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Fix building issue for examples (#66)

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* fix build issue
This commit is contained in:
Chao Liu
2022-01-17 23:49:04 -06:00
committed by GitHub
parent acbd7bd7c5
commit 6260ced2f3
3 changed files with 43 additions and 145 deletions
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16
example/1_gemm_xdl/gemm_xdl.cpp
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@@ -34,11 +34,11 @@ using CElementOp = ck::tensor_operation::element_wise::PassThrough;
// Compilation parameters for NT problem
// clang-format off
using DeviceGemmInstance =
//#########################################| AData| BData| CData| AccData| ALayout| BLayout| CLayout| AElementwise| BElementwise| CElementwise| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| CThreadTransfer| CThreadTransfer| ABlockLds| BBlockLds|
//#########################################| Type| Type| Type| Type| | | | Operation| Operation| Operation| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| SrcDstVectorDim| DstScalar| AddExtraM| AddExtraN|
//#########################################| | | | | | | | | | | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerVector| | |
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
ck::tensor_operation::device::DeviceGemmXdl< ADataType, BDataType, CDataType, AccDataType, ALayout, BLayout, CLayout, AElementOp, BElementOp, CElementOp, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 7, 1, true, true>;
//#########################################| AData| BData| CData| AccData| ALayout| BLayout| CLayout| AElementwise| BElementwise| CElementwise| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
//#########################################| Type| Type| Type| Type| | | | Operation| Operation| Operation| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
//#########################################| | | | | | | | | | | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
ck::tensor_operation::device::DeviceGemmXdl< ADataType, BDataType, CDataType, AccDataType, ALayout, BLayout, CLayout, AElementOp, BElementOp, CElementOp, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>;
// clang-format on
template <typename AType,
@@ -90,9 +90,9 @@ int main(int argc, char* argv[])
if(argc == 4)
{
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
}
else if(argc == 10)
{

109
example/3_gemm_xdl_bias_relu_add/gemm_xdl_bias_relu_add.cpp
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@@ -37,7 +37,7 @@ struct BiasReluAdd
{
#if 0
float a = v1 + v0;
float b = max(a, float(0));
float b = a > 0 ? a : 0;
float c = b + v2;
return c;
@@ -52,70 +52,13 @@ struct BiasReluAdd
}
};
// v0 is from A * B
// v1 is from C0
// v2 is from C1
struct BiasLeakyReluAdd
{
template <typename T1, typename T2>
__host__ constexpr float operator()(float v0, T1 v1, T2 v2) const
{
float a = v0 + v1;
float b = 0.1 * a;
float c = b > 0 ? b : 0;
float d = c + v2;
return d;
}
template <typename T1, typename T2>
__device__ constexpr float operator()(float v0, T1 v1, T2 v2) const
{
constexpr float alpha = 0.1;
constexpr float alpha_inv = 1.0 / alpha;
float a = v2 * alpha_inv;
float b = v1 + v0;
float c = max(b, float(0));
float d = alpha * (a + c);
return d;
}
};
struct BiasLeakyRelu
{
template <typename T1, typename T2>
__host__ constexpr float operator()(float v0, T1 v1, T2) const
{
float a = v0 + v1;
float b = 0.1 * a;
float c = b > 0 ? b : 0;
return c;
}
template <typename T1, typename T2>
__device__ constexpr float operator()(float v0, T1 v1, T2) const
{
constexpr float alpha = 0.1;
float b = v1 + v0;
float c = max(b, float(0));
float d = alpha * c;
return d;
}
};
struct BiasAdd
struct DoSomething
{
#if 1
// correct result
// no scratch memory, good VGPR allocation (59)
// good perf (101Tflops)
template <typename T1, typename T2>
__host__ __device__ constexpr float operator()(float v0, T1 v1, T2 v2) const
// good perf (101Tflops @ 1089Mhz)
__host__ __device__ constexpr float operator()(float v0, ck::half_t v1, ck::half_t v2) const
{
constexpr float alpha = 0.1;
constexpr float beta = 0.2;
@@ -124,7 +67,7 @@ struct BiasAdd
// compiler seems very volatile to the order of these calculation:
// compiler is very eager to read AccVgpr (v0) out prematurely, resulting in register
// over-allocation. Therefore, move v0 calculation to the very end
float a = T1(beta) * v1 + T2(gamma) * v2;
float a = ck::half_t(beta) * v1 + ck::half_t(gamma) * v2;
float b = a + float(alpha) * v0;
return b;
@@ -137,15 +80,14 @@ struct BiasAdd
// wrong result
// lots of scratch memory
// huge perf drop
template <typename T1, typename T2>
__host__ __device__ constexpr float operator()(float v0, T1 v1, T2 v2) const
__host__ __device__ constexpr float operator()(float v0, ck::half_t v1, ck::half_t v2) const
{
return alpha * v0 + beta * v1 + gamma * v2;
}
#elif 0
// correct result
// some scratch memory (68 dword)
// some perf drop (94Tflops)
// some perf drop (94Tflops @ 1089MHz)
// fp64 instructions are used
__host__ __device__ constexpr auto operator()(float v0, ck::half_t v1, ck::half_t v2) const
{
@@ -185,16 +127,20 @@ using CLayout = ck::tensor_layout::gemm::RowMajor;
using AOp = PassThrough;
using BOp = PassThrough;
#if 1
using COp = BiasReluAdd;
#else
using COp = DoSomething;
#endif
// Compilation parameters for NT problem
// clang-format off
using DeviceGemmInstance =
//#################################################################| AData| BData| CData| AccData| ALayout| BLayout| CLayout| AElementwise| BElementwise| CElementwise| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| CThreadTransfer| CThreadTransfer| ABlockLds| BBlockLds|
//#################################################################| Type| Type| Type| Type| | | | Operation| Operation| Operation| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| SrcDstVectorDim| DstScalar| AddExtraM| AddExtraN|
//#################################################################| | | | | | | | | | | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerVector| | |
//#################################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
ck::tensor_operation::device::DeviceGemmXdl_two_extra_source_reduce< ADataType, BDataType, CDataType, AccDataType, ALayout, BLayout, CLayout, AOp, BOp, COp, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 7, 1, true, true>;
//#################################################################| AData| BData| CData| AccData| ALayout| BLayout| CLayout| AElementwise| BElementwise| CElementwise| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
//#################################################################| Type| Type| Type| Type| | | | Operation| Operation| Operation| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
//#################################################################| | | | | | | | | | | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
//#################################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
ck::tensor_operation::device::DeviceGemmXdl_two_extra_source_reduce< ADataType, BDataType, CDataType, AccDataType, ALayout, BLayout, CLayout, AOp, BOp, COp, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>;
// clang-format on
template <typename AType,
@@ -215,16 +161,15 @@ static void host_verify(const Tensor<AType>& a_m_k,
auto f_mk_kn_mn = [&](auto m, auto n) {
const int K = a_m_k.mDesc.GetLengths()[1];
double v = 0;
float acc = 0;
for(int k = 0; k < K; ++k)
{
v += static_cast<const double>(a_element_op(a_m_k(m, k))) *
static_cast<const double>(b_element_op(b_k_n(k, n)));
acc += static_cast<const double>(a_element_op(a_m_k(m, k))) *
static_cast<const double>(b_element_op(b_k_n(k, n)));
}
c_m_n(m, n) = c_element_op(
v, static_cast<const double>(c0_m_n(m, n)), static_cast<const double>(c1_m_n(m, n)));
c_m_n(m, n) = c_element_op(acc, c0_m_n(m, n), c1_m_n(m, n));
};
make_ParallelTensorFunctor(f_mk_kn_mn,
@@ -249,9 +194,9 @@ int main(int argc, char* argv[])
if(argc == 4)
{
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
nrepeat = std::stoi(argv[3]);
}
else if(argc == 10)
{
@@ -337,7 +282,9 @@ int main(int argc, char* argv[])
c0_m_n_device_buf.ToDevice(c0_m_n.mData.data());
c1_m_n_device_buf.ToDevice(c1_m_n.mData.data());
auto c_element_op = BiasReluAdd{};
auto a_element_op = AOp{};
auto b_element_op = BOp{};
auto c_element_op = COp{};
// do GEMM
auto gemm = DeviceGemmInstance{};
@@ -354,8 +301,8 @@ int main(int argc, char* argv[])
StrideA,
StrideB,
StrideC,
PassThrough{},
PassThrough{},
a_element_op,
b_element_op,
c_element_op);
if(!gemm.IsSupportedArgument(argument))

63
example/3_gemm_xdl_bias_relu_add/include/device_gemm_xdl_two_extra_source_reduce.hpp
View File

@@ -35,24 +35,22 @@ template <typename ADataType,
ck::index_t NPerXDL,
ck::index_t MXdlPerWave,
ck::index_t NXdlPerWave,
typename ABlockTransferThreadSliceLengths_K0_M_K1,
typename ABlockTransferThreadClusterLengths_K0_M_K1,
typename ABlockTransferThreadClusterArrangeOrder,
typename ABlockTransferSrcAccessOrder,
ck::index_t ABlockTransferSrcVectorDim,
ck::index_t ABlockTransferSrcScalarPerVector,
ck::index_t ABlockTransferDstScalarPerVector_K1,
typename BBlockTransferThreadSliceLengths_K0_N_K1,
bool ABlockLdsAddExtraM,
typename BBlockTransferThreadClusterLengths_K0_N_K1,
typename BBlockTransferThreadClusterArrangeOrder,
typename BBlockTransferSrcAccessOrder,
ck::index_t BBlockTransferSrcVectorDim,
ck::index_t BBlockTransferSrcScalarPerVector,
ck::index_t BBlockTransferDstScalarPerVector_K1,
bool BBlockLdsAddExtraN,
ck::index_t CThreadTransferSrcDstVectorDim,
ck::index_t CThreadTransferDstScalarPerVector,
bool ABlockLdsAddExtraM,
bool BBlockLdsAddExtraN>
ck::index_t CThreadTransferDstScalarPerVector>
struct DeviceGemmXdl_two_extra_source_reduce : public BaseOperator
{
static constexpr auto I0 = Number<0>{};
@@ -137,45 +135,6 @@ struct DeviceGemmXdl_two_extra_source_reduce : public BaseOperator
using C1GridDesc_M_N =
decltype(make_naive_tensor_descriptor(make_tuple(1, 1), make_tuple(I1, I0)));
// TODO remove these hacks
static constexpr auto a_k0_m_k1_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0>{}, // 0+: K0
Sequence<0, 0, 0>{}, // 1+: M
Sequence<0, 0, 0>{}), // 2+: K1
make_tuple(Sequence<0, 0, 0>{}, // 0-: K0
Sequence<0, 0, 0>{}, // 1-: M
Sequence<0, 0, 0>{})); // 2-: K1
static constexpr auto b_k0_n_k1_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0>{}, // 0+: K0
Sequence<0, 0, 0>{}, // 1+: N
Sequence<0, 0, 0>{}), // 2+: K1
make_tuple(Sequence<0, 0, 0>{}, // 0-: K0
Sequence<0, 0, 0>{}, // 1-: N
Sequence<0, 0, 0>{})); // 2-: K1
static constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1+: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2+: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3+: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4+: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5+: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6+: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 7+: N2
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0-: M0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1-: N0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 2-: M1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 3-: N1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 4-: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 5-: M3
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6-: M4
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
static constexpr auto a_k0_m_k1_grid_move_slice_window_step_hacks = Sequence<0, 0, 0>{};
static constexpr auto b_k0_n_k1_grid_move_slice_window_step_hacks = Sequence<0, 0, 0>{};
// GridwiseGemm
using GridwiseGemm = GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r5<
BlockSize,
@@ -199,7 +158,6 @@ struct DeviceGemmXdl_two_extra_source_reduce : public BaseOperator
K1,
MXdlPerWave,
NXdlPerWave,
ABlockTransferThreadSliceLengths_K0_M_K1,
ABlockTransferThreadClusterLengths_K0_M_K1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
@@ -207,25 +165,18 @@ struct DeviceGemmXdl_two_extra_source_reduce : public BaseOperator
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_K1,
false, // AThreadTransferSrcResetCoordinateAfterRun,
BBlockTransferThreadSliceLengths_K0_N_K1,
ABlockLdsAddExtraM,
BBlockTransferThreadClusterLengths_K0_N_K1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_K1,
false, // BThreadTransferSrcResetCoordinateAfterRun,
false, // BThreadTransferSrcResetCoordinateAfterRun,
BBlockLdsAddExtraN,
Sequence<0, 2, 4, 5, 6, 1, 3, 7>, // CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
decltype(a_k0_m_k1_grid_step_hacks), // AGridStepHacks,
decltype(b_k0_n_k1_grid_step_hacks), // BGridStepHacks,
decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks), // CGridStepHacks,
decltype(a_k0_m_k1_grid_move_slice_window_step_hacks), // AGridMoveSliceWindowStepHacks,
decltype(b_k0_n_k1_grid_move_slice_window_step_hacks), // BGridMoveSliceWindowStepHacks,
false, // CAccessOrderMRepeatNRepeat,
ABlockLdsAddExtraM,
BBlockLdsAddExtraN>;
CThreadTransferDstScalarPerVector>;
using CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2 =
decltype(GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(CGridDesc_M_N{}));