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Commit 419e41c5 authored by muozturk's avatar muozturk
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validation issue

parent c0ff964b
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Showing with 62 additions and 50 deletions
example/64_complex_contraction/complex_contraction_bilinear_xdl_fp32.cpp
View file @ 419e41c5
......@@ -34,6 +34,7 @@ using CShuffleDataType = F32;
using DDataType = F32;
using DsDataType = ck::Tuple<DDataType>;
using EDataType = F32;
using ComputeDataType = F32;
static constexpr ck::index_t NumDimM = 2;
static constexpr ck::index_t NumDimN = 2;
......@@ -51,7 +52,7 @@ using DeviceOpInstanceKKNN = ck::tensor_operation::device::
//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceContractionMultipleD_Xdl_CShuffle< NumDimM, NumDimN, NumDimK, F32, F32, F32, F32, DsDataType, F32, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 256, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4>;
DeviceContractionMultipleD_Xdl_CShuffle< NumDimM, NumDimN, NumDimK, F32, F32, F32, F32, DsDataType, F32, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 256, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, 4, ComputeDataType>;
using DeviceOpInstanceKNNN = ck::tensor_operation::device::
//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
......@@ -77,11 +78,13 @@ using DeviceOpInstanceMNNN = ck::tensor_operation::device::
using DeviceOpInstance = DeviceOpInstanceKKNN;
// using DeviceOpInstance = DeviceOpInstanceMNNN;
int main(int argc, char* argv[])
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
bool time_kernel = true;
// A[M0, M1, K0, K1]
std::vector<ck::index_t> a_ms_ks_lengths{30, 128, 32, 64};
......@@ -173,19 +176,19 @@ int main(int argc, char* argv[])
Tensor<EDataType> e_ms_ns_host_result_img(e_ms_ns_lengths, e_ms_ns_strides);
Tensor<EDataType> e_ms_ns_device_result_img(e_ms_ns_lengths, e_ms_ns_strides);
// Intermediate E tensor Definition
Tensor<EDataType> e_ms_ns_device_result_re1(e_ms_ns_lengths, e_ms_ns_strides);
// Tensor<EDataType> e_ms_ns_device_result_re2(e_ms_ns_lengths, e_ms_ns_strides);
Tensor<EDataType> e_ms_ns_device_result_img1(e_ms_ns_lengths, e_ms_ns_strides);
// Tensor<EDataType> e_ms_ns_device_result_img2(e_ms_ns_lengths, e_ms_ns_strides);
std::cout << "a_ms_ks_re: " << a_ms_ks_re.mDesc << std::endl;
std::cout << "b_ns_ks_re: " << b_ns_ks_re.mDesc << std::endl;
std::cout << "d_ms_ns_re: " << d_ms_ns_re.mDesc << std::endl;
std::cout << "e_ms_ns_re: " << e_ms_ns_host_result_re.mDesc << std::endl;
std::cout << "a_ms_ks: " << a_ms_ks_re.mDesc << std::endl;
std::cout << "b_ns_ks: " << b_ns_ks_re.mDesc << std::endl;
std::cout << "d_ms_ns: " << d_ms_ns_re.mDesc << std::endl;
std::cout << "e_ms_ns: " << e_ms_ns_host_result_re.mDesc << std::endl;
std::cout << "a_ms_ks_img: " << a_ms_ks_img.mDesc << std::endl;
std::cout << "b_ns_ks_img: " << b_ns_ks_img.mDesc << std::endl;
std::cout << "d_ms_ns_img: " << d_ms_ns_img.mDesc << std::endl;
std::cout << "e_ms_ns_img: " << e_ms_ns_host_result_img.mDesc << std::endl;
switch(init_method)
{
......@@ -241,13 +244,8 @@ int main(int argc, char* argv[])
e_device_buf_img.SetZero();
// set zero for intermediate values
// LookAtHere
e_device_buf_re1.SetZero();
// e_device_buf_re2.SetZero();
e_device_buf_img1.SetZero();
// e_device_buf_img2.SetZero();
// LookAtHere
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
......@@ -409,13 +407,13 @@ int main(int argc, char* argv[])
e_device_buf_img.FromDevice(e_ms_ns_device_result_img.mData.data());
auto isRealOk = 0;
if(do_verification)
{
// Real Part Verification
Tensor<CShuffleDataType> c_ms_ns_host_result_re(e_ms_ns_lengths, e_ms_ns_strides);
Tensor<CShuffleDataType> c_ms_ns_host_result_re1(e_ms_ns_lengths, e_ms_ns_strides);
using ReferenceOpInstance =
ck::tensor_operation::host::ReferenceContraction_M2_N2_K2<NumDimM,
NumDimN,
......@@ -437,17 +435,6 @@ int main(int argc, char* argv[])
ref_invoker.Run(ref_argument_re);
auto ref_argument_re1 =
ref_op.MakeArgument(a_ms_ks_img, b_ns_ks_img, c_ms_ns_host_result_re1, a_element_op, b_element_op);
ref_invoker.Run(ref_argument_re1);
// Image Part Verification
Tensor<CShuffleDataType> c_ms_ns_host_result_img(e_ms_ns_lengths, e_ms_ns_strides);
auto ref_argument_img =
ref_op.MakeArgument(a_ms_ks_img, b_ns_ks_img, c_ms_ns_host_result_img, a_element_op, b_element_op);
for(size_t m0 = 0; m0 < e_ms_ns_host_result_re.mDesc.GetLengths()[0]; ++m0)
{
for(size_t m1 = 0; m1 < e_ms_ns_host_result_re.mDesc.GetLengths()[1]; ++m1)
......@@ -469,8 +456,33 @@ int main(int argc, char* argv[])
cde_element_op = CDEElementOp{alpha, beta};
isRealOk = ck::utils::check_err(e_ms_ns_device_result_re, e_ms_ns_host_result_re) ? 0 : 1;
auto ref_argument_re1 =
ref_op.MakeArgument(a_ms_ks_img, b_ns_ks_img, c_ms_ns_host_result_re1, a_element_op, b_element_op);
ref_invoker.Run(ref_argument_re1);
for(size_t m0 = 0; m0 < e_ms_ns_host_result_re.mDesc.GetLengths()[0]; ++m0)
{
for(size_t m1 = 0; m1 < e_ms_ns_host_result_re.mDesc.GetLengths()[1]; ++m1)
{
for(size_t n0 = 0; n0 < e_ms_ns_host_result_re.mDesc.GetLengths()[2]; ++n0)
{
for(size_t n1 = 0; n1 < e_ms_ns_host_result_re.mDesc.GetLengths()[3]; ++n1)
{
cde_element_op(e_ms_ns_host_result_re(m0, m1, n0, n1),
c_ms_ns_host_result_re(m0, m1, n0, n1),
c_ms_ns_host_result_re1(m0, m1, n0, n1));
}
}
}
}
isRealOk = ck::utils::check_err(e_ms_ns_device_result_re, e_ms_ns_host_result_re) ? 0 : 1;
Tensor<CShuffleDataType> c_ms_ns_host_result_img(e_ms_ns_lengths, e_ms_ns_strides);
return isRealOk;
}
return 0;
}
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