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Commit 308146e7 authored by Jianfeng yan's avatar Jianfeng yan
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turned on other operations

parent 8e3c41a5
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include/ck/tensor_operation/gpu/device/device_conv2d_backward_weight_xdl_c_shuffle_nhwc_kyxc_nhwk.hpp
View file @ 308146e7
......@@ -3,7 +3,6 @@
#include <iostream>
#include <sstream>
#include "batched_gemm_util.hpp"
#include "device.hpp"
#include "device_base.hpp"
#include "device_conv_backward_weight.hpp"
......@@ -13,7 +12,6 @@
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm_xdlops_v2r4r2.hpp"
#include "batched_gemm_util.hpp"
namespace ck {
namespace tensor_operation {
......@@ -27,34 +25,33 @@ template <typename InDataType,
typename InElementwiseOperation,
typename WeiElementwiseOperation,
typename OutElementwiseOperation,
index_t NumGemmKPrefetchStage,
index_t BlockSize,
index_t MPerBlock,
index_t NPerBlock,
index_t K0PerBlock,
index_t AK1,
ck::index_t BlockSize,
ck::index_t MPerBlock,
ck::index_t NPerBlock,
ck::index_t K0PerBlock,
ck::index_t K1,
ck::index_t MPerXdl,
ck::index_t NPerXdl,
ck::index_t MXdlPerWave,
ck::index_t NXdlPerWave,
typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
typename ABlockTransferThreadClusterLengths_K0_M_K1,
typename ABlockTransferThreadClusterArrangeOrder,
typename ABlockTransferSrcAccessOrder,
index_t ABlockTransferSrcVectorDim,
index_t ABlockTransferSrcScalarPerVector,
index_t ABlockTransferDstScalarPerVector_AK1,
bool ABlockLdsExtraM,
typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
ck::index_t ABlockTransferSrcVectorDim,
ck::index_t ABlockTransferSrcScalarPerVector,
ck::index_t ABlockTransferDstScalarPerVector_K1,
bool ABlockLdsAddExtraM,
typename BBlockTransferThreadClusterLengths_K0_N_K1,
typename BBlockTransferThreadClusterArrangeOrder,
typename BBlockTransferSrcAccessOrder,
index_t BBlockTransferSrcVectorDim,
index_t BBlockTransferSrcScalarPerVector,
index_t BBlockTransferDstScalarPerVector_BK1,
bool BBlockLdsExtraN,
ck::index_t BBlockTransferSrcVectorDim,
ck::index_t BBlockTransferSrcScalarPerVector,
ck::index_t BBlockTransferDstScalarPerVector_K1,
bool BBlockLdsAddExtraN,
index_t CShuffleMXdlPerWavePerShuffle,
index_t CShuffleNXdlPerWavePerShuffle,
typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
index_t CShuffleBlockTransferScalarPerVector_NPerBlock>
typename CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
index_t CBlockTransferScalarPerVector_NWaveNPerXdl>
struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
: public DeviceConvBwdWeight<InElementwiseOperation,
WeiElementwiseOperation,
......@@ -95,7 +92,7 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
std::vector<ck::index_t> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads,
std::vector<ck::index_t> input_right_pads,
ck::index_t k_batch)
ck::index_t batch_k)
{
using namespace ck;
......@@ -120,40 +117,35 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
const index_t InRightPadH = input_right_pads[0];
const index_t InRightPadW = input_right_pads[1];
const index_t GemmKTotal = N * Ho * Wo;
const index_t GemmM = K;
const index_t GemmN = C * X * Y;
const index_t GemmAKPerBatch = GemmAK0 * GemmAK1Number;
const index_t GemmBKPerBatch = GemmBK0 * GemmBK1Number;
const index_t GemmKTotal = N * Ho * Wo;
const index_t GemmM = K;
const index_t GemmN = C * X * Y;
const index_t GemmAK0 =
math::integer_divide_ceil(GemmKTotal, GemmAK1Number * K0PerBlock * k_batch) *
const index_t GemmKBatch = batch_k;
const index_t GemmK0 =
math::integer_divide_ceil(GemmKTotal, GemmK1Number * K0PerBlock * GemmKBatch) *
K0PerBlock;
const index_t GemmBK0 =
math::integer_divide_ceil(GemmKTotal, GemmBK1Number * K0PerBlock * k_batch) *
K0PerBlock;
const index_t GemmAKPad = GemmKBatch * GemmAK0 * GemmAK1Number;
const index_t GemmBKPad = GemmKBatch * GemmBK0 * GemmBK1Number;
const index_t GemmKPad = GemmKBatch * GemmK0 * GemmK1Number;
const auto out_gemmk_gemmm_grid_desc =
const auto out_gemmktotal_gemmm_grid_desc =
make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K));
const auto in_n_hi_wi_c_grid_desc =
make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C));
// A: output tensor
const auto out_gemmkpad_gemmm_grid_desc = transform_tensor_descriptor(
out_gemmk_gemmm_grid_desc,
make_tuple(make_right_pad_transform(GemmKTotal, GemmAKPad - GemmKTotal),
out_gemmktotal_gemmm_grid_desc,
make_tuple(make_right_pad_transform(GemmKTotal, GemmKPad - GemmKTotal),
make_pass_through_transform(GemmM)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
const auto out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
out_gemmkpad_gemmm_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(GemmAK0, GemmAK1Number)),
make_tuple(make_unmerge_transform(make_tuple(GemmKBatch, GemmK0, GemmK1Number)),
make_pass_through_transform(GemmM)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
// B: input tensor
const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
......@@ -184,24 +176,24 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
const auto in_gemmkpad_gemmn_grid_desc = transform_tensor_descriptor(
in_gemmktotal_gemmn_grid_desc,
make_tuple(make_right_pad_transform(GemmKTotal, GemmBKPad - GemmKTotal),
make_tuple(make_right_pad_transform(GemmKTotal, GemmKPad - GemmKTotal),
make_pass_through_transform(GemmN)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
const auto in_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
const auto in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
in_gemmkpad_gemmn_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(GemmBK0, GemmBK1Number)),
make_tuple(make_unmerge_transform(make_tuple(GemmKBatch, GemmK0, GemmK1Number)),
make_pass_through_transform(GemmN)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
// C: weight tensor
const auto wei_gemmm_gemmn_grid_desc =
make_naive_tensor_descriptor_packed(make_tuple(K, Y * X * C));
return make_tuple(out_gemmk0_gemmm_gemmk1_grid_desc,
in_gemmk0_gemmn_gemmk1_grid_desc,
return make_tuple(out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc,
in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc,
wei_gemmm_gemmn_grid_desc);
}
......@@ -213,97 +205,93 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
using CGridDesc_M_N = remove_cvref_t<decltype(ABCGridDescs{}[I2])>;
// GridwiseGemm
using GridwiseGemm = GridwiseGemm_k0mk1_k0nk1_mn_xdl_cshuffle_v1<
using GridwiseGemm = GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2<
BlockSize,
ADataType, // TODO: distinguish A/B datatype
GemmAccDataType,
CShuffleDataType,
AccDataType,
CDataType,
InMemoryDataOperationEnum::Set,
AGridDesc_K0_M_K1,
BGridDesc_K0_N_K1,
CGridDesc_M_N,
AElementwiseOperation,
BElementwiseOperation,
CElementwiseOperation,
InMemoryDataOperationEnum::Set,
AGridDesc_AK0_M_AK1,
BGridDesc_BK0_N_BK1,
CGridDesc_M_N,
NumGemmKPrefetchStage,
BlockSize,
MPerBlock,
NPerBlock,
KPerBlock,
AK1,
BK1,
MPerXDL,
NPerXDL,
K0PerBlock,
MPerXdl,
NPerXdl,
K1,
MXdlPerWave,
NXdlPerWave,
ABlockTransferThreadClusterLengths_AK0_M_AK1,
ABlockTransferThreadClusterLengths_K0_M_K1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_AK1,
false,
ABlockLdsExtraM,
BBlockTransferThreadClusterLengths_BK0_N_BK1,
ABlockTransferDstScalarPerVector_K1,
false, // AThreadTransferSrcResetCoordinateAfterRun,
ABlockLdsAddExtraM,
BBlockTransferThreadClusterLengths_K0_N_K1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_BK1,
false,
BBlockLdsExtraN,
BBlockTransferDstScalarPerVector_K1,
false, // BThreadTransferSrcResetCoordinateAfterRun,
BBlockLdsAddExtraN,
CShuffleMXdlPerWavePerShuffle,
CShuffleNXdlPerWavePerShuffle,
CBlockTransferScalarPerVector_NWaveNPerXdl,
CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock>;
CShuffleBlockTransferScalarPerVector_NPerBlock > ;
using GridwiseGemmAtomicAdd = GridwiseGemm_k0mk1_k0nk1_mn_xdl_cshuffle_v1<
using GridwiseGemmAtomicAdd = GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2<
BlockSize,
ADataType, // TODO: distinguish A/B datatype
GemmAccDataType,
CShuffleDataType,
AccDataType,
CDataType,
InMemoryDataOperationEnum::AtomicAdd,
AGridDesc_K0_M_K1,
BGridDesc_K0_N_K1,
CGridDesc_M_N,
AElementwiseOperation,
BElementwiseOperation,
CElementwiseOperation,
InMemoryDataOperationEnum::AtomicAdd,
AGridDesc_AK0_M_AK1,
BGridDesc_BK0_N_BK1,
CGridDesc_M_N,
NumGemmKPrefetchStage,
BlockSize,
MPerBlock,
NPerBlock,
KPerBlock,
AK1,
BK1,
MPerXDL,
NPerXDL,
K0PerBlock,
MPerXdl,
NPerXdl,
K1,
MXdlPerWave,
NXdlPerWave,
ABlockTransferThreadClusterLengths_AK0_M_AK1,
ABlockTransferThreadClusterLengths_K0_M_K1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_AK1,
false,
ABlockLdsExtraM,
BBlockTransferThreadClusterLengths_BK0_N_BK1,
ABlockTransferDstScalarPerVector_K1,
false, // AThreadTransferSrcResetCoordinateAfterRun,
ABlockLdsAddExtraM,
BBlockTransferThreadClusterLengths_K0_N_K1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_BK1,
false,
BBlockLdsExtraN,
BBlockTransferDstScalarPerVector_K1,
false, // BThreadTransferSrcResetCoordinateAfterRun,
BBlockLdsAddExtraN,
CShuffleMXdlPerWavePerShuffle,
CShuffleNXdlPerWavePerShuffle,
CBlockTransferScalarPerVector_NWaveNPerXdl,
CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock>;
CShuffleBlockTransferScalarPerVector_NPerBlock > ;
// Argument
using CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock =
decltype(GridwiseGemm::MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(CGridDesc_M_N{}));
using Block2CTileMap =
decltype(BatchedGemmUtil::MakeBlock2CTileMap<MPerBlock, NPerBlock>(1, 1, 1));
decltype(GridwiseGemm::MakeCBlockClusterAdaptor(CGridDesc_M_N{}, 1, 1, 1));
struct Argument : public BaseArgument
{
Argument(const InDataType* p_in_grid,
......@@ -328,8 +316,8 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
: p_a_grid_{p_out_grid},
p_b_grid_{p_in_grid},
p_c_grid_{p_wei_grid},
a_grid_desc_k0_m_k1_{},
b_grid_desc_k0_n_k1_{},
a_grid_desc_kbatch_k0_m_k1_{},
b_grid_desc_kbatch_k0_n_k1_{},
c_grid_desc_m_n_{},
c_grid_desc_mblock_mperblock_nblock_nperblock_{},
block_2_ctile_map_{},
......@@ -361,36 +349,31 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
input_right_pads,
k_batch_);
a_grid_desc_k0_m_k1_ = descs[I0];
b_grid_desc_k0_n_k1_ = descs[I1];
c_grid_desc_m_n_ = descs[I2];
a_grid_desc_kbatch_k0_m_k1_ = descs[I0];
b_grid_desc_kbatch_k0_n_k1_ = descs[I1];
c_grid_desc_m_n_ = descs[I2];
if(GridwiseGemm::CheckValidity(
a_grid_desc_k0_m_k1_, b_grid_desc_k0_n_k1_, c_grid_desc_m_n_, M01_, N01_))
if(GridwiseGemm::CheckValidity(a_grid_desc_kbatch_k0_m_k1_,
b_grid_desc_kbatch_k0_n_k1_,
c_grid_desc_m_n_,
M01_,
N01_))
{
c_grid_desc_mblock_mperblock_nblock_nperblock_ =
GridwiseGemm::MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
c_grid_desc_m_n_);
block_2_ctile_map_ = BatchedGemmUtil::MakeBlock2CTileMap<MPerBlock, NPerBlock>(
k_batch_,
c_grid_desc_m_n_.GetLength(I0),
c_grid_desc_m_n_.GetLength(I1),
M01_,
N01_);
GridwiseGemm::MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(c_grid_desc_m_n_);
block_2_ctile_map_ =
GridwiseGemm::MakeCBlockClusterAdaptor(c_grid_desc_m_n_, M01, N01, k_batch_);
}
}
const ADataType* p_a_grid_;
const BDataType* p_b_grid_;
CDataType* p_c_grid_;
index_t k_batch_;
AGridDesc_K0_M_K1 a_grid_desc_k0_m_k1_;
BGridDesc_K0_N_K1 b_grid_desc_k0_n_k1_;
AGridDesc_K0_M_K1 a_grid_desc_kbatch_k0_m_k1_;
BGridDesc_K0_N_K1 b_grid_desc_kbatch_k0_n_k1_;
CGridDesc_M_N c_grid_desc_m_n_;
typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
c_grid_desc_mblock_mperblock_nblock_nperblock_;
BatchedGemmUtil::ComputePtrOffsetOfStridedBatch compute_ptr_offset_of_batch_;
CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock c_grid_desc_mblock_mperblock_nblock_nperblock_;
Block2CTileMap block_2_ctile_map_;
index_t M01_;
index_t N01_;
......@@ -416,14 +399,17 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
void ShowInfo(const Argument& arg)
{
std::cout << "k_batch = " << arg.BatchCount_ << "\n";
std::cout << "arg.a_grid_desc_k0_m_k1_{" << arg.a_grid_desc_k0_m_k1_.GetLength(I0)
<< ", " << arg.a_grid_desc_k0_m_k1_.GetLength(I1) << ", "
<< arg.a_grid_desc_k0_m_k1_.GetLength(I2) << "}" << std::endl;
std::cout << "arg.b_grid_desc_k0_n_k1_{" << arg.b_grid_desc_k0_n_k1_.GetLength(I0)
<< ", " << arg.b_grid_desc_k0_n_k1_.GetLength(I1) << ", "
<< arg.b_grid_desc_k0_n_k1_.GetLength(I2) << "}" << std::endl;
std::cout << "arg.a_grid_desc_kbatch_k0_m_k1_{"
<< arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I0) << ", "
<< arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I1) << ", "
<< arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I2) << ", "
<< arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I3) << "}" << std::endl;
std::cout << "arg.b_grid_desc_kbatch_k0_n_k1_{"
<< arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I0) << ", "
<< arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I1) << ", "
<< arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I2) << ", "
<< arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I3) << "}" << std::endl;
std::cout << "arg.c_grid_desc_m_n_{ " << arg.c_grid_desc_m_n_.GetLength(I0) << ", "
<< arg.c_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
......@@ -432,8 +418,8 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
float Run(const Argument& arg, int nrepeat = 1)
{
ShowInfo(arg);
if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_,
arg.b_grid_desc_k0_n_k1_,
if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_kbatch_k0_m_k1_,
arg.b_grid_desc_kbatch_k0_n_k1_,
arg.c_grid_desc_m_n_,
arg.M01_,
arg.N01_))
......@@ -441,11 +427,10 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
throw std::runtime_error(
"wrong! GridwiseGemm_km_kn_m0m1n0n1_xdlops_v3r1 has invalid setting");
}
const auto k_batch = arg.k_batch_;
const index_t grid_size =
GridwiseGemm::CalculateGridSize(arg.c_grid_desc_m_n_) * k_batch;
const auto kbatch = arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I0);
const index_t grid_size = GridwiseGemm::CalculateGridSize(arg.c_grid_desc_m_n_, kbatch);
const auto K0 = arg.a_grid_desc_ak0_m_ak1_.GetLength(I0);
const auto K0 = arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I1);
const bool has_main_k0_block_loop = GridwiseGemm::CalculateHasMainK0BlockLoop(K0);
......@@ -463,8 +448,8 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_k0_m_k1_,
arg.b_grid_desc_k0_n_k1_,
arg.a_grid_desc_kbatch_k0_m_k1_,
arg.b_grid_desc_kbatch_k0_n_k1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.a_element_op_,
arg.b_element_op_,
......@@ -472,7 +457,7 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
arg.block_2_ctile_map_);
}
if(k_batch > 1 || nrepeat <= 0)
if(kbatch > 1 || nrepeat <= 0)
{
hipGetErrorString(hipMemset(
arg.p_c_grid_,
......@@ -487,8 +472,8 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_c_grid_,
arg.a_grid_desc_k0_m_k1_,
arg.b_grid_desc_k0_n_k1_,
arg.a_grid_desc_kbatch_k0_m_k1_,
arg.b_grid_desc_kbatch_k0_n_k1_,
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.a_element_op_,
arg.b_element_op_,
......@@ -499,38 +484,36 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
if(has_main_k0_block_loop)
{
if(k_batch == 1)
if(kbatch == 1)
{
const auto kernel = kernel_batched_gemm_xdl_cshuffle_v1<
const auto kernel = kernel_gemm_xdlops_v2r4r2<
GridwiseGemm,
ADataType, // TODO: distiguish A/B datatype
CDataType,
AElementwiseOperation,
BElementwiseOperation,
CElementwiseOperation,
DeviceOp::AGridDesc_AK0_M_AK1,
DeviceOp::BGridDesc_BK0_N_BK1,
typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
ComputePtrOffsetOfStridedBatch,
Block2CTileMap,
remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
remove_reference_t<DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
OutElementwiseOperation,
InElementwiseOperation,
WeiElementwiseOperation,
remove_reference_t<DeviceOp::Block2CTileMap>,
true>;
Run(kernel);
}
else
{
const auto kernel = kernel_batched_gemm_xdl_cshuffle_v1<
const auto kernel = kernel_gemm_xdlops_v2r4r2<
GridwiseGemmAtomicAdd,
ADataType, // TODO: distiguish A/B datatype
CDataType,
AElementwiseOperation,
BElementwiseOperation,
CElementwiseOperation,
DeviceOp::AGridDesc_AK0_M_AK1,
DeviceOp::BGridDesc_BK0_N_BK1,
typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
ComputePtrOffsetOfStridedBatch,
Block2CTileMap,
remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
remove_reference_t<DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
OutElementwiseOperation,
InElementwiseOperation,
WeiElementwiseOperation,
remove_reference_t<DeviceOp::Block2CTileMap>,
true>;
Run(kernel);
......@@ -538,38 +521,36 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
}
else
{
if(k_batch == 1)
if(kbatch == 1)
{
const auto kernel = kernel_batched_gemm_xdl_cshuffle_v1<
const auto kernel = kernel_gemm_xdlops_v2r4r2<
GridwiseGemm,
ADataType, // TODO: distiguish A/B datatype
CDataType,
AElementwiseOperation,
BElementwiseOperation,
CElementwiseOperation,
DeviceOp::AGridDesc_AK0_M_AK1,
DeviceOp::BGridDesc_BK0_N_BK1,
typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
ComputePtrOffsetOfStridedBatch,
Block2CTileMap,
remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
remove_reference_t<DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
OutElementwiseOperation,
InElementwiseOperation,
WeiElementwiseOperation,
remove_reference_t<DeviceOp::Block2CTileMap>,
false>;
Run(kernel);
}
else
{
const auto kernel = kernel_batched_gemm_xdl_cshuffle_v1<
const auto kernel = kernel_gemm_xdlops_v2r4r2<
GridwiseGemmAtomicAdd,
ADataType, // TODO: distiguish A/B datatype
CDataType,
AElementwiseOperation,
BElementwiseOperation,
CElementwiseOperation,
DeviceOp::AGridDesc_AK0_M_AK1,
DeviceOp::BGridDesc_BK0_N_BK1,
typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
ComputePtrOffsetOfStridedBatch,
Block2CTileMap,
remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
remove_reference_t<DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
OutElementwiseOperation,
InElementwiseOperation,
WeiElementwiseOperation,
remove_reference_t<DeviceOp::Block2CTileMap>,
false>;
Run(kernel);
......@@ -602,14 +583,14 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
}
// vector store C matrix into global memory
if(!(arg.Conv_C_ % CShuffleBlockTransferScalarPerVector_NPerBlock == 0))
if(!(arg.Conv_C_ % CBlockTransferScalarPerVector_NWaveNPerXdl == 0))
{
return false;
}
// Gridwise GEMM size
return GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_,
arg.b_grid_desc_k0_n_k1_,
return GridwiseGemm::CheckValidity(arg.a_grid_desc_kbatch_k0_m_k1_,
arg.b_grid_desc_kbatch_k0_n_k1_,
arg.c_grid_desc_m_n_,
arg.M01_,
arg.N01_);
......
profiler/CMakeLists.txt
View file @ 308146e7
......@@ -24,40 +24,40 @@ include_directories(BEFORE
set(PROFILER_SOURCE
src/profiler.cpp
src/profile_gemm.cpp
# src/profile_gemm_bias_2d.cpp
# src/profile_gemm_bias_relu.cpp
# src/profile_gemm_bias_relu_add.cpp
# src/profile_gemm_reduce.cpp
# src/profile_batched_gemm.cpp
# src/profile_conv_fwd_bias_relu.cpp
# src/profile_conv_fwd_bias_relu_add.cpp
# src/profile_conv_fwd_bias_relu_atomic_add.cpp
# src/profile_convnd_fwd.cpp
# src/profile_convnd_bwd_data.cpp
# src/profile_reduce.cpp
# src/profile_grouped_gemm.cpp
# src/profile_conv_bwd_weight.cpp
# src/profile_batched_gemm_reduce.cpp
src/profile_gemm_bias_2d.cpp
src/profile_gemm_bias_relu.cpp
src/profile_gemm_bias_relu_add.cpp
src/profile_gemm_reduce.cpp
src/profile_batched_gemm.cpp
src/profile_conv_fwd_bias_relu.cpp
src/profile_conv_fwd_bias_relu_add.cpp
src/profile_conv_fwd_bias_relu_atomic_add.cpp
src/profile_convnd_fwd.cpp
src/profile_convnd_bwd_data.cpp
src/profile_reduce.cpp
src/profile_grouped_gemm.cpp
src/profile_conv_bwd_weight.cpp
src/profile_batched_gemm_reduce.cpp
)
add_executable(ckProfiler ${PROFILER_SOURCE})
target_link_libraries(ckProfiler PRIVATE host_tensor)
# target_link_libraries(ckProfiler PRIVATE conv_fwd_util)
# target_link_libraries(ckProfiler PRIVATE device_gemm_reduce_instance)
target_link_libraries(ckProfiler PRIVATE conv_fwd_util)
target_link_libraries(ckProfiler PRIVATE device_gemm_reduce_instance)
target_link_libraries(ckProfiler PRIVATE device_gemm_instance)
# target_link_libraries(ckProfiler PRIVATE device_gemm_bias2d_instance)
# target_link_libraries(ckProfiler PRIVATE device_gemm_bias_relu_instance)
# target_link_libraries(ckProfiler PRIVATE device_gemm_bias_relu_add_instance)
# target_link_libraries(ckProfiler PRIVATE device_batched_gemm_instance)
# target_link_libraries(ckProfiler PRIVATE device_conv1d_fwd_instance)
# target_link_libraries(ckProfiler PRIVATE device_conv2d_fwd_instance)
# target_link_libraries(ckProfiler PRIVATE device_conv3d_fwd_instance)
# target_link_libraries(ckProfiler PRIVATE device_conv2d_fwd_bias_relu_instance)
# target_link_libraries(ckProfiler PRIVATE device_conv2d_fwd_bias_relu_add_instance)
# target_link_libraries(ckProfiler PRIVATE device_conv2d_fwd_bias_relu_atomic_add_instance)
# target_link_libraries(ckProfiler PRIVATE device_convnd_bwd_data_instance)
# target_link_libraries(ckProfiler PRIVATE device_reduce_instance)
# target_link_libraries(ckProfiler PRIVATE device_grouped_gemm_instance)
# target_link_libraries(ckProfiler PRIVATE device_conv2d_bwd_weight_instance)
# target_link_libraries(ckProfiler PRIVATE device_batched_gemm_reduce_instance)
target_link_libraries(ckProfiler PRIVATE device_gemm_bias2d_instance)
target_link_libraries(ckProfiler PRIVATE device_gemm_bias_relu_instance)
target_link_libraries(ckProfiler PRIVATE device_gemm_bias_relu_add_instance)
target_link_libraries(ckProfiler PRIVATE device_batched_gemm_instance)
target_link_libraries(ckProfiler PRIVATE device_conv1d_fwd_instance)
target_link_libraries(ckProfiler PRIVATE device_conv2d_fwd_instance)
target_link_libraries(ckProfiler PRIVATE device_conv3d_fwd_instance)
target_link_libraries(ckProfiler PRIVATE device_conv2d_fwd_bias_relu_instance)
target_link_libraries(ckProfiler PRIVATE device_conv2d_fwd_bias_relu_add_instance)
target_link_libraries(ckProfiler PRIVATE device_conv2d_fwd_bias_relu_atomic_add_instance)
target_link_libraries(ckProfiler PRIVATE device_convnd_bwd_data_instance)
target_link_libraries(ckProfiler PRIVATE device_reduce_instance)
target_link_libraries(ckProfiler PRIVATE device_grouped_gemm_instance)
target_link_libraries(ckProfiler PRIVATE device_conv2d_bwd_weight_instance)
target_link_libraries(ckProfiler PRIVATE device_batched_gemm_reduce_instance)
profiler/src/profiler.cpp
View file @ 308146e7
......@@ -7,19 +7,19 @@
#include "profile_convnd_fwd.hpp"
int profile_gemm(int, char*[]);
// int profile_gemm_bias_2d(int, char*[]);
// int profile_gemm_bias_relu(int, char*[]);
// int profile_gemm_bias_relu_add(int, char*[]);
// int profile_gemm_reduce(int, char*[]);
// int profile_batched_gemm(int, char*[]);
// int profile_grouped_gemm(int, char*[]);
// int profile_conv_fwd_bias_relu(int, char*[]);
// int profile_conv_fwd_bias_relu_add(int, char*[]);
// int profile_conv_fwd_bias_relu_atomic_add(int, char*[]);
// int profile_convnd_bwd_data(int, char*[], int);
// int profile_reduce(int, char*[]);
// int profile_conv_bwd_weight(int, char*[]);
// int profile_batched_gemm_reduce(int, char*[]);
int profile_gemm_bias_2d(int, char*[]);
int profile_gemm_bias_relu(int, char*[]);
int profile_gemm_bias_relu_add(int, char*[]);
int profile_gemm_reduce(int, char*[]);
int profile_batched_gemm(int, char*[]);
int profile_grouped_gemm(int, char*[]);
int profile_conv_fwd_bias_relu(int, char*[]);
int profile_conv_fwd_bias_relu_add(int, char*[]);
int profile_conv_fwd_bias_relu_atomic_add(int, char*[]);
int profile_convnd_bwd_data(int, char*[], int);
int profile_reduce(int, char*[]);
int profile_conv_bwd_weight(int, char*[]);
int profile_batched_gemm_reduce(int, char*[]);
int main(int argc, char* argv[])
{
......@@ -27,70 +27,70 @@ int main(int argc, char* argv[])
{
return profile_gemm(argc, argv);
}
// else if(strcmp(argv[1], "gemm_bias_2d") == 0)
// {
// return profile_gemm_bias_2d(argc, argv);
// }
// else if(strcmp(argv[1], "gemm_bias_relu") == 0)
// {
// return profile_gemm_bias_relu(argc, argv);
// }
// else if(strcmp(argv[1], "gemm_bias_relu_add") == 0)
// {
// return profile_gemm_bias_relu_add(argc, argv);
// }
// else if(strcmp(argv[1], "gemm_reduce") == 0)
// {
// return profile_gemm_reduce(argc, argv);
// }
// else if(strcmp(argv[1], "batched_gemm") == 0)
// {
// return profile_batched_gemm(argc, argv);
// }
// else if(strcmp(argv[1], "batched_gemm_reduce") == 0)
// {
// return profile_batched_gemm_reduce(argc, argv);
// }
// else if(strcmp(argv[1], "grouped_gemm") == 0)
// {
// profile_grouped_gemm(argc, argv);
// }
// else if(strcmp(argv[1], "conv_fwd") == 0)
// {
// return ck::profiler::profile_convnd_fwd(argc, argv);
// }
// else if(strcmp(argv[1], "conv_fwd_bias_relu") == 0)
// {
// return profile_conv_fwd_bias_relu(argc, argv);
// }
// else if(strcmp(argv[1], "conv_fwd_bias_relu_add") == 0)
// {
// return profile_conv_fwd_bias_relu_add(argc, argv);
// }
// else if(strcmp(argv[1], "conv_fwd_bias_relu_atomic_add") == 0)
// {
// return profile_conv_fwd_bias_relu_atomic_add(argc, argv);
// }
// else if(strcmp(argv[1], "conv1d_bwd_data") == 0)
// {
// return profile_convnd_bwd_data(argc, argv, 1);
// }
// else if(strcmp(argv[1], "conv2d_bwd_data") == 0)
// {
// return profile_convnd_bwd_data(argc, argv, 2);
// }
// else if(strcmp(argv[1], "conv3d_bwd_data") == 0)
// {
// return profile_convnd_bwd_data(argc, argv, 3);
// }
// else if(strcmp(argv[1], "reduce") == 0)
// {
// return profile_reduce(argc, argv);
// }
// else if(strcmp(argv[1], "conv2d_bwd_weight") == 0)
// {
// return profile_conv_bwd_weight(argc, argv);
// }
else if(strcmp(argv[1], "gemm_bias_2d") == 0)
{
return profile_gemm_bias_2d(argc, argv);
}
else if(strcmp(argv[1], "gemm_bias_relu") == 0)
{
return profile_gemm_bias_relu(argc, argv);
}
else if(strcmp(argv[1], "gemm_bias_relu_add") == 0)
{
return profile_gemm_bias_relu_add(argc, argv);
}
else if(strcmp(argv[1], "gemm_reduce") == 0)
{
return profile_gemm_reduce(argc, argv);
}
else if(strcmp(argv[1], "batched_gemm") == 0)
{
return profile_batched_gemm(argc, argv);
}
else if(strcmp(argv[1], "batched_gemm_reduce") == 0)
{
return profile_batched_gemm_reduce(argc, argv);
}
else if(strcmp(argv[1], "grouped_gemm") == 0)
{
profile_grouped_gemm(argc, argv);
}
else if(strcmp(argv[1], "conv_fwd") == 0)
{
return ck::profiler::profile_convnd_fwd(argc, argv);
}
else if(strcmp(argv[1], "conv_fwd_bias_relu") == 0)
{
return profile_conv_fwd_bias_relu(argc, argv);
}
else if(strcmp(argv[1], "conv_fwd_bias_relu_add") == 0)
{
return profile_conv_fwd_bias_relu_add(argc, argv);
}
else if(strcmp(argv[1], "conv_fwd_bias_relu_atomic_add") == 0)
{
return profile_conv_fwd_bias_relu_atomic_add(argc, argv);
}
else if(strcmp(argv[1], "conv1d_bwd_data") == 0)
{
return profile_convnd_bwd_data(argc, argv, 1);
}
else if(strcmp(argv[1], "conv2d_bwd_data") == 0)
{
return profile_convnd_bwd_data(argc, argv, 2);
}
else if(strcmp(argv[1], "conv3d_bwd_data") == 0)
{
return profile_convnd_bwd_data(argc, argv, 3);
}
else if(strcmp(argv[1], "reduce") == 0)
{
return profile_reduce(argc, argv);
}
else if(strcmp(argv[1], "conv2d_bwd_weight") == 0)
{
return profile_conv_bwd_weight(argc, argv);
}
else
{
// clang-format off
......
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