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Commit de6a70f7 authored by Jing Zhang's avatar Jing Zhang
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add ds

parent 1d11426a
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Showing with 193 additions and 158 deletions
example/28_batched_gemm_multi_d/batched_gemm_multi_d_xdl_fp16.cpp
View file @ de6a70f7
......@@ -33,9 +33,9 @@ using CShuffleDataType = F16;
using DsDataType = ck::Tuple<>;
using EDataType = F16;
using ALayout = Row;
using BLayout = Col;
using ELayout = Row;
using ALayout = Row;
using BLayout = Col;
using ELayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
......@@ -63,9 +63,9 @@ int main(int argc, char* argv[])
int init_method = 1;
bool time_kernel = false;
const int M = 256;
const int N = 128;
const int K = 64;
const int M = 256 * (rand() % 16 + 1);
const int N = 128 * (rand() % 16 + 1);
const int K = 64 * (rand() % 16 + 1);
const int stride_A = K;
const int stride_B = K;
......@@ -112,12 +112,12 @@ int main(int argc, char* argv[])
Tensor<ADataType> a_g_m_k(f_host_tensor_descriptor(batch_count, M, K, stride_A, ALayout{}));
Tensor<BDataType> b_g_k_n(f_host_tensor_descriptor(batch_count, K, N, stride_B, BLayout{}));
Tensor<EDataType> c_g_m_n_device_result(
Tensor<EDataType> e_g_m_n_device_result(
f_host_tensor_descriptor(batch_count, M, N, stride_C, ELayout{}));
std::cout << "a_g_m_k: " << a_g_m_k.mDesc << std::endl;
std::cout << "b_g_k_n: " << b_g_k_n.mDesc << std::endl;
std::cout << "c_g_m_n: " << c_g_m_n_device_result.mDesc << std::endl;
std::cout << "e_g_m_n: " << e_g_m_n_device_result.mDesc << std::endl;
switch(init_method)
{
......@@ -134,35 +134,38 @@ int main(int argc, char* argv[])
DeviceMem a_device_buf(sizeof(ADataType) * a_g_m_k.mDesc.GetElementSpace());
DeviceMem b_device_buf(sizeof(BDataType) * b_g_k_n.mDesc.GetElementSpace());
DeviceMem c_device_buf(sizeof(EDataType) * c_g_m_n_device_result.mDesc.GetElementSpace());
DeviceMem c_device_buf(sizeof(EDataType) * e_g_m_n_device_result.mDesc.GetElementSpace());
a_device_buf.ToDevice(a_g_m_k.mData.data());
b_device_buf.ToDevice(b_g_k_n.mData.data());
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto c_element_op = CDEElementOp{};
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto cde_element_op = CDEElementOp{};
auto gemm = DeviceGemmInstance{};
auto invoker = gemm.MakeInvoker();
// do GEMM
auto argument = gemm.MakeArgument(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
static_cast<EDataType*>(c_device_buf.GetDeviceBuffer()),
auto argument = gemm.MakeArgument(a_device_buf.GetDeviceBuffer(),
b_device_buf.GetDeviceBuffer(),
{},
c_device_buf.GetDeviceBuffer(),
M,
N,
K,
stride_A,
stride_B,
{},
stride_C,
batch_stride_A,
batch_stride_B,
{},
batch_stride_C,
batch_count,
a_element_op,
b_element_op,
c_element_op);
cde_element_op);
if(!gemm.IsSupportedArgument(argument))
{
......@@ -189,32 +192,21 @@ int main(int argc, char* argv[])
if(do_verification)
{
c_device_buf.FromDevice(c_g_m_n_device_result.mData.data());
c_device_buf.FromDevice(e_g_m_n_device_result.mData.data());
auto ref_batched_gemm = ReferenceBatchedGemmInstance{};
auto ref_invoker = ref_batched_gemm.MakeInvoker();
Tensor<EDataType> c_g_m_n_host_result = HostTensorDescriptor(
std::vector<std::size_t>({batch_count, M, N}), std::vector<std::size_t>({M * N, N, 1}));
Tensor<EDataType> e_g_m_n_host_result(
f_host_tensor_descriptor(batch_count, M, N, stride_C, ELayout{}));
auto ref_argument = ref_batched_gemm.MakeArgument(
a_g_m_k, b_g_k_n, c_g_m_n_host_result, a_element_op, b_element_op, c_element_op);
a_g_m_k, b_g_k_n, e_g_m_n_host_result, a_element_op, b_element_op, cde_element_op);
ref_invoker.Run(ref_argument);
//for(int b = 0; b < batch_count; b++)
//{
//for(int m = 0; m < M; m++)
//{
//for(int n = 0; n < N; n++)
//{
//c_g_m_n_host_result(b, m, n) = c_g_m_n_host_result(b, m, n);
//}
//}
//}
pass = ck::utils::check_err(
c_g_m_n_host_result.mData, c_g_m_n_device_result.mData, "Error: Incorrect results c");
e_g_m_n_host_result.mData, e_g_m_n_device_result.mData, "Error: Incorrect results c");
}
return pass ? 0 : 1;
......
include/ck/tensor_operation/gpu/device/device_batched_gemm_multi_d.hpp
View file @ de6a70f7
......@@ -29,16 +29,18 @@ struct DeviceBatchedGemmMultiD : public BaseOperator
virtual std::unique_ptr<BaseArgument>
MakeArgumentPointer(const void* p_a,
const void* p_b,
std::array<const void*, NumDTensor> p_ds,
void* p_c,
ck::index_t M,
ck::index_t N,
ck::index_t K,
ck::index_t StrideA,
ck::index_t StrideB,
//std::array<ck::index_t, NumDTensor> StrideDs,
std::array<ck::index_t, NumDTensor> StrideDs,
ck::index_t StrideE,
ck::index_t BatchStrideA,
ck::index_t BatchStrideB,
std::array<ck::index_t, NumDTensor> BatchStrideDs,
ck::index_t BatchStrideE,
ck::index_t Batch,
AElementwiseOperation a_element_op,
......@@ -58,16 +60,17 @@ template <typename ALayout,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CDEElementwiseOperation>
using DeviceBatchedGemmMultiDPtr = std::unique_ptr<DeviceBatchedGemmMultiD<ALayout,
BLayout,
CLayout,
ADataType,
BDataType,
DsDataType,
EDataType,
AElementwiseOperation,
BElementwiseOperation,
CDEElementwiseOperation>>;
using DeviceBatchedGemmMultiDPtr =
std::unique_ptr<DeviceBatchedGemmMultiD<ALayout,
BLayout,
CLayout,
ADataType,
BDataType,
DsDataType,
EDataType,
AElementwiseOperation,
BElementwiseOperation,
CDEElementwiseOperation>>;
} // namespace device
} // namespace tensor_operation
......
include/ck/tensor_operation/gpu/device/device_batched_gemm_multi_d_xdl.hpp
View file @ de6a70f7
......@@ -47,10 +47,12 @@ namespace device {
*/
template <typename GridwiseGemm,
typename FloatAB,
typename FloatDsPointer,
typename FloatC,
typename AGridDesc_AK0_M_AK1,
typename BGridDesc_BK0_N_BK1,
typename CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
typename DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
typename EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CDEElementwiseOperation,
......@@ -63,18 +65,22 @@ __global__ void
#endif
kernel_batched_gemm_xdl(const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatDsPointer p_ds_grid,
FloatC* __restrict__ p_e_grid,
const index_t batch_count,
const AGridDesc_AK0_M_AK1 a_grid_desc_k0_m_k1,
const BGridDesc_BK0_N_BK1 b_grid_desc_k0_n_k1,
const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
const DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
ds_grid_desc_mblock_mperblock_nblock_nperblock,
const EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
e_grid_desc_mblock_mperblock_nblock_nperblock_,
const AElementwiseOperation a_element_op,
const BElementwiseOperation b_element_op,
const CDEElementwiseOperation c_element_op,
const CDEElementwiseOperation cde_element_op,
const ComputePtrOffsetOfBatch compute_ptr_offset_of_batch,
const Block2CTileMap block_2_ctile_map)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
const index_t num_blocks_per_batch =
__builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
......@@ -84,38 +90,47 @@ __global__ void
static_cast<long_index_t>(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)));
const long_index_t b_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)));
const long_index_t c_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_ptr_offset_of_batch.GetCPtrOffset(g_idx)));
const long_index_t e_batch_offset = __builtin_amdgcn_readfirstlane(
static_cast<long_index_t>(compute_ptr_offset_of_batch.GetEPtrOffset(g_idx)));
const auto ds_batch_offset = compute_ptr_offset_of_batch.GetDsPtrOffset(g_idx);
__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
GridwiseGemm::template Run<HasMainKBlockLoop>(
p_a_grid + a_batch_offset,
p_b_grid + b_batch_offset,
ck::Tuple<>{},
p_e_grid + c_batch_offset,
p_shared,
a_element_op,
b_element_op,
c_element_op,
a_grid_desc_k0_m_k1,
b_grid_desc_k0_n_k1,
ck::StaticallyIndexedArray<
typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
0>{},
e_grid_desc_mblock_mperblock_nblock_nperblock_,
block_2_ctile_map);
FloatDsPointer p_ds_grid_grp;
static constexpr index_t NumDTensor =
DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock::Size();
static_for<0, NumDTensor, 1>{}(
[&](auto i) { p_ds_grid_grp(i) = p_ds_grid[i] + ds_batch_offset[i]; });
GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid + a_batch_offset,
p_b_grid + b_batch_offset,
p_ds_grid_grp,
p_e_grid + e_batch_offset,
p_shared,
a_element_op,
b_element_op,
cde_element_op,
a_grid_desc_k0_m_k1,
b_grid_desc_k0_n_k1,
ds_grid_desc_mblock_mperblock_nblock_nperblock,
e_grid_desc_mblock_mperblock_nblock_nperblock_,
block_2_ctile_map);
#else
ignore = p_a_grid;
ignore = p_b_grid;
ignore = p_ds_grid;
ignore = p_e_grid;
ignore = batch_count;
ignore = a_grid_desc_k0_m_k1;
ignore = b_grid_desc_k0_n_k1;
ignore = ds_grid_desc_mblock_mperblock_nblock_nperblock;
ignore = e_grid_desc_mblock_mperblock_nblock_nperblock_;
ignore = a_element_op;
ignore = b_element_op;
ignore = c_element_op;
ignore = cde_element_op;
ignore = compute_ptr_offset_of_batch;
ignore = block_2_ctile_map;
#endif
......@@ -456,8 +471,12 @@ struct DeviceBatchedGemmMultiDXdl : public DeviceBatchedGemmMultiD<ALayout,
{
ComputePtrOffsetOfStridedBatch(index_t BatchStrideA,
index_t BatchStrideB,
std::array<ck::index_t, NumDTensor> BatchStrideDs,
index_t BatchStrideE)
: BatchStrideA_(BatchStrideA), BatchStrideB_(BatchStrideB), BatchStrideE_(BatchStrideE)
: BatchStrideA_(BatchStrideA),
BatchStrideB_(BatchStrideB),
BatchStrideDs_(BatchStrideDs),
BatchStrideE_(BatchStrideE)
{
}
......@@ -471,7 +490,16 @@ struct DeviceBatchedGemmMultiDXdl : public DeviceBatchedGemmMultiD<ALayout,
return g_idx * static_cast<long_index_t>(BatchStrideB_);
}
__host__ __device__ constexpr long_index_t GetCPtrOffset(index_t g_idx) const
__host__ __device__ constexpr auto GetDsPtrOffset(index_t g_idx) const
{
std::array<long_index_t, NumDTensor> ds_offset;
static_for<0, NumDTensor, 1>{}([&](auto i) {
ds_offset[i] = g_idx * static_cast<long_index_t>(BatchStrideDs_[i]);
});
return ds_offset;
}
__host__ __device__ constexpr long_index_t GetEPtrOffset(index_t g_idx) const
{
return g_idx * static_cast<long_index_t>(BatchStrideE_);
}
......@@ -479,6 +507,7 @@ struct DeviceBatchedGemmMultiDXdl : public DeviceBatchedGemmMultiD<ALayout,
private:
index_t BatchStrideA_;
index_t BatchStrideB_;
std::array<ck::index_t, NumDTensor> BatchStrideDs_;
index_t BatchStrideE_;
};
......@@ -535,41 +564,46 @@ struct DeviceBatchedGemmMultiDXdl : public DeviceBatchedGemmMultiD<ALayout,
// Argument
struct Argument : public BaseArgument
{
Argument(const ADataType* p_a_grid,
const BDataType* p_b_grid,
EDataType* p_e_grid,
Argument(const void* p_a_grid,
const void* p_b_grid,
std::array<const void*, NumDTensor> p_ds_grid,
void* p_e_grid,
index_t M,
index_t N,
index_t K,
index_t StrideA,
index_t StrideB,
std::array<ck::index_t, NumDTensor> StrideDs,
index_t StrideE,
index_t BatchStrideA,
index_t BatchStrideB,
std::array<ck::index_t, NumDTensor> BatchStrideDs,
index_t BatchStrideE,
index_t Batch,
index_t M01,
index_t N01,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation c_element_op)
: p_a_grid_{p_a_grid},
p_b_grid_{p_b_grid},
p_e_grid_{p_e_grid},
CDEElementwiseOperation cde_element_op)
: p_a_grid_{static_cast<const ADataType*>(p_a_grid)},
p_b_grid_{static_cast<const BDataType*>(p_b_grid)},
p_ds_grid_{}, // FIXME
p_e_grid_{static_cast<EDataType*>(p_e_grid)},
Batch_(Batch),
a_grid_desc_ak0_m_ak1_{
DeviceBatchedGemmMultiDXdl::MakeAGridDescriptor_AK0_M_AK1(M, K, StrideA)},
b_grid_desc_bk0_n_bk1_{
DeviceBatchedGemmMultiDXdl::MakeBGridDescriptor_BK0_N_BK1(K, N, StrideB)},
ds_grid_desc_mblock_mperblock_nblock_nperblock_{},
e_grid_desc_m_n_{DeviceBatchedGemmMultiDXdl::MakeEGridDescriptor_M_N(M, N, StrideE)},
e_grid_desc_mblock_mperblock_nblock_nperblock_{},
compute_ptr_offset_of_batch_{BatchStrideA, BatchStrideB, BatchStrideE},
compute_ptr_offset_of_batch_{BatchStrideA, BatchStrideB, BatchStrideDs, BatchStrideE},
block_2_ctile_map_{GridwiseGemm::MakeDefaultBlock2ETileMap(e_grid_desc_m_n_)},
M01_{M01},
N01_{N01},
a_element_op_{a_element_op},
b_element_op_{b_element_op},
cde_element_op_{c_element_op}
cde_element_op_{cde_element_op}
{
if(GridwiseGemm::CheckValidity(a_grid_desc_ak0_m_ak1_,
b_grid_desc_bk0_n_bk1_,
......@@ -579,6 +613,19 @@ struct DeviceBatchedGemmMultiDXdl : public DeviceBatchedGemmMultiD<ALayout,
e_grid_desc_mblock_mperblock_nblock_nperblock_ =
GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
e_grid_desc_m_n_);
static_for<0, NumDTensor, 1>{}([&](auto i) {
using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
p_ds_grid_(i) = static_cast<const DDataType*>(p_ds_grid[i]);
const auto d_grid_desc_m_n =
DeviceOp::MakeEGridDescriptor_M_N(M, N, StrideDs[i]);
ds_grid_desc_mblock_mperblock_nblock_nperblock_(i) =
GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
d_grid_desc_m_n);
});
}
}
......@@ -646,77 +693,57 @@ struct DeviceBatchedGemmMultiDXdl : public DeviceBatchedGemmMultiD<ALayout,
const auto K =
arg.a_grid_desc_ak0_m_ak1_.GetLength(I0) * arg.a_grid_desc_ak0_m_ak1_.GetLength(I2);
float ave_time = 0;
auto launch_kernel = [&](auto has_main_k_block_loop) {
constexpr bool has_main_loop = has_main_k_block_loop.value;
if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
{
const auto kernel = kernel_batched_gemm_xdl<
GridwiseGemm,
ADataType, // TODO: distiguish A/B datatype
typename GridwiseGemm::DsGridPointer,
EDataType,
remove_reference_t<DeviceBatchedGemmMultiDXdl::AGridDesc_AK0_M_AK1>,
remove_reference_t<DeviceBatchedGemmMultiDXdl::BGridDesc_BK0_N_BK1>,
DeviceOp::AGridDesc_AK0_M_AK1,
DeviceOp::BGridDesc_BK0_N_BK1,
ck::StaticallyIndexedArray<
typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
NumDTensor>,
typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
AElementwiseOperation,
BElementwiseOperation,
CDEElementwiseOperation,
ComputePtrOffsetOfStridedBatch,
remove_reference_t<Block2CTileMap>,
true>;
ave_time =
launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_e_grid_,
arg.Batch_,
arg.a_grid_desc_ak0_m_ak1_,
arg.b_grid_desc_bk0_n_bk1_,
arg.e_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.a_element_op_,
arg.b_element_op_,
arg.cde_element_op_,
arg.compute_ptr_offset_of_batch_,
arg.block_2_ctile_map_);
has_main_loop>;
return launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_ds_grid_,
arg.p_e_grid_,
arg.Batch_,
arg.a_grid_desc_ak0_m_ak1_,
arg.b_grid_desc_bk0_n_bk1_,
arg.ds_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.e_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.a_element_op_,
arg.b_element_op_,
arg.cde_element_op_,
arg.compute_ptr_offset_of_batch_,
arg.block_2_ctile_map_);
};
float ave_time = 0;
if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
{
ave_time = launch_kernel(integral_constant<bool, true>{});
}
else
{
const auto kernel = kernel_batched_gemm_xdl<
GridwiseGemm,
ADataType, // TODO: distiguish A/B datatype
EDataType,
remove_reference_t<DeviceBatchedGemmMultiDXdl::AGridDesc_AK0_M_AK1>,
remove_reference_t<DeviceBatchedGemmMultiDXdl::BGridDesc_BK0_N_BK1>,
typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
AElementwiseOperation,
BElementwiseOperation,
CDEElementwiseOperation,
ComputePtrOffsetOfStridedBatch,
remove_reference_t<Block2CTileMap>,
false>;
ave_time =
launch_and_time_kernel(stream_config,
kernel,
dim3(grid_size),
dim3(BlockSize),
0,
arg.p_a_grid_,
arg.p_b_grid_,
arg.p_e_grid_,
arg.Batch_,
arg.a_grid_desc_ak0_m_ak1_,
arg.b_grid_desc_bk0_n_bk1_,
arg.e_grid_desc_mblock_mperblock_nblock_nperblock_,
arg.a_element_op_,
arg.b_element_op_,
arg.cde_element_op_,
arg.compute_ptr_offset_of_batch_,
arg.block_2_ctile_map_);
ave_time = launch_kernel(integral_constant<bool, false>{});
}
return ave_time;
......@@ -750,81 +777,94 @@ struct DeviceBatchedGemmMultiDXdl : public DeviceBatchedGemmMultiD<ALayout,
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
}
static auto MakeArgument(const ADataType* p_a,
const BDataType* p_b,
EDataType* p_c,
static auto MakeArgument(const void* p_a,
const void* p_b,
std::array<const void*, NumDTensor> p_ds,
void* p_c,
index_t M,
index_t N,
index_t K,
index_t StrideA,
index_t StrideB,
std::array<index_t, NumDTensor> StrideDs,
index_t StrideE,
index_t BatchStrideA,
index_t BatchStrideB,
std::array<ck::index_t, NumDTensor> BatchStrideDs,
index_t BatchStrideE,
index_t Batch,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation c_element_op)
CDEElementwiseOperation cde_element_op)
{
return Argument{p_a,
p_b,
p_ds,
p_c,
M,
N,
K,
StrideA,
StrideB,
StrideDs,
StrideE,
BatchStrideA,
BatchStrideB,
BatchStrideDs,
BatchStrideE,
Batch,
1,
1,
a_element_op,
b_element_op,
c_element_op};
cde_element_op};
}
static auto MakeInvoker() { return Invoker{}; }
// polymorphic
std::unique_ptr<BaseArgument> MakeArgumentPointer(const void* p_a,
const void* p_b,
void* p_c,
index_t M,
index_t N,
index_t K,
index_t StrideA,
index_t StrideB,
index_t StrideE,
index_t BatchStrideA,
index_t BatchStrideB,
index_t BatchStrideE,
index_t Batch,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation c_element_op) override
std::unique_ptr<BaseArgument>
MakeArgumentPointer(const void* p_a,
const void* p_b,
std::array<const void*, NumDTensor> p_ds,
void* p_c,
index_t M,
index_t N,
index_t K,
index_t StrideA,
index_t StrideB,
std::array<ck::index_t, NumDTensor> StrideDs,
index_t StrideE,
index_t BatchStrideA,
index_t BatchStrideB,
std::array<ck::index_t, NumDTensor> BatchStrideDs,
index_t BatchStrideE,
index_t Batch,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation cde_element_op) override
{
return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
static_cast<const BDataType*>(p_b),
static_cast<EDataType*>(p_c),
return std::make_unique<Argument>(p_a,
p_b,
p_ds,
p_c,
M,
N,
K,
StrideA,
StrideB,
StrideDs,
StrideE,
BatchStrideA,
BatchStrideB,
BatchStrideDs,
BatchStrideE,
Batch,
1,
1,
a_element_op,
b_element_op,
c_element_op);
cde_element_op);
}
// polymorphic
......
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