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Unverified Commit 35e49f2d authored by zjing14's avatar zjing14 Committed by GitHub
Browse files

add g; fixed strides (#355)

parent de60d290
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Showing with 279 additions and 250 deletions
example/25_gemm_bias_e_permute/CMakeLists.txt
View file @ 35e49f2d
add_example_executable(example_gemm_bias_e_permute_m3n2_xdl_fp16 gemm_bias_e_permute_m3n2_xdl_fp16.cpp)
add_example_executable(example_gemm_bias_e_permute_m2n3_xdl_fp16 gemm_bias_e_permute_m2n3_xdl_fp16.cpp)
add_example_executable(example_gemm_bias_e_permute_g1m3n2k1_xdl_fp16 gemm_bias_e_permute_g1m3n2k1_xdl_fp16.cpp)
add_example_executable(example_gemm_bias_e_permute_g1m2n3k1_xdl_fp16 gemm_bias_e_permute_g1m2n3k1_xdl_fp16.cpp)
example/25_gemm_bias_e_permute/gemm_bias_e_permute_m2n3_xdl_fp16.cpp → example/25_gemm_bias_e_permute/gemm_bias_e_permute_g1m2n3k1_xdl_fp16.cpp
View file @ 35e49f2d
......@@ -16,6 +16,8 @@
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
......@@ -33,7 +35,7 @@ using DDataType = F16;
using DsDataType = ck::Tuple<DDataType>;
using EDataType = F16;
static constexpr ck::index_t NumDimG = 0;
static constexpr ck::index_t NumDimG = 1;
static constexpr ck::index_t NumDimM = 2;
static constexpr ck::index_t NumDimN = 3;
static constexpr ck::index_t NumDimK = 1;
......@@ -69,30 +71,31 @@ template <ck::index_t NumDimM,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CDEElementwiseOperation,
ck::enable_if_t<NumDimM == 2 && NumDimN == 3 && NumDimK == 1, bool> = false>
struct ReferenceContraction_M2_N3_K1 : public ck::tensor_operation::device::BaseOperator
ck::enable_if_t<NumDimG == 1 && NumDimM == 2 && NumDimN == 3 && NumDimK == 1, bool> =
false>
struct ReferenceContraction_G1_M2_N3_K1 : public ck::tensor_operation::device::BaseOperator
{
// Argument
struct Argument : public ck::tensor_operation::device::BaseArgument
{
Argument(const Tensor<ADataType>& a_ms_ks,
const Tensor<BDataType>& b_ns_ks,
Tensor<EDataType>& e_ms_ns,
Argument(const Tensor<ADataType>& a_gs_ms_ks,
const Tensor<BDataType>& b_gs_ns_ks,
Tensor<EDataType>& e_gs_ms_ns,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation cde_element_op)
: a_ms_ks_{a_ms_ks},
b_ns_ks_{b_ns_ks},
e_ms_ns_{e_ms_ns},
: a_gs_ms_ks_{a_gs_ms_ks},
b_gs_ns_ks_{b_gs_ns_ks},
e_gs_ms_ns_{e_gs_ms_ns},
a_element_op_{a_element_op},
b_element_op_{b_element_op},
cde_element_op_{cde_element_op}
{
}
const Tensor<ADataType>& a_ms_ks_;
const Tensor<BDataType>& b_ns_ks_;
Tensor<EDataType>& e_ms_ns_;
const Tensor<ADataType>& a_gs_ms_ks_;
const Tensor<BDataType>& b_gs_ns_ks_;
Tensor<EDataType>& e_gs_ms_ns_;
AElementwiseOperation a_element_op_;
BElementwiseOperation b_element_op_;
......@@ -102,12 +105,12 @@ struct ReferenceContraction_M2_N3_K1 : public ck::tensor_operation::device::Base
// Invoker
struct Invoker : public ck::tensor_operation::device::BaseInvoker
{
using Argument = ReferenceContraction_M2_N3_K1::Argument;
using Argument = ReferenceContraction_G1_M2_N3_K1::Argument;
float Run(const Argument& arg)
{
auto f_ms_ns = [&](auto m0, auto m1, auto n0, auto n1, auto n2) {
const int K0 = arg.a_ms_ks_.mDesc.GetLengths()[2];
auto f_gs_ms_ns = [&](auto g0, auto m0, auto m1, auto n0, auto n1, auto n2) {
const int K0 = arg.a_gs_ms_ks_.mDesc.GetLengths()[3];
AccDataType v_acc = 0;
......@@ -117,9 +120,10 @@ struct ReferenceContraction_M2_N3_K1 : public ck::tensor_operation::device::Base
AccDataType v_b;
arg.a_element_op_(
v_a, ck::type_convert<const AccDataType>(arg.a_ms_ks_(m0, m1, k0)));
v_a, ck::type_convert<const AccDataType>(arg.a_gs_ms_ks_(g0, m0, m1, k0)));
arg.b_element_op_(
v_b, ck::type_convert<const AccDataType>(arg.b_ns_ks_(n0, n1, n2, k0)));
v_b,
ck::type_convert<const AccDataType>(arg.b_gs_ns_ks_(g0, n0, n1, n2, k0)));
v_acc += v_a * v_b;
}
......@@ -128,15 +132,16 @@ struct ReferenceContraction_M2_N3_K1 : public ck::tensor_operation::device::Base
arg.cde_element_op_(v_c, v_acc);
arg.e_ms_ns_(m0, m1, n0, n1, n2) = v_c;
arg.e_gs_ms_ns_(g0, m0, m1, n0, n1, n2) = v_c;
};
make_ParallelTensorFunctor(f_ms_ns,
arg.e_ms_ns_.mDesc.GetLengths()[0],
arg.e_ms_ns_.mDesc.GetLengths()[1],
arg.e_ms_ns_.mDesc.GetLengths()[2],
arg.e_ms_ns_.mDesc.GetLengths()[3],
arg.e_ms_ns_.mDesc.GetLengths()[4])(
make_ParallelTensorFunctor(f_gs_ms_ns,
arg.e_gs_ms_ns_.mDesc.GetLengths()[0],
arg.e_gs_ms_ns_.mDesc.GetLengths()[1],
arg.e_gs_ms_ns_.mDesc.GetLengths()[2],
arg.e_gs_ms_ns_.mDesc.GetLengths()[3],
arg.e_gs_ms_ns_.mDesc.GetLengths()[4],
arg.e_gs_ms_ns_.mDesc.GetLengths()[5])(
std::thread::hardware_concurrency());
return 0;
......@@ -160,14 +165,15 @@ struct ReferenceContraction_M2_N3_K1 : public ck::tensor_operation::device::Base
return true;
}
static auto MakeArgument(const Tensor<ADataType>& a_ms_ks,
const Tensor<BDataType>& b_ns_ks,
Tensor<EDataType>& e_ms_ns,
static auto MakeArgument(const Tensor<ADataType>& a_gs_ms_ks,
const Tensor<BDataType>& b_gs_ns_ks,
Tensor<EDataType>& e_gs_ms_ns,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation cde_element_op)
{
return Argument{a_ms_ks, b_ns_ks, e_ms_ns, a_element_op, b_element_op, cde_element_op};
return Argument{
a_gs_ms_ks, b_gs_ns_ks, e_gs_ms_ns, a_element_op, b_element_op, cde_element_op};
}
static auto MakeInvoker() { return Invoker{}; }
......@@ -196,28 +202,31 @@ int main(int argc, char* argv[])
int init_method = 1;
bool time_kernel = false;
ck::index_t G0 = 1;
ck::index_t M0 = 4;
ck::index_t M1 = 256;
ck::index_t N0 = 4;
ck::index_t N1 = 8;
ck::index_t N2 = 128;
ck::index_t N1 = 16;
ck::index_t N2 = 32;
ck::index_t K0 = 256;
// A[M0, M1, M2, K0]
std::vector<ck::index_t> a_ms_ks_lengths{M0, M1, K0};
std::vector<ck::index_t> a_ms_ks_strides{M1 * K0, K0, 1};
std::vector<ck::index_t> a_gs_ms_ks_lengths{G0, M0, M1, K0};
std::vector<ck::index_t> a_gs_ms_ks_strides{M0 * M1 * K0, M1 * K0, K0, 1};
// B[N0, N1, K0]
std::vector<ck::index_t> b_ns_ks_lengths{N0, N1, N2, K0};
std::vector<ck::index_t> b_ns_ks_strides{N1 * N2 * K0, N2 * K0, K0, 1};
std::vector<ck::index_t> b_gs_ns_ks_lengths{G0, N0, N1, N2, K0};
std::vector<ck::index_t> b_gs_ns_ks_strides{N0 * N1 * N2 * K0, N1 * N2 * K0, N2 * K0, K0, 1};
// D[N0, M0, N1, M1, N2]
std::vector<ck::index_t> d_ms_ns_lengths{M0, M1, N0, N1, N2};
std::vector<ck::index_t> d_ms_ns_strides{0, 0, N1 * N2, N1, 1};
std::vector<ck::index_t> d_gs_ms_ns_lengths{G0, M0, M1, N0, N1, N2};
std::vector<ck::index_t> d_gs_ms_ns_strides{N0 * N1 * N2, 0, 0, N1 * N2, N2, 1};
// E[N0, M0, N1, M1, N2]
std::vector<ck::index_t> e_ms_ns_lengths{M0, M1, N0, N1, N2};
std::vector<ck::index_t> e_ms_ns_strides{N1 * M1 * N2, N2, M0 * N1 * M1 * N2, M1 * N2, 1};
std::vector<ck::index_t> e_gs_ms_ns_lengths{G0, M0, M1, N0, N1, N2};
std::vector<ck::index_t> e_gs_ms_ns_strides{
M0 * M1 * N0 * N1 * N2, N1 * M1 * N2, N2, M0 * N1 * M1 * N2, M1 * N2, 1};
if(argc == 1)
{
......@@ -237,50 +246,51 @@ int main(int argc, char* argv[])
exit(0);
}
Tensor<ADataType> a_ms_ks(
std::vector<std::size_t>(a_ms_ks_lengths.begin(), a_ms_ks_lengths.end()),
std::vector<std::size_t>(a_ms_ks_strides.begin(), a_ms_ks_strides.end()));
Tensor<BDataType> b_ns_ks(
std::vector<std::size_t>(b_ns_ks_lengths.begin(), b_ns_ks_lengths.end()),
std::vector<std::size_t>(b_ns_ks_strides.begin(), b_ns_ks_strides.end()));
Tensor<DDataType> d_ms_ns(
std::vector<std::size_t>(d_ms_ns_lengths.begin(), d_ms_ns_lengths.end()),
std::vector<std::size_t>(d_ms_ns_strides.begin(), d_ms_ns_strides.end()));
Tensor<EDataType> e_ms_ns_host_result(
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
Tensor<EDataType> e_ms_ns_device_result(
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
std::cout << "a_ms_ks: " << a_ms_ks.mDesc << std::endl;
std::cout << "b_ns_ks: " << b_ns_ks.mDesc << std::endl;
std::cout << "d_ms_ns: " << d_ms_ns.mDesc << std::endl;
std::cout << "e_ms_ns: " << e_ms_ns_host_result.mDesc << std::endl;
Tensor<ADataType> a_gs_ms_ks(
std::vector<std::size_t>(a_gs_ms_ks_lengths.begin(), a_gs_ms_ks_lengths.end()),
std::vector<std::size_t>(a_gs_ms_ks_strides.begin(), a_gs_ms_ks_strides.end()));
Tensor<BDataType> b_gs_ns_ks(
std::vector<std::size_t>(b_gs_ns_ks_lengths.begin(), b_gs_ns_ks_lengths.end()),
std::vector<std::size_t>(b_gs_ns_ks_strides.begin(), b_gs_ns_ks_strides.end()));
Tensor<DDataType> d_gs_ms_ns(
std::vector<std::size_t>(d_gs_ms_ns_lengths.begin(), d_gs_ms_ns_lengths.end()),
std::vector<std::size_t>(d_gs_ms_ns_strides.begin(), d_gs_ms_ns_strides.end()));
Tensor<EDataType> e_gs_ms_ns_host_result(
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
Tensor<EDataType> e_gs_ms_ns_device_result(
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
std::cout << "a_gs_ms_ks: " << a_gs_ms_ks.mDesc << std::endl;
std::cout << "b_gs_ns_ks: " << b_gs_ns_ks.mDesc << std::endl;
std::cout << "d_gs_ms_ns: " << d_gs_ms_ns.mDesc << std::endl;
std::cout << "e_gs_ms_ns: " << e_gs_ms_ns_host_result.mDesc << std::endl;
switch(init_method)
{
case 0: break;
case 1:
a_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
b_ns_ks.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
d_ms_ns.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
b_gs_ns_ks.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
d_gs_ms_ns.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
break;
default:
a_ms_ks.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_ns_ks.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
d_ms_ns.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_gs_ns_ks.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
d_gs_ms_ns.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
break;
}
DeviceMem a_device_buf(sizeof(ADataType) * a_ms_ks.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf(sizeof(BDataType) * b_ns_ks.mDesc.GetElementSpaceSize());
DeviceMem d_device_buf(sizeof(DDataType) * d_ms_ns.mDesc.GetElementSpaceSize());
DeviceMem e_device_buf(sizeof(EDataType) * e_ms_ns_device_result.mDesc.GetElementSpaceSize());
DeviceMem a_device_buf(sizeof(ADataType) * a_gs_ms_ks.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf(sizeof(BDataType) * b_gs_ns_ks.mDesc.GetElementSpaceSize());
DeviceMem d_device_buf(sizeof(DDataType) * d_gs_ms_ns.mDesc.GetElementSpaceSize());
DeviceMem e_device_buf(sizeof(EDataType) *
e_gs_ms_ns_device_result.mDesc.GetElementSpaceSize());
a_device_buf.ToDevice(a_ms_ks.mData.data());
b_device_buf.ToDevice(b_ns_ks.mData.data());
d_device_buf.ToDevice(d_ms_ns.mData.data());
a_device_buf.ToDevice(a_gs_ms_ks.mData.data());
b_device_buf.ToDevice(b_gs_ns_ks.mData.data());
d_device_buf.ToDevice(d_gs_ms_ns.mData.data());
// set zero
e_device_buf.SetZero();
......@@ -296,14 +306,14 @@ int main(int argc, char* argv[])
b_device_buf.GetDeviceBuffer(),
std::array<const void*, 1>{d_device_buf.GetDeviceBuffer()},
e_device_buf.GetDeviceBuffer(),
a_ms_ks_lengths,
a_ms_ks_strides,
b_ns_ks_lengths,
b_ns_ks_strides,
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_lengths},
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_strides},
e_ms_ns_lengths,
e_ms_ns_strides,
a_gs_ms_ks_lengths,
a_gs_ms_ks_strides,
b_gs_ns_ks_lengths,
b_gs_ns_ks_strides,
std::array<std::vector<ck::index_t>, 1>{d_gs_ms_ns_lengths},
std::array<std::vector<ck::index_t>, 1>{d_gs_ms_ns_strides},
e_gs_ms_ns_lengths,
e_gs_ms_ns_strides,
a_element_op,
b_element_op,
cde_element_op);
......@@ -317,18 +327,18 @@ int main(int argc, char* argv[])
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
ck::index_t M = std::accumulate(e_ms_ns_lengths.begin(),
e_ms_ns_lengths.begin() + NumDimM,
std::size_t M = std::accumulate(e_gs_ms_ns_lengths.begin() + NumDimG,
e_gs_ms_ns_lengths.begin() + NumDimG + NumDimM,
ck::index_t{1},
std::multiplies<ck::index_t>{});
ck::index_t N = std::accumulate(e_ms_ns_lengths.begin() + NumDimM,
e_ms_ns_lengths.begin() + NumDimM + NumDimN,
std::size_t N = std::accumulate(e_gs_ms_ns_lengths.begin() + NumDimG + NumDimM,
e_gs_ms_ns_lengths.begin() + NumDimG + NumDimM + NumDimN,
ck::index_t{1},
std::multiplies<ck::index_t>{});
ck::index_t K = std::accumulate(a_ms_ks_lengths.begin() + NumDimM,
a_ms_ks_lengths.begin() + NumDimM + NumDimK,
std::size_t K = std::accumulate(a_gs_ms_ks_lengths.begin() + NumDimG + NumDimM,
a_gs_ms_ks_lengths.begin() + NumDimG + NumDimM + NumDimK,
ck::index_t{1},
std::multiplies<ck::index_t>{});
......@@ -343,15 +353,15 @@ int main(int argc, char* argv[])
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
<< op.GetTypeString() << std::endl;
e_device_buf.FromDevice(e_ms_ns_device_result.mData.data());
e_device_buf.FromDevice(e_gs_ms_ns_device_result.mData.data());
if(do_verification)
{
Tensor<CShuffleDataType> c_ms_ns_host_result(
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
Tensor<CShuffleDataType> c_gs_ms_ns_host_result(
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
using ReferenceOpInstance = ReferenceContraction_M2_N3_K1<NumDimM,
using ReferenceOpInstance = ReferenceContraction_G1_M2_N3_K1<NumDimM,
NumDimN,
NumDimK,
ADataType,
......@@ -365,31 +375,41 @@ int main(int argc, char* argv[])
auto ref_gemm = ReferenceOpInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(
a_ms_ks, b_ns_ks, c_ms_ns_host_result, a_element_op, b_element_op, PassThrough{});
auto ref_argument = ref_gemm.MakeArgument(a_gs_ms_ks,
b_gs_ns_ks,
c_gs_ms_ns_host_result,
a_element_op,
b_element_op,
PassThrough{});
ref_invoker.Run(ref_argument);
for(size_t m0 = 0; m0 < e_ms_ns_host_result.mDesc.GetLengths()[0]; ++m0)
for(size_t g0 = 0; g0 < e_gs_ms_ns_host_result.mDesc.GetLengths()[0]; ++g0)
{
for(size_t m0 = 0; m0 < e_gs_ms_ns_host_result.mDesc.GetLengths()[1]; ++m0)
{
for(size_t m1 = 0; m1 < e_ms_ns_host_result.mDesc.GetLengths()[1]; ++m1)
for(size_t m1 = 0; m1 < e_gs_ms_ns_host_result.mDesc.GetLengths()[2]; ++m1)
{
for(size_t n0 = 0; n0 < e_ms_ns_host_result.mDesc.GetLengths()[2]; ++n0)
for(size_t n0 = 0; n0 < e_gs_ms_ns_host_result.mDesc.GetLengths()[3]; ++n0)
{
for(size_t n1 = 0; n1 < e_ms_ns_host_result.mDesc.GetLengths()[3]; ++n1)
for(size_t n1 = 0; n1 < e_gs_ms_ns_host_result.mDesc.GetLengths()[4]; ++n1)
{
for(size_t n2 = 0; n2 < e_ms_ns_host_result.mDesc.GetLengths()[4]; ++n2)
for(size_t n2 = 0; n2 < e_gs_ms_ns_host_result.mDesc.GetLengths()[5];
++n2)
{
cde_element_op(e_ms_ns_host_result(m0, m1, n0, n1, n2),
c_ms_ns_host_result(m0, m1, n0, n1, n2),
d_ms_ns(m0, m1, n0, n1, n2));
cde_element_op(e_gs_ms_ns_host_result(g0, m0, m1, n0, n1, n2),
c_gs_ms_ns_host_result(g0, m0, m1, n0, n1, n2),
d_gs_ms_ns(g0, m0, m1, n0, n1, n2));
}
}
}
}
}
}
return ck::utils::check_err(e_ms_ns_device_result.mData, e_ms_ns_host_result.mData) ? 0 : 1;
return ck::utils::check_err(e_gs_ms_ns_device_result.mData, e_gs_ms_ns_host_result.mData)
? 0
: 1;
}
return 0;
......
include/ck/tensor_operation/gpu/device/device_batched_contraction_multiple_d_xdl_cshuffle.hpp
View file @ 35e49f2d
......@@ -500,11 +500,8 @@ struct DeviceBatchedContractionMultipleD_Xdl_CShuffle
std::array<long_index_t, NumDTensor> ds_offset;
static_for<0, NumDTensor, 1>{}([&](auto i) {
if constexpr(NumDimG > 0)
ds_offset[i] =
ds_grid_desc_g_m_n_[i].CalculateOffset(make_multi_index(g_idx, 0, 0));
else
ds_offset[i] = 0;
});
return ds_offset;
......@@ -512,10 +509,7 @@ struct DeviceBatchedContractionMultipleD_Xdl_CShuffle
__host__ __device__ constexpr long_index_t GetEPtrOffset(index_t g_idx) const
{
if constexpr(NumDimG > 0)
return e_grid_desc_g_m_n_.CalculateOffset(make_multi_index(g_idx, 0, 0));
else
return 0;
}
private:
......@@ -634,6 +628,8 @@ struct DeviceBatchedContractionMultipleD_Xdl_CShuffle
compute_ptr_offset_of_batch_{
a_batch_stride_, b_batch_stride_, ds_grid_desc_g_m_n_, e_grid_desc_g_m_n_}
{
static_assert(NumDimG > 0 && NumDimM > 0 && NumDimN > 0 && NumDimK > 0, "");
// populate pointer, batch stride, desc for Ds
static_for<0, NumDTensor, 1>{}([&](auto i) {
using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
......
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