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tidy

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This commit is contained in:
Chao Liu
2021-08-09 19:27:49 +00:00
parent 54fba515b3
commit 56fc0842b3
23 changed files with 82 additions and 136 deletions
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3
CMakeLists.txt
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@@ -142,12 +142,11 @@ enable_clang_tidy(
-cppcoreguidelines-prefer-member-initializer
${MIOPEN_TIDY_CHECKS}
${MIOPEN_TIDY_ERRORS}
${MIOPEN_TIDY_ERRORS}
HEADER_FILTER
"\.hpp$"
EXTRA_ARGS
-DMIOPEN_USE_CLANG_TIDY
)
include(CppCheck)

6
composable_kernel/include/tensor_operation/blockwise_gemm_dlops_v2r2.hpp
View File

@@ -71,7 +71,7 @@ struct BlockwiseGemmDlops_km_kn_m0m1n0n1_v2r2_pipeline_2x2
static constexpr index_t N0 = N / N1;
__host__ __device__ static constexpr auto
MakeAKM0M1BlockDescriptor(const AKMBlockDesc& a_k_m_block_desc)
MakeAKM0M1BlockDescriptor(const AKMBlockDesc& /* a_k_m_block_desc */)
{
const auto a_k_m0_m1_block_desc = transform_dynamic_tensor_descriptor(
AKMBlockDesc{},
@@ -84,7 +84,7 @@ struct BlockwiseGemmDlops_km_kn_m0m1n0n1_v2r2_pipeline_2x2
}
__host__ __device__ static constexpr auto
MakeBKN0N1BlockDescriptor(const BKNBlockDesc& b_k_n_block_desc)
MakeBKN0N1BlockDescriptor(const BKNBlockDesc& /* b_k_n_block_desc */)
{
const auto b_k_n0_n1_block_desc = transform_dynamic_tensor_descriptor(
BKNBlockDesc{},
@@ -194,7 +194,7 @@ struct BlockwiseGemmDlops_km_kn_m0m1n0n1_v2r2_pipeline_2x2
typename ABlockBuffer,
typename BBlockBuffer,
typename CThreadBuffer>
__device__ void Run(const CM0M1N0N1ThreadDesc& c_m0_m1_n0_n1_thread_desc,
__device__ void Run(const CM0M1N0N1ThreadDesc& /* c_m0_m1_n0_n1_thread_desc */,
const ABlockBuffer& a_block_buf,
const BBlockBuffer& b_block_buf,
CThreadBuffer& c_thread_buf) const

3
composable_kernel/include/tensor_operation/blockwise_gemm_dlops_v3.hpp
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@@ -120,9 +120,6 @@ struct BlockwiseGemmDlops_km_kn_m0m1n0n1_v3
"wrong! inconsistent type");
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto a_block_mtx = BlockMatrixA{};

1
composable_kernel/include/tensor_operation/blockwise_gemm_xdlops.hpp
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@@ -270,7 +270,6 @@ struct BlockwiseGemmXdlops_km_kn_m0m1m2n_v1_2x2pipeline
const index_t waveId = thread_id / WaveSize;
const index_t laneId = thread_id % WaveSize;
const index_t waveId_m = waveId / NWaves;
const index_t waveId_n = waveId % NWaves;
if constexpr(xdlops_gemm.IsKReduction)
{

11
composable_kernel/include/tensor_operation/gridwise_dynamic_gemm_dlops_v1r2.hpp
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@@ -619,17 +619,6 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r2
// output: register to global memory
{
constexpr index_t M11 =
M1PerThreadM111 * M11N11ThreadClusterM1100 * M11N11ThreadClusterM1101;
constexpr index_t N11 =
N1PerThreadN111 * M11N11ThreadClusterN1100 * M11N11ThreadClusterN1101;
constexpr index_t M10 = MPerBlockM1 / M11;
constexpr index_t N10 = NPerBlockN1 / N11;
constexpr index_t M111 = M1PerThreadM111;
constexpr index_t N111 = N1PerThreadN111;
constexpr auto c_m0_m10_m11_n0_n10_n11_thread_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(
make_tuple(I1,

15
composable_kernel/include/tensor_operation/gridwise_dynamic_gemm_dlops_v1r3.hpp
View File

@@ -191,12 +191,12 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r3
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);
const auto K1 = a_k0_m_k1_grid_desc.GetLength(I2);
// TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
return (M == c_m_n_grid_desc.GetLength(I0) && N == c_m_n_grid_desc.GetLength(I1) &&
K0 == b_k0_n_k1_grid_desc.GetLength(I0) &&
K1 == a_k0_m_k1_grid_desc.GetLength(I2) &&
K1 == b_k0_n_k1_grid_desc.GetLength(I2)) &&
(M % MPerBlockM1 == 0 && N % NPerBlockN1 == 0 && K0 % KPerBlock == 0);
}
@@ -608,19 +608,6 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r3
// output: register to global memory
{
constexpr auto M11 =
Number<container_reduce(M11N11ThreadClusterM110Xs{}, math::multiplies_v2{}, I1) *
M1PerThreadM111>{};
constexpr auto N11 =
Number<container_reduce(M11N11ThreadClusterN110Xs{}, math::multiplies_v2{}, I1) *
N1PerThreadN111>{};
constexpr index_t M10 = MPerBlockM1 / M11;
constexpr index_t N10 = NPerBlockN1 / N11;
constexpr index_t M111 = M1PerThreadM111;
constexpr index_t N111 = N1PerThreadN111;
constexpr auto c_m0_m10_m11_n0_n10_n11_thread_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(
make_tuple(I1,

2
composable_kernel/include/tensor_operation/gridwise_dynamic_gemm_dlops_v2.hpp
View File

@@ -102,7 +102,6 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v3
// divide block work by [M, N]
#if 0
const auto k_block_work_num = K / Number<KPerBlock>{};
const auto ho_block_work_num = Ho / Number<HoPerBlock>{};
const auto wo_block_work_num = Wo / Number<WoPerBlock>{};
const auto hwo_block_work_num = ho_block_work_num * wo_block_work_num;
@@ -114,7 +113,6 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v3
const index_t wo_block_work_id = hwo_block_work_id - ho_block_work_id * wo_block_work_num;
#else
// Hack: this force result into SGPR
const index_t k_block_work_num = __builtin_amdgcn_readfirstlane(K / KPerBlock);
const index_t ho_block_work_num = __builtin_amdgcn_readfirstlane(Ho / HoPerBlock);
const index_t wo_block_work_num = __builtin_amdgcn_readfirstlane(Wo / WoPerBlock);
const index_t hwo_block_work_num = ho_block_work_num * wo_block_work_num;

5
composable_kernel/include/tensor_operation/gridwise_dynamic_gemm_xdlops_v2r3.hpp
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@@ -269,11 +269,6 @@ struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
const CM0M1M2NGridDesc& c_m0_m1_m2_n_grid_desc,
const CBlockClusterAdaptor& c_block_cluster_adaptor)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum_t::Global>(
p_a_grid, a_k0_m_k1_grid_desc.GetElementSpaceSize());
const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum_t::Global>(

2
composable_kernel/include/tensor_operation/threadwise_gemm_dlops_v3.hpp
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@@ -57,8 +57,6 @@ struct ThreadwiseGemmDlops_km_kn_mn_v3
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto E = ADesc{}.GetLength(I0);
constexpr auto K = ADesc{}.GetLength(I1);

10
host/driver_offline/include/device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw.hpp
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@@ -34,12 +34,6 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw(
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>{};
constexpr auto I6 = Number<6>{};
constexpr auto I7 = Number<7>{};
constexpr auto I8 = Number<8>{};
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());
@@ -198,8 +192,8 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw(
in_gemmk_gemmn0_gemmn1_grid_move_slice_window_iterator_hacks,
nrepeat);
float perf = (float)calculate_convolution_flops(
in_n_c_hi_wi_desc, wei_k_c_y_x_desc, out_n_k_ho_wo_desc) /
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;

7
host/driver_offline/include/device_dynamic_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhwk.hpp
View File

@@ -35,11 +35,6 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhw
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>{};
constexpr auto I6 = Number<6>{};
constexpr auto I7 = Number<7>{};
constexpr auto I8 = Number<8>{};
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());
@@ -271,7 +266,7 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhw
const auto Y = wei_k_y_x_c_lengths[I1];
const auto X = wei_k_y_x_c_lengths[I2];
float perf = (float)(std::size_t(2) * N * K * Ho * Wo * C * Y * X) /
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"

10
host/driver_offline/include/device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw.hpp
View File

@@ -34,12 +34,6 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nk
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>{};
constexpr auto I6 = Number<6>{};
constexpr auto I7 = Number<7>{};
constexpr auto I8 = Number<8>{};
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());
@@ -194,8 +188,8 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nk
in_gemmk0_gemmn_gemmk1_grid_move_slice_window_iterator_hacks,
nrepeat);
float perf = (float)calculate_convolution_flops(
in_n_c_hi_wi_desc, wei_k_c_y_x_desc, out_n_k_ho_wo_desc) /
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;

7
host/driver_offline/include/device_dynamic_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk.hpp
View File

@@ -35,11 +35,6 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nh
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>{};
constexpr auto I6 = Number<6>{};
constexpr auto I7 = Number<7>{};
constexpr auto I8 = Number<8>{};
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());
@@ -352,7 +347,7 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nh
const auto Y = wei_k_y_x_c_lengths[I1];
const auto X = wei_k_y_x_c_lengths[I2];
float perf = (float)(std::size_t(2) * N * K * Ho * Wo * C * Y * X) /
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"

2
host/driver_offline/include/device_dynamic_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw.hpp
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@@ -26,7 +26,7 @@ void device_dynamic_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw(
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)
ck::index_t /* nrepeat */)
{
using namespace ck;

4
host/driver_offline/include/device_dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw.hpp
View File

@@ -232,8 +232,8 @@ void device_dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw(
in_grid_move_slice_window_iterator_hacks,
nrepeat);
float perf = (float)calculate_convolution_flops(
in_desc_n_c_hi_wi, wei_desc_k_c_y_x, out_desc_n_k_ho_wo) /
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;

9
host/driver_offline/include/driver_dynamic_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw.hpp
View File

@@ -338,10 +338,11 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_pad
float ave_time = timer.GetElapsedTime() / nrepeat;
float perf = (float)calculate_convolution_flops(in_n_c_hi_wi_global_desc,
wei_k_c_y_x_global_desc,
out_n_k0_ho_wo_k1_global_desc) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
float perf =
static_cast<float>(calculate_convolution_flops(in_n_c_hi_wi_global_desc,
wei_k_c_y_x_global_desc,
out_n_k0_ho_wo_k1_global_desc)) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s"
<< std::endl;

9
host/driver_offline/include/driver_dynamic_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw_outpad.hpp
View File

@@ -354,10 +354,11 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_outp
float ave_time = timer.GetElapsedTime() / nrepeat;
float perf = (float)calculate_convolution_flops(in_n_c_hi_wi_global_desc,
wei_k_c_y_x_global_desc,
out_n_k0_ho_wo_k1_global_desc) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
float perf =
static_cast<float>(calculate_convolution_flops(in_n_c_hi_wi_global_desc,
wei_k_c_y_x_global_desc,
out_n_k0_ho_wo_k1_global_desc)) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s"
<< std::endl;

16
host/driver_offline/src/conv_bwd_driver_offline.cpp
View File

@@ -128,10 +128,8 @@ int main(int argc, char* argv[])
std::vector<std::size_t> in_lengths_host(4), wei_lengths_host(4), out_lengths_host(4);
switch(layout)
if(layout == ConvTensorLayout::NCHW)
{
case ConvTensorLayout::NCHW:
// NCHW
in_lengths_host[0] = static_cast<std::size_t>(N);
in_lengths_host[1] = static_cast<std::size_t>(C);
in_lengths_host[2] = static_cast<std::size_t>(Hi);
@@ -144,9 +142,9 @@ int main(int argc, char* argv[])
out_lengths_host[1] = static_cast<std::size_t>(K);
out_lengths_host[2] = static_cast<std::size_t>(Ho);
out_lengths_host[3] = static_cast<std::size_t>(Wo);
break;
case ConvTensorLayout::NHWC:
// NHWC
}
else if(layout == ConvTensorLayout::NHWC)
{
in_lengths_host[0] = static_cast<std::size_t>(N);
in_lengths_host[1] = static_cast<std::size_t>(Hi);
in_lengths_host[2] = static_cast<std::size_t>(Wi);
@@ -159,8 +157,10 @@ int main(int argc, char* argv[])
out_lengths_host[1] = static_cast<std::size_t>(Ho);
out_lengths_host[2] = static_cast<std::size_t>(Wo);
out_lengths_host[3] = static_cast<std::size_t>(K);
break;
default: throw std::runtime_error("wrong! not implemented");
}
else
{
throw std::runtime_error("wrong! not implemented");
}
Tensor<in_data_t> in_host(in_lengths_host);

2
host/driver_offline/src/conv_fwd_driver_offline.cpp
View File

@@ -467,7 +467,6 @@ int main(int argc, char* argv[])
check_error(out_host, out_device);
#if 0
if(do_log)
{
LogRangeAsType<float>(std::cout << "in : ", in.mData, ",") << std::endl;
@@ -475,6 +474,5 @@ int main(int argc, char* argv[])
LogRangeAsType<float>(std::cout << "out_host : ", out_host.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "out_device: ", out_device.mData, ",") << std::endl;
}
#endif
}
}

2
host/host_tensor/include/conv_common.hpp
View File

@@ -62,7 +62,7 @@ constexpr auto get_convolution_output_default_4d_tensor_descriptor(
template <class InDesc, class WeiDesc, class OutDesc>
constexpr std::size_t
calculate_convolution_flops(const InDesc& in_desc, const WeiDesc& wei_desc, const OutDesc& out_desc)
calculate_convolution_flops(const InDesc&, const WeiDesc& wei_desc, const OutDesc& out_desc)
{
using namespace ck;

32
host/host_tensor/include/host_conv.hpp
View File

@@ -14,15 +14,13 @@ void host_direct_convolution(const Tensor<TIn>& in,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const InRightPads&,
const ConvTensorLayout layout = ConvTensorLayout::NCHW)
{
using namespace ck;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
auto f_nchw = [&](auto n, auto k, auto ho, auto wo) {
double v = 0;
@@ -68,23 +66,25 @@ void host_direct_convolution(const Tensor<TIn>& in,
out(n, ho, wo, k) = v;
};
switch(layout)
if(layout == ConvTensorLayout::NCHW)
{
case ConvTensorLayout::NCHW:
make_ParallelTensorFunctor(f_nchw,
out.mDesc.GetLengths()[0],
out.mDesc.GetLengths()[1],
out.mDesc.GetLengths()[2],
out.mDesc.GetLengths()[3])(std::thread::hardware_concurrency());
break;
case ConvTensorLayout::NHWC:
}
else if(layout == ConvTensorLayout::NHWC)
{
make_ParallelTensorFunctor(f_nhwc,
out.mDesc.GetLengths()[0],
out.mDesc.GetLengths()[1],
out.mDesc.GetLengths()[2],
out.mDesc.GetLengths()[3])(std::thread::hardware_concurrency());
break;
default: throw std::runtime_error("wrong! not supported layout");
}
else
{
throw std::runtime_error("wrong! not supported layout");
}
}
@@ -100,17 +100,15 @@ void host_winograd_3x3_convolution(const Tensor<TIn>& in_nchw,
constexpr std::size_t HoPerTile = 2;
constexpr std::size_t WoPerTile = 2;
std::size_t N = in_nchw.mDesc.GetLengths()[0];
std::size_t C = in_nchw.mDesc.GetLengths()[1];
std::size_t HI = in_nchw.mDesc.GetLengths()[2];
std::size_t WI = in_nchw.mDesc.GetLengths()[3];
std::size_t N = in_nchw.mDesc.GetLengths()[0];
std::size_t C = in_nchw.mDesc.GetLengths()[1];
std::size_t K = wei_kcyx.mDesc.GetLengths()[0];
std::size_t Y = wei_kcyx.mDesc.GetLengths()[2];
std::size_t X = wei_kcyx.mDesc.GetLengths()[3];
std::size_t HO = out_nkhw.mDesc.GetLengths()[2];
std::size_t WO = out_nkhw.mDesc.GetLengths()[3];
std::size_t Ho = out_nkhw.mDesc.GetLengths()[2];
std::size_t Wo = out_nkhw.mDesc.GetLengths()[3];
index_t h_pad_low = InLeftPads{}.Get(Number<0>{});
index_t w_pad_low = InLeftPads{}.Get(Number<1>{});
@@ -118,8 +116,8 @@ void host_winograd_3x3_convolution(const Tensor<TIn>& in_nchw,
std::size_t HiPerTile = HoPerTile + Y - 1;
std::size_t WiPerTile = WoPerTile + X - 1;
std::size_t HTile = (HO + HoPerTile - 1) / HoPerTile;
std::size_t WTile = (WO + WoPerTile - 1) / WoPerTile;
std::size_t HTile = (Ho + HoPerTile - 1) / HoPerTile;
std::size_t WTile = (Wo + WoPerTile - 1) / WoPerTile;
Tensor<double> in_hold({N, C, HTile, WTile, HiPerTile, WiPerTile});
Tensor<double> in_transform({N, C, HTile, WTile, HiPerTile, WiPerTile});

2
host/host_tensor/include/host_tensor_generator.hpp
View File

@@ -9,7 +9,7 @@ struct GeneratorTensor_1
int value = 1;
template <typename... Is>
float operator()(Is... is)
float operator()(Is...)
{
return value;
}

58
host/solver/include/conv_igemm_fwd_v6r1_dlops_nchw_kcyx_nkhw.hpp
View File

@@ -99,40 +99,48 @@ struct CompileParameterConvIgemmFwdV6r1DlopsNchwKcyxNkhw
// clang-format on
}
ck::DataTypeEnum_t ABDataTypeEnum;
ck::DataTypeEnum_t AccDataTypeEnum;
ck::DataTypeEnum_t CDataTypeEnum;
ck::DataTypeEnum_t ABDataTypeEnum = ck::DataTypeEnum_t::Unknown;
ck::DataTypeEnum_t AccDataTypeEnum = ck::DataTypeEnum_t::Unknown;
ck::DataTypeEnum_t CDataTypeEnum = ck::DataTypeEnum_t::Unknown;
int BlockSize;
int BlockSize = 1;
int GN0;
int GK1;
int GN0 = -1;
int GK1 = -1;
int GM1PerBlockGM11;
int GN1PerBlockGN11;
int GK0PerBlock;
int GM1PerBlockGM11 = -1;
int GN1PerBlockGN11 = -1;
int GK0PerBlock = -1;
int BM1PerThreadBM11;
int BN1PerThreadBN11;
int BK0PerThread;
int BM1PerThreadBM11 = -1;
int BN1PerThreadBN11 = -1;
int BK0PerThread = -1;
std::array<int, 2> BM10BN10ThreadClusterBM10Xs;
std::array<int, 2> BM10BN10ThreadClusterBN10Xs;
std::array<int, 2> BM10BN10ThreadClusterBM10Xs = {-1, -1};
std::array<int, 2> BM10BN10ThreadClusterBN10Xs = {-1, -1};
std::array<int, 5> ABlockTransferThreadSliceLengths_GK0_GM0_GM10_GM11_GK1;
std::array<int, 5> ABlockTransferThreadClusterLengths_GK0_GM0_GM10_GM11_GK1;
std::array<int, 5> ABlockTransferSrcVectorTensorLengths_GK0_GM0_GM10_GM11_GK1;
std::array<int, 5> ABlockTransferDstVectorTensorLengths_GK0_GM0_GM10_GM11_GK1;
std::array<int, 5> ABlockTransferThreadSliceLengths_GK0_GM0_GM10_GM11_GK1 = {
-1, -1, -1, -1, -1};
std::array<int, 5> ABlockTransferThreadClusterLengths_GK0_GM0_GM10_GM11_GK1 = {
-1, -1, -1, -1, -1};
std::array<int, 5> ABlockTransferSrcVectorTensorLengths_GK0_GM0_GM10_GM11_GK1 = {
-1, -1, -1, -1, -1};
std::array<int, 5> ABlockTransferDstVectorTensorLengths_GK0_GM0_GM10_GM11_GK1 = {
-1, -1, -1, -1, -1};
std::array<int, 5> BBlockTransferThreadSliceLengths_GK0_GN0_GN10_GN11_GK1;
std::array<int, 5> BBlockTransferThreadClusterLengths_GK0_GN0_GN10_GN11_GK1;
std::array<int, 5> BBlockTransferSrcVectorTensorLengths_GK0_GN0_GN10_GN11_GK1;
std::array<int, 5> BBlockTransferDstVectorTensorLengths_GK0_GN0_GN10_GN11_GK1;
std::array<int, 5> BBlockTransferThreadSliceLengths_GK0_GN0_GN10_GN11_GK1 = {
-1, -1, -1, -1, -1};
std::array<int, 5> BBlockTransferThreadClusterLengths_GK0_GN0_GN10_GN11_GK1 = {
-1, -1, -1, -1, -1};
std::array<int, 5> BBlockTransferSrcVectorTensorLengths_GK0_GN0_GN10_GN11_GK1 = {
-1, -1, -1, -1, -1};
std::array<int, 5> BBlockTransferDstVectorTensorLengths_GK0_GN0_GN10_GN11_GK1 = {
-1, -1, -1, -1, -1};
int CThreadTransferDstScalarPerVector;
int CThreadTransferDstScalarPerVector = -1;
bool HasMainKBlockLoop;
bool HasDoubleTailKBlockLoop;
bool HasMainKBlockLoop = false;
bool HasDoubleTailKBlockLoop = false;
};
struct TunableConvIgemmFwdV6r1DlopsNchwKcyxNkhw