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Unverified Commit 8346af9c authored by Bartłomiej Kocot's avatar Bartłomiej Kocot Committed by GitHub
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Change output gemm type to AccDataType in two stage conv bwd wei (#1283)

parent a0ae1c61
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Showing with 19 additions and 9 deletions
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_multiple_d_xdl_cshuffle.hpp
View file @ 8346af9c
......@@ -197,6 +197,12 @@ struct DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle
K0PerBlock,
ConvBackwardWeightSpecialization>{};
static constexpr index_t MaxScalarPerVectorFP32 = 4;
static constexpr index_t WorkspaceInOutScalarPerVector =
is_same_v<AccDataType, float>
? math::min(CBlockTransferScalarPerVector_NWaveNPerXdl, MaxScalarPerVectorFP32)
: CBlockTransferScalarPerVector_NWaveNPerXdl;
// Bytes per 32 lds bank: 32 * 4 bytes
static constexpr auto BankLength = 128;
static constexpr auto ElePerBank = BankLength / sizeof(ADataType);
......@@ -297,7 +303,7 @@ struct DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle
ADataType,
BDataType,
AccDataType,
EDataType,
AccDataType,
InMemoryDataOperationEnum::AtomicAdd,
AGridDesc_K0_M_K1,
BGridDesc_K0_N_K1,
......@@ -337,7 +343,7 @@ struct DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle
BBlockLdsN1Padding,
CShuffleMXdlPerWavePerShuffle,
CShuffleNXdlPerWavePerShuffle,
CBlockTransferScalarPerVector_NWaveNPerXdl,
WorkspaceInOutScalarPerVector,
CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
true,
true,
......@@ -349,7 +355,7 @@ struct DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle
static constexpr auto MakeElementwiseInputSequence()
{
return generate_sequence_v2(
[&](auto) constexpr { return Number<CBlockTransferScalarPerVector_NWaveNPerXdl>{}; },
[&](auto) constexpr { return Number<WorkspaceInOutScalarPerVector>{}; },
Number<NumDTensor + 1>{});
}
......@@ -499,7 +505,7 @@ struct DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle
using DsGridDesc_M_N = decltype(MakeDsGridDescriptor_M_N<NDimSpatial>({}, {}));
using CDGridDesc_M_N = decltype(concat_tuple(Tuple<CGridDesc_M_N>{}, DsGridDesc_M_N{}));
using DsGridPointerTuple = decltype(GetDsGridPointerTuple());
using CDDataTypes = decltype(concat_tuple(Tuple<const EDataType*>{}, DsGridPointerTuple{}));
using CDDataTypes = decltype(concat_tuple(Tuple<const AccDataType*>{}, DsGridPointerTuple{}));
using EGridDesc_M_N = CGridDesc_M_N;
static constexpr index_t ClusterLengthMPerBlock =
CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock::At(1);
......@@ -659,7 +665,7 @@ struct DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle
std::size_t GetWorkspaceSizeBytes() const
{
return sizeof(EDataType) * ce_grid_desc_m_n_.GetElementSpaceSize() * Conv_G_;
return sizeof(AccDataType) * ce_grid_desc_m_n_.GetElementSpaceSize() * Conv_G_;
}
const ADataType* p_a_grid_;
......@@ -738,7 +744,7 @@ struct DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle
const bool has_main_k0_block_loop = GridwiseGemm::CalculateHasMainK0BlockLoop(K0);
auto launch_gemm_kernel = [&](auto has_main_k_block_loop) {
EDataType* p_c_grid = type_convert<EDataType*>(arg.p_workspace_);
AccDataType* p_c_grid = type_convert<AccDataType*>(arg.p_workspace_);
const index_t grid_size =
arg.block_2_ctile_map_.CalculateGridSize(arg.ce_grid_desc_m_n_) * arg.Conv_G_;
......@@ -753,7 +759,7 @@ struct DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle
GridwiseGemm,
ADataType,
BDataType,
EDataType,
AccDataType,
OutElementwiseOperation,
InElementwiseOperation,
element_wise::PassThrough,
......@@ -786,7 +792,7 @@ struct DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle
};
auto launch_elementwise_kernel = [&]() {
const EDataType* p_c_grid = type_convert<const EDataType*>(arg.p_workspace_);
const AccDataType* p_c_grid = type_convert<const AccDataType*>(arg.p_workspace_);
const index_t grid_size =
arg.elementwise_block_2_ctile_map_.CalculateGridSize(arg.ce_grid_desc_m_n_) *
arg.Conv_G_;
......@@ -907,7 +913,8 @@ struct DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle
}
// vector store C matrix into global memory
if(!(arg.Conv_C_ % CBlockTransferScalarPerVector_NWaveNPerXdl == 0))
if(!(arg.Conv_C_ % CBlockTransferScalarPerVector_NWaveNPerXdl == 0 &&
arg.Conv_C_ % WorkspaceInOutScalarPerVector == 0))
{
return false;
}
......
library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_xdl_bilinear_instance.hpp
View file @ 8346af9c
......@@ -86,6 +86,7 @@ using device_grouped_conv_bwd_weight_xdl_c_shuffle_f16_bilinear_instances = std:
//#########################################| Spatial| | | | | | | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| |
// generic instance
DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, Tuple<BLayout>, F16, F16, F16, F32, Tuple<F16>, PassThrough, Bilinear, PassThrough, ConvSpec, 64, 64, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 1, 4, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 1, 4, true, 1, 1, S<1, 16, 1, 4>, 1>,
DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, Tuple<BLayout>, F16, F16, F16, F32, Tuple<F16>, PassThrough, Bilinear, PassThrough, ConvSpec, 64, 64, 64, 4, 8, 32, 32, 2, 2, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 2, 4, true, S<1, 4, 8, 2>, S<0, 3, 1, 2>, S<0, 2, 1, 3>, 2, 2, 4, true, 1, 1, S<1, 16, 1, 4>, 2>,
// instance for small conv.K
// for fp16 conv.K and conv.C must be divisible by 2
......
test/grouped_convnd_bwd_weight/test_grouped_conv_bwd_weight_xdl_bilinear.cpp
View file @ 8346af9c
......@@ -264,5 +264,7 @@ TYPED_TEST(TestGroupedConvndBwdWeight3d, Test3D)
{3, 1, 1, 64, 3, {3, 3, 3}, {32, 32, 32}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}});
this->conv_params.push_back(
{3, 1, 1, 1, 1, {3, 3, 3}, {32, 32, 32}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}});
this->conv_params.push_back(
{3, 1, 1, 4, 4, {3, 3, 3}, {14, 28, 28}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}});
this->Run();
}
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