
template <typename TInWei,
typename TAcc,
typename TOut,
ck::ActivTypeEnum activ_type,
typename InLengths,
typename WeiLengths,
typename OutLengths,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void device_convolution_forward_implicit_gemm_v5r1_dlops_nc0hwc1_kc0yxc1_nk0hwk1(
const InLengths& in_n_c0_hi_wi_c1_lengths,
const WeiLengths& wei_k_c0_y_x_c1_lengths,
const OutLengths& out_n_k0_ho_wo_k1_lengths,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const Tensor<TInWei>& in_n_c0_hi_wi_c1,
const Tensor<TInWei>& wei_k_c0_y_x_c1,
const Tensor<TOut>& bias_k0_k1,
Tensor<TOut>& out_n_k0_ho_wo_k1,
ck::index_t nrepeat)
{
using namespace ck;
std::cout << __func__ << std::endl;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
const auto N = out_n_k0_ho_wo_k1_lengths[I0];
const auto K0 = out_n_k0_ho_wo_k1_lengths[I1];
const auto Ho = out_n_k0_ho_wo_k1_lengths[I2];
const auto Wo = out_n_k0_ho_wo_k1_lengths[I3];
const auto K1 = out_n_k0_ho_wo_k1_lengths[I4];
const auto C0 = in_n_c0_hi_wi_c1_lengths[I1];
const auto Hi = in_n_c0_hi_wi_c1_lengths[I2];
const auto Wi = in_n_c0_hi_wi_c1_lengths[I3];
const auto C1 = in_n_c0_hi_wi_c1_lengths[I4];
const auto K = wei_k_c0_y_x_c1_lengths[I0];
const auto Y = wei_k_c0_y_x_c1_lengths[I2];
const auto X = wei_k_c0_y_x_c1_lengths[I3];
DeviceMem in_n_c0_hi_wi_c1_device_buf(sizeof(TInWei) *
in_n_c0_hi_wi_c1.mDesc.GetElementSpace());
DeviceMem wei_k_c0_y_x_c1_device_buf(sizeof(TInWei) * wei_k_c0_y_x_c1.mDesc.GetElementSpace());
DeviceMem bias_k0_k1_device_buf(sizeof(TOut) * bias_k0_k1.mDesc.GetElementSpace());
DeviceMem out_n_k0_ho_wo_k1_device_buf(sizeof(TOut) *
out_n_k0_ho_wo_k1.mDesc.GetElementSpace());
in_n_c0_hi_wi_c1_device_buf.ToDevice(in_n_c0_hi_wi_c1.mData.data());
wei_k_c0_y_x_c1_device_buf.ToDevice(wei_k_c0_y_x_c1.mData.data());
bias_k0_k1_device_buf.ToDevice(bias_k0_k1.mData.data());
constexpr index_t InWeiVectorSize = 8;
if(C1 % InWeiVectorSize != 0)
{
throw std::runtime_error("wrong! C1 cannot be divided by InWeiVectorSize");
}
#if 0
constexpr index_t BlockSize = 256;
constexpr index_t KPerBlock = 32;
constexpr index_t HoPerBlock = 8;
constexpr index_t WoPerBlock = 64;
constexpr index_t E1 = C0 * 9;
constexpr index_t E2 = 1;
constexpr index_t E1PerBlock = C0;
constexpr index_t KPerThread = 16;
constexpr index_t HoPerThread = 2;
constexpr index_t WoPerThread = 2;
constexpr index_t EPerThread = 1;
using ABlockTransferThreadSliceLengths_E0_E1_K0_K1_E2 = Sequence<1, 9, 1, E2>;
using ABlockTransferThreadClusterLengths_E0_E1_K0_K1_E2 = Sequence<1, E1PerBlock, KPerBlock, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_E2 = E2;
constexpr index_t ABlockTransferDstScalarPerVector_E2 = E2;
constexpr index_t BThreadTransferSrcScalarPerVector_E2 = E2;
constexpr index_t CThreadTransferDstScalarPerVector_K = K1;
#elif 1
constexpr index_t BlockSize = 64;
constexpr index_t KPerBlock = 8;
constexpr index_t HoPerBlock = 8;
constexpr index_t WoPerBlock = 32;
constexpr index_t E1 = 2 * 9;
constexpr index_t E2 = 1;
constexpr index_t K2 = 2;
constexpr index_t E1PerBlock = 2;
constexpr index_t KPerThread = KPerBlock;
constexpr index_t HoPerThread = 2;
constexpr index_t WoPerThread = 2;
constexpr index_t EPerThread = 1;
using ABlockTransferThreadSliceLengths_E0_E1_K0_K1_E2 = Sequence<1, 9, 1, 1, E2>;
using ABlockTransferThreadClusterLengths_E0_E1_K0_K1_E2 =
Sequence<1, E1PerBlock, 1, KPerBlock, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_E2 = E2;
constexpr index_t ABlockTransferDstScalarPerVector_E2 = E2;
constexpr index_t BThreadTransferSrcScalarPerVector_E2 = E2;
constexpr index_t CThreadTransferDstScalarPerVector_K = InWeiVectorSize;
#endif
if(KPerThread % InWeiVectorSize != 0)
{
throw std::runtime_error("wrong! C1 cannot be divided by InWeiVectorSize");
}
const auto in_n_c0_hi_wi_c1_desc =
make_naive_tensor_descriptor_packed(make_tuple(N, C0, Hi, Wi, E2));
const auto wei_k_c0_y_x_c1_desc =
make_naive_tensor_descriptor_packed(make_tuple(K, C0, Y, X, E2));
const auto out_n_k0_ho_wo_k1_desc =
make_naive_tensor_descriptor_packed(make_tuple(N, K0, Ho, Wo, K1));
constexpr auto conv_driver =
DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nc0hwc1_kc0yxc1_nk0hwk1_outpad<
BlockSize,
typename vector_type<TInWei, InWeiVectorSize>::type,
TAcc,
TOut,
E1,
E2,
K2,
KPerBlock,
HoPerBlock,
WoPerBlock,
E1PerBlock,
KPerThread,
HoPerThread,
WoPerThread,
EPerThread,
ABlockTransferThreadSliceLengths_E0_E1_K0_K1_E2,
ABlockTransferThreadClusterLengths_E0_E1_K0_K1_E2,
ABlockTransferSrcScalarPerVector_E2,
ABlockTransferDstScalarPerVector_E2,
BThreadTransferSrcScalarPerVector_E2,
CThreadTransferDstScalarPerVector_K,
activ_type>{};
std::cerr << "conv_bias_activ_input_"
<< "n" << N << "c" << C0 << "h" << Hi << "w" << Wi << "c" << C1 << "_filter_k" << K
<< "c" << C0 << "y" << Y << "x" << X << "c" << C1 << "_convout_n" << N << "k" << K0
<< "h" << Ho << "w" << Wo << "k" << K1 << std::endl;
for(int i = 0; i < 5; i++)
{
const auto ave_time =
conv_driver.Run(wei_k_c0_y_x_c1_desc,
in_n_c0_hi_wi_c1_desc,
out_n_k0_ho_wo_k1_desc,
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads,
static_cast<typename vector_type<TInWei, InWeiVectorSize>::type*>(
wei_k_c0_y_x_c1_device_buf.GetDeviceBuffer()),
static_cast<typename vector_type<TInWei, InWeiVectorSize>::type*>(
in_n_c0_hi_wi_c1_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(bias_k0_k1_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(out_n_k0_ho_wo_k1_device_buf.GetDeviceBuffer()),
nrepeat);
{
float perf = static_cast<float>(std::size_t(2) * N * K * Ho * Wo * C0 * C1 * Y * X) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s"
<< std::endl;
}
}
out_n_k0_ho_wo_k1_device_buf.FromDevice(out_n_k0_ho_wo_k1.mData.data());
}
library/include/ck/library/obselete_driver_offline/device_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw.hpp deleted 100644 → 0
View file @ 05d38218
#pragma once
#include <unistd.h>
#include "device.hpp"
#include "host_tensor.hpp"
#include "transform_forward_convolution_into_gemm_v6r1_nchw_kcyx_nkhw.hpp"
#include "driver_contraction_dlops_v1r2.hpp"
template <typename TInWei,
typename TAcc,
typename TOut,
typename InLengths,
typename WeiLengths,
typename OutLengths,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void device_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw(
const InLengths& in_n_c_hi_wi_lengths,
const WeiLengths& wei_k_c_y_x_lengths,
const OutLengths& out_n_k_ho_wo_lengths,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const Tensor<TInWei>& in_n_c_hi_wi,
const Tensor<TInWei>& wei_k_c_y_x,
Tensor<TOut>& out_n_k_ho_wo,
ck::index_t nrepeat)
{
using namespace ck;
std::cout << __func__ << std::endl;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
DeviceMem in_n_c_hi_wi_device_buf(sizeof(TInWei) * in_n_c_hi_wi.mDesc.GetElementSpace());
DeviceMem wei_k_c_y_x_device_buf(sizeof(TInWei) * wei_k_c_y_x.mDesc.GetElementSpace());
DeviceMem out_n_k_ho_wo_device_buf(sizeof(TOut) * out_n_k_ho_wo.mDesc.GetElementSpace());
in_n_c_hi_wi_device_buf.ToDevice(in_n_c_hi_wi.mData.data());
wei_k_c_y_x_device_buf.ToDevice(wei_k_c_y_x.mData.data());
out_n_k_ho_wo_device_buf.ToDevice(out_n_k_ho_wo.mData.data());
const auto in_desc_n_c_hi_wi = make_naive_tensor_descriptor_packed(in_n_c_hi_wi_lengths);
const auto wei_desc_k_c_y_x = make_naive_tensor_descriptor_packed(wei_k_c_y_x_lengths);
const auto out_desc_n_k_ho_wo = make_naive_tensor_descriptor_packed(out_n_k_ho_wo_lengths);
#if 1
// [8, 1, 128, 1] * [8, 4, 32, 1] = [1, 128, 4, 32] for fp32
// cdata = 64, BlockSize = 256
constexpr index_t BlockSize = 256;
constexpr index_t GN0 = 4;
constexpr index_t GK1 = 1;
constexpr index_t GM1PerBlockGM11 = 128;
constexpr index_t GN1PerBlockGN11 = 32;
constexpr index_t GK0PerBlock = 8;
constexpr index_t BM1PerThreadBM11 = 4;
constexpr index_t BN1PerThreadBN11 = 4;
constexpr index_t BK0PerThread = 1;
using BM10BN10ThreadClusterBM10Xs = Sequence<8, 2>;
using BM10BN10ThreadClusterBN10Xs = Sequence<8, 2>;
using ABlockTransferThreadSliceLengths_GK0_GM0_GM10_GM11_GK1 = Sequence<4, 1, 1, 1, 1>;
using ABlockTransferThreadClusterLengths_GK0_GM0_GM10_GM11_GK1 = Sequence<2, 1, 1, 128, 1>;
using ABlockTransferSrcVectorTensorLengths_GK0_GM0_GM10_GM11_GK1 = Sequence<4, 1, 1, 1, 1>;
using ABlockTransferDstVectorTensorLengths_GK0_GM0_GM10_GM11_GK1 = Sequence<1, 1, 1, 1, 1>;
using BBlockTransferThreadSliceLengths_GK0_GN0_GN10_GN11_GK1 = Sequence<1, 4, 1, 1, 1>;
using BBlockTransferThreadClusterLengths_GK0_GN0_GN10_GN11_GK1 = Sequence<8, 1, 1, 32, 1>;
using BBlockTransferSrcVectorTensorLengths_GK0_GN0_GN10_GN11_GK1 = Sequence<1, 1, 1, 1, 1>;
using BBlockTransferDstVectorTensorLengths_GK0_GN0_GN10_GN11_GK1 = Sequence<1, 1, 1, 1, 1>;
constexpr index_t CThreadTransferDstScalarPerVector_BN1 = 1;
#elif 1
// [8, 1, 128, 2] * [8, 4, 32, 2] = [1, 128, 4, 32] for fp16
// cdata = 64, BlockSize = 256
constexpr index_t BlockSize = 256;
constexpr index_t GN0 = 4;
constexpr index_t GK1 = 2;
constexpr index_t GM1PerBlockGM11 = 128;
constexpr index_t GN1PerBlockGN11 = 32;
constexpr index_t GK0PerBlock = 8;
constexpr index_t BM1PerThreadBM11 = 4;
constexpr index_t BN1PerThreadBN11 = 4;
constexpr index_t BK0PerThread = 1;
using BM10BN10ThreadClusterBM10Xs = Sequence<8, 2>;
using BM10BN10ThreadClusterBN10Xs = Sequence<8, 2>;
using ABlockTransferThreadSliceLengths_GK0_GM0_GM10_GM11_GK1 = Sequence<4, 1, 1, 1, 2>;
using ABlockTransferThreadClusterLengths_GK0_GM0_GM10_GM11_GK1 = Sequence<2, 1, 1, 128, 1>;
using ABlockTransferSrcVectorTensorLengths_GK0_GM0_GM10_GM11_GK1 = Sequence<4, 1, 1, 1, 1>;
using ABlockTransferDstVectorTensorLengths_GK0_GM0_GM10_GM11_GK1 = Sequence<1, 1, 1, 1, 2>;
using BBlockTransferThreadSliceLengths_GK0_GN0_GN10_GN11_GK1 = Sequence<1, 4, 1, 1, 2>;
using BBlockTransferThreadClusterLengths_GK0_GN0_GN10_GN11_GK1 = Sequence<8, 1, 1, 32, 1>;
using BBlockTransferSrcVectorTensorLengths_GK0_GN0_GN10_GN11_GK1 = Sequence<1, 1, 1, 1, 1>;
using BBlockTransferDstVectorTensorLengths_GK0_GN0_GN10_GN11_GK1 = Sequence<1, 1, 1, 1, 2>;
constexpr index_t CThreadTransferDstScalarPerVector_BN1 = 1;
#endif
const auto descs =
transform_forward_convolution_into_contraction_v6r1_nchw_kcyx_nkhw_pad(wei_desc_k_c_y_x,
in_desc_n_c_hi_wi,
out_desc_n_k_ho_wo,
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads,
Number<GN0>{},
Number<GK1>{});
const auto wei_grid_desc_gk0_gm0_gm1_gk1 = descs[I0];
const auto in_grid_desc_gk0_gn0_gn1_gk1 = descs[I1];
const auto out_grid_desc_gm0_gm1_gn0_gn1 = descs[I2];
// HACK: hacks that control index calculation when iterating over A, B, C matrix
constexpr auto wei_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0>{}, // 0+: GK0
Sequence<0, 0, 0, 0, 0, 0, 0>{}, // 1+: GM0
Sequence<0, 0, 0, 0, 0, 0, 0>{}, // 2+: GM10
Sequence<0, 0, 0, 0, 0, 0, 0>{}, // 3+: GM11
Sequence<0, 0, 0, 0, 0, 0, 0>{}), // 4+: GK1
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0>{}, // 0-: GK0
Sequence<0, 0, 0, 0, 0, 0, 0>{}, // 1-: GM0
Sequence<0, 0, 0, 0, 0, 0, 0>{}, // 2-: GM10
Sequence<0, 0, 0, 0, 0, 0, 0>{}, // 3-: GM11
Sequence<0, 0, 0, 0, 0, 0, 0>{})); // 4-: GK1
constexpr auto in_grid_step_hacks = make_tuple(
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: GK0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0>{}, // 1+: GN0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0>{}, // 2+: GN10
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0>{}, // 3+: GN11
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 4+: GK1
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0>{}, // 0-: GK0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0>{}, // 1-: GN0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0>{}, // 2-: GN10
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0>{}, // 3-: GN11
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 4-: GK1
constexpr auto out_grid_step_hacks = make_tuple(
make_tuple(
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: GM10
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0>{}, // 1+: BM0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0>{}, // 2+: BM1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3+: GN10
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0>{}, // 4+: BN0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0>{}), // 5+: GN1
make_tuple(
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0-: GM10
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0>{}, // 1-: BM0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0>{}, // 2-: BM1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3-: GN10
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0>{}, // 4-: BN0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0>{})); // 5-: GN1
constexpr auto wei_grid_move_slice_window_step_hacks = Sequence<0, 0, 0, 0, 0, 0, 0>{};
constexpr auto in_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0>{};
for(index_t i = 0; i < 5; ++i)
{
float ave_time = driver_contraction_dlops_v1r2<
BlockSize,
TInWei,
TAcc,
TOut,
InMemoryDataOperationEnum::Set,
decltype(wei_grid_desc_gk0_gm0_gm1_gk1),
decltype(in_grid_desc_gk0_gn0_gn1_gk1),
decltype(out_grid_desc_gm0_gm1_gn0_gn1),
GM1PerBlockGM11,
GN1PerBlockGN11,
GK0PerBlock,
BM1PerThreadBM11,
BN1PerThreadBN11,
BK0PerThread,
BM10BN10ThreadClusterBM10Xs,
BM10BN10ThreadClusterBN10Xs,
ABlockTransferThreadSliceLengths_GK0_GM0_GM10_GM11_GK1,
ABlockTransferThreadClusterLengths_GK0_GM0_GM10_GM11_GK1,
Sequence<1, 2, 3, 0, 4>, // ABlockTransferThreadClusterArrangeOrder
Sequence<3, 2, 1, 0, 4>, // ABlockTransferSrcAccessOrder
ABlockTransferSrcVectorTensorLengths_GK0_GM0_GM10_GM11_GK1,
ABlockTransferDstVectorTensorLengths_GK0_GM0_GM10_GM11_GK1,
Sequence<0, 1, 2, 3, 4>, // ABlockTransferSrcVectorTensorContiguousDimOrder
BBlockTransferThreadSliceLengths_GK0_GN0_GN10_GN11_GK1,
BBlockTransferThreadClusterLengths_GK0_GN0_GN10_GN11_GK1,
Sequence<0, 4, 1, 2, 3>, // BBlockTransferThreadClusterArrangeOrder
Sequence<4, 3, 2, 0, 1>, // BBlockTransferSrcAccessOrder
BBlockTransferSrcVectorTensorLengths_GK0_GN0_GN10_GN11_GK1,
BBlockTransferDstVectorTensorLengths_GK0_GN0_GN10_GN11_GK1,
Sequence<0, 1, 2, 3, 4>, // BBlockTransferSrcVectorTensorContiguousDimOrder
Sequence<3, 4, 5, 0, 1, 2>, // CThreadTransferSrcDstAccessOrder
5, // CThreadTransferSrcDstVectorDim
CThreadTransferDstScalarPerVector_BN1,
decltype(wei_grid_step_hacks),
decltype(in_grid_step_hacks),
decltype(out_grid_step_hacks),
decltype(wei_grid_move_slice_window_step_hacks),
decltype(in_grid_move_slice_window_step_hacks)>(
static_cast<TInWei*>(wei_k_c_y_x_device_buf.GetDeviceBuffer()),
static_cast<TInWei*>(in_n_c_hi_wi_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(out_n_k_ho_wo_device_buf.GetDeviceBuffer()),
wei_grid_desc_gk0_gm0_gm1_gk1,
in_grid_desc_gk0_gn0_gn1_gk1,
out_grid_desc_gm0_gm1_gn0_gn1,
wei_grid_step_hacks,
in_grid_step_hacks,
out_grid_step_hacks,
wei_grid_move_slice_window_step_hacks,
in_grid_move_slice_window_step_hacks,
nrepeat);
float perf = static_cast<float>(calculate_convolution_flops(
in_desc_n_c_hi_wi, wei_desc_k_c_y_x, out_desc_n_k_ho_wo)) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s" << std::endl;
}
// copy result back to host
out_n_k_ho_wo_device_buf.FromDevice(out_n_k_ho_wo.mData.data());
}
library/include/ck/library/obselete_driver_offline/device_convolution_maxpool_forward_implicit_gemm_v5r1_dlops_nc0hwc1_kc0yxc1_nk0hwk1.hpp deleted 100644 → 0
View file @ 05d38218
#include <unistd.h>
#include "device.hpp"
#include "host_tensor.hpp"
#include "driver_convolution_maxpool_forward_implicit_gemm_v5r1_dlops_nc0hwc1_kc0yxc1_nk0hwk1.hpp"
template <typename TInWei,
typename TAcc,
typename TOut,
ck::ActivTypeEnum activ_type,
typename InLengths,
typename WeiLengths,
typename MaxLengths,
typename OutLengths,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void device_convolution_maxpool_forward_implicit_gemm_v5r1_dlops_nc0hwc1_kc0yxc1_nk0hwk1(
const InLengths& in_n_c0_hi_wi_c1_lengths,
const WeiLengths& wei_k_c0_y_x_c1_lengths,
const MaxLengths& max_n_k0_hx_wx_k1_lengths,
const OutLengths& out_n_k0_ho_wo_k1_lengths,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const Tensor<TInWei>& in_n_c0_hi_wi_c1,
const Tensor<TInWei>& wei_k_c0_y_x_c1,
const Tensor<TOut>& bias_k0_k1,
Tensor<TOut>& out_n_k0_ho_wo_k1,
Tensor<TOut>& max_n_k0_hx_wx_k1,
ck::index_t nrepeat)
{
using namespace ck;
std::cout << __func__ << std::endl;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
const auto N = out_n_k0_ho_wo_k1_lengths[I0];
const auto K0 = out_n_k0_ho_wo_k1_lengths[I1];
const auto Ho = out_n_k0_ho_wo_k1_lengths[I2];
const auto Wo = out_n_k0_ho_wo_k1_lengths[I3];
const auto K1 = out_n_k0_ho_wo_k1_lengths[I4];
const auto C0 = in_n_c0_hi_wi_c1_lengths[I1];
const auto Hi = in_n_c0_hi_wi_c1_lengths[I2];
const auto Wi = in_n_c0_hi_wi_c1_lengths[I3];
const auto C1 = in_n_c0_hi_wi_c1_lengths[I4];
const auto K = wei_k_c0_y_x_c1_lengths[I0];
const auto Y = wei_k_c0_y_x_c1_lengths[I2];
const auto X = wei_k_c0_y_x_c1_lengths[I3];
const auto Hx = max_n_k0_hx_wx_k1_lengths[I2];
const auto Wx = max_n_k0_hx_wx_k1_lengths[I3];
DeviceMem in_n_c0_hi_wi_c1_device_buf(sizeof(TInWei) *
in_n_c0_hi_wi_c1.mDesc.GetElementSpace());
DeviceMem wei_k_c0_y_x_c1_device_buf(sizeof(TInWei) * wei_k_c0_y_x_c1.mDesc.GetElementSpace());
DeviceMem bias_k0_k1_device_buf(sizeof(TOut) * bias_k0_k1.mDesc.GetElementSpace());
DeviceMem out_n_k0_ho_wo_k1_device_buf(sizeof(TOut) *
out_n_k0_ho_wo_k1.mDesc.GetElementSpace());
DeviceMem max_n_k0_hx_wx_k1_device_buf(sizeof(TOut) *
max_n_k0_hx_wx_k1.mDesc.GetElementSpace());
in_n_c0_hi_wi_c1_device_buf.ToDevice(in_n_c0_hi_wi_c1.mData.data());
wei_k_c0_y_x_c1_device_buf.ToDevice(wei_k_c0_y_x_c1.mData.data());
bias_k0_k1_device_buf.ToDevice(bias_k0_k1.mData.data());
max_n_k0_hx_wx_k1_device_buf.ToDevice(max_n_k0_hx_wx_k1.mData.data());
constexpr index_t InWeiVectorSize = 8;
if(C1 % InWeiVectorSize != 0)
{
throw std::runtime_error("wrong! C1 cannot be divided by InWeiVectorSize");
}
#if 0
constexpr index_t BlockSize = 256;
constexpr index_t KPerBlock = 32;
constexpr index_t HoPerBlock = 8;
constexpr index_t WoPerBlock = 64;
constexpr index_t E1 = C0 * 9;
constexpr index_t E2 = 1;
constexpr index_t E1PerBlock = C0;
constexpr index_t KPerThread = 16;
constexpr index_t HoPerThread = 2;
constexpr index_t WoPerThread = 2;
constexpr index_t EPerThread = 1;
using ABlockTransferThreadSliceLengths_E0_E1_K0_K1_E2 = Sequence<1, 9, 1, E2>;
using ABlockTransferThreadClusterLengths_E0_E1_K0_K1_E2 = Sequence<1, E1PerBlock, KPerBlock, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_E2 = E2;
constexpr index_t ABlockTransferDstScalarPerVector_E2 = E2;
constexpr index_t BThreadTransferSrcScalarPerVector_E2 = E2;
constexpr index_t CThreadTransferDstScalarPerVector_K = K1;
#elif 1
constexpr index_t BlockSize = 64;
constexpr index_t KPerBlock = 8;
constexpr index_t HoPerBlock = 8;
constexpr index_t WoPerBlock = 32;
constexpr index_t E1 = 2 * 9;
constexpr index_t E2 = 1;
constexpr index_t K2 = 2;
constexpr index_t E1PerBlock = 2;
constexpr index_t KPerThread = KPerBlock;
constexpr index_t HoPerThread = 2;
constexpr index_t WoPerThread = 2;
constexpr index_t EPerThread = 1;
using ABlockTransferThreadSliceLengths_E0_E1_K0_K1_E2 = Sequence<1, 9, 1, 1, E2>;
using ABlockTransferThreadClusterLengths_E0_E1_K0_K1_E2 =
Sequence<1, E1PerBlock, 1, KPerBlock, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_E2 = E2;
constexpr index_t ABlockTransferDstScalarPerVector_E2 = E2;
constexpr index_t BThreadTransferSrcScalarPerVector_E2 = E2;
constexpr index_t CThreadTransferDstScalarPerVector_K = InWeiVectorSize;
#endif
if(KPerThread % InWeiVectorSize != 0)
{
throw std::runtime_error("wrong! C1 cannot be divided by InWeiVectorSize");
}
const auto in_n_c0_hi_wi_c1_desc =
make_naive_tensor_descriptor_packed(make_tuple(N, C0, Hi, Wi, E2));
const auto wei_k_c0_y_x_c1_desc =
make_naive_tensor_descriptor_packed(make_tuple(K, C0, Y, X, E2));
const auto max_n_k0_hx_wx_k1_desc =
make_naive_tensor_descriptor_packed(make_tuple(N, K0, Hx, Wx, K1));
const auto out_n_k0_ho_wo_k1_desc =
make_naive_tensor_descriptor_packed(make_tuple(N, K0, Ho, Wo, K1));
constexpr auto conv_driver =
DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nc0hwc1_kc0yxc1_nk0hwk1_maxpool<
BlockSize,
typename vector_type<TInWei, InWeiVectorSize>::type,
TAcc,
TOut,
E1,
E2,
K2,
KPerBlock,
HoPerBlock,
WoPerBlock,
E1PerBlock,
KPerThread,
HoPerThread,
WoPerThread,
EPerThread,
ABlockTransferThreadSliceLengths_E0_E1_K0_K1_E2,
ABlockTransferThreadClusterLengths_E0_E1_K0_K1_E2,
ABlockTransferSrcScalarPerVector_E2,
ABlockTransferDstScalarPerVector_E2,
BThreadTransferSrcScalarPerVector_E2,
CThreadTransferDstScalarPerVector_K,
activ_type>{};
std::cerr << "conv_bias_activ_maxpool_input_"
<< "n" << N << "c" << C0 << "h" << Hi << "w" << Wi << "c" << C1 << "_filter_k" << K
<< "c" << C0 << "y" << Y << "x" << X << "c" << C1 << "_convout_n" << N << "k" << K0
<< "h" << Ho << "w" << Wo << "k" << K1 << "_maxpoolout_n" << N << "k" << K0 << "h"
<< Ho / 2 << "w" << Wo / 2 << "k" << K1 << std::endl;
for(int i = 0; i < 5; i++)
{
const auto ave_time =
conv_driver.Run(wei_k_c0_y_x_c1_desc,
in_n_c0_hi_wi_c1_desc,
out_n_k0_ho_wo_k1_desc,
max_n_k0_hx_wx_k1_desc,
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads,
static_cast<typename vector_type<TInWei, InWeiVectorSize>::type*>(
wei_k_c0_y_x_c1_device_buf.GetDeviceBuffer()),
static_cast<typename vector_type<TInWei, InWeiVectorSize>::type*>(
in_n_c0_hi_wi_c1_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(bias_k0_k1_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(out_n_k0_ho_wo_k1_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(max_n_k0_hx_wx_k1_device_buf.GetDeviceBuffer()),
nrepeat);
{
float perf = static_cast<float>(std::size_t(2) * N * K * Ho * Wo * C0 * C1 * Y * X) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s"
<< std::endl;
}
}
out_n_k0_ho_wo_k1_device_buf.FromDevice(out_n_k0_ho_wo_k1.mData.data());
max_n_k0_hx_wx_k1_device_buf.FromDevice(max_n_k0_hx_wx_k1.mData.data());
}
library/include/ck/library/obselete_driver_offline/device_gemm_xdlops_km_kn_mn.hpp deleted 100644 → 0
View file @ 05d38218
#pragma once
#include <unistd.h>
#include "device.hpp"
#include "host_tensor.hpp"
#include "driver_gemm_xdlops_v2r3.hpp"
template <typename ABType, typename AccType, typename CType>
void device_gemm_xdlops_km_kn_mn(const Tensor<ABType>& a_k_m,
const Tensor<ABType>& b_k_n,
Tensor<CType>& c_m_n,
ck::index_t nrepeat)
{
using namespace ck;
std::cout << __func__ << std::endl;
DeviceMem a_k_m_device_buf(sizeof(ABType) * a_k_m.mDesc.GetElementSpace());
DeviceMem b_k_n_device_buf(sizeof(ABType) * b_k_n.mDesc.GetElementSpace());
DeviceMem c_m_n_device_buf(sizeof(CType) * c_m_n.mDesc.GetElementSpace());
a_k_m_device_buf.ToDevice(a_k_m.mData.data());
b_k_n_device_buf.ToDevice(b_k_n.mData.data());
c_m_n_device_buf.ToDevice(c_m_n.mData.data());
#if 0
// [M, N, K0, K1] = [256, 128, 4, 4] for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 256;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 4, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 2;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 256, 4, 4], C = 128, for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 256;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 4;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 2;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 4, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 128, 4, 4], C = 64, for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 2;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 2;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 64, 4, 4], C = 32, for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 64;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 1;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 2;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 1, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 1;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [64, 128, 4, 4], C = 32, for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 64;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 1;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 1, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 1;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 2;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 1
// [M, N, K0, K1] = [256, 128, 4, 8], C = 128, for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 256;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 4, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 2;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 256, 4, 8] for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 256;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 4;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 2;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 4, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 128, 4, 8], C = 128, for fp16
constexpr index_t BlockSize = 128;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 4, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 32, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 4, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 32, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 128, 4, 8], C = 64, for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 2;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 2;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 1
// [M, N, K0, K1] = [128, 64, 4, 8], C = 32, for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 64;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 1;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 2;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 1, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 1;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [64, 128, 4, 8], C = 32, for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 64;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 1;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 1, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 1;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 2;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#endif
const auto K = a_k_m.mDesc.GetLengths()[0];
const auto M = a_k_m.mDesc.GetLengths()[1];
const auto N = b_k_n.mDesc.GetLengths()[1];
constexpr auto K1Number = Number<K1>{};
const auto K0 = K / K1Number;
const auto a_k0_m_k1_grid_desc =
make_naive_tensor_descriptor(make_tuple(K0, M, K1Number),
make_tuple(K1Number * a_k_m.mDesc.GetStrides()[0],
a_k_m.mDesc.GetStrides()[1],
a_k_m.mDesc.GetStrides()[0]));
const auto b_k0_n_k1_grid_desc =
make_naive_tensor_descriptor(make_tuple(K0, N, K1Number),
make_tuple(K1Number * b_k_n.mDesc.GetStrides()[0],
b_k_n.mDesc.GetStrides()[1],
b_k_n.mDesc.GetStrides()[0]));
const auto c_m_n_grid_desc = make_naive_tensor_descriptor(
make_tuple(M, N), make_tuple(c_m_n.mDesc.GetStrides()[0], c_m_n.mDesc.GetStrides()[1]));
// HACK: hacks that control index calculation when iterating over A, B, C matrix
constexpr auto a_k0_m_k1_grid_step_hacks = make_tuple(make_tuple(Sequence<0>{}, // 0+: K0
Sequence<0>{}, // 1+: M
Sequence<0>{}), // 2+: K1
make_tuple(Sequence<0>{}, // 0-: K0
Sequence<0>{}, // 1-: M
Sequence<0>{})); // 2-: K1
constexpr auto b_k0_n_k1_grid_step_hacks = make_tuple(make_tuple(Sequence<0>{}, // 0+: K0
Sequence<0>{}, // 1+: N
Sequence<0>{}), // 2+: K1
make_tuple(Sequence<0>{}, // 0-: K0
Sequence<0>{}, // 1-: N
Sequence<0>{})); // 2-: K1
constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1+: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2+: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3+: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4+: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5+: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6+: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 7+: N2
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0-: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1-: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2-: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3-: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4-: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5-: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6-: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
constexpr auto a_k0_m_k1_grid_move_slice_window_step_hacks = Sequence<0>{};
constexpr auto b_k0_n_k1_grid_move_slice_window_step_hacks = Sequence<0>{};
for(index_t i = 0; i < 5; ++i)
{
float ave_time =
driver_gemm_xdlops_v2r3<BlockSize,
ABType,
AccType,
CType,
InMemoryDataOperationEnum::Set,
decltype(a_k0_m_k1_grid_desc),
decltype(b_k0_n_k1_grid_desc),
decltype(c_m_n_grid_desc),
MPerBlock,
NPerBlock,
KPerBlock,
MPerXDL,
NPerXDL,
K1,
MRepeat,
NRepeat,
ABlockTransferThreadSliceLengths_K0_M_K1,
ABlockTransferThreadClusterLengths_K0_M_K1,
Sequence<0, 2, 1>,
Sequence<0, 2, 1>,
1,
ABlockTransferSrcScalarPerVector_M,
ABlockTransferDstScalarPerVector_K1,
false, // don't move back src coordinate after threadwise copy
BBlockTransferThreadSliceLengths_K0_N_K1,
BBlockTransferThreadClusterLengths_K0_N_K1,
Sequence<0, 2, 1>,
Sequence<0, 2, 1>,
1,
BBlockTransferSrcScalarPerVector_N,
BBlockTransferDstScalarPerVector_K1,
false, // don't move back src coordinate after threadwise copy
Sequence<0, 2, 4, 5, 6, 1, 3, 7>,
7,
CThreadTransferDstScalarPerVector,
decltype(a_k0_m_k1_grid_step_hacks),
decltype(b_k0_n_k1_grid_step_hacks),
decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks),
decltype(a_k0_m_k1_grid_move_slice_window_step_hacks),
decltype(b_k0_n_k1_grid_move_slice_window_step_hacks),
false, // CAccessOrderMRepeatNRepeat
true, // ABlockLdsExtraM
true // BBlockLdsExtraN
>(static_cast<ABType*>(a_k_m_device_buf.GetDeviceBuffer()),
static_cast<ABType*>(b_k_n_device_buf.GetDeviceBuffer()),
static_cast<CType*>(c_m_n_device_buf.GetDeviceBuffer()),
a_k0_m_k1_grid_desc,
b_k0_n_k1_grid_desc,
c_m_n_grid_desc,
debug::debug_driver_gemm_xdlops_v2r3::M01,
debug::debug_driver_gemm_xdlops_v2r3::N01,
a_k0_m_k1_grid_step_hacks,
b_k0_n_k1_grid_step_hacks,
c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks,
a_k0_m_k1_grid_move_slice_window_step_hacks,
b_k0_n_k1_grid_move_slice_window_step_hacks,
nrepeat);
float perf = static_cast<float>((std::size_t(2) * M * N * K)) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s" << std::endl;
}
// copy result back to host
c_m_n_device_buf.FromDevice(c_m_n.mData.data());
}
library/include/ck/library/obselete_driver_offline/device_gemm_xdlops_km_kn_nm.hpp deleted 100644 → 0
View file @ 05d38218
#pragma once
#include <unistd.h>
#include "device.hpp"
#include "host_tensor.hpp"
#include "driver_gemm_xdlops_v2r3.hpp"
template <typename ABType, typename AccType, typename CType>
void device_gemm_xdlops_km_kn_nm(const Tensor<ABType>& a_k_m,
const Tensor<ABType>& b_k_n,
Tensor<CType>& c_n_m,
ck::index_t nrepeat)
{
using namespace ck;
std::cout << __func__ << std::endl;
DeviceMem a_k_m_device_buf(sizeof(ABType) * a_k_m.mDesc.GetElementSpace());
DeviceMem b_k_n_device_buf(sizeof(ABType) * b_k_n.mDesc.GetElementSpace());
DeviceMem c_n_m_device_buf(sizeof(CType) * c_n_m.mDesc.GetElementSpace());
a_k_m_device_buf.ToDevice(a_k_m.mData.data());
b_k_n_device_buf.ToDevice(b_k_n.mData.data());
c_n_m_device_buf.ToDevice(c_n_m.mData.data());
#if 0
// [M, N, K0, K1] = [256, 128, 4, 4] for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 256;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 4, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 2;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 4;
#elif 0
// [M, N, K0, K1] = [128, 256, 4, 4] for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 256;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 4;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 2;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 4, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 4;
#elif 1
// [M, N, K0, K1] = [256, 128, 4, 8] for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 256;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 4, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 2;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 4;
#elif 1
// [M, N, K0, K1] = [128, 128, 4, 8] for fp16
constexpr index_t BlockSize = 128;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 4, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 32, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 4, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 32, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 4;
#endif
const auto K = a_k_m.mDesc.GetLengths()[0];
const auto M = a_k_m.mDesc.GetLengths()[1];
const auto N = b_k_n.mDesc.GetLengths()[1];
constexpr auto K1Number = Number<K1>{};
const auto K0 = K / K1Number;
const auto a_k0_m_k1_grid_desc =
make_naive_tensor_descriptor(make_tuple(K0, M, K1Number),
make_tuple(K1Number * a_k_m.mDesc.GetStrides()[0],
a_k_m.mDesc.GetStrides()[1],
a_k_m.mDesc.GetStrides()[0]));
const auto b_k0_n_k1_grid_desc =
make_naive_tensor_descriptor(make_tuple(K0, N, K1Number),
make_tuple(K1Number * b_k_n.mDesc.GetStrides()[0],
b_k_n.mDesc.GetStrides()[1],
b_k_n.mDesc.GetStrides()[0]));
const auto c_m_n_grid_desc = make_naive_tensor_descriptor(
make_tuple(M, N), make_tuple(c_n_m.mDesc.GetStrides()[1], c_n_m.mDesc.GetStrides()[0]));
// HACK: hacks that control index calculation when iterating over A, B, C matrix
constexpr auto a_k0_m_k1_grid_step_hacks = make_tuple(make_tuple(Sequence<0>{}, // 0+: K0
Sequence<0>{}, // 1+: M
Sequence<0>{}), // 2+: K1
make_tuple(Sequence<0>{}, // 0-: K0
Sequence<0>{}, // 1-: M
Sequence<0>{})); // 2-: K1
constexpr auto b_k0_n_k1_grid_step_hacks = make_tuple(make_tuple(Sequence<0>{}, // 0+: K0
Sequence<0>{}, // 1+: N
Sequence<0>{}), // 2+: K1
make_tuple(Sequence<0>{}, // 0-: K0
Sequence<0>{}, // 1-: N
Sequence<0>{})); // 2-: K1
constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1+: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2+: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3+: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4+: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5+: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6+: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 7+: N2
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0-: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1-: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2-: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3-: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4-: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5-: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6-: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
constexpr auto a_k0_m_k1_grid_move_slice_window_step_hacks = Sequence<0>{};
constexpr auto b_k0_n_k1_grid_move_slice_window_step_hacks = Sequence<0>{};
for(index_t i = 0; i < 5; ++i)
{
float ave_time =
driver_gemm_xdlops_v2r3<BlockSize,
ABType,
AccType,
CType,
InMemoryDataOperationEnum::Set,
decltype(a_k0_m_k1_grid_desc),
decltype(b_k0_n_k1_grid_desc),
decltype(c_m_n_grid_desc),
MPerBlock,
NPerBlock,
KPerBlock,
MPerXDL,
NPerXDL,
K1,
MRepeat,
NRepeat,
ABlockTransferThreadSliceLengths_K0_M_K1,
ABlockTransferThreadClusterLengths_K0_M_K1,
Sequence<0, 2, 1>,
Sequence<0, 2, 1>,
1,
ABlockTransferSrcScalarPerVector_M,
ABlockTransferDstScalarPerVector_K1,
false, // don't move back src coordinate after threadwise copy
BBlockTransferThreadSliceLengths_K0_N_K1,
BBlockTransferThreadClusterLengths_K0_N_K1,
Sequence<0, 2, 1>,
Sequence<0, 2, 1>,
1,
BBlockTransferSrcScalarPerVector_N,
BBlockTransferDstScalarPerVector_K1,
false, // don't move back src coordinate after threadwise copy
Sequence<2, 3, 0, 1, 7, 5, 4, 6>,
6,
CThreadTransferDstScalarPerVector,
decltype(a_k0_m_k1_grid_step_hacks),
decltype(b_k0_n_k1_grid_step_hacks),
decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks),
decltype(a_k0_m_k1_grid_move_slice_window_step_hacks),
decltype(b_k0_n_k1_grid_move_slice_window_step_hacks),
false // CAccessOrderMRepeatNRepeat
>(static_cast<ABType*>(a_k_m_device_buf.GetDeviceBuffer()),
static_cast<ABType*>(b_k_n_device_buf.GetDeviceBuffer()),
static_cast<CType*>(c_n_m_device_buf.GetDeviceBuffer()),
a_k0_m_k1_grid_desc,
b_k0_n_k1_grid_desc,
c_m_n_grid_desc,
a_k0_m_k1_grid_step_hacks,
b_k0_n_k1_grid_step_hacks,
c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks,
a_k0_m_k1_grid_move_slice_window_step_hacks,
b_k0_n_k1_grid_move_slice_window_step_hacks,
nrepeat);
float perf = static_cast<float>((std::size_t(2) * M * N * K)) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s" << std::endl;
}
// copy result back to host
c_n_m_device_buf.FromDevice(c_n_m.mData.data());
}
library/include/ck/library/obselete_driver_offline/device_gemm_xdlops_km_nk_mn.hpp deleted 100644 → 0
View file @ 05d38218
#pragma once
#include <unistd.h>
#include "device.hpp"
#include "host_tensor.hpp"
#include "driver_gemm_xdlops_v2r3.hpp"
template <typename ABType, typename AccType, typename CType>
void device_gemm_xdlops_km_nk_mn(const Tensor<ABType>& a_k_m,
const Tensor<ABType>& b_n_k,
Tensor<CType>& c_m_n,
ck::index_t nrepeat)
{
using namespace ck;
std::cout << __func__ << std::endl;
DeviceMem a_k_m_device_buf(sizeof(ABType) * a_k_m.mDesc.GetElementSpace());
DeviceMem b_n_k_device_buf(sizeof(ABType) * b_n_k.mDesc.GetElementSpace());
DeviceMem c_m_n_device_buf(sizeof(CType) * c_m_n.mDesc.GetElementSpace());
a_k_m_device_buf.ToDevice(a_k_m.mData.data());
b_n_k_device_buf.ToDevice(b_n_k.mData.data());
c_m_n_device_buf.ToDevice(c_m_n.mData.data());
#if 0
// [M, N, K0, K1] = [256, 128, 4, 4] for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 256;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 4, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 256, 4, 4] for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 256;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 4;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 2;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 4, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 128, 4, 4], C = 64, for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 2;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 64, 4, 4], C = 32, for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 64;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 1;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 2;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 1, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [64, 128, 4, 4], C = 32, for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 64;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 1;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 1, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 1;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 1
// [M, N, K0, K1] = [256, 128, 4, 8], C = 128, for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 256;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 4, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 256, 4, 8] for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 256;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 4;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 2;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 4, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 128, 4, 8], C = 128, for fp16
constexpr index_t BlockSize = 128;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 4, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 32, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 4, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 32, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 128, 4, 8], C = 64, for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 2;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 1
// [M, N, K0, K1] = [128, 64, 4, 8], C = 32, for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 64;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 1;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 2;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 1, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [64, 128, 4, 8], C = 32, for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 64;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 1;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 1, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 1;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#endif
const auto K = a_k_m.mDesc.GetLengths()[0];
const auto M = a_k_m.mDesc.GetLengths()[1];
const auto N = b_n_k.mDesc.GetLengths()[0];
constexpr auto K1Number = Number<K1>{};
const auto K0 = K / K1Number;
const auto a_k0_m_k1_grid_desc =
make_naive_tensor_descriptor(make_tuple(K0, M, K1Number),
make_tuple(K1Number * a_k_m.mDesc.GetStrides()[0],
a_k_m.mDesc.GetStrides()[1],
a_k_m.mDesc.GetStrides()[0]));
const auto b_k0_n_k1_grid_desc =
make_naive_tensor_descriptor(make_tuple(K0, N, K1Number),
make_tuple(K1Number * b_n_k.mDesc.GetStrides()[1],
b_n_k.mDesc.GetStrides()[0],
b_n_k.mDesc.GetStrides()[1]));
const auto c_m_n_grid_desc = make_naive_tensor_descriptor(
make_tuple(M, N), make_tuple(c_m_n.mDesc.GetStrides()[0], c_m_n.mDesc.GetStrides()[1]));
// HACK: hacks that control index calculation when iterating over A, B, C matrix
constexpr auto a_k0_m_k1_grid_step_hacks = make_tuple(make_tuple(Sequence<0>{}, // 0+: K0
Sequence<0>{}, // 1+: M
Sequence<0>{}), // 2+: K1
make_tuple(Sequence<0>{}, // 0-: K0
Sequence<0>{}, // 1-: M
Sequence<0>{})); // 2-: K1
constexpr auto b_k0_n_k1_grid_step_hacks = make_tuple(make_tuple(Sequence<0>{}, // 0+: K0
Sequence<0>{}, // 1+: N
Sequence<0>{}), // 2+: K1
make_tuple(Sequence<0>{}, // 0-: K0
Sequence<0>{}, // 1-: N
Sequence<0>{})); // 2-: K1
constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1+: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2+: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3+: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4+: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5+: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6+: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 7+: N2
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0-: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1-: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2-: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3-: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4-: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5-: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6-: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
constexpr auto a_k0_m_k1_grid_move_slice_window_step_hacks = Sequence<0>{};
constexpr auto b_k0_n_k1_grid_move_slice_window_step_hacks = Sequence<0>{};
for(index_t i = 0; i < 5; ++i)
{
float ave_time =
driver_gemm_xdlops_v2r3<BlockSize,
ABType,
AccType,
CType,
InMemoryDataOperationEnum::Set,
decltype(a_k0_m_k1_grid_desc),
decltype(b_k0_n_k1_grid_desc),
decltype(c_m_n_grid_desc),
MPerBlock,
NPerBlock,
KPerBlock,
MPerXDL,
NPerXDL,
K1,
MRepeat,
NRepeat,
ABlockTransferThreadSliceLengths_K0_M_K1,
ABlockTransferThreadClusterLengths_K0_M_K1,
Sequence<0, 2, 1>,
Sequence<0, 2, 1>,
1,
ABlockTransferSrcScalarPerVector_M,
ABlockTransferDstScalarPerVector_K1,
false, // don't move back src coordinate after threadwise copy
BBlockTransferThreadSliceLengths_K0_N_K1,
BBlockTransferThreadClusterLengths_K0_N_K1,
Sequence<1, 0, 2>,
Sequence<1, 0, 2>,
2,
BBlockTransferSrcScalarPerVector_K1,
BBlockTransferDstScalarPerVector_K1,
false, // don't move back src coordinate after threadwise copy
Sequence<0, 2, 4, 5, 6, 1, 3, 7>,
7,
CThreadTransferDstScalarPerVector,
decltype(a_k0_m_k1_grid_step_hacks),
decltype(b_k0_n_k1_grid_step_hacks),
decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks),
decltype(a_k0_m_k1_grid_move_slice_window_step_hacks),
decltype(b_k0_n_k1_grid_move_slice_window_step_hacks),
false, // CAccessOrderMRepeatNRepeat
true, // ABlockLdsExtraM
true // BBlockLdsExtraN
>(static_cast<ABType*>(a_k_m_device_buf.GetDeviceBuffer()),
static_cast<ABType*>(b_n_k_device_buf.GetDeviceBuffer()),
static_cast<CType*>(c_m_n_device_buf.GetDeviceBuffer()),
a_k0_m_k1_grid_desc,
b_k0_n_k1_grid_desc,
c_m_n_grid_desc,
debug::debug_driver_gemm_xdlops_v2r3::M01,
debug::debug_driver_gemm_xdlops_v2r3::N01,
a_k0_m_k1_grid_step_hacks,
b_k0_n_k1_grid_step_hacks,
c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks,
a_k0_m_k1_grid_move_slice_window_step_hacks,
b_k0_n_k1_grid_move_slice_window_step_hacks,
nrepeat);
float perf = static_cast<float>((std::size_t(2) * M * N * K)) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s" << std::endl;
}
// copy result back to host
c_m_n_device_buf.FromDevice(c_m_n.mData.data());
}
library/include/ck/library/obselete_driver_offline/device_gemm_xdlops_km_nk_nm.hpp deleted 100644 → 0
View file @ 05d38218
#pragma once
#include <unistd.h>
#include "device.hpp"
#include "host_tensor.hpp"
#include "driver_gemm_xdlops_v2r3.hpp"
template <typename ABType, typename AccType, typename CType>
void device_gemm_xdlops_km_nk_nm(const Tensor<ABType>& a_k_m,
const Tensor<ABType>& b_n_k,
Tensor<CType>& c_n_m,
ck::index_t nrepeat)
{
using namespace ck;
std::cout << __func__ << std::endl;
DeviceMem a_k_m_device_buf(sizeof(ABType) * a_k_m.mDesc.GetElementSpace());
DeviceMem b_n_k_device_buf(sizeof(ABType) * b_n_k.mDesc.GetElementSpace());
DeviceMem c_n_m_device_buf(sizeof(CType) * c_n_m.mDesc.GetElementSpace());
a_k_m_device_buf.ToDevice(a_k_m.mData.data());
b_n_k_device_buf.ToDevice(b_n_k.mData.data());
c_n_m_device_buf.ToDevice(c_n_m.mData.data());
#if 0
// [M, N, K0, K1] = [256, 128, 4, 4] for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 256;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 4, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 4;
#elif 0
// [M, N, K0, K1] = [128, 256, 4, 4] for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 256;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 4;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 2;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 4, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 4;
#elif 1
// [M, N, K0, K1] = [256, 128, 4, 8] for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 256;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 4, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 4;
#elif 1
// [M, N, K0, K1] = [128, 128, 4, 8] for fp16
constexpr index_t BlockSize = 128;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 4, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 32, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_M = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 4, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 32, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 4;
#endif
const auto K = a_k_m.mDesc.GetLengths()[0];
const auto M = a_k_m.mDesc.GetLengths()[1];
const auto N = b_n_k.mDesc.GetLengths()[0];
constexpr auto K1Number = Number<K1>{};
const auto K0 = K / K1Number;
const auto a_k0_m_k1_grid_desc =
make_naive_tensor_descriptor(make_tuple(K0, M, K1Number),
make_tuple(K1Number * a_k_m.mDesc.GetStrides()[0],
a_k_m.mDesc.GetStrides()[1],
a_k_m.mDesc.GetStrides()[0]));
const auto b_k0_n_k1_grid_desc =
make_naive_tensor_descriptor(make_tuple(K0, N, K1Number),
make_tuple(K1Number * b_n_k.mDesc.GetStrides()[1],
b_n_k.mDesc.GetStrides()[0],
b_n_k.mDesc.GetStrides()[1]));
const auto c_m_n_grid_desc = make_naive_tensor_descriptor(
make_tuple(M, N), make_tuple(c_n_m.mDesc.GetStrides()[1], c_n_m.mDesc.GetStrides()[0]));
// HACK: hacks that control index calculation when iterating over A, B, C matrix
constexpr auto a_k0_m_k1_grid_step_hacks = make_tuple(make_tuple(Sequence<0>{}, // 0+: K0
Sequence<0>{}, // 1+: M
Sequence<0>{}), // 2+: K1
make_tuple(Sequence<0>{}, // 0-: K0
Sequence<0>{}, // 1-: M
Sequence<0>{})); // 2-: K1
constexpr auto b_k0_n_k1_grid_step_hacks = make_tuple(make_tuple(Sequence<0>{}, // 0+: K0
Sequence<0>{}, // 1+: N
Sequence<0>{}), // 2+: K1
make_tuple(Sequence<0>{}, // 0-: K0
Sequence<0>{}, // 1-: N
Sequence<0>{})); // 2-: K1
constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1+: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2+: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3+: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4+: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5+: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6+: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 7+: N2
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0-: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1-: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2-: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3-: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4-: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5-: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6-: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
constexpr auto a_k0_m_k1_grid_move_slice_window_step_hacks = Sequence<0>{};
constexpr auto b_k0_n_k1_grid_move_slice_window_step_hacks = Sequence<0>{};
for(index_t i = 0; i < 5; ++i)
{
float ave_time =
driver_gemm_xdlops_v2r3<BlockSize,
ABType,
AccType,
CType,
InMemoryDataOperationEnum::Set,
decltype(a_k0_m_k1_grid_desc),
decltype(b_k0_n_k1_grid_desc),
decltype(c_m_n_grid_desc),
MPerBlock,
NPerBlock,
KPerBlock,
MPerXDL,
NPerXDL,
K1,
MRepeat,
NRepeat,
ABlockTransferThreadSliceLengths_K0_M_K1,
ABlockTransferThreadClusterLengths_K0_M_K1,
Sequence<0, 2, 1>,
Sequence<0, 2, 1>,
1,
ABlockTransferSrcScalarPerVector_M,
ABlockTransferDstScalarPerVector_K1,
false, // don't move back src coordinate after threadwise copy
BBlockTransferThreadSliceLengths_K0_N_K1,
BBlockTransferThreadClusterLengths_K0_N_K1,
Sequence<1, 0, 2>,
Sequence<1, 0, 2>,
2,
BBlockTransferSrcScalarPerVector_K1,
BBlockTransferDstScalarPerVector_K1,
false, // don't move back src coordinate after threadwise copy
Sequence<2, 3, 0, 1, 7, 5, 4, 6>,
6,
CThreadTransferDstScalarPerVector,
decltype(a_k0_m_k1_grid_step_hacks),
decltype(b_k0_n_k1_grid_step_hacks),
decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks),
decltype(a_k0_m_k1_grid_move_slice_window_step_hacks),
decltype(b_k0_n_k1_grid_move_slice_window_step_hacks),
false // CAccessOrderMRepeatNRepeat
>(static_cast<ABType*>(a_k_m_device_buf.GetDeviceBuffer()),
static_cast<ABType*>(b_n_k_device_buf.GetDeviceBuffer()),
static_cast<CType*>(c_n_m_device_buf.GetDeviceBuffer()),
a_k0_m_k1_grid_desc,
b_k0_n_k1_grid_desc,
c_m_n_grid_desc,
a_k0_m_k1_grid_step_hacks,
b_k0_n_k1_grid_step_hacks,
c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks,
a_k0_m_k1_grid_move_slice_window_step_hacks,
b_k0_n_k1_grid_move_slice_window_step_hacks,
nrepeat);
float perf = static_cast<float>((std::size_t(2) * M * N * K)) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s" << std::endl;
}
// copy result back to host
c_n_m_device_buf.FromDevice(c_n_m.mData.data());
}
library/include/ck/library/obselete_driver_offline/device_gemm_xdlops_mk_kn_mn.hpp deleted 100644 → 0
View file @ 05d38218
#pragma once
#include <unistd.h>
#include "device.hpp"
#include "host_tensor.hpp"
#include "driver_gemm_xdlops_v2r3.hpp"
template <typename ABType, typename AccType, typename CType>
void device_gemm_xdlops_mk_kn_mn(const Tensor<ABType>& a_m_k,
const Tensor<ABType>& b_k_n,
Tensor<CType>& c_m_n,
ck::index_t nrepeat)
{
using namespace ck;
std::cout << __func__ << std::endl;
DeviceMem a_m_k_device_buf(sizeof(ABType) * a_m_k.mDesc.GetElementSpace());
DeviceMem b_k_n_device_buf(sizeof(ABType) * b_k_n.mDesc.GetElementSpace());
DeviceMem c_m_n_device_buf(sizeof(CType) * c_m_n.mDesc.GetElementSpace());
a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_k_n_device_buf.ToDevice(b_k_n.mData.data());
c_m_n_device_buf.ToDevice(c_m_n.mData.data());
#if 0
// [M, N, K0, K1] = [256, 128, 4, 4] for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 256;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 4, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 2;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 256, 4, 4] for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 256;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 4;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 4, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 128, 4, 4], C = 64, for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 2;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 64, 4, 4], C = 32, for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 64;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 1;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 1, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 1;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [64, 128, 4, 4], C = 32, for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 64;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 1;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 1, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 2;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 1
// [M, N, K0, K1] = [256, 128, 4, 8], C = 128, for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 256;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 4, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 2;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 256, 4, 8] for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 256;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 4;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 4, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 128, 4, 8], C = 128, for fp16
constexpr index_t BlockSize = 128;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 4, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 32, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 4, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 32, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 128, 4, 8], C = 64, for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 2;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 1
// [M, N, K0, K1] = [128, 64, 4, 8], C = 32, for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 64;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 1;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 1, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 1;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 1
// [M, N, K0, K1] = [64, 128, 4, 8], C = 32, for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 64;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 1;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 1, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 2;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#endif
const auto K = a_m_k.mDesc.GetLengths()[1];
const auto M = a_m_k.mDesc.GetLengths()[0];
const auto N = b_k_n.mDesc.GetLengths()[1];
constexpr auto K1Number = Number<K1>{};
const auto K0 = K / K1Number;
const auto a_k0_m_k1_grid_desc =
make_naive_tensor_descriptor(make_tuple(K0, M, K1Number),
make_tuple(K1Number * a_m_k.mDesc.GetStrides()[1],
a_m_k.mDesc.GetStrides()[0],
a_m_k.mDesc.GetStrides()[1]));
const auto b_k0_n_k1_grid_desc =
make_naive_tensor_descriptor(make_tuple(K0, N, K1Number),
make_tuple(K1Number * b_k_n.mDesc.GetStrides()[0],
b_k_n.mDesc.GetStrides()[1],
b_k_n.mDesc.GetStrides()[0]));
const auto c_m_n_grid_desc = make_naive_tensor_descriptor(
make_tuple(M, N), make_tuple(c_m_n.mDesc.GetStrides()[0], c_m_n.mDesc.GetStrides()[1]));
// HACK: hacks that control index calculation when iterating over A, B, C matrix
constexpr auto a_k0_m_k1_grid_step_hacks = make_tuple(make_tuple(Sequence<0>{}, // 0+: K0
Sequence<0>{}, // 1+: M
Sequence<0>{}), // 2+: K1
make_tuple(Sequence<0>{}, // 0-: K0
Sequence<0>{}, // 1-: M
Sequence<0>{})); // 2-: K1
constexpr auto b_k0_n_k1_grid_step_hacks = make_tuple(make_tuple(Sequence<0>{}, // 0+: K0
Sequence<0>{}, // 1+: N
Sequence<0>{}), // 2+: K1
make_tuple(Sequence<0>{}, // 0-: K0
Sequence<0>{}, // 1-: N
Sequence<0>{})); // 2-: K1
constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1+: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2+: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3+: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4+: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5+: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6+: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 7+: N2
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0-: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1-: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2-: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3-: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4-: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5-: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6-: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
constexpr auto a_k0_m_k1_grid_move_slice_window_step_hacks = Sequence<0>{};
constexpr auto b_k0_n_k1_grid_move_slice_window_step_hacks = Sequence<0>{};
for(index_t i = 0; i < 5; ++i)
{
float ave_time =
driver_gemm_xdlops_v2r3<BlockSize,
ABType,
AccType,
CType,
InMemoryDataOperationEnum::Set,
decltype(a_k0_m_k1_grid_desc),
decltype(b_k0_n_k1_grid_desc),
decltype(c_m_n_grid_desc),
MPerBlock,
NPerBlock,
KPerBlock,
MPerXDL,
NPerXDL,
K1,
MRepeat,
NRepeat,
ABlockTransferThreadSliceLengths_K0_M_K1,
ABlockTransferThreadClusterLengths_K0_M_K1,
Sequence<1, 0, 2>,
Sequence<1, 0, 2>,
2,
ABlockTransferSrcScalarPerVector_K1,
ABlockTransferDstScalarPerVector_K1,
false, // don't move back src coordinate after threadwise copy
BBlockTransferThreadSliceLengths_K0_N_K1,
BBlockTransferThreadClusterLengths_K0_N_K1,
Sequence<0, 2, 1>,
Sequence<0, 2, 1>,
1,
BBlockTransferSrcScalarPerVector_N,
BBlockTransferDstScalarPerVector_K1,
false, // don't move back src coordinate after threadwise copy
Sequence<0, 2, 4, 5, 6, 1, 3, 7>,
7,
CThreadTransferDstScalarPerVector,
decltype(a_k0_m_k1_grid_step_hacks),
decltype(b_k0_n_k1_grid_step_hacks),
decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks),
decltype(a_k0_m_k1_grid_move_slice_window_step_hacks),
decltype(b_k0_n_k1_grid_move_slice_window_step_hacks),
false, // CAccessOrderMRepeatNRepeat
true, // ABlockLdsExtraM
true // BBlockLdsExtraN
>(static_cast<ABType*>(a_m_k_device_buf.GetDeviceBuffer()),
static_cast<ABType*>(b_k_n_device_buf.GetDeviceBuffer()),
static_cast<CType*>(c_m_n_device_buf.GetDeviceBuffer()),
a_k0_m_k1_grid_desc,
b_k0_n_k1_grid_desc,
c_m_n_grid_desc,
debug::debug_driver_gemm_xdlops_v2r3::M01,
debug::debug_driver_gemm_xdlops_v2r3::N01,
a_k0_m_k1_grid_step_hacks,
b_k0_n_k1_grid_step_hacks,
c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks,
a_k0_m_k1_grid_move_slice_window_step_hacks,
b_k0_n_k1_grid_move_slice_window_step_hacks,
nrepeat);
float perf = static_cast<float>((std::size_t(2) * M * N * K)) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s" << std::endl;
}
// copy result back to host
c_m_n_device_buf.FromDevice(c_m_n.mData.data());
}
library/include/ck/library/obselete_driver_offline/device_gemm_xdlops_mk_kn_nm.hpp deleted 100644 → 0
View file @ 05d38218
#pragma once
#include <unistd.h>
#include "device.hpp"
#include "host_tensor.hpp"
#include "driver_gemm_xdlops_v2r3.hpp"
template <typename ABType, typename AccType, typename CType>
void device_gemm_xdlops_mk_kn_nm(const Tensor<ABType>& a_m_k,
const Tensor<ABType>& b_k_n,
Tensor<CType>& c_n_m,
ck::index_t nrepeat)
{
using namespace ck;
std::cout << __func__ << std::endl;
DeviceMem a_m_k_device_buf(sizeof(ABType) * a_m_k.mDesc.GetElementSpace());
DeviceMem b_k_n_device_buf(sizeof(ABType) * b_k_n.mDesc.GetElementSpace());
DeviceMem c_n_m_device_buf(sizeof(CType) * c_n_m.mDesc.GetElementSpace());
a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_k_n_device_buf.ToDevice(b_k_n.mData.data());
c_n_m_device_buf.ToDevice(c_n_m.mData.data());
#if 0
// [M, N, K0, K1] = [256, 128, 4, 4] for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 256;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 4, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 2;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 4;
#elif 0
// [M, N, K0, K1] = [128, 256, 4, 4] for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 256;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 4;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 4, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 4;
#elif 1
// [M, N, K0, K1] = [256, 128, 4, 8] for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 256;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 4, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 2;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 4;
#elif 1
// [M, N, K0, K1] = [128, 256, 4, 8] for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 256;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 4;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 4, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 4;
#elif 1
// [M, N, K0, K1] = [128, 128, 4, 8] for fp16
constexpr index_t BlockSize = 128;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 4, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 32, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 4, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 32, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_N = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 4;
#endif
const auto K = a_m_k.mDesc.GetLengths()[1];
const auto M = a_m_k.mDesc.GetLengths()[0];
const auto N = b_k_n.mDesc.GetLengths()[1];
constexpr auto K1Number = Number<K1>{};
const auto K0 = K / K1Number;
const auto a_k0_m_k1_grid_desc =
make_naive_tensor_descriptor(make_tuple(K0, M, K1Number),
make_tuple(K1Number * a_m_k.mDesc.GetStrides()[1],
a_m_k.mDesc.GetStrides()[0],
a_m_k.mDesc.GetStrides()[1]));
const auto b_k0_n_k1_grid_desc =
make_naive_tensor_descriptor(make_tuple(K0, N, K1Number),
make_tuple(K1Number * b_k_n.mDesc.GetStrides()[0],
b_k_n.mDesc.GetStrides()[1],
b_k_n.mDesc.GetStrides()[0]));
const auto c_m_n_grid_desc = make_naive_tensor_descriptor(
make_tuple(M, N), make_tuple(c_n_m.mDesc.GetStrides()[1], c_n_m.mDesc.GetStrides()[0]));
// HACK: hacks that control index calculation when iterating over A, B, C matrix
constexpr auto a_k0_m_k1_grid_step_hacks = make_tuple(make_tuple(Sequence<0>{}, // 0+: K0
Sequence<0>{}, // 1+: M
Sequence<0>{}), // 2+: K1
make_tuple(Sequence<0>{}, // 0-: K0
Sequence<0>{}, // 1-: M
Sequence<0>{})); // 2-: K1
constexpr auto b_k0_n_k1_grid_step_hacks = make_tuple(make_tuple(Sequence<0>{}, // 0+: K0
Sequence<0>{}, // 1+: N
Sequence<0>{}), // 2+: K1
make_tuple(Sequence<0>{}, // 0-: K0
Sequence<0>{}, // 1-: N
Sequence<0>{})); // 2-: K1
constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1+: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2+: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3+: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4+: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5+: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6+: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 7+: N2
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0-: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1-: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2-: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3-: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4-: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5-: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6-: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
constexpr auto a_k0_m_k1_grid_move_slice_window_step_hacks = Sequence<0>{};
constexpr auto b_k0_n_k1_grid_move_slice_window_step_hacks = Sequence<0>{};
for(index_t i = 0; i < 5; ++i)
{
float ave_time =
driver_gemm_xdlops_v2r3<BlockSize,
ABType,
AccType,
CType,
InMemoryDataOperationEnum::Set,
decltype(a_k0_m_k1_grid_desc),
decltype(b_k0_n_k1_grid_desc),
decltype(c_m_n_grid_desc),
MPerBlock,
NPerBlock,
KPerBlock,
MPerXDL,
NPerXDL,
K1,
MRepeat,
NRepeat,
ABlockTransferThreadSliceLengths_K0_M_K1,
ABlockTransferThreadClusterLengths_K0_M_K1,
Sequence<1, 0, 2>,
Sequence<1, 0, 2>,
2,
ABlockTransferSrcScalarPerVector_K1,
ABlockTransferDstScalarPerVector_K1,
false, // don't move back src coordinate after threadwise copy
BBlockTransferThreadSliceLengths_K0_N_K1,
BBlockTransferThreadClusterLengths_K0_N_K1,
Sequence<0, 2, 1>,
Sequence<0, 2, 1>,
1,
BBlockTransferSrcScalarPerVector_N,
BBlockTransferDstScalarPerVector_K1,
false, // don't move back src coordinate after threadwise copy
Sequence<2, 3, 0, 1, 7, 5, 4, 6>,
6,
CThreadTransferDstScalarPerVector,
decltype(a_k0_m_k1_grid_step_hacks),
decltype(b_k0_n_k1_grid_step_hacks),
decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks),
decltype(a_k0_m_k1_grid_move_slice_window_step_hacks),
decltype(b_k0_n_k1_grid_move_slice_window_step_hacks),
false // CAccessOrderMRepeatNRepeat
>(static_cast<ABType*>(a_m_k_device_buf.GetDeviceBuffer()),
static_cast<ABType*>(b_k_n_device_buf.GetDeviceBuffer()),
static_cast<CType*>(c_n_m_device_buf.GetDeviceBuffer()),
a_k0_m_k1_grid_desc,
b_k0_n_k1_grid_desc,
c_m_n_grid_desc,
a_k0_m_k1_grid_step_hacks,
b_k0_n_k1_grid_step_hacks,
c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks,
a_k0_m_k1_grid_move_slice_window_step_hacks,
b_k0_n_k1_grid_move_slice_window_step_hacks,
nrepeat);
float perf = static_cast<float>((std::size_t(2) * M * N * K)) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s" << std::endl;
}
// copy result back to host
c_n_m_device_buf.FromDevice(c_n_m.mData.data());
}
library/include/ck/library/obselete_driver_offline/device_gemm_xdlops_mk_nk_mn.hpp deleted 100644 → 0
View file @ 05d38218
#pragma once
#include <unistd.h>
#include "device.hpp"
#include "host_tensor.hpp"
#include "driver_gemm_xdlops_v2r3.hpp"
template <typename ABType, typename AccType, typename CType>
void device_gemm_xdlops_mk_nk_mn(const Tensor<ABType>& a_m_k,
const Tensor<ABType>& b_n_k,
Tensor<CType>& c_m_n,
ck::index_t nrepeat)
{
using namespace ck;
std::cout << __func__ << std::endl;
DeviceMem a_m_k_device_buf(sizeof(ABType) * a_m_k.mDesc.GetElementSpace());
DeviceMem b_n_k_device_buf(sizeof(ABType) * b_n_k.mDesc.GetElementSpace());
DeviceMem c_m_n_device_buf(sizeof(CType) * c_m_n.mDesc.GetElementSpace());
a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_n_k_device_buf.ToDevice(b_n_k.mData.data());
c_m_n_device_buf.ToDevice(c_m_n.mData.data());
#if 0
// [M, N, K0, K1] = [256, 128, 4, 4] for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 256;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 4, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 256, 4, 4] for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 256;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 4;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 4, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 128, 4, 4], C = 64, for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 64, 4, 4], C = 32, for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 64;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 1;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 1, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [64, 128, 4, 4], C = 32, for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 64;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 1;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 1, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 1
// [M, N, K0, K1] = [256, 128, 4, 8], C = 128, for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 256;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 4, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 256, 4, 8], C = 128, for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 256;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 4;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 4, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 128, 4, 8], C = 128, for fp16
constexpr index_t BlockSize = 128;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 4, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 32, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 4, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 32, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [128, 128, 4, 8], C = 64, for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 0
// [M, N, K0, K1] = [64, 128, 4, 8], C = 64, for fp16
constexpr index_t BlockSize = 128;
constexpr index_t MPerBlock = 64;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 32, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 4, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 32, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 1
// [M, N, K0, K1] = [128, 64, 4, 8], C = 32, for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 64;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 1;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 1, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#elif 1
// [M, N, K0, K1] = [64, 128, 4, 8], C = 32, for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 64;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 1;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 1, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 1;
#endif
const auto K = a_m_k.mDesc.GetLengths()[1];
const auto M = a_m_k.mDesc.GetLengths()[0];
const auto N = b_n_k.mDesc.GetLengths()[0];
constexpr auto K1Number = Number<K1>{};
const auto K0 = K / K1Number;
#if 1
// non-padded GEMM
const auto a_k0_m_k1_grid_desc =
make_naive_tensor_descriptor(make_tuple(K0, M, K1Number),
make_tuple(K1Number * a_m_k.mDesc.GetStrides()[1],
a_m_k.mDesc.GetStrides()[0],
a_m_k.mDesc.GetStrides()[1]));
const auto b_k0_n_k1_grid_desc =
make_naive_tensor_descriptor(make_tuple(K0, N, K1Number),
make_tuple(K1Number * b_n_k.mDesc.GetStrides()[1],
b_n_k.mDesc.GetStrides()[0],
b_n_k.mDesc.GetStrides()[1]));
const auto c_m_n_grid_desc = make_naive_tensor_descriptor(
make_tuple(M, N), make_tuple(c_m_n.mDesc.GetStrides()[0], c_m_n.mDesc.GetStrides()[1]));
// HACK: hacks that control index calculation when iterating over A, B, C matrix
constexpr auto a_k0_m_k1_grid_step_hacks = make_tuple(make_tuple(Sequence<0>{}, // 0+: K0
Sequence<0>{}, // 1+: M
Sequence<0>{}), // 2+: K1
make_tuple(Sequence<0>{}, // 0-: K0
Sequence<0>{}, // 1-: M
Sequence<0>{})); // 2-: K1
constexpr auto b_k0_n_k1_grid_step_hacks = make_tuple(make_tuple(Sequence<0>{}, // 0+: K0
Sequence<0>{}, // 1+: N
Sequence<0>{}), // 2+: K1
make_tuple(Sequence<0>{}, // 0-: K0
Sequence<0>{}, // 1-: N
Sequence<0>{})); // 2-: K1
constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1+: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2+: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3+: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4+: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5+: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6+: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 7+: N2
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0-: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1-: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2-: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3-: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4-: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5-: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6-: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
constexpr auto a_k0_m_k1_grid_move_slice_window_step_hacks = Sequence<0>{};
constexpr auto b_k0_n_k1_grid_move_slice_window_step_hacks = Sequence<0>{};
#else
// padded GEMM
const auto a_k0_m_k1_grid_desc_tmp =
make_naive_tensor_descriptor(make_tuple(K0, M, K1Number),
make_tuple(K1Number * a_m_k.mDesc.GetStrides()[1],
a_m_k.mDesc.GetStrides()[0],
a_m_k.mDesc.GetStrides()[1]));
const auto MRightPad = math::integer_divide_ceil(M, MPerBlock) * MPerBlock - M;
const auto a_k0_m_k1_grid_desc =
transform_tensor_descriptor(a_k0_m_k1_grid_desc_tmp,
make_tuple(make_pass_through_transform(K0),
make_right_pad_transform(M, MRightPad),
make_pass_through_transform(K1Number)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
const auto b_k0_n_k1_grid_desc =
make_naive_tensor_descriptor(make_tuple(K0, N, K1Number),
make_tuple(K1Number * b_n_k.mDesc.GetStrides()[1],
b_n_k.mDesc.GetStrides()[0],
b_n_k.mDesc.GetStrides()[1]));
const auto c_m_n_grid_desc_tmp = make_naive_tensor_descriptor(
make_tuple(M, N), make_tuple(c_m_n.mDesc.GetStrides()[0], c_m_n.mDesc.GetStrides()[1]));
const auto c_m_n_grid_desc = transform_tensor_descriptor(
c_m_n_grid_desc_tmp,
make_tuple(make_right_pad_transform(M, MRightPad), make_pass_through_transform(N)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
// HACK: hacks that control index calculation when iterating over A, B, C matrix
constexpr auto a_k0_m_k1_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0>{}, // 0+: K0
Sequence<0, 0, 0, 0>{}, // 1+: M
Sequence<0, 0, 0, 0>{}), // 2+: K1
make_tuple(Sequence<0, 0, 0, 0>{}, // 0-: K0
Sequence<0, 0, 0, 0>{}, // 1-: M
Sequence<0, 0, 0, 0>{})); // 2-: K1
constexpr auto b_k0_n_k1_grid_step_hacks = make_tuple(make_tuple(Sequence<0>{}, // 0+: K0
Sequence<0>{}, // 1+: N
Sequence<0>{}), // 2+: K1
make_tuple(Sequence<0>{}, // 0-: K0
Sequence<0>{}, // 1-: N
Sequence<0>{})); // 2-: K1
constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1+: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2+: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3+: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4+: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5+: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6+: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 7+: N2
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0-: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1-: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2-: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3-: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4-: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5-: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6-: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
constexpr auto a_k0_m_k1_grid_move_slice_window_step_hacks = Sequence<0, 0, 0, 0>{};
constexpr auto b_k0_n_k1_grid_move_slice_window_step_hacks = Sequence<0>{};
#endif
for(index_t i = 0; i < 5; ++i)
{
float ave_time =
driver_gemm_xdlops_v2r3<BlockSize,
ABType,
AccType,
CType,
InMemoryDataOperationEnum::Set,
decltype(a_k0_m_k1_grid_desc),
decltype(b_k0_n_k1_grid_desc),
decltype(c_m_n_grid_desc),
MPerBlock,
NPerBlock,
KPerBlock,
MPerXDL,
NPerXDL,
K1,
MRepeat,
NRepeat,
ABlockTransferThreadSliceLengths_K0_M_K1,
ABlockTransferThreadClusterLengths_K0_M_K1,
Sequence<1, 0, 2>,
Sequence<1, 0, 2>,
2,
ABlockTransferSrcScalarPerVector_K1,
ABlockTransferDstScalarPerVector_K1,
false, // don't move back src coordinate after threadwise copy
BBlockTransferThreadSliceLengths_K0_N_K1,
BBlockTransferThreadClusterLengths_K0_N_K1,
Sequence<1, 0, 2>,
Sequence<1, 0, 2>,
2,
BBlockTransferSrcScalarPerVector_K1,
BBlockTransferDstScalarPerVector_K1,
false, // don't move back src coordinate after threadwise copy
Sequence<0, 2, 4, 5, 6, 1, 3, 7>,
7,
CThreadTransferDstScalarPerVector,
decltype(a_k0_m_k1_grid_step_hacks),
decltype(b_k0_n_k1_grid_step_hacks),
decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks),
decltype(a_k0_m_k1_grid_move_slice_window_step_hacks),
decltype(b_k0_n_k1_grid_move_slice_window_step_hacks),
false, // CAccessOrderMRepeatNRepeat
true, // ABlockLdsExtraM
true // BBlockLdsExtraN
>(static_cast<ABType*>(a_m_k_device_buf.GetDeviceBuffer()),
static_cast<ABType*>(b_n_k_device_buf.GetDeviceBuffer()),
static_cast<CType*>(c_m_n_device_buf.GetDeviceBuffer()),
a_k0_m_k1_grid_desc,
b_k0_n_k1_grid_desc,
c_m_n_grid_desc,
debug::debug_driver_gemm_xdlops_v2r3::M01,
debug::debug_driver_gemm_xdlops_v2r3::N01,
a_k0_m_k1_grid_step_hacks,
b_k0_n_k1_grid_step_hacks,
c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks,
a_k0_m_k1_grid_move_slice_window_step_hacks,
b_k0_n_k1_grid_move_slice_window_step_hacks,
nrepeat);
float perf = static_cast<float>((std::size_t(2) * M * N * K)) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s" << std::endl;
}
// copy result back to host
c_m_n_device_buf.FromDevice(c_m_n.mData.data());
}
library/include/ck/library/obselete_driver_offline/device_gemm_xdlops_mk_nk_nm.hpp deleted 100644 → 0
View file @ 05d38218
#pragma once
#include <unistd.h>
#include "device.hpp"
#include "host_tensor.hpp"
#include "driver_gemm_xdlops_v2r3.hpp"
template <typename ABType, typename AccType, typename CType>
void device_gemm_xdlops_mk_nk_nm(const Tensor<ABType>& a_m_k,
const Tensor<ABType>& b_n_k,
Tensor<CType>& c_n_m,
ck::index_t nrepeat)
{
using namespace ck;
std::cout << __func__ << std::endl;
DeviceMem a_m_k_device_buf(sizeof(ABType) * a_m_k.mDesc.GetElementSpace());
DeviceMem b_n_k_device_buf(sizeof(ABType) * b_n_k.mDesc.GetElementSpace());
DeviceMem c_n_m_device_buf(sizeof(CType) * c_n_m.mDesc.GetElementSpace());
a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_n_k_device_buf.ToDevice(b_n_k.mData.data());
c_n_m_device_buf.ToDevice(c_n_m.mData.data());
#if 0
// [M, N, K0, K1] = [256, 128, 4, 4] for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 256;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 4, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 4;
#elif 0
// [M, N, K0, K1] = [128, 256, 4, 4] for fp32
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 256;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 4;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 4;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 4>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 4, 4>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 4;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
constexpr index_t CThreadTransferDstScalarPerVector = 4;
#elif 0
// [M, N, K0, K1] = [256, 128, 4, 8] for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 256;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 4, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 4;
#elif 0
// [M, N, K0, K1] = [128, 256, 4, 8] for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 256;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 4;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 4, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 4;
#elif 0
// [M, N, K0, K1] = [128, 128, 4, 8], C = 128, for fp16
constexpr index_t BlockSize = 128;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 4;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 4, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 32, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 4, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 32, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 4;
#elif 0
// [M, N, K0, K1] = [128, 128, 4, 8], C = 64, for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 128;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 2;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 2, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 4;
#elif 1
// [M, N, K0, K1] = [64, 128, 4, 8], C = 32, for fp16
constexpr index_t BlockSize = 256;
constexpr index_t MPerBlock = 64;
constexpr index_t NPerBlock = 128;
constexpr index_t KPerBlock = 4;
constexpr index_t MPerXDL = 32;
constexpr index_t NPerXDL = 32;
constexpr index_t K1 = 8;
constexpr index_t MRepeat = 1;
constexpr index_t NRepeat = 2;
using ABlockTransferThreadSliceLengths_K0_M_K1 = Sequence<1, 1, 8>;
using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
using BBlockTransferThreadSliceLengths_K0_N_K1 = Sequence<1, 2, 8>;
using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 8;
constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
constexpr index_t CThreadTransferDstScalarPerVector = 4;
#endif
const auto K = a_m_k.mDesc.GetLengths()[1];
const auto M = a_m_k.mDesc.GetLengths()[0];
const auto N = b_n_k.mDesc.GetLengths()[0];
constexpr auto K1Number = Number<K1>{};
const auto K0 = K / K1Number;
const auto a_k0_m_k1_grid_desc =
make_naive_tensor_descriptor(make_tuple(K0, M, K1Number),
make_tuple(K1Number * a_m_k.mDesc.GetStrides()[1],
a_m_k.mDesc.GetStrides()[0],
a_m_k.mDesc.GetStrides()[1]));
const auto b_k0_n_k1_grid_desc =
make_naive_tensor_descriptor(make_tuple(K0, N, K1Number),
make_tuple(K1Number * b_n_k.mDesc.GetStrides()[1],
b_n_k.mDesc.GetStrides()[0],
b_n_k.mDesc.GetStrides()[1]));
const auto c_m_n_grid_desc = make_naive_tensor_descriptor(
make_tuple(M, N), make_tuple(c_n_m.mDesc.GetStrides()[1], c_n_m.mDesc.GetStrides()[0]));
// HACK: hacks that control index calculation when iterating over A, B, C matrix
constexpr auto a_k0_m_k1_grid_step_hacks = make_tuple(make_tuple(Sequence<0>{}, // 0+: K0
Sequence<0>{}, // 1+: M
Sequence<0>{}), // 2+: K1
make_tuple(Sequence<0>{}, // 0-: K0
Sequence<0>{}, // 1-: M
Sequence<0>{})); // 2-: K1
constexpr auto b_k0_n_k1_grid_step_hacks = make_tuple(make_tuple(Sequence<0>{}, // 0+: K0
Sequence<0>{}, // 1+: N
Sequence<0>{}), // 2+: K1
make_tuple(Sequence<0>{}, // 0-: K0
Sequence<0>{}, // 1-: N
Sequence<0>{})); // 2-: K1
constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1+: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2+: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3+: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4+: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5+: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6+: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 7+: N2
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0-: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1-: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2-: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3-: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4-: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5-: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6-: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
constexpr auto a_k0_m_k1_grid_move_slice_window_step_hacks = Sequence<0>{};
constexpr auto b_k0_n_k1_grid_move_slice_window_step_hacks = Sequence<0>{};
for(index_t i = 0; i < 5; ++i)
{
float ave_time =
driver_gemm_xdlops_v2r3<BlockSize,
ABType,
AccType,
CType,
InMemoryDataOperationEnum::Set,
decltype(a_k0_m_k1_grid_desc),
decltype(b_k0_n_k1_grid_desc),
decltype(c_m_n_grid_desc),
MPerBlock,
NPerBlock,
KPerBlock,
MPerXDL,
NPerXDL,
K1,
MRepeat,
NRepeat,
ABlockTransferThreadSliceLengths_K0_M_K1,
ABlockTransferThreadClusterLengths_K0_M_K1,
Sequence<1, 0, 2>,
Sequence<1, 0, 2>,
2,
ABlockTransferSrcScalarPerVector_K1,
ABlockTransferDstScalarPerVector_K1,
false, // don't move back src coordinate after threadwise copy
BBlockTransferThreadSliceLengths_K0_N_K1,
BBlockTransferThreadClusterLengths_K0_N_K1,
Sequence<1, 0, 2>,
Sequence<1, 0, 2>,
2,
BBlockTransferSrcScalarPerVector_K1,
BBlockTransferDstScalarPerVector_K1,
false, // don't move back src coordinate after threadwise copy
Sequence<2, 3, 0, 1, 7, 5, 4, 6>,
6,
CThreadTransferDstScalarPerVector,
decltype(a_k0_m_k1_grid_step_hacks),
decltype(b_k0_n_k1_grid_step_hacks),
decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks),
decltype(a_k0_m_k1_grid_move_slice_window_step_hacks),
decltype(b_k0_n_k1_grid_move_slice_window_step_hacks),
false // CAccessOrderMRepeatNRepeat
>(static_cast<ABType*>(a_m_k_device_buf.GetDeviceBuffer()),
static_cast<ABType*>(b_n_k_device_buf.GetDeviceBuffer()),
static_cast<CType*>(c_n_m_device_buf.GetDeviceBuffer()),
a_k0_m_k1_grid_desc,
b_k0_n_k1_grid_desc,
c_m_n_grid_desc,
a_k0_m_k1_grid_step_hacks,
b_k0_n_k1_grid_step_hacks,
c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks,
a_k0_m_k1_grid_move_slice_window_step_hacks,
b_k0_n_k1_grid_move_slice_window_step_hacks,
nrepeat);
float perf = static_cast<float>((std::size_t(2) * M * N * K)) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s" << std::endl;
}
// copy result back to host
c_n_m_device_buf.FromDevice(c_n_m.mData.data());
}
library/include/ck/library/obselete_driver_offline/driver_contraction_dlops_v1r2.hpp deleted 100644 → 0
View file @ 05d38218
#ifndef DRIVER_CONTRACTION_DLOPS_V1R2_HPP
#define DRIVER_CONTRACTION_DLOPS_V1R2_HPP
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_contraction_dlops_v1r2.hpp"
template <ck::index_t BlockSize,
typename FloatAB,
typename FloatAcc,
typename FloatC,
ck::InMemoryDataOperationEnum CGlobalMemoryDataOperation,
typename AGridDesc_GK0_GM0_GM1_GK1,
typename BGridDesc_GK0_GN0_GN1_GK1,
typename CGridDesc_GM0_GM1_GN0_GN1,
ck::index_t GM1PerBlockGM11,
ck::index_t GN1PerBlockGN11,
ck::index_t GK0PerBlock,
ck::index_t BM1PerThreadBM11,
ck::index_t BN1PerThreadBN11,
ck::index_t BK0PerThread,
typename BM10BN10ThreadClusterBM10Xs,
typename BM10BN10ThreadClusterBN10Xs,
typename ABlockTransferThreadSliceLengths_GK0_GM0_GM10_GM11_GK1,
typename ABlockTransferThreadClusterLengths_GK0_GM0_GM10_GM11_GK1,
typename ABlockTransferThreadClusterArrangeOrder,
typename ABlockTransferSrcAccessOrder,
typename ABlockTransferSrcVectorTensorLengths_GK0_GM0_GM10_GM11_GK1,
typename ABlockTransferDstVectorTensorLengths_GK0_GM0_GM10_GM11_GK1,
typename ABlockTransferSrcVectorTensorContiguousDimOrder,
typename BBlockTransferThreadSliceLengths_GK0_GN0_GN10_GN11_GK1,
typename BBlockTransferThreadClusterLengths_GK0_GN0_GN10_GN11_GK1,
typename BBlockTransferThreadClusterArrangeOrder,
typename BBlockTransferSrcAccessOrder,
typename BBlockTransferSrcVectorTensorLengths_GK0_GN0_GN10_GN11_GK1,
typename BBlockTransferDstVectorTensorLengths_GK0_GN0_GN10_GN11_GK1,
typename BBlockTransferSrcVectorTensorContiguousDimOrder,
typename CThreadTransferSrcDstAccessOrder,
ck::index_t CThreadTransferSrcDstVectorDim,
ck::index_t CThreadTransferDstScalarPerVector,
typename AGridStepHacks,
typename BGridStepHacks,
typename CGridStepHacks,
typename AGridMoveSliceWindowStepHacks,
typename BGridMoveSliceWindowStepHacks>
__host__ float
driver_contraction_dlops_v1r2(const FloatAB* p_a_grid,
const FloatAB* p_b_grid,
FloatC* p_c_grid,
const AGridDesc_GK0_GM0_GM1_GK1& a_grid_desc_gk0_gm0_gm1_gk1,
const BGridDesc_GK0_GN0_GN1_GK1& b_grid_desc_gk0_gn0_gn1_gk1,
const CGridDesc_GM0_GM1_GN0_GN1& c_grid_desc_gm0_gm1_gn0_gn1,
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks,
ck::index_t nrepeat)
{
using namespace ck;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
// GEMM
using GridwiseContraction =
GridwiseContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0_GM1_GN0_GN1<
BlockSize,
FloatAB,
FloatAcc,
FloatC,
CGlobalMemoryDataOperation,
AGridDesc_GK0_GM0_GM1_GK1,
BGridDesc_GK0_GN0_GN1_GK1,
CGridDesc_GM0_GM1_GN0_GN1,
GM1PerBlockGM11,
GN1PerBlockGN11,
GK0PerBlock,
BM1PerThreadBM11,
BN1PerThreadBN11,
BK0PerThread,
BM10BN10ThreadClusterBM10Xs,
BM10BN10ThreadClusterBN10Xs,
ABlockTransferThreadSliceLengths_GK0_GM0_GM10_GM11_GK1,
ABlockTransferThreadClusterLengths_GK0_GM0_GM10_GM11_GK1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorTensorLengths_GK0_GM0_GM10_GM11_GK1,
ABlockTransferDstVectorTensorLengths_GK0_GM0_GM10_GM11_GK1,
ABlockTransferSrcVectorTensorContiguousDimOrder,
BBlockTransferThreadSliceLengths_GK0_GN0_GN10_GN11_GK1,
BBlockTransferThreadClusterLengths_GK0_GN0_GN10_GN11_GK1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorTensorLengths_GK0_GN0_GN10_GN11_GK1,
BBlockTransferDstVectorTensorLengths_GK0_GN0_GN10_GN11_GK1,
BBlockTransferSrcVectorTensorContiguousDimOrder,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks>;
const auto GK0 = a_grid_desc_gk0_gm0_gm1_gk1.GetLength(I0);
if(!GridwiseContraction::CheckValidity(
a_grid_desc_gk0_gm0_gm1_gk1, b_grid_desc_gk0_gn0_gn1_gk1, c_grid_desc_gm0_gm1_gn0_gn1))
{
throw std::runtime_error("wrong! "
"GridwiseContraction_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_"
"GM0_GM1_GN0_GN1 has invalid setting");
}
const auto a_grid_desc_gk0_gm0_gm10_gm11_gk1 =
GridwiseContraction::MakeAGridDescriptor_GK0_GM0_GM10_GM11_GK1(a_grid_desc_gk0_gm0_gm1_gk1);
const auto b_grid_desc_gk0_gn0_gn10_gn11_gk1 =
GridwiseContraction::MakeBGridDescriptor_GK0_GN0_GN10_GN11_GK1(b_grid_desc_gk0_gn0_gn1_gk1);
using AGridDesc_GK0_GM0_GM10_GM11_GK1 = decltype(a_grid_desc_gk0_gm0_gm10_gm11_gk1);
using BGridDesc_GK0_GN0_GN10_GN11_GK1 = decltype(b_grid_desc_gk0_gn0_gn10_gn11_gk1);
// c_grid_desc_gm10_bm0_bm1_gn10_bn0_bn1
const auto c_grid_desc_gm10_bm0_bm1_gn10_bn0_bn1 =
GridwiseContraction::MakeCGridDescriptor_GM10_BM0_BM1_GN10_BN0_BN1(
c_grid_desc_gm0_gm1_gn0_gn1);
using CGridDesc_GM10_BM0_BM1_GN10_BN0_BN1 = decltype(c_grid_desc_gm10_bm0_bm1_gn10_bn0_bn1);
// c_grid_block_cluster_blockid_to_gm10_gn10
const auto c_grid_block_cluster_blockid_to_gm10_gn10 =
GridwiseContraction::MakeCGridBlockCluster_BlockId_To_GM10_GN10(
c_grid_desc_gm0_gm1_gn0_gn1);
using CGridBlockCluster_BlockId_To_GM10_GN10 =
decltype(c_grid_block_cluster_blockid_to_gm10_gn10);
const index_t grid_size = GridwiseContraction::CalculateGridSize(c_grid_desc_gm0_gm1_gn0_gn1);
const bool has_main_k_block_loop = GridwiseContraction::CalculateHasMainKBlockLoop(GK0);
const bool has_double_tail_k_block_loop =
GridwiseContraction::CalculateHasDoubleTailKBlockLoop(GK0);
{
std::cout << "a_grid_desc_gk0_gm0_gm10_gm11_gk1{"
<< a_grid_desc_gk0_gm0_gm10_gm11_gk1.GetLength(I0) << ", "
<< a_grid_desc_gk0_gm0_gm10_gm11_gk1.GetLength(I1) << ", "
<< a_grid_desc_gk0_gm0_gm10_gm11_gk1.GetLength(I2) << ", "
<< a_grid_desc_gk0_gm0_gm10_gm11_gk1.GetLength(I3) << ", "
<< a_grid_desc_gk0_gm0_gm10_gm11_gk1.GetLength(I4) << "}" << std::endl;
std::cout << "b_grid_desc_gk0_gn0_gn10_gn11_gk1{"
<< b_grid_desc_gk0_gn0_gn10_gn11_gk1.GetLength(I0) << ", "
<< b_grid_desc_gk0_gn0_gn10_gn11_gk1.GetLength(I1) << ", "
<< b_grid_desc_gk0_gn0_gn10_gn11_gk1.GetLength(I2) << ", "
<< b_grid_desc_gk0_gn0_gn10_gn11_gk1.GetLength(I3) << ", "
<< b_grid_desc_gk0_gn0_gn10_gn11_gk1.GetLength(I4) << "}" << std::endl;
std::cout << "c_grid_desc_gm10_bm0_bm1_gn10_bn0_bn1{ "
<< c_grid_desc_gm10_bm0_bm1_gn10_bn0_bn1.GetLength(I0) << ", "
<< c_grid_desc_gm10_bm0_bm1_gn10_bn0_bn1.GetLength(I1) << ", "
<< c_grid_desc_gm10_bm0_bm1_gn10_bn0_bn1.GetLength(I2) << ", "
<< c_grid_desc_gm10_bm0_bm1_gn10_bn0_bn1.GetLength(I3) << ", "
<< c_grid_desc_gm10_bm0_bm1_gn10_bn0_bn1.GetLength(I4) << ", "
<< c_grid_desc_gm10_bm0_bm1_gn10_bn0_bn1.GetLength(I5) << "}" << std::endl;
}
float ave_time = 0;
if(has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel = kernel_contraction_dlops_v1r2<
GridwiseContraction,
FloatAB,
FloatC,
remove_reference_t<AGridDesc_GK0_GM0_GM10_GM11_GK1>,
remove_reference_t<BGridDesc_GK0_GN0_GN10_GN11_GK1>,
remove_reference_t<CGridDesc_GM10_BM0_BM1_GN10_BN0_BN1>,
remove_reference_t<CGridBlockCluster_BlockId_To_GM10_GN10>,
true,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_grid_desc_gk0_gm0_gm10_gm11_gk1,
b_grid_desc_gk0_gn0_gn10_gn11_gk1,
c_grid_desc_gm10_bm0_bm1_gn10_bn0_bn1,
c_grid_block_cluster_blockid_to_gm10_gn10);
}
else if(has_main_k_block_loop && !has_double_tail_k_block_loop)
{
const auto kernel = kernel_contraction_dlops_v1r2<
GridwiseContraction,
FloatAB,
FloatC,
remove_reference_t<AGridDesc_GK0_GM0_GM10_GM11_GK1>,
remove_reference_t<BGridDesc_GK0_GN0_GN10_GN11_GK1>,
remove_reference_t<CGridDesc_GM10_BM0_BM1_GN10_BN0_BN1>,
remove_reference_t<CGridBlockCluster_BlockId_To_GM10_GN10>,
true,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_grid_desc_gk0_gm0_gm10_gm11_gk1,
b_grid_desc_gk0_gn0_gn10_gn11_gk1,
c_grid_desc_gm10_bm0_bm1_gn10_bn0_bn1,
c_grid_block_cluster_blockid_to_gm10_gn10);
}
else if(!has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel = kernel_contraction_dlops_v1r2<
GridwiseContraction,
FloatAB,
FloatC,
remove_reference_t<AGridDesc_GK0_GM0_GM10_GM11_GK1>,
remove_reference_t<BGridDesc_GK0_GN0_GN10_GN11_GK1>,
remove_reference_t<CGridDesc_GM10_BM0_BM1_GN10_BN0_BN1>,
remove_reference_t<CGridBlockCluster_BlockId_To_GM10_GN10>,
false,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_grid_desc_gk0_gm0_gm10_gm11_gk1,
b_grid_desc_gk0_gn0_gn10_gn11_gk1,
c_grid_desc_gm10_bm0_bm1_gn10_bn0_bn1,
c_grid_block_cluster_blockid_to_gm10_gn10);
}
else
{
const auto kernel = kernel_contraction_dlops_v1r2<
GridwiseContraction,
FloatAB,
FloatC,
remove_reference_t<AGridDesc_GK0_GM0_GM10_GM11_GK1>,
remove_reference_t<BGridDesc_GK0_GN0_GN10_GN11_GK1>,
remove_reference_t<CGridDesc_GM10_BM0_BM1_GN10_BN0_BN1>,
remove_reference_t<CGridBlockCluster_BlockId_To_GM10_GN10>,
false,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_grid_desc_gk0_gm0_gm10_gm11_gk1,
b_grid_desc_gk0_gn0_gn10_gn11_gk1,
c_grid_desc_gm10_bm0_bm1_gn10_bn0_bn1,
c_grid_block_cluster_blockid_to_gm10_gn10);
}
return ave_time;
}
#endif
library/include/ck/library/obselete_driver_offline/driver_convolution_add_forward_implicit_gemm_v5r1_dlops_nc0hwc1_kc0yxc1_nk0hwk1.hpp deleted 100644 → 0
View file @ 05d38218
#ifndef DRIVER_CONVOLUTION_ADD_FORWARD_IMPLICIT_GEMM_V5R1_DLOPS_NC0HWc1_KC0YXC1_NK0HWK1_HPP
#define DRIVER_CONVOLUTION_ADD_FORWARD_IMPLICIT_GEMM_V5R1_DLOPS_NC0HWc1_KC0YXC1_NK0HWK1_HPP
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm_dlops_v3.hpp"
template <ck::index_t BlockSize,
typename FloatAB,
typename FloatAcc,
typename FloatC,
ck::index_t E1_,
ck::index_t E2_,
ck::index_t K2_,
ck::index_t KPerBlock,
ck::index_t HoPerBlock,
ck::index_t WoPerBlock,
ck::index_t E1PerBlock,
ck::index_t KPerThread,
ck::index_t HoPerThread,
ck::index_t WoPerThread,
ck::index_t EPerThread,
typename ABlockTransferThreadSliceLengths_E0_E1_K0_K1_E2,
typename ABlockTransferThreadClusterLengths_E0_E1_K0_K1_E2,
ck::index_t ABlockTransferSrcScalarPerVector_E2,
ck::index_t ABlockTransferDstScalarPerVector_E2,
ck::index_t BThreadTransferSrcScalarPerVector_E2,
ck::index_t CThreadTransferDstScalarPerVector_K,
ck::ActivTypeEnum activ_type>
struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nc0hwc1_kc0yxc1_nk0hwk1_add
{
template <typename... Wei,
typename... In,
typename... Add,
typename... Out,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
__host__ float Run(const ck::TensorDescriptor<Wei...>& wei_k_c0_y_x_c1_global_desc,
const ck::TensorDescriptor<In...>& in_n_c0_hi_wi_c1_global_desc,
const ck::TensorDescriptor<Out...>& out_n_k0_ho_wo_k1_global_desc,
const ck::TensorDescriptor<Add...>& add_n_k0_hox2_wox2_k1_global_desc,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
const FloatC* __restrict__ p_bias_grid,
FloatC* __restrict__ p_d_grid,
const int nrepeat) const
{
using namespace ck;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
const auto N = in_n_c0_hi_wi_c1_global_desc.GetLength(I0);
const auto C0 = in_n_c0_hi_wi_c1_global_desc.GetLength(I1);
const auto Hi = in_n_c0_hi_wi_c1_global_desc.GetLength(I2);
const auto Wi = in_n_c0_hi_wi_c1_global_desc.GetLength(I3);
// const auto C1 = in_n_c0_hi_wi_c1_global_desc.GetLength(I4);
const auto K0 = out_n_k0_ho_wo_k1_global_desc.GetLength(I1);
const auto Ho = out_n_k0_ho_wo_k1_global_desc.GetLength(I2);
const auto Wo = out_n_k0_ho_wo_k1_global_desc.GetLength(I3);
const auto K1 = out_n_k0_ho_wo_k1_global_desc.GetLength(I4);
const auto Hox2 = add_n_k0_hox2_wox2_k1_global_desc.GetLength(I2);
const auto Wox2 = add_n_k0_hox2_wox2_k1_global_desc.GetLength(I3);
const auto K = wei_k_c0_y_x_c1_global_desc.GetLength(I0);
const auto Y = wei_k_c0_y_x_c1_global_desc.GetLength(I2);
const auto X = wei_k_c0_y_x_c1_global_desc.GetLength(I3);
const auto ConvStrideH = conv_strides[I0];
const auto ConvStrideW = conv_strides[I1];
const auto ConvDilationH = conv_dilations[I0];
const auto ConvDilationW = conv_dilations[I1];
const auto Hop = (Ho + HoPerBlock - 1) / HoPerBlock * HoPerBlock;
const auto Wop = (Wo + WoPerBlock - 1) / WoPerBlock * WoPerBlock;
const auto OutRightPadH = Hop - Ho;
const auto OutRightPadW = Wop - Wo;
const auto OutRightPadHx = OutRightPadH * 2;
const auto OutRightPadWx = OutRightPadW * 2;
const auto InLeftPadH = in_left_pads[I0];
const auto InLeftPadW = in_left_pads[I1];
const auto InRightPadH = in_right_pads[I0] + OutRightPadH * ConvStrideH;
const auto InRightPadW = in_right_pads[I1] + OutRightPadW * ConvStrideW;
const auto E = C0 * Y * X;
constexpr auto E1 = Number<E1_>{};
constexpr auto E2 = Number<E2_>{};
constexpr auto K2 = Number<K2_>{};
const auto E0 = E / E1;
// weight tensor
const auto a_e_k_e2_grid_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(K, C0 * Y * X, E2)),
make_tuple(make_pass_through_transform(K),
make_pass_through_transform(C0 * Y * X),
make_pass_through_transform(E2)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<1>{}, Sequence<0>{}, Sequence<2>{}));
const auto a_e0_e1_k_e2_grid_desc =
transform_tensor_descriptor(a_e_k_e2_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(E0, E1)),
make_pass_through_transform(K),
make_pass_through_transform(E2)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0, 1>{}, Sequence<2>{}, Sequence<3>{}));
// input tensor
const auto in_n_c0_hip_wip_e2_global_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(N, C0, Hi, Wi, E2)),
make_tuple(make_pass_through_transform(N),
make_pass_through_transform(C0),
make_pad_transform(Hi, InLeftPadH, InRightPadH),
make_pad_transform(Wi, InLeftPadW, InRightPadW),
make_pass_through_transform(E2)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}));
const auto in_n_c0_y_ho_x_wo_e2_global_desc = transform_tensor_descriptor(
in_n_c0_hip_wip_e2_global_desc,
make_tuple(
make_pass_through_transform(N),
make_pass_through_transform(C0),
make_embed_transform(make_tuple(Y, Hop), make_tuple(ConvDilationH, ConvStrideH)),
make_embed_transform(make_tuple(X, Wop), make_tuple(ConvDilationW, ConvStrideW)),
make_pass_through_transform(E2)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4, 5>{}, Sequence<6>{}));
const auto in_e_n_ho_wo_e2_grid_desc = transform_tensor_descriptor(
in_n_c0_y_ho_x_wo_e2_global_desc,
make_tuple(make_merge_transform(make_tuple(C0, Y, X)),
make_pass_through_transform(N),
make_pass_through_transform(Hop),
make_pass_through_transform(Wop),
make_pass_through_transform(E2)),
make_tuple(
Sequence<1, 2, 4>{}, Sequence<0>{}, Sequence<3>{}, Sequence<5>{}, Sequence<6>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}));
const auto b_e0_e1_n_ho_wo_e2_grid_desc = transform_tensor_descriptor(
in_e_n_ho_wo_e2_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(E0, E1)),
make_pass_through_transform(N),
make_pass_through_transform(Hop),
make_pass_through_transform(Wop),
make_pass_through_transform(E2)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
make_tuple(
Sequence<0, 1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}, Sequence<5>{}));
// output tensor
const auto c_k_n_hop_wop_grid_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(N, K0, Ho, Wo, K1)),
make_tuple(make_merge_transform(make_tuple(K0, K1)),
make_pass_through_transform(N),
make_pad_transform(Ho, I0, OutRightPadH),
make_pad_transform(Wo, I0, OutRightPadW)),
make_tuple(Sequence<1, 4>{}, Sequence<0>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
// add tensor
const auto d_k_n_hopx2_wopx2_grid_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(N, K0, Hox2, Wox2, K1)),
make_tuple(make_merge_transform(make_tuple(K0, K1)),
make_pass_through_transform(N),
make_pad_transform(Hox2, I0, OutRightPadHx),
make_pad_transform(Wox2, I0, OutRightPadWx)),
make_tuple(Sequence<1, 4>{}, Sequence<0>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
std::cerr << "Hop = " << Hop << " Wop = " << Wop << std::endl;
if(!((K % KPerBlock) == 0 && (Hop % HoPerBlock) == 0 && (Wop % WoPerBlock) == 0 &&
(E1 % E1PerBlock) == 0))
{
throw std::runtime_error("wrong! GEMM size no divisible");
}
// clang-format off
// hack to control index calculation when iterating over a_e0_e1_k_e2_global tensor
constexpr auto a_e0_e1_k_e2_global_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}));
constexpr auto a_e0_e1_k_e2_global_move_slice_window_step_hack =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{};
// hack to control index calculation when iterating over b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_global tensor
constexpr auto b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_global_step_hacks =
make_tuple(
make_tuple(
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}),
make_tuple(
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})
);
constexpr auto b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_global_move_slice_window_step_hack =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{};
// hack to control index calculation when iterating over c_k0_k1_n_h0_h1_h2_w0_w1_w2_global tensor
constexpr auto c_k0_k1_n_h0_h1_h2_w0_w1_w2_global_tensor_step_hacks =
make_tuple(make_tuple(Sequence<0, 1, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 1, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}),
make_tuple(Sequence<0, 2, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 2, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}));
constexpr auto d_k0_k1_n_h0_h1_h2x2_w0_w1_w2x2_global_tensor_step_hacks =
make_tuple(make_tuple(Sequence<0, 1, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 1, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}),
make_tuple(Sequence<0, 2, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 2, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}));
// clang-format on
// GEMM
using GridwiseGemm = GridwiseGemmDlops_km_kn_mn_v3<
BlockSize,
FloatAB,
FloatAcc,
FloatC,
InMemoryDataOperationEnum::Set,
decltype(a_e0_e1_k_e2_grid_desc),
decltype(b_e0_e1_n_ho_wo_e2_grid_desc),
decltype(c_k_n_hop_wop_grid_desc),
decltype(d_k_n_hopx2_wopx2_grid_desc),
E1,
E2,
K2,
KPerBlock,
HoPerBlock,
WoPerBlock,
E1PerBlock,
KPerThread,
HoPerThread,
WoPerThread,
EPerThread,
ABlockTransferThreadSliceLengths_E0_E1_K0_K1_E2,
ABlockTransferThreadClusterLengths_E0_E1_K0_K1_E2,
Sequence<2, 3, 0, 1, 4>,
Sequence<0, 1, 2, 3, 4>,
4,
ABlockTransferSrcScalarPerVector_E2,
ABlockTransferDstScalarPerVector_E2,
false, // don't move back src coordinate after threadwise copy
Sequence<0, 1, 2, 3, 4, 5, 6, 7, 8, 9>, // E0, E1, N, H0, H1, H2, W0, W1, W2, E2
9,
BThreadTransferSrcScalarPerVector_E2,
false, // don't move back src coordinate after threadwise copy, which will be fused with
// MoveSrcSliceWindow() to save addr computation
Sequence<0, 1, 2, 3, 4, 5, 6, 7, 8>, // K0, K1, N, H0, H1, I2, H2, W0, W1, I2, W2
1,
CThreadTransferDstScalarPerVector_K,
decltype(a_e0_e1_k_e2_global_step_hacks),
decltype(b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_global_step_hacks),
decltype(c_k0_k1_n_h0_h1_h2_w0_w1_w2_global_tensor_step_hacks),
decltype(d_k0_k1_n_h0_h1_h2x2_w0_w1_w2x2_global_tensor_step_hacks),
decltype(a_e0_e1_k_e2_global_move_slice_window_step_hack),
decltype(b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_global_move_slice_window_step_hack)>;
const auto a_e0_e1_k0_k1_e2_grid_desc =
GridwiseGemm::MakeAE0E1K0K1E2GridDescriptor(a_e0_e1_k_e2_grid_desc);
const auto b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_grid_desc =
GridwiseGemm::MakeBE0E1NH0H1H2W0W1W2E2GridDescriptor(b_e0_e1_n_ho_wo_e2_grid_desc);
const auto c_k0_k1_n_h0_h1_h2_w0_w1_w2_grid_desc =
GridwiseGemm::MakeCK0K1NH0H1H2W0W1W2GridDescriptor(c_k_n_hop_wop_grid_desc);
const auto d_k0_k1_n_h0_h1_h2x2_w0_w1_w2x2_grid_desc =
GridwiseGemm::MakeDK0K1NH0H1HxW0W1WxGridDescriptorResizeAdd(
d_k_n_hopx2_wopx2_grid_desc);
using AGridDesc_E0_E1_K0_K1_E2 = decltype(a_e0_e1_k0_k1_e2_grid_desc);
using BGridDesc_E0_E1_N_H0_H1_H2_W0_W1_W2_E2 =
decltype(b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_grid_desc);
using CGridDesc_K0_K1_N_H0_H1_H2_W0_W1_W2 = decltype(c_k0_k1_n_h0_h1_h2_w0_w1_w2_grid_desc);
using DGridDesc_K0_K1_N_H0_H1_H2x2_W0_W1_W2x2 =
decltype(d_k0_k1_n_h0_h1_h2x2_w0_w1_w2x2_grid_desc);
const auto grid_size = (K / KPerBlock) * (Hop / HoPerBlock) * (Wop / WoPerBlock) * N;
const bool has_main_e0_block_loop = E0 > 1;
std::cerr << "has_main_e0_block_loop = " << has_main_e0_block_loop << std::endl;
const auto cblockid_to_k_n_h_w_block_cluster_adaptor =
GridwiseGemm::MakeCBlockIdToKNHoWoBlockClusterAdaptor(c_k_n_hop_wop_grid_desc);
using CBlockIdToBlockClusterAdaptor_K_N_H_W =
decltype(cblockid_to_k_n_h_w_block_cluster_adaptor);
float ave_time = 0;
if(has_main_e0_block_loop)
{
const auto kernel = kernel_gemm_dlops_v3_resize_add<
GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AGridDesc_E0_E1_K0_K1_E2>,
remove_reference_t<BGridDesc_E0_E1_N_H0_H1_H2_W0_W1_W2_E2>,
remove_reference_t<CGridDesc_K0_K1_N_H0_H1_H2_W0_W1_W2>,
remove_reference_t<DGridDesc_K0_K1_N_H0_H1_H2x2_W0_W1_W2x2>,
remove_reference_t<CBlockIdToBlockClusterAdaptor_K_N_H_W>,
true,
activ_type>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_bias_grid,
p_d_grid,
a_e0_e1_k0_k1_e2_grid_desc,
b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_grid_desc,
c_k0_k1_n_h0_h1_h2_w0_w1_w2_grid_desc,
d_k0_k1_n_h0_h1_h2x2_w0_w1_w2x2_grid_desc,
cblockid_to_k_n_h_w_block_cluster_adaptor);
}
else
{
const auto kernel = kernel_gemm_dlops_v3_resize_add<
GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AGridDesc_E0_E1_K0_K1_E2>,
remove_reference_t<BGridDesc_E0_E1_N_H0_H1_H2_W0_W1_W2_E2>,
remove_reference_t<CGridDesc_K0_K1_N_H0_H1_H2_W0_W1_W2>,
remove_reference_t<DGridDesc_K0_K1_N_H0_H1_H2x2_W0_W1_W2x2>,
remove_reference_t<CBlockIdToBlockClusterAdaptor_K_N_H_W>,
false,
activ_type>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_bias_grid,
p_d_grid,
a_e0_e1_k0_k1_e2_grid_desc,
b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_grid_desc,
c_k0_k1_n_h0_h1_h2_w0_w1_w2_grid_desc,
d_k0_k1_n_h0_h1_h2x2_w0_w1_w2x2_grid_desc,
cblockid_to_k_n_h_w_block_cluster_adaptor);
}
return ave_time;
}
};
#endif
library/include/ck/library/obselete_driver_offline/driver_convolution_forward_implicit_gemm_v5r1_dlops_nc0hwc1_kc0yxc1_nk0hwk1.hpp deleted 100644 → 0
View file @ 05d38218
#ifndef DRIVER_CONVOLUTION_FORWARD_IMPLICIT_GEMM_V5R1_DLOPS_NC0HWc1_KC0YXC1_NK0HWK1_HPP
#define DRIVER_CONVOLUTION_FORWARD_IMPLICIT_GEMM_V5R1_DLOPS_NC0HWc1_KC0YXC1_NK0HWK1_HPP
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm_dlops_v3.hpp"
template <ck::index_t BlockSize,
typename FloatAB,
typename FloatAcc,
typename FloatC,
ck::index_t E1_,
ck::index_t E2_,
ck::index_t K2_,
ck::index_t KPerBlock,
ck::index_t HoPerBlock,
ck::index_t WoPerBlock,
ck::index_t E1PerBlock,
ck::index_t KPerThread,
ck::index_t HoPerThread,
ck::index_t WoPerThread,
ck::index_t EPerThread,
typename ABlockTransferThreadSliceLengths_E0_E1_K0_K1_E2,
typename ABlockTransferThreadClusterLengths_E0_E1_K0_K1_E2,
ck::index_t ABlockTransferSrcScalarPerVector_E2,
ck::index_t ABlockTransferDstScalarPerVector_E2,
ck::index_t BThreadTransferSrcScalarPerVector_E2,
ck::index_t CThreadTransferDstScalarPerVector_K,
ck::ActivTypeEnum activ_type>
struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nc0hwc1_kc0yxc1_nk0hwk1_outpad
{
template <typename... Wei,
typename... In,
typename... Out,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
__host__ float Run(const ck::TensorDescriptor<Wei...>& wei_k_c0_y_x_c1_global_desc,
const ck::TensorDescriptor<In...>& in_n_c0_hi_wi_c1_global_desc,
const ck::TensorDescriptor<Out...>& out_n_k0_ho_wo_k1_global_desc,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
const FloatC* __restrict__ p_bias_grid,
FloatC* __restrict__ p_c_grid,
const int nrepeat) const
{
using namespace ck;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
const auto N = in_n_c0_hi_wi_c1_global_desc.GetLength(I0);
const auto C0 = in_n_c0_hi_wi_c1_global_desc.GetLength(I1);
const auto Hi = in_n_c0_hi_wi_c1_global_desc.GetLength(I2);
const auto Wi = in_n_c0_hi_wi_c1_global_desc.GetLength(I3);
// const auto C1 = in_n_c0_hi_wi_c1_global_desc.GetLength(I4);
const auto K0 = out_n_k0_ho_wo_k1_global_desc.GetLength(I1);
const auto Ho = out_n_k0_ho_wo_k1_global_desc.GetLength(I2);
const auto Wo = out_n_k0_ho_wo_k1_global_desc.GetLength(I3);
const auto K1 = out_n_k0_ho_wo_k1_global_desc.GetLength(I4);
const auto K = wei_k_c0_y_x_c1_global_desc.GetLength(I0);
const auto Y = wei_k_c0_y_x_c1_global_desc.GetLength(I2);
const auto X = wei_k_c0_y_x_c1_global_desc.GetLength(I3);
const auto ConvStrideH = conv_strides[I0];
const auto ConvStrideW = conv_strides[I1];
const auto ConvDilationH = conv_dilations[I0];
const auto ConvDilationW = conv_dilations[I1];
#if CK_EXPERIMENTAL_STATIC_TENSOR_DESCRIPTOR
const auto Hop = Number<(Ho + HoPerBlock - 1) / HoPerBlock * HoPerBlock>{};
const auto Wop = Number<(Wo + WoPerBlock - 1) / WoPerBlock * WoPerBlock>{};
#else
const auto Hop = (Ho + HoPerBlock - 1) / HoPerBlock * HoPerBlock;
const auto Wop = (Wo + WoPerBlock - 1) / WoPerBlock * WoPerBlock;
#endif
const auto OutRightPadH = Hop - Ho;
const auto OutRightPadW = Wop - Wo;
const auto InLeftPadH = in_left_pads[I0];
const auto InLeftPadW = in_left_pads[I1];
const auto InRightPadH = in_right_pads[I0] + OutRightPadH * ConvStrideH;
const auto InRightPadW = in_right_pads[I1] + OutRightPadW * ConvStrideW;
const auto E = C0 * Y * X;
constexpr auto E1 = Number<E1_>{};
constexpr auto E2 = Number<E2_>{};
constexpr auto K2 = Number<K2_>{};
const auto E0 = E / E1;
// weight tensor
const auto a_e_k_e2_grid_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(K, C0 * Y * X, E2)),
make_tuple(make_pass_through_transform(K),
make_pass_through_transform(C0 * Y * X),
make_pass_through_transform(E2)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<1>{}, Sequence<0>{}, Sequence<2>{}));
const auto a_e0_e1_k_e2_grid_desc =
transform_tensor_descriptor(a_e_k_e2_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(E0, E1)),
make_pass_through_transform(K),
make_pass_through_transform(E2)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0, 1>{}, Sequence<2>{}, Sequence<3>{}));
// input tensor
const auto in_n_c0_hip_wip_e2_global_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(N, C0, Hi, Wi, E2)),
make_tuple(make_pass_through_transform(N),
make_pass_through_transform(C0),
make_pad_transform(Hi, InLeftPadH, InRightPadH),
make_pad_transform(Wi, InLeftPadW, InRightPadW),
make_pass_through_transform(E2)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}));
const auto in_n_c0_y_ho_x_wo_e2_global_desc = transform_tensor_descriptor(
in_n_c0_hip_wip_e2_global_desc,
make_tuple(
make_pass_through_transform(N),
make_pass_through_transform(C0),
make_embed_transform(make_tuple(Y, Hop), make_tuple(ConvDilationH, ConvStrideH)),
make_embed_transform(make_tuple(X, Wop), make_tuple(ConvDilationW, ConvStrideW)),
make_pass_through_transform(E2)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4, 5>{}, Sequence<6>{}));
const auto in_e_n_ho_wo_e2_grid_desc = transform_tensor_descriptor(
in_n_c0_y_ho_x_wo_e2_global_desc,
make_tuple(make_merge_transform(make_tuple(C0, Y, X)),
make_pass_through_transform(N),
make_pass_through_transform(Hop),
make_pass_through_transform(Wop),
make_pass_through_transform(E2)),
make_tuple(
Sequence<1, 2, 4>{}, Sequence<0>{}, Sequence<3>{}, Sequence<5>{}, Sequence<6>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}));
const auto b_e0_e1_n_ho_wo_e2_grid_desc = transform_tensor_descriptor(
in_e_n_ho_wo_e2_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(E0, E1)),
make_pass_through_transform(N),
make_pass_through_transform(Hop),
make_pass_through_transform(Wop),
make_pass_through_transform(E2)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
make_tuple(
Sequence<0, 1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}, Sequence<5>{}));
// output tensor
const auto c_k_n_hop_wop_grid_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(N, K0, Ho, Wo, K1)),
make_tuple(make_merge_transform(make_tuple(K0, K1)),
make_pass_through_transform(N),
make_pad_transform(Ho, I0, OutRightPadH),
make_pad_transform(Wo, I0, OutRightPadW)),
make_tuple(Sequence<1, 4>{}, Sequence<0>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
std::cerr << "Hop = " << Hop << " Wop = " << Wop << std::endl;
if(!((K % KPerBlock) == 0 && (Hop % HoPerBlock) == 0 && (Wop % WoPerBlock) == 0 &&
(E1 % E1PerBlock) == 0))
{
throw std::runtime_error("wrong! GEMM size no divisible");
}
// clang-format off
// hack to control index calculation when iterating over a_e0_e1_k_e2_global tensor
constexpr auto a_e0_e1_k_e2_global_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}));
constexpr auto a_e0_e1_k_e2_global_move_slice_window_step_hack =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{};
// hack to control index calculation when iterating over b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_global tensor
constexpr auto b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_global_step_hacks =
make_tuple(
make_tuple(
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}),
make_tuple(
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})
);
constexpr auto b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_global_move_slice_window_step_hack =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{};
// hack to control index calculation when iterating over c_k0_k1_n_h0_h1_h2_w0_w1_w2_global tensor
constexpr auto c_k0_k1_n_h0_h1_h2_w0_w1_w2_global_tensor_step_hacks =
make_tuple(make_tuple(Sequence<0, 1, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 1, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}),
make_tuple(Sequence<0, 2, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 2, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}));
// clang-format on
// GEMM
using GridwiseGemm = GridwiseGemmDlops_km_kn_mn_v3<
BlockSize,
FloatAB,
FloatAcc,
FloatC,
InMemoryDataOperationEnum::Set,
decltype(a_e0_e1_k_e2_grid_desc),
decltype(b_e0_e1_n_ho_wo_e2_grid_desc),
decltype(c_k_n_hop_wop_grid_desc),
decltype(c_k_n_hop_wop_grid_desc),
E1,
E2,
K2,
KPerBlock,
HoPerBlock,
WoPerBlock,
E1PerBlock,
KPerThread,
HoPerThread,
WoPerThread,
EPerThread,
ABlockTransferThreadSliceLengths_E0_E1_K0_K1_E2,
ABlockTransferThreadClusterLengths_E0_E1_K0_K1_E2,
Sequence<2, 3, 0, 1, 4>,
Sequence<0, 1, 2, 3, 4>,
4,
ABlockTransferSrcScalarPerVector_E2,
ABlockTransferDstScalarPerVector_E2,
false, // don't move back src coordinate after threadwise copy
Sequence<0, 1, 2, 3, 4, 5, 6, 7, 8, 9>, // E0, E1, N, H0, H1, H2, W0, W1, W2, E2
9,
BThreadTransferSrcScalarPerVector_E2,
false, // don't move back src coordinate after threadwise copy, which will be fused with
// MoveSrcSliceWindow() to save addr computation
Sequence<0, 1, 2, 3, 4, 5, 6, 7, 8>, // K0, K1, N, H0, H1, H2, W0, W1, W2
1,
CThreadTransferDstScalarPerVector_K,
decltype(a_e0_e1_k_e2_global_step_hacks),
decltype(b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_global_step_hacks),
decltype(c_k0_k1_n_h0_h1_h2_w0_w1_w2_global_tensor_step_hacks),
decltype(c_k0_k1_n_h0_h1_h2_w0_w1_w2_global_tensor_step_hacks),
decltype(a_e0_e1_k_e2_global_move_slice_window_step_hack),
decltype(b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_global_move_slice_window_step_hack)>;
const auto a_e0_e1_k0_k1_e2_grid_desc =
GridwiseGemm::MakeAE0E1K0K1E2GridDescriptor(a_e0_e1_k_e2_grid_desc);
const auto b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_grid_desc =
GridwiseGemm::MakeBE0E1NH0H1H2W0W1W2E2GridDescriptor(b_e0_e1_n_ho_wo_e2_grid_desc);
const auto c_k0_k1_n_h0_h1_h2_w0_w1_w2_grid_desc =
GridwiseGemm::MakeCK0K1NH0H1H2W0W1W2GridDescriptor(c_k_n_hop_wop_grid_desc);
using AGridDesc_E0_E1_K0_K1_E2 = decltype(a_e0_e1_k0_k1_e2_grid_desc);
using BGridDesc_E0_E1_N_H0_H1_H2_W0_W1_W2_E2 =
decltype(b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_grid_desc);
using CGridDesc_K0_K1_N_H0_H1_H2_W0_W1_W2 = decltype(c_k0_k1_n_h0_h1_h2_w0_w1_w2_grid_desc);
const auto grid_size = (K / KPerBlock) * (Hop / HoPerBlock) * (Wop / WoPerBlock) * N;
const bool has_main_e0_block_loop = E0 > 1;
std::cerr << "has_main_e0_block_loop = " << has_main_e0_block_loop << std::endl;
const auto cblockid_to_k_n_h_w_block_cluster_adaptor =
GridwiseGemm::MakeCBlockIdToKNHoWoBlockClusterAdaptor(c_k_n_hop_wop_grid_desc);
using CBlockIdToBlockClusterAdaptor_K_N_H_W =
decltype(cblockid_to_k_n_h_w_block_cluster_adaptor);
float ave_time = 0;
if(has_main_e0_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AGridDesc_E0_E1_K0_K1_E2>,
remove_reference_t<BGridDesc_E0_E1_N_H0_H1_H2_W0_W1_W2_E2>,
remove_reference_t<CGridDesc_K0_K1_N_H0_H1_H2_W0_W1_W2>,
remove_reference_t<CBlockIdToBlockClusterAdaptor_K_N_H_W>,
true,
activ_type>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_bias_grid,
p_c_grid,
a_e0_e1_k0_k1_e2_grid_desc,
b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_grid_desc,
c_k0_k1_n_h0_h1_h2_w0_w1_w2_grid_desc,
cblockid_to_k_n_h_w_block_cluster_adaptor);
}
else
{
const auto kernel =
kernel_gemm_dlops_v3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AGridDesc_E0_E1_K0_K1_E2>,
remove_reference_t<BGridDesc_E0_E1_N_H0_H1_H2_W0_W1_W2_E2>,
remove_reference_t<CGridDesc_K0_K1_N_H0_H1_H2_W0_W1_W2>,
remove_reference_t<CBlockIdToBlockClusterAdaptor_K_N_H_W>,
false,
activ_type>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_bias_grid,
p_c_grid,
a_e0_e1_k0_k1_e2_grid_desc,
b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_grid_desc,
c_k0_k1_n_h0_h1_h2_w0_w1_w2_grid_desc,
cblockid_to_k_n_h_w_block_cluster_adaptor);
}
return ave_time;
}
};
#endif
library/include/ck/library/obselete_driver_offline/driver_convolution_maxpool_forward_implicit_gemm_v5r1_dlops_nc0hwc1_kc0yxc1_nk0hwk1.hpp deleted 100644 → 0
View file @ 05d38218
#ifndef DRIVER_CONVOLUTION_MAXPOOL_FORWARD_IMPLICIT_GEMM_V5R1_DLOPS_NC0HWc1_KC0YXC1_NK0HWK1_HPP
#define DRIVER_CONVOLUTION_MAXPOOL_FORWARD_IMPLICIT_GEMM_V5R1_DLOPS_NC0HWc1_KC0YXC1_NK0HWK1_HPP
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm_dlops_v3.hpp"
template <ck::index_t BlockSize,
typename FloatAB,
typename FloatAcc,
typename FloatC,
ck::index_t E1_,
ck::index_t E2_,
ck::index_t K2_,
ck::index_t KPerBlock,
ck::index_t HoPerBlock,
ck::index_t WoPerBlock,
ck::index_t E1PerBlock,
ck::index_t KPerThread,
ck::index_t HoPerThread,
ck::index_t WoPerThread,
ck::index_t EPerThread,
typename ABlockTransferThreadSliceLengths_E0_E1_K0_K1_E2,
typename ABlockTransferThreadClusterLengths_E0_E1_K0_K1_E2,
ck::index_t ABlockTransferSrcScalarPerVector_E2,
ck::index_t ABlockTransferDstScalarPerVector_E2,
ck::index_t BThreadTransferSrcScalarPerVector_E2,
ck::index_t CThreadTransferDstScalarPerVector_K,
ck::ActivTypeEnum activ_type>
struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nc0hwc1_kc0yxc1_nk0hwk1_maxpool
{
template <typename... Wei,
typename... In,
typename... MaxPool,
typename... Out,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
__host__ float Run(const ck::TensorDescriptor<Wei...>& wei_k_c0_y_x_c1_global_desc,
const ck::TensorDescriptor<In...>& in_n_c0_hi_wi_c1_global_desc,
const ck::TensorDescriptor<Out...>& out_n_k0_ho_wo_k1_global_desc,
const ck::TensorDescriptor<MaxPool...>& max_n_k0_hx_wx_k1_global_desc,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
const FloatC* __restrict__ p_bias_grid,
FloatC* __restrict__ p_c_grid,
FloatC* __restrict__ p_d_grid,
const int nrepeat) const
{
using namespace ck;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
const auto N = in_n_c0_hi_wi_c1_global_desc.GetLength(I0);
const auto C0 = in_n_c0_hi_wi_c1_global_desc.GetLength(I1);
const auto Hi = in_n_c0_hi_wi_c1_global_desc.GetLength(I2);
const auto Wi = in_n_c0_hi_wi_c1_global_desc.GetLength(I3);
// const auto C1 = in_n_c0_hi_wi_c1_global_desc.GetLength(I4);
const auto K0 = out_n_k0_ho_wo_k1_global_desc.GetLength(I1);
const auto Ho = out_n_k0_ho_wo_k1_global_desc.GetLength(I2);
const auto Wo = out_n_k0_ho_wo_k1_global_desc.GetLength(I3);
const auto K1 = out_n_k0_ho_wo_k1_global_desc.GetLength(I4);
const auto Hx = max_n_k0_hx_wx_k1_global_desc.GetLength(I2);
const auto Wx = max_n_k0_hx_wx_k1_global_desc.GetLength(I3);
const auto K = wei_k_c0_y_x_c1_global_desc.GetLength(I0);
const auto Y = wei_k_c0_y_x_c1_global_desc.GetLength(I2);
const auto X = wei_k_c0_y_x_c1_global_desc.GetLength(I3);
const auto ConvStrideH = conv_strides[I0];
const auto ConvStrideW = conv_strides[I1];
const auto ConvDilationH = conv_dilations[I0];
const auto ConvDilationW = conv_dilations[I1];
#if CK_EXPERIMENTAL_STATIC_TENSOR_DESCRIPTOR
const auto Hop = Number<(Ho + HoPerBlock - 1) / HoPerBlock * HoPerBlock>{};
const auto Wop = Number<(Wo + WoPerBlock - 1) / WoPerBlock * WoPerBlock>{};
const auto OutRightPadH = Hop - Ho;
const auto OutRightPadW = Wop - Wo;
const auto OutRightPadHx = Number<OutRightPadH / 2>{};
const auto OutRightPadWx = Number<OutRightPadW / 2>{};
#else
const auto Hop = (Ho + HoPerBlock - 1) / HoPerBlock * HoPerBlock;
const auto Wop = (Wo + WoPerBlock - 1) / WoPerBlock * WoPerBlock;
const auto OutRightPadH = Hop - Ho;
const auto OutRightPadW = Wop - Wo;
const auto OutRightPadHx = OutRightPadH / 2;
const auto OutRightPadWx = OutRightPadW / 2;
#endif
const auto InLeftPadH = in_left_pads[I0];
const auto InLeftPadW = in_left_pads[I1];
const auto InRightPadH = in_right_pads[I0] + OutRightPadH * ConvStrideH;
const auto InRightPadW = in_right_pads[I1] + OutRightPadW * ConvStrideW;
const auto E = C0 * Y * X;
constexpr auto E1 = Number<E1_>{};
constexpr auto E2 = Number<E2_>{};
constexpr auto K2 = Number<K2_>{};
const auto E0 = E / E1;
// weight tensor
const auto a_e_k_e2_grid_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(K, C0 * Y * X, E2)),
make_tuple(make_pass_through_transform(K),
make_pass_through_transform(C0 * Y * X),
make_pass_through_transform(E2)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<1>{}, Sequence<0>{}, Sequence<2>{}));
const auto a_e0_e1_k_e2_grid_desc =
transform_tensor_descriptor(a_e_k_e2_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(E0, E1)),
make_pass_through_transform(K),
make_pass_through_transform(E2)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0, 1>{}, Sequence<2>{}, Sequence<3>{}));
// input tensor
const auto in_n_c0_hip_wip_e2_global_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(N, C0, Hi, Wi, E2)),
make_tuple(make_pass_through_transform(N),
make_pass_through_transform(C0),
make_pad_transform(Hi, InLeftPadH, InRightPadH),
make_pad_transform(Wi, InLeftPadW, InRightPadW),
make_pass_through_transform(E2)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}));
const auto in_n_c0_y_ho_x_wo_e2_global_desc = transform_tensor_descriptor(
in_n_c0_hip_wip_e2_global_desc,
make_tuple(
make_pass_through_transform(N),
make_pass_through_transform(C0),
make_embed_transform(make_tuple(Y, Hop), make_tuple(ConvDilationH, ConvStrideH)),
make_embed_transform(make_tuple(X, Wop), make_tuple(ConvDilationW, ConvStrideW)),
make_pass_through_transform(E2)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
make_tuple(
Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4, 5>{}, Sequence<6>{}));
const auto in_e_n_ho_wo_e2_grid_desc = transform_tensor_descriptor(
in_n_c0_y_ho_x_wo_e2_global_desc,
make_tuple(make_merge_transform(make_tuple(C0, Y, X)),
make_pass_through_transform(N),
make_pass_through_transform(Hop),
make_pass_through_transform(Wop),
make_pass_through_transform(E2)),
make_tuple(
Sequence<1, 2, 4>{}, Sequence<0>{}, Sequence<3>{}, Sequence<5>{}, Sequence<6>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}));
const auto b_e0_e1_n_ho_wo_e2_grid_desc = transform_tensor_descriptor(
in_e_n_ho_wo_e2_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(E0, E1)),
make_pass_through_transform(N),
make_pass_through_transform(Hop),
make_pass_through_transform(Wop),
make_pass_through_transform(E2)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
make_tuple(
Sequence<0, 1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}, Sequence<5>{}));
// output tensor
const auto c_k_n_hop_wop_grid_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(N, K0, Ho, Wo, K1)),
make_tuple(make_merge_transform(make_tuple(K0, K1)),
make_pass_through_transform(N),
make_pad_transform(Ho, I0, OutRightPadH),
make_pad_transform(Wo, I0, OutRightPadW)),
make_tuple(Sequence<1, 4>{}, Sequence<0>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
// max tensor
const auto d_k_n_hx_wx_grid_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(N, K0, Hx, Wx, K1)),
make_tuple(make_merge_transform(make_tuple(K0, K1)),
make_pass_through_transform(N),
make_pad_transform(Hx, I0, OutRightPadHx),
make_pad_transform(Wx, I0, OutRightPadWx)),
make_tuple(Sequence<1, 4>{}, Sequence<0>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
std::cerr << "Hop = " << Hop << " Wop = " << Wop << std::endl;
if(!((K % KPerBlock) == 0 && (Hop % HoPerBlock) == 0 && (Wop % WoPerBlock) == 0 &&
(E1 % E1PerBlock) == 0))
{
throw std::runtime_error("wrong! GEMM size no divisible");
}
// clang-format off
// hack to control index calculation when iterating over a_e0_e1_k_e2_global tensor
constexpr auto a_e0_e1_k_e2_global_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}));
constexpr auto a_e0_e1_k_e2_global_move_slice_window_step_hack =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{};
// hack to control index calculation when iterating over b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_global tensor
constexpr auto b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_global_step_hacks =
make_tuple(
make_tuple(
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}),
make_tuple(
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})
);
constexpr auto b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_global_move_slice_window_step_hack =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{};
constexpr auto c_k0_k1_n_h0_h1_h2_w0_w1_w2_global_tensor_step_hacks =
make_tuple(make_tuple(Sequence<0, 1, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 1, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}),
make_tuple(Sequence<0, 2, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 2, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}));
constexpr auto d_k0_k1_n_h0_h1_hx_w0_w1_wx_global_tensor_step_hacks =
make_tuple(make_tuple(Sequence<0, 1, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 1, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}),
make_tuple(Sequence<0, 2, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 2, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}));
// clang-format on
// GEMM
using GridwiseGemm = GridwiseGemmDlops_km_kn_mn_v3<
BlockSize,
FloatAB,
FloatAcc,
FloatC,
InMemoryDataOperationEnum::Set,
decltype(a_e0_e1_k_e2_grid_desc),
decltype(b_e0_e1_n_ho_wo_e2_grid_desc),
decltype(c_k_n_hop_wop_grid_desc),
decltype(d_k_n_hx_wx_grid_desc),
E1,
E2,
K2,
KPerBlock,
HoPerBlock,
WoPerBlock,
E1PerBlock,
KPerThread,
HoPerThread,
WoPerThread,
EPerThread,
ABlockTransferThreadSliceLengths_E0_E1_K0_K1_E2,
ABlockTransferThreadClusterLengths_E0_E1_K0_K1_E2,
Sequence<2, 3, 0, 1, 4>,
Sequence<0, 1, 2, 3, 4>,
4,
ABlockTransferSrcScalarPerVector_E2,
ABlockTransferDstScalarPerVector_E2,
false, // don't move back src coordinate after threadwise copy
Sequence<0, 1, 2, 3, 4, 5, 6, 7, 8, 9>, // E0, E1, N, H0, H1, H2, W0, W1, W2, E2
9,
BThreadTransferSrcScalarPerVector_E2,
false, // don't move back src coordinate after threadwise copy, which will be fused
// with MoveSrcSliceWindow() to save addr computation
Sequence<0, 1, 2, 3, 4, 5, 6, 7, 8>, // K0, K1, N, H0, H1, I2, H2, W0, W1, I2, W2
1,
CThreadTransferDstScalarPerVector_K,
decltype(a_e0_e1_k_e2_global_step_hacks),
decltype(b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_global_step_hacks),
decltype(c_k0_k1_n_h0_h1_h2_w0_w1_w2_global_tensor_step_hacks),
decltype(d_k0_k1_n_h0_h1_hx_w0_w1_wx_global_tensor_step_hacks),
decltype(a_e0_e1_k_e2_global_move_slice_window_step_hack),
decltype(b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_global_move_slice_window_step_hack)>;
const auto a_e0_e1_k0_k1_e2_grid_desc =
GridwiseGemm::MakeAE0E1K0K1E2GridDescriptor(a_e0_e1_k_e2_grid_desc);
const auto b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_grid_desc =
GridwiseGemm::MakeBE0E1NH0H1H2W0W1W2E2GridDescriptor(b_e0_e1_n_ho_wo_e2_grid_desc);
const auto c_k0_k1_n_h0_h1_h2_w0_w1_w2_grid_desc =
GridwiseGemm::MakeCK0K1NH0H1H2W0W1W2GridDescriptor(c_k_n_hop_wop_grid_desc);
const auto d_k0_k1_n_h0_h1_hx_w0_w1_wx_grid_desc =
GridwiseGemm::MakeDK0K1NH0H1HxW0W1WxGridDescriptorMaxPool(d_k_n_hx_wx_grid_desc);
using AGridDesc_E0_E1_K0_K1_E2 = decltype(a_e0_e1_k0_k1_e2_grid_desc);
using BGridDesc_E0_E1_N_H0_H1_H2_W0_W1_W2_E2 =
decltype(b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_grid_desc);
using CGridDesc_K0_K1_N_H0_H1_H2_W0_W1_W2 = decltype(c_k0_k1_n_h0_h1_h2_w0_w1_w2_grid_desc);
using DGridDesc_K0_K1_N_H0_H1_Hx_W0_W1_Wx = decltype(d_k0_k1_n_h0_h1_hx_w0_w1_wx_grid_desc);
const auto grid_size = (K / KPerBlock) * (Hop / HoPerBlock) * (Wop / WoPerBlock) * N;
const bool has_main_e0_block_loop = E0 > 1;
std::cerr << "has_main_e0_block_loop = " << has_main_e0_block_loop << std::endl;
const auto cblockid_to_k_n_h_w_block_cluster_adaptor =
GridwiseGemm::MakeCBlockIdToKNHoWoBlockClusterAdaptor(c_k_n_hop_wop_grid_desc);
using CBlockIdToBlockClusterAdaptor_K_N_H_W =
decltype(cblockid_to_k_n_h_w_block_cluster_adaptor);
float ave_time = 0;
if(has_main_e0_block_loop)
{
const auto kernel = kernel_gemm_dlops_v3_maxpool<
GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AGridDesc_E0_E1_K0_K1_E2>,
remove_reference_t<BGridDesc_E0_E1_N_H0_H1_H2_W0_W1_W2_E2>,
remove_reference_t<CGridDesc_K0_K1_N_H0_H1_H2_W0_W1_W2>,
remove_reference_t<DGridDesc_K0_K1_N_H0_H1_Hx_W0_W1_Wx>,
remove_reference_t<CBlockIdToBlockClusterAdaptor_K_N_H_W>,
true,
activ_type>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_bias_grid,
p_c_grid,
p_d_grid,
a_e0_e1_k0_k1_e2_grid_desc,
b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_grid_desc,
c_k0_k1_n_h0_h1_h2_w0_w1_w2_grid_desc,
d_k0_k1_n_h0_h1_hx_w0_w1_wx_grid_desc,
cblockid_to_k_n_h_w_block_cluster_adaptor);
}
else
{
const auto kernel = kernel_gemm_dlops_v3_maxpool<
GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AGridDesc_E0_E1_K0_K1_E2>,
remove_reference_t<BGridDesc_E0_E1_N_H0_H1_H2_W0_W1_W2_E2>,
remove_reference_t<CGridDesc_K0_K1_N_H0_H1_H2_W0_W1_W2>,
remove_reference_t<DGridDesc_K0_K1_N_H0_H1_Hx_W0_W1_Wx>,
remove_reference_t<CBlockIdToBlockClusterAdaptor_K_N_H_W>,
false,
activ_type>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_bias_grid,
p_c_grid,
p_d_grid,
a_e0_e1_k0_k1_e2_grid_desc,
b_e0_e1_n_h0_h1_h2_w0_w1_w2_e2_grid_desc,
c_k0_k1_n_h0_h1_h2_w0_w1_w2_grid_desc,
d_k0_k1_n_h0_h1_hx_w0_w1_wx_grid_desc,
cblockid_to_k_n_h_w_block_cluster_adaptor);
}
return ave_time;
}
};
#endif
library/include/ck/library/obselete_driver_offline/driver_gemm_dlops_v1r2.hpp deleted 100644 → 0
View file @ 05d38218
#ifndef DRIVER_GEMM_DLOPS_V1R2
#define DRIVER_GEMM_DLOPS_V1R2
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm_dlops_v1r2.hpp"
template <ck::index_t BlockSize,
typename FloatAB,
typename FloatAcc,
typename FloatC,
ck::InMemoryDataOperationEnum CGlobalMemoryDataOperation,
typename AKMGridDesc,
typename BKNGridDesc,
typename CMNGridDesc,
ck::index_t MPerBlock,
ck::index_t NPerBlock,
ck::index_t KPerBlock,
ck::index_t M1PerThread,
ck::index_t N1PerThread,
ck::index_t KPerThread,
ck::index_t M1N1ThreadClusterM10,
ck::index_t M1N1ThreadClusterN10,
ck::index_t M1N1ThreadClusterM11,
ck::index_t M1N1ThreadClusterN11,
typename ABlockTransferThreadSliceLengths_K_M0_M1,
typename ABlockTransferThreadClusterLengths_K_M0_M1,
typename ABlockTransferThreadClusterArrangeOrder,
typename ABlockTransferSrcAccessOrder,
ck::index_t ABlockTransferSrcVectorDim,
ck::index_t ABlockTransferSrcScalarPerVector,
ck::index_t ABlockTransferDstScalarPerVector_M1,
bool AThreadTransferSrcResetCoordinateAfterRun,
typename BBlockTransferThreadSliceLengths_K_N0_N1,
typename BBlockTransferThreadClusterLengths_K_N0_N1,
typename BBlockTransferThreadClusterArrangeOrder,
typename BBlockTransferSrcAccessOrder,
ck::index_t BBlockTransferSrcVectorDim,
ck::index_t BBlockTransferSrcScalarPerVector,
ck::index_t BBlockTransferDstScalarPerVector_N1,
bool BThreadTransferSrcResetCoordinateAfterRun,
typename CThreadTransferSrcDstAccessOrder,
ck::index_t CThreadTransferSrcDstVectorDim,
ck::index_t CThreadTransferDstScalarPerVector,
typename AGridStepHacks,
typename BGridStepHacks,
typename CGridStepHacks,
typename AGridMoveSliceWindowStepHacks,
typename BGridMoveSliceWindowStepHacks>
__host__ float driver_gemm_dlops_v1r2(const FloatAB* p_a_grid,
const FloatAB* p_b_grid,
FloatC* p_c_grid,
const AKMGridDesc& a_k_m_grid_desc,
const BKNGridDesc& b_k_n_grid_desc,
const CMNGridDesc& c_m_n_grid_desc,
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks,
ck::index_t nrepeat)
{
using namespace ck;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
// GEMM
using GridwiseGemm = GridwiseGemmDlops_km_kn_mn_v1r2<BlockSize,
FloatAB,
FloatAcc,
FloatC,
CGlobalMemoryDataOperation,
AKMGridDesc,
BKNGridDesc,
CMNGridDesc,
MPerBlock,
NPerBlock,
KPerBlock,
M1PerThread,
N1PerThread,
KPerThread,
M1N1ThreadClusterM10,
M1N1ThreadClusterN10,
M1N1ThreadClusterM11,
M1N1ThreadClusterN11,
ABlockTransferThreadSliceLengths_K_M0_M1,
ABlockTransferThreadClusterLengths_K_M0_M1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_M1,
AThreadTransferSrcResetCoordinateAfterRun,
BBlockTransferThreadSliceLengths_K_N0_N1,
BBlockTransferThreadClusterLengths_K_N0_N1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_N1,
BThreadTransferSrcResetCoordinateAfterRun,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks>;
const auto M = a_k_m_grid_desc.GetLength(I1);
const auto N = b_k_n_grid_desc.GetLength(I1);
const auto K = a_k_m_grid_desc.GetLength(I0);
if(!GridwiseGemm::CheckValidity(a_k_m_grid_desc, b_k_n_grid_desc, c_m_n_grid_desc))
{
throw std::runtime_error("wrong! GridwiseGemmDlops_km_kn_mn_v1r2 has invalid setting");
}
const auto a_k_m0_m1_grid_desc = GridwiseGemm::MakeAKM0M1GridDescriptor(a_k_m_grid_desc);
const auto b_k_n0_n1_grid_desc = GridwiseGemm::MakeBKN0N1GridDescriptor(b_k_n_grid_desc);
using AKM0M1GridDesc = decltype(a_k_m0_m1_grid_desc);
using BKN0N1GridDesc = decltype(b_k_n0_n1_grid_desc);
// c_m0_m10_m11_n0_n10_n11_grid_desc
const auto c_m0_m10_m11_n0_n10_n11_grid_desc =
GridwiseGemm::MakeCM0M10M11N0N10N11GridDescriptor(c_m_n_grid_desc);
using CM0M10M11N0N10N11GridDesc = decltype(c_m0_m10_m11_n0_n10_n11_grid_desc);
// cblockid_to_m0_n0_block_cluster_adaptor
const auto cblockid_to_m0_n0_block_cluster_adaptor =
GridwiseGemm::MakeCBlockIdToM0N0BlockClusterAdaptor(c_m_n_grid_desc);
using CBlockIdToM0N0BlockClusterAdaptor = decltype(cblockid_to_m0_n0_block_cluster_adaptor);
const index_t grid_size = GridwiseGemm::CalculateGridSize(M, N);
const bool has_main_k_block_loop = GridwiseGemm::CalculateHasMainKBlockLoop(K);
const bool has_double_tail_k_block_loop = GridwiseGemm::CalculateHasDoubleTailKBlockLoop(K);
{
std::cout << "a_k_m0_m1_grid_desc{" << a_k_m0_m1_grid_desc.GetLength(I0) << ", "
<< a_k_m0_m1_grid_desc.GetLength(I1) << ", " << a_k_m0_m1_grid_desc.GetLength(I2)
<< "}" << std::endl;
std::cout << "b_k_n0_n1_grid_desc{" << b_k_n0_n1_grid_desc.GetLength(I0) << ", "
<< b_k_n0_n1_grid_desc.GetLength(I1) << ", " << b_k_n0_n1_grid_desc.GetLength(I2)
<< "}" << std::endl;
std::cout << "c_m0_m10_m11_n0_n10_n11_grid_desc{ "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I0) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I1) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I2) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I3) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I4) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I5) << "}" << std::endl;
}
float ave_time = 0;
if(has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
remove_reference_t<BKN0N1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
true,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k_m0_m1_grid_desc,
b_k_n0_n1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
cblockid_to_m0_n0_block_cluster_adaptor);
}
else if(has_main_k_block_loop && !has_double_tail_k_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
remove_reference_t<BKN0N1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
true,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k_m0_m1_grid_desc,
b_k_n0_n1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
cblockid_to_m0_n0_block_cluster_adaptor);
}
else if(!has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
remove_reference_t<BKN0N1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
false,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k_m0_m1_grid_desc,
b_k_n0_n1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
cblockid_to_m0_n0_block_cluster_adaptor);
}
else
{
const auto kernel =
kernel_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
remove_reference_t<BKN0N1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
false,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k_m0_m1_grid_desc,
b_k_n0_n1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
cblockid_to_m0_n0_block_cluster_adaptor);
}
return ave_time;
}
#endif
library/include/ck/library/obselete_driver_offline/driver_gemm_dlops_v1r3.hpp deleted 100644 → 0
View file @ 05d38218
#ifndef DRIVER_GEMM_DLOPS_V1R3
#define DRIVER_GEMM_DLOPS_V1R3
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm_dlops_v1r3.hpp"
template <ck::index_t BlockSize,
typename FloatAB,
typename FloatAcc,
typename FloatC,
ck::InMemoryDataOperationEnum CGlobalMemoryDataOperation,
typename AK0MK1GridDesc,
typename BK0NK1GridDesc,
typename CMNGridDesc,
ck::index_t MPerBlock,
ck::index_t NPerBlock,
ck::index_t KPerBlock,
ck::index_t M1PerThread,
ck::index_t N1PerThread,
ck::index_t KPerThread,
typename M1N1ThreadClusterM1Xs,
typename M1N1ThreadClusterN1Xs,
typename ABlockTransferThreadSliceLengths_K0_M0_M1_K1,
typename ABlockTransferThreadClusterLengths_K0_M0_M1_K1,
typename ABlockTransferThreadClusterArrangeOrder,
typename ABlockTransferSrcAccessOrder,
typename ABlockTransferSrcVectorTensorLengths_K0_M0_M1_K1,
typename ABlockTransferSrcVectorTensorContiguousDimOrder,
typename ABlockTransferDstVectorTensorLengths_K0_M0_M1_K1,
typename BBlockTransferThreadSliceLengths_K0_N0_N1_K1,
typename BBlockTransferThreadClusterLengths_K0_N0_N1_K1,
typename BBlockTransferThreadClusterArrangeOrder,
typename BBlockTransferSrcAccessOrder,
typename BBlockTransferSrcVectorTensorLengths_K0_N0_N1_K1,
typename BBlockTransferSrcVectorTensorContiguousDimOrder,
typename BBlockTransferDstVectorTensorLengths_K0_N0_N1_K1,
typename CThreadTransferSrcDstAccessOrder,
ck::index_t CThreadTransferSrcDstVectorDim,
ck::index_t CThreadTransferDstScalarPerVector,
typename AGridStepHacks,
typename BGridStepHacks,
typename CGridStepHacks,
typename AGridMoveSliceWindowStepHacks,
typename BGridMoveSliceWindowStepHacks>
__host__ float driver_gemm_dlops_v1r3(const FloatAB* p_a_grid,
const FloatAB* p_b_grid,
FloatC* p_c_grid,
const AK0MK1GridDesc& a_k0_m_k1_grid_desc,
const BK0NK1GridDesc& b_k0_n_k1_grid_desc,
const CMNGridDesc& c_m_n_grid_desc,
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks,
ck::index_t nrepeat)
{
using namespace ck;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
// GEMM
using GridwiseGemm =
GridwiseGemmDlops_km_kn_mn_v1r3<BlockSize,
FloatAB,
FloatAcc,
FloatC,
CGlobalMemoryDataOperation,
AK0MK1GridDesc,
BK0NK1GridDesc,
CMNGridDesc,
MPerBlock,
NPerBlock,
KPerBlock,
M1PerThread,
N1PerThread,
KPerThread,
M1N1ThreadClusterM1Xs,
M1N1ThreadClusterN1Xs,
ABlockTransferThreadSliceLengths_K0_M0_M1_K1,
ABlockTransferThreadClusterLengths_K0_M0_M1_K1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorTensorLengths_K0_M0_M1_K1,
ABlockTransferSrcVectorTensorContiguousDimOrder,
ABlockTransferDstVectorTensorLengths_K0_M0_M1_K1,
BBlockTransferThreadSliceLengths_K0_N0_N1_K1,
BBlockTransferThreadClusterLengths_K0_N0_N1_K1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorTensorLengths_K0_N0_N1_K1,
BBlockTransferSrcVectorTensorContiguousDimOrder,
BBlockTransferDstVectorTensorLengths_K0_N0_N1_K1,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks>;
const auto M = a_k0_m_k1_grid_desc.GetLength(I1);
const auto N = b_k0_n_k1_grid_desc.GetLength(I1);
const auto K0 = a_k0_m_k1_grid_desc.GetLength(I0);
if(!GridwiseGemm::CheckValidity(a_k0_m_k1_grid_desc, b_k0_n_k1_grid_desc, c_m_n_grid_desc))
{
throw std::runtime_error("wrong! GridwiseGemmDlops_km_kn_mn_v1r3 has invalid setting");
}
const auto a_k0_m0_m1_k1_grid_desc =
GridwiseGemm::MakeAK0M0M1K1GridDescriptor(a_k0_m_k1_grid_desc);
const auto b_k0_n0_n1_k1_grid_desc =
GridwiseGemm::MakeBK0N0N1K1GridDescriptor(b_k0_n_k1_grid_desc);
using AK0M0M1K1GridDesc = decltype(a_k0_m0_m1_k1_grid_desc);
using BK0N0N1K1GridDesc = decltype(b_k0_n0_n1_k1_grid_desc);
// c_m0_m10_m11_n0_n10_n11_grid_desc
const auto c_m0_m10_m11_n0_n10_n11_grid_desc =
GridwiseGemm::MakeCM0M10M11N0N10N11GridDescriptor(c_m_n_grid_desc);
using CM0M10M11N0N10N11GridDesc = decltype(c_m0_m10_m11_n0_n10_n11_grid_desc);
// cblockid_to_m0_n0_block_cluster_adaptor
const auto cblockid_to_m0_n0_block_cluster_adaptor =
GridwiseGemm::MakeCBlockIdToM0N0BlockClusterAdaptor(c_m_n_grid_desc);
using CBlockIdToM0N0BlockClusterAdaptor = decltype(cblockid_to_m0_n0_block_cluster_adaptor);
const index_t grid_size = GridwiseGemm::CalculateGridSize(M, N);
const bool has_main_k_block_loop = GridwiseGemm::CalculateHasMainKBlockLoop(K0);
const bool has_double_tail_k_block_loop = GridwiseGemm::CalculateHasDoubleTailKBlockLoop(K0);
{
std::cout << "a_k0_m0_m1_k1_grid_desc{" << a_k0_m0_m1_k1_grid_desc.GetLength(I0) << ", "
<< a_k0_m0_m1_k1_grid_desc.GetLength(I1) << ", "
<< a_k0_m0_m1_k1_grid_desc.GetLength(I2) << ", "
<< a_k0_m0_m1_k1_grid_desc.GetLength(I3) << "}" << std::endl;
std::cout << "b_k0_n0_n1_k1_grid_desc{" << b_k0_n0_n1_k1_grid_desc.GetLength(I0) << ", "
<< b_k0_n0_n1_k1_grid_desc.GetLength(I1) << ", "
<< b_k0_n0_n1_k1_grid_desc.GetLength(I2) << ", "
<< b_k0_n0_n1_k1_grid_desc.GetLength(I3) << "}" << std::endl;
std::cout << "c_m0_m10_m11_n0_n10_n11_grid_desc{ "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I0) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I1) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I2) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I3) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I4) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I5) << "}" << std::endl;
}
float ave_time = 0;
if(has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
remove_reference_t<BK0N0N1K1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
true,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k0_m0_m1_k1_grid_desc,
b_k0_n0_n1_k1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
cblockid_to_m0_n0_block_cluster_adaptor);
}
else if(has_main_k_block_loop && !has_double_tail_k_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
remove_reference_t<BK0N0N1K1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
true,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k0_m0_m1_k1_grid_desc,
b_k0_n0_n1_k1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
cblockid_to_m0_n0_block_cluster_adaptor);
}
else if(!has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
remove_reference_t<BK0N0N1K1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
false,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k0_m0_m1_k1_grid_desc,
b_k0_n0_n1_k1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
cblockid_to_m0_n0_block_cluster_adaptor);
}
else
{
const auto kernel =
kernel_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
remove_reference_t<BK0N0N1K1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
false,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k0_m0_m1_k1_grid_desc,
b_k0_n0_n1_k1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
cblockid_to_m0_n0_block_cluster_adaptor);
}
return ave_time;
}
#endif
library/include/ck/library/obselete_driver_offline/driver_gemm_xdlops_v2r3.hpp deleted 100644 → 0
View file @ 05d38218
#ifndef DRIVER_GEMM_XDLOPS_V2R3_HPP
#define DRIVER_GEMM_XDLOPS_V2R3_HPP
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm_xdlops_v2r3.hpp"
#include "element_wise_operation.hpp"
template <ck::index_t BlockSize,
typename FloatAB,
typename FloatAcc,
typename FloatC,
ck::InMemoryDataOperationEnum CGlobalMemoryDataOperation,
typename AGridDesc_K0_M_K1,
typename BGridDesc_K0_N_K,
typename CMNGridDesc,
ck::index_t MPerBlock,
ck::index_t NPerBlock,
ck::index_t KPerBlock,
ck::index_t MPerXDL,
ck::index_t NPerXDL,
ck::index_t K1,
ck::index_t MRepeat,
ck::index_t NRepeat,
typename ABlockTransferThreadSliceLengths_K0_M_K1,
typename ABlockTransferThreadClusterLengths_K0_M_K1,
typename ABlockTransferThreadClusterArrangeOrder,
typename ABlockTransferSrcAccessOrder,
ck::index_t ABlockTransferSrcVectorDim,
ck::index_t ABlockTransferSrcScalarPerVector,
ck::index_t ABlockTransferDstScalarPerVector_K1,
bool AThreadTransferSrcResetCoordinateAfterRun,
typename BBlockTransferThreadSliceLengths_K0_N_K1,
typename BBlockTransferThreadClusterLengths_K0_N_K1,
typename BBlockTransferThreadClusterArrangeOrder,
typename BBlockTransferSrcAccessOrder,
ck::index_t BBlockTransferSrcVectorDim,
ck::index_t BBlockTransferSrcScalarPerVector,
ck::index_t BBlockTransferDstScalarPerVector_K1,
bool BThreadTransferSrcResetCoordinateAfterRun,
typename CThreadTransferSrcDstAccessOrder,
ck::index_t CThreadTransferSrcDstVectorDim,
ck::index_t CThreadTransferDstScalarPerVector,
typename AGridStepHacks,
typename BGridStepHacks,
typename CGridStepHacks,
typename AGridMoveSliceWindowStepHacks,
typename BGridMoveSliceWindowStepHacks,
bool CAccessOrderMRepeatNRepeat,
bool ABlockLdsAddExtraM,
bool BBlockLdsAddExtraN>
__host__ float driver_gemm_xdlops_v2r3(const FloatAB* p_a_grid,
const FloatAB* p_b_grid,
FloatC* p_c_grid,
const AGridDesc_K0_M_K1& a_grid_desc_k0_m_k1,
const BGridDesc_K0_N_K& b_grid_desc_k0_n_k1,
const CMNGridDesc& c_grid_desc_m_n,
ck::index_t M01,
ck::index_t N01,
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks,
ck::index_t nrepeat)
{
using namespace ck;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
using ElementwiseOperation = ck::tensor_operation::element_wise::PassThrough;
using GridwiseGemm =
GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3<BlockSize,
FloatAB,
FloatAcc,
FloatC,
CGlobalMemoryDataOperation,
AGridDesc_K0_M_K1,
BGridDesc_K0_N_K,
CMNGridDesc,
ElementwiseOperation,
ElementwiseOperation,
ElementwiseOperation,
MPerBlock,
NPerBlock,
KPerBlock,
MPerXDL,
NPerXDL,
K1,
MRepeat,
NRepeat,
ABlockTransferThreadClusterLengths_K0_M_K1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_K1,
AThreadTransferSrcResetCoordinateAfterRun,
ABlockLdsAddExtraM,
BBlockTransferThreadClusterLengths_K0_N_K1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_K1,
BThreadTransferSrcResetCoordinateAfterRun,
BBlockLdsAddExtraN,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector>;
{
std::cout << "a_grid_desc_k0_m_k1{" << a_grid_desc_k0_m_k1.GetLength(I0) << ", "
<< a_grid_desc_k0_m_k1.GetLength(I1) << ", " << a_grid_desc_k0_m_k1.GetLength(I2)
<< "}" << std::endl;
std::cout << "b_grid_desc_k0_n_k1{" << b_grid_desc_k0_n_k1.GetLength(I0) << ", "
<< b_grid_desc_k0_n_k1.GetLength(I1) << ", " << b_grid_desc_k0_n_k1.GetLength(I2)
<< "}" << std::endl;
std::cout << "c_grid_desc_m_n{ " << c_grid_desc_m_n.GetLength(I0) << ", "
<< c_grid_desc_m_n.GetLength(I1) << "}" << std::endl;
}
if(!GridwiseGemm::CheckValidity(
a_grid_desc_k0_m_k1, b_grid_desc_k0_n_k1, c_grid_desc_m_n, M01, N01))
{
throw std::runtime_error(
"wrong! GridwiseGemm_km_kn_m0m1n0n1_xdlops_v2r3 has invalid setting");
}
const auto c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc =
GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(c_grid_desc_m_n);
using CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2 = decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc);
const auto block_2_ctile_map =
GridwiseGemm::MakeDefaultBlock2CTileMap(c_grid_desc_m_n, M01, N01);
using Block2CTileMap = decltype(block_2_ctile_map);
const index_t grid_size = GridwiseGemm::CalculateGridSize(c_grid_desc_m_n);
const auto K0 = a_grid_desc_k0_m_k1.GetLength(I0);
const bool has_main_k0_block_loop = GridwiseGemm::CalculateHasMainK0BlockLoop(K0);
float ave_time = 0;
auto element_op_ = ElementwiseOperation{};
if(has_main_k0_block_loop)
{
const auto kernel =
kernel_gemm_xdlops_v2r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AGridDesc_K0_M_K1>,
remove_reference_t<BGridDesc_K0_N_K>,
remove_reference_t<CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2>,
ElementwiseOperation,
ElementwiseOperation,
ElementwiseOperation,
remove_reference_t<Block2CTileMap>,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_grid_desc_k0_m_k1,
b_grid_desc_k0_n_k1,
c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
element_op_,
element_op_,
element_op_,
block_2_ctile_map);
}
else
{
const auto kernel =
kernel_gemm_xdlops_v2r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AGridDesc_K0_M_K1>,
remove_reference_t<BGridDesc_K0_N_K>,
remove_reference_t<CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2>,
ElementwiseOperation,
ElementwiseOperation,
ElementwiseOperation,
remove_reference_t<Block2CTileMap>,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_grid_desc_k0_m_k1,
b_grid_desc_k0_n_k1,
c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
element_op_,
element_op_,
element_op_,
block_2_ctile_map);
}
return ave_time;
}
#endif
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