Skip to content

GitLab

Commit 07a673c6 authored by carlushuang's avatar carlushuang
Browse files

Merge remote-tracking branch 'origin/develop' into cpu_avx2

parents c0f698d5 ac0d8066
Hide whitespace changes
Inline Side-by-side
Showing with 778 additions and 220 deletions
library/include/ck/library/obselete_driver_offline/driver_convolution_maxpool_forward_implicit_gemm_v5r1_dlops_nc0hwc1_kc0yxc1_nk0hwk1.hpp
View file @ 07a673c6
......@@ -27,7 +27,7 @@ template <ck::index_t BlockSize,
ck::index_t ABlockTransferDstScalarPerVector_E2,
ck::index_t BThreadTransferSrcScalarPerVector_E2,
ck::index_t CThreadTransferDstScalarPerVector_K,
ck::ActivTypeEnum_t activ_type>
ck::ActivTypeEnum activ_type>
struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nc0hwc1_kc0yxc1_nk0hwk1_maxpool
{
template <typename... Wei,
......@@ -305,7 +305,7 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nc0hwc1_kc0yxc1_nk0
FloatAB,
FloatAcc,
FloatC,
InMemoryDataOperationEnum_t::Set,
InMemoryDataOperationEnum::Set,
decltype(a_e0_e1_k_e2_grid_desc),
decltype(b_e0_e1_n_ho_wo_e2_grid_desc),
decltype(c_k_n_hop_wop_grid_desc),
......
library/include/ck/library/obselete_driver_offline/driver_gemm_dlops_v1r2.hpp
View file @ 07a673c6
......@@ -10,7 +10,7 @@ template <ck::index_t BlockSize,
typename FloatAB,
typename FloatAcc,
typename FloatC,
ck::InMemoryDataOperationEnum_t CGlobalMemoryDataOperation,
ck::InMemoryDataOperationEnum CGlobalMemoryDataOperation,
typename AKMGridDesc,
typename BKNGridDesc,
typename CMNGridDesc,
......
library/include/ck/library/obselete_driver_offline/driver_gemm_dlops_v1r3.hpp
View file @ 07a673c6
......@@ -10,7 +10,7 @@ template <ck::index_t BlockSize,
typename FloatAB,
typename FloatAcc,
typename FloatC,
ck::InMemoryDataOperationEnum_t CGlobalMemoryDataOperation,
ck::InMemoryDataOperationEnum CGlobalMemoryDataOperation,
typename AK0MK1GridDesc,
typename BK0NK1GridDesc,
typename CMNGridDesc,
......
library/include/ck/library/obselete_driver_offline/driver_gemm_xdlops_v2r3.hpp
View file @ 07a673c6
......@@ -11,7 +11,7 @@ template <ck::index_t BlockSize,
typename FloatAB,
typename FloatAcc,
typename FloatC,
ck::InMemoryDataOperationEnum_t CGlobalMemoryDataOperation,
ck::InMemoryDataOperationEnum CGlobalMemoryDataOperation,
typename AGridDesc_K0_M_K1,
typename BGridDesc_K0_N_K,
typename CMNGridDesc,
......
library/include/ck/library/obselete_driver_offline/driver_gemm_xdlops_v2r4.hpp
View file @ 07a673c6
......@@ -10,7 +10,7 @@ template <ck::index_t BlockSize,
typename FloatAB,
typename FloatAcc,
typename FloatC,
ck::InMemoryDataOperationEnum_t CGlobalMemoryDataOperation,
ck::InMemoryDataOperationEnum CGlobalMemoryDataOperation,
typename ABK0MK1GridDesc,
typename BBK0NK1GridDesc,
typename CMNGridDesc,
......
library/include/ck/library/reference_tensor_operation/cpu/reference_conv_backward_weight.hpp
View file @ 07a673c6
......@@ -17,7 +17,7 @@ template <typename InDataType,
typename InElementwiseOperation,
typename WeiElementwiseOperation,
typename OutElementwiseOperation>
struct ReferenceConvWrw : public device::BaseOperator
struct ReferenceConvBwdWeight : public device::BaseOperator
{
// Argument
struct Argument : public device::BaseArgument
......@@ -62,7 +62,7 @@ struct ReferenceConvWrw : public device::BaseOperator
// Invoker
struct Invoker : public device::BaseInvoker
{
using Argument = ReferenceConvWrw::Argument;
using Argument = ReferenceConvBwdWeight::Argument;
float Run(const Argument& arg)
{
......@@ -163,7 +163,7 @@ struct ReferenceConvWrw : public device::BaseOperator
auto str = std::stringstream();
// clang-format off
str << "ReferenceConvFwd"
str << "ReferenceConvBwdWeight"
<< std::endl;
// clang-format on
......
library/include/ck/library/reference_tensor_operation/cpu/reference_conv_bwd_data.hpp
View file @ 07a673c6
......@@ -18,8 +18,8 @@ template <typename InDataType,
typename InElementwiseOperation,
typename WeiElementwiseOperation,
typename OutElementwiseOperation,
ck::index_t NumDimSpatial = 2,
typename std::enable_if<NumDimSpatial >= 1 && NumDimSpatial <= 3, bool>::type = false>
ck::index_t NumDimSpatial = 2,
typename ck::enable_if<NumDimSpatial >= 1 && NumDimSpatial <= 3, bool>::type = false>
struct ReferenceConvBwdData : public device::BaseOperator
{
// Argument
......@@ -71,7 +71,7 @@ struct ReferenceConvBwdData : public device::BaseOperator
{
if constexpr(NumDimSpatial == 1)
{
auto f_nchw = [&](auto n, auto c, auto wi) {
auto f_ncw = [&](auto n, auto c, auto wi) {
std::size_t K = arg.weight_.mDesc.GetLengths()[0];
std::size_t X = arg.weight_.mDesc.GetLengths()[2];
std::size_t Wo = arg.output_.mDesc.GetLengths()[2];
......@@ -108,7 +108,7 @@ struct ReferenceConvBwdData : public device::BaseOperator
arg.input_(n, c, wi) = ck::type_convert<InDataType>(v_in);
};
make_ParallelTensorFunctor(f_nchw,
make_ParallelTensorFunctor(f_ncw,
arg.input_.mDesc.GetLengths()[0],
arg.input_.mDesc.GetLengths()[1],
arg.input_.mDesc.GetLengths()[2])(
......@@ -182,7 +182,7 @@ struct ReferenceConvBwdData : public device::BaseOperator
}
else if constexpr(NumDimSpatial == 3)
{
auto f_nchw = [&](auto n, auto c, auto di, auto hi, auto wi) {
auto f_ncdhw = [&](auto n, auto c, auto di, auto hi, auto wi) {
std::size_t K = arg.weight_.mDesc.GetLengths()[0];
std::size_t Z = arg.weight_.mDesc.GetLengths()[2];
std::size_t Y = arg.weight_.mDesc.GetLengths()[3];
......@@ -252,7 +252,7 @@ struct ReferenceConvBwdData : public device::BaseOperator
arg.input_(n, c, di, hi, wi) = ck::type_convert<InDataType>(v_in);
};
make_ParallelTensorFunctor(f_nchw,
make_ParallelTensorFunctor(f_ncdhw,
arg.input_.mDesc.GetLengths()[0],
arg.input_.mDesc.GetLengths()[1],
arg.input_.mDesc.GetLengths()[2],
......
library/include/ck/library/tensor_operation_instance/gpu/reduce/device_reduce_instance_blockwise.hpp
View file @ 07a673c6
......@@ -47,7 +47,7 @@ using reduce_configuration_2_instances_blockwise = std::tuple<
>;
#endif
template <typename AccDataType, ReduceTensorOp_t ReduceOpId>
template <typename AccDataType, ReduceTensorOp ReduceOpId>
using deviceReduceBlockWisePtrType = DeviceReducePtr<
typename reduce_unary_operator<AccDataType, ReduceOpId, true, true>::InElementwiseOperation,
typename reduce_unary_operator<AccDataType, ReduceOpId, true, true>::AccElementwiseOperation>;
......@@ -57,9 +57,9 @@ template <typename InDataType,
typename OutDataType,
int Rank,
int NumReduceDim,
ReduceTensorOp_t ReduceOpId,
NanPropagation_t NanOpt,
ReduceTensorIndices_t IndicesOpt>
ReduceTensorOp ReduceOpId,
NanPropagation NanOpt,
ReduceTensorIndices IndicesOpt>
void add_device_reduce_instance_blockwise(
std::vector<deviceReduceBlockWisePtrType<AccDataType, ReduceOpId>>& device_op_instances)
{
......@@ -71,11 +71,11 @@ void add_device_reduce_instance_blockwise(
AccElementwiseOperation;
constexpr bool Indexable =
(ReduceOpId == ReduceTensorOp_t::MIN || ReduceOpId == ReduceTensorOp_t::MAX ||
ReduceOpId == ReduceTensorOp_t::AMAX);
constexpr bool NeedIndices = Indexable && (IndicesOpt != ReduceTensorIndices_t::NO_INDICES);
(ReduceOpId == ReduceTensorOp::MIN || ReduceOpId == ReduceTensorOp::MAX ||
ReduceOpId == ReduceTensorOp::AMAX);
constexpr bool NeedIndices = Indexable && (IndicesOpt != ReduceTensorIndices::NO_INDICES);
constexpr bool PropagateNan = (NanOpt == NanPropagation_t::NOT_PROPAGATE_NAN) ? false : true;
constexpr bool PropagateNan = (NanOpt == NanPropagation::NOT_PROPAGATE_NAN) ? false : true;
static_for<0, std::tuple_size<reduce_configuration_1_instances>::value, 1>{}([&](auto i) {
using cfg1 =
......@@ -123,15 +123,15 @@ void add_device_reduce_instance_blockwise(
IndicesOpt>( \
std::vector<deviceReduceBlockWisePtrType<compT, ReduceOpId>> & device_op_instances)
#define ADD_BLOCKWISE_INST_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_BLOCKWISE_INST_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp_t>(ReduceOpId), \
static_cast<NanPropagation_t>(NanOpt), \
static_cast<ReduceTensorIndices_t>(IndicesOpt), \
Rank, \
#define ADD_BLOCKWISE_INST_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_BLOCKWISE_INST_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp>(ReduceOpId), \
static_cast<NanPropagation>(NanOpt), \
static_cast<ReduceTensorIndices>(IndicesOpt), \
Rank, \
NumReduceDim)
#define ADD_BLOCKWISE_INST_REF_BY_TYPE( \
......@@ -150,15 +150,15 @@ void add_device_reduce_instance_blockwise(
AccElementwiseOperation>> & \
device_op_instances)
#define ADD_BLOCKWISE_INST_REF_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_BLOCKWISE_INST_REF_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp_t>(ReduceOpId), \
static_cast<NanPropagation_t>(NanOpt), \
static_cast<ReduceTensorIndices_t>(IndicesOpt), \
Rank, \
#define ADD_BLOCKWISE_INST_REF_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_BLOCKWISE_INST_REF_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp>(ReduceOpId), \
static_cast<NanPropagation>(NanOpt), \
static_cast<ReduceTensorIndices>(IndicesOpt), \
Rank, \
NumReduceDim)
} // namespace device_reduce_instance
......
library/include/ck/library/tensor_operation_instance/gpu/reduce/device_reduce_instance_blockwise_second_call.hpp
View file @ 07a673c6
......@@ -34,7 +34,7 @@ using reduce_configuration_2_instances_blockwise_second_call = std::tuple<
>;
#endif
template <typename AccDataType, ReduceTensorOp_t ReduceOpId>
template <typename AccDataType, ReduceTensorOp ReduceOpId>
using deviceReduceBlockWiseSecondCallPtrType = DeviceReducePtr<
typename reduce_unary_operator<AccDataType, ReduceOpId, false, true>::InElementwiseOperation,
typename reduce_unary_operator<AccDataType, ReduceOpId, false, true>::AccElementwiseOperation>;
......@@ -44,9 +44,9 @@ template <typename InDataType,
typename OutDataType,
int Rank,
int NumReduceDim,
ReduceTensorOp_t ReduceOpId,
NanPropagation_t NanOpt,
ReduceTensorIndices_t IndicesOpt>
ReduceTensorOp ReduceOpId,
NanPropagation NanOpt,
ReduceTensorIndices IndicesOpt>
void add_device_reduce_instance_blockwise_second_call(
std::vector<deviceReduceBlockWiseSecondCallPtrType<AccDataType, ReduceOpId>>&
device_op_instances)
......@@ -60,11 +60,11 @@ void add_device_reduce_instance_blockwise_second_call(
AccElementwiseOperation;
constexpr bool Indexable =
(ReduceOpId == ReduceTensorOp_t::MIN || ReduceOpId == ReduceTensorOp_t::MAX ||
ReduceOpId == ReduceTensorOp_t::AMAX);
constexpr bool NeedIndices = Indexable && (IndicesOpt != ReduceTensorIndices_t::NO_INDICES);
(ReduceOpId == ReduceTensorOp::MIN || ReduceOpId == ReduceTensorOp::MAX ||
ReduceOpId == ReduceTensorOp::AMAX);
constexpr bool NeedIndices = Indexable && (IndicesOpt != ReduceTensorIndices::NO_INDICES);
constexpr bool PropagateNan = (NanOpt == NanPropagation_t::NOT_PROPAGATE_NAN) ? false : true;
constexpr bool PropagateNan = (NanOpt == NanPropagation::NOT_PROPAGATE_NAN) ? false : true;
static_assert(std::is_same<InDataType, AccDataType>::value,
"InDataType and AccDataType should be the same to use "
......@@ -117,15 +117,15 @@ void add_device_reduce_instance_blockwise_second_call(
std::vector<deviceReduceBlockWiseSecondCallPtrType<compT, ReduceOpId>> & \
device_op_instances)
#define ADD_BLOCKWISE_SECOND_CALL_INST_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_BLOCKWISE_SECOND_CALL_INST_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp_t>(ReduceOpId), \
static_cast<NanPropagation_t>(NanOpt), \
static_cast<ReduceTensorIndices_t>(IndicesOpt), \
Rank, \
#define ADD_BLOCKWISE_SECOND_CALL_INST_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_BLOCKWISE_SECOND_CALL_INST_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp>(ReduceOpId), \
static_cast<NanPropagation>(NanOpt), \
static_cast<ReduceTensorIndices>(IndicesOpt), \
Rank, \
NumReduceDim)
#define ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_TYPE( \
......@@ -145,15 +145,15 @@ void add_device_reduce_instance_blockwise_second_call(
AccElementwiseOperation>> & \
device_op_instances)
#define ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp_t>(ReduceOpId), \
static_cast<NanPropagation_t>(NanOpt), \
static_cast<ReduceTensorIndices_t>(IndicesOpt), \
Rank, \
#define ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp>(ReduceOpId), \
static_cast<NanPropagation>(NanOpt), \
static_cast<ReduceTensorIndices>(IndicesOpt), \
Rank, \
NumReduceDim)
} // namespace device_reduce_instance
......
library/include/ck/library/tensor_operation_instance/gpu/reduce/device_reduce_instance_multiblock_atomic_add.hpp
View file @ 07a673c6
......@@ -47,7 +47,7 @@ using reduce_configuration_2_instances_multiblock_atomic_add = std::tuple<
>;
#endif
template <typename AccDataType, ReduceTensorOp_t ReduceOperation>
template <typename AccDataType, ReduceTensorOp ReduceOperation>
using deviceReduceMultiBlockAtomicAddPtrType =
DeviceReducePtr<typename reduce_unary_operator<AccDataType, ReduceOperation, true, true>::
InElementwiseOperation,
......@@ -59,9 +59,9 @@ template <typename InDataType,
typename OutDataType,
int Rank,
int NumReduceDim,
ReduceTensorOp_t ReduceOpId,
NanPropagation_t NanOpt,
ReduceTensorIndices_t IndicesOpt>
ReduceTensorOp ReduceOpId,
NanPropagation NanOpt,
ReduceTensorIndices IndicesOpt>
void add_device_reduce_instance_multiblock_atomic_add(
std::vector<deviceReduceMultiBlockAtomicAddPtrType<AccDataType, ReduceOpId>>&
device_op_instances)
......@@ -74,18 +74,18 @@ void add_device_reduce_instance_multiblock_atomic_add(
AccElementwiseOperation;
constexpr bool Indexable =
(ReduceOpId == ReduceTensorOp_t::MIN || ReduceOpId == ReduceTensorOp_t::MAX ||
ReduceOpId == ReduceTensorOp_t::AMAX);
constexpr bool NeedIndices = Indexable && (IndicesOpt != ReduceTensorIndices_t::NO_INDICES);
(ReduceOpId == ReduceTensorOp::MIN || ReduceOpId == ReduceTensorOp::MAX ||
ReduceOpId == ReduceTensorOp::AMAX);
constexpr bool NeedIndices = Indexable && (IndicesOpt != ReduceTensorIndices::NO_INDICES);
constexpr bool PropagateNan = (NanOpt == NanPropagation_t::NOT_PROPAGATE_NAN) ? false : true;
constexpr bool PropagateNan = (NanOpt == NanPropagation::NOT_PROPAGATE_NAN) ? false : true;
static_assert(IndicesOpt == ReduceTensorIndices_t::NO_INDICES,
static_assert(IndicesOpt == ReduceTensorIndices::NO_INDICES,
"AtomicAdd can only be used with reduction operations without indices!");
constexpr bool op_acceptable =
(ReduceOpId == ReduceTensorOp_t::ADD || ReduceOpId == ReduceTensorOp_t::MUL ||
ReduceOpId == ReduceTensorOp_t::AVG || ReduceOpId == ReduceTensorOp_t::NORM1);
(ReduceOpId == ReduceTensorOp::ADD || ReduceOpId == ReduceTensorOp::MUL ||
ReduceOpId == ReduceTensorOp::AVG || ReduceOpId == ReduceTensorOp::NORM1);
constexpr bool out_type_acceptable =
(std::is_same<OutDataType, float>::value || std::is_same<OutDataType, double>::value);
......@@ -144,15 +144,15 @@ void add_device_reduce_instance_multiblock_atomic_add(
std::vector<deviceReduceMultiBlockAtomicAddPtrType<compT, ReduceOpId>> & \
device_op_instances)
#define ADD_MULTIBLOCK_ATOMIC_ADD_INST_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_MULTIBLOCK_ATOMIC_ADD_INST_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp_t>(ReduceOpId), \
static_cast<NanPropagation_t>(NanOpt), \
static_cast<ReduceTensorIndices_t>(IndicesOpt), \
Rank, \
#define ADD_MULTIBLOCK_ATOMIC_ADD_INST_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_MULTIBLOCK_ATOMIC_ADD_INST_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp>(ReduceOpId), \
static_cast<NanPropagation>(NanOpt), \
static_cast<ReduceTensorIndices>(IndicesOpt), \
Rank, \
NumReduceDim)
#define ADD_MULTIBLOCK_ATOMIC_ADD_INST_REF_BY_TYPE( \
......@@ -171,15 +171,15 @@ void add_device_reduce_instance_multiblock_atomic_add(
AccElementwiseOperation>> & \
device_op_instances)
#define ADD_MULTIBLOCK_ATOMIC_ADD_INST_REF_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_MULTIBLOCK_ATOMIC_ADD_INST_REF_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp_t>(ReduceOpId), \
static_cast<NanPropagation_t>(NanOpt), \
static_cast<ReduceTensorIndices_t>(IndicesOpt), \
Rank, \
#define ADD_MULTIBLOCK_ATOMIC_ADD_INST_REF_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_MULTIBLOCK_ATOMIC_ADD_INST_REF_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp>(ReduceOpId), \
static_cast<NanPropagation>(NanOpt), \
static_cast<ReduceTensorIndices>(IndicesOpt), \
Rank, \
NumReduceDim)
} // namespace device_reduce_instance
......
library/include/ck/library/tensor_operation_instance/gpu/reduce/device_reduce_instance_multiblock_partial_reduce.hpp
View file @ 07a673c6
......@@ -46,7 +46,7 @@ using reduce_configuration_2_instances_multiblock_partial_reduce = std::tuple<
>;
#endif
template <typename AccDataType, ReduceTensorOp_t ReduceOpId>
template <typename AccDataType, ReduceTensorOp ReduceOpId>
using deviceReduceMultiBlockPartialReducePtrType = DeviceReducePtr<
typename reduce_unary_operator<AccDataType, ReduceOpId, true, false>::InElementwiseOperation,
typename reduce_unary_operator<AccDataType, ReduceOpId, true, false>::AccElementwiseOperation>;
......@@ -56,9 +56,9 @@ template <typename InDataType,
typename OutDataType,
int Rank,
int NumReduceDim,
ReduceTensorOp_t ReduceOpId,
NanPropagation_t NanOpt,
ReduceTensorIndices_t IndicesOpt>
ReduceTensorOp ReduceOpId,
NanPropagation NanOpt,
ReduceTensorIndices IndicesOpt>
void add_device_reduce_instance_multiblock_partial_reduce(
std::vector<deviceReduceMultiBlockPartialReducePtrType<AccDataType, ReduceOpId>>&
device_op_instances)
......@@ -72,11 +72,11 @@ void add_device_reduce_instance_multiblock_partial_reduce(
AccElementwiseOperation;
constexpr bool Indexable =
(ReduceOpId == ReduceTensorOp_t::MIN || ReduceOpId == ReduceTensorOp_t::MAX ||
ReduceOpId == ReduceTensorOp_t::AMAX);
constexpr bool NeedIndices = Indexable && (IndicesOpt != ReduceTensorIndices_t::NO_INDICES);
(ReduceOpId == ReduceTensorOp::MIN || ReduceOpId == ReduceTensorOp::MAX ||
ReduceOpId == ReduceTensorOp::AMAX);
constexpr bool NeedIndices = Indexable && (IndicesOpt != ReduceTensorIndices::NO_INDICES);
constexpr bool PropagateNan = (NanOpt == NanPropagation_t::NOT_PROPAGATE_NAN) ? false : true;
constexpr bool PropagateNan = (NanOpt == NanPropagation::NOT_PROPAGATE_NAN) ? false : true;
static_for<0, std::tuple_size<reduce_configuration_1_instances>::value, 1>{}([&](auto i) {
using cfg1 =
......@@ -126,15 +126,15 @@ void add_device_reduce_instance_multiblock_partial_reduce(
std::vector<deviceReduceMultiBlockPartialReducePtrType<compT, ReduceOpId>> & \
device_op_instances)
#define ADD_MULTIBLOCK_PARTIAL_REDUCE_INST_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_MULTIBLOCK_PARTIAL_REDUCE_INST_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp_t>(ReduceOpId), \
static_cast<NanPropagation_t>(NanOpt), \
static_cast<ReduceTensorIndices_t>(IndicesOpt), \
Rank, \
#define ADD_MULTIBLOCK_PARTIAL_REDUCE_INST_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_MULTIBLOCK_PARTIAL_REDUCE_INST_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp>(ReduceOpId), \
static_cast<NanPropagation>(NanOpt), \
static_cast<ReduceTensorIndices>(IndicesOpt), \
Rank, \
NumReduceDim)
#define ADD_MULTIBLOCK_PARTIAL_REDUCE_INST_REF_BY_TYPE( \
......@@ -154,15 +154,15 @@ void add_device_reduce_instance_multiblock_partial_reduce(
AccElementwiseOperation>> & \
device_op_instances)
#define ADD_MULTIBLOCK_PARTIAL_REDUCE_INST_REF_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_MULTIBLOCK_PARTIAL_REDUCE_INST_REF_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp_t>(ReduceOpId), \
static_cast<NanPropagation_t>(NanOpt), \
static_cast<ReduceTensorIndices_t>(IndicesOpt), \
Rank, \
#define ADD_MULTIBLOCK_PARTIAL_REDUCE_INST_REF_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_MULTIBLOCK_PARTIAL_REDUCE_INST_REF_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp>(ReduceOpId), \
static_cast<NanPropagation>(NanOpt), \
static_cast<ReduceTensorIndices>(IndicesOpt), \
Rank, \
NumReduceDim)
} // namespace device_reduce_instance
......
library/include/ck/library/tensor_operation_instance/gpu/reduce/device_reduce_instance_threadwise.hpp
View file @ 07a673c6
......@@ -47,7 +47,7 @@ using reduce_configuration_2_instances_threadwise = std::tuple<
>;
#endif
template <typename AccDataType, ReduceTensorOp_t ReduceOpId>
template <typename AccDataType, ReduceTensorOp ReduceOpId>
using deviceReduceThreadWisePtrType = DeviceReducePtr<
typename reduce_unary_operator<AccDataType, ReduceOpId, true, true>::InElementwiseOperation,
typename reduce_unary_operator<AccDataType, ReduceOpId, true, true>::AccElementwiseOperation>;
......@@ -57,9 +57,9 @@ template <typename InDataType,
typename OutDataType,
int Rank,
int NumReduceDim,
ReduceTensorOp_t ReduceOpId,
NanPropagation_t NanOpt,
ReduceTensorIndices_t IndicesOpt>
ReduceTensorOp ReduceOpId,
NanPropagation NanOpt,
ReduceTensorIndices IndicesOpt>
void add_device_reduce_instance_threadwise(
std::vector<deviceReduceThreadWisePtrType<AccDataType, ReduceOpId>>& device_op_instances)
{
......@@ -71,11 +71,11 @@ void add_device_reduce_instance_threadwise(
AccElementwiseOperation;
constexpr bool Indexable =
(ReduceOpId == ReduceTensorOp_t::MIN || ReduceOpId == ReduceTensorOp_t::MAX ||
ReduceOpId == ReduceTensorOp_t::AMAX);
constexpr bool NeedIndices = Indexable && (IndicesOpt != ReduceTensorIndices_t::NO_INDICES);
(ReduceOpId == ReduceTensorOp::MIN || ReduceOpId == ReduceTensorOp::MAX ||
ReduceOpId == ReduceTensorOp::AMAX);
constexpr bool NeedIndices = Indexable && (IndicesOpt != ReduceTensorIndices::NO_INDICES);
constexpr bool PropagateNan = (NanOpt == NanPropagation_t::NOT_PROPAGATE_NAN) ? false : true;
constexpr bool PropagateNan = (NanOpt == NanPropagation::NOT_PROPAGATE_NAN) ? false : true;
using cfg1 = ReductionConfiguration_1<256, 256, 1>;
......@@ -119,15 +119,15 @@ void add_device_reduce_instance_threadwise(
IndicesOpt>( \
std::vector<deviceReduceThreadWisePtrType<compT, ReduceOpId>> & device_op_instances)
#define ADD_THREADWISE_INST_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_THREADWISE_INST_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp_t>(ReduceOpId), \
static_cast<NanPropagation_t>(NanOpt), \
static_cast<ReduceTensorIndices_t>(IndicesOpt), \
Rank, \
#define ADD_THREADWISE_INST_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_THREADWISE_INST_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp>(ReduceOpId), \
static_cast<NanPropagation>(NanOpt), \
static_cast<ReduceTensorIndices>(IndicesOpt), \
Rank, \
NumReduceDim)
#define ADD_THREADWISE_INST_REF_BY_TYPE( \
......@@ -146,15 +146,15 @@ void add_device_reduce_instance_threadwise(
AccElementwiseOperation>> & \
device_op_instances)
#define ADD_THREADWISE_INST_REF_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_THREADWISE_INST_REF_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp_t>(ReduceOpId), \
static_cast<NanPropagation_t>(NanOpt), \
static_cast<ReduceTensorIndices_t>(IndicesOpt), \
Rank, \
#define ADD_THREADWISE_INST_REF_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_THREADWISE_INST_REF_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp>(ReduceOpId), \
static_cast<NanPropagation>(NanOpt), \
static_cast<ReduceTensorIndices>(IndicesOpt), \
Rank, \
NumReduceDim)
} // namespace device_reduce_instance
......
test/include/test_util.hpp → library/include/ck/library/utility/check_err.hpp
View file @ 07a673c6
#ifndef TEST_UTIL_HPP
#define TEST_UTIL_HPP
#ifndef CHECK_ERR_HPP
#define CHECK_ERR_HPP
#include <algorithm>
#include <cmath>
#include <cstdlib>
#include <half.hpp>
#include <iostream>
#include <iomanip>
#include <iterator>
......@@ -13,16 +14,17 @@
#include "data_type.hpp"
namespace test {
namespace ck {
namespace utils {
template <typename T>
typename std::enable_if<std::is_floating_point<T>::value && !std::is_same<T, ck::half_t>::value,
typename std::enable_if<std::is_floating_point<T>::value && !std::is_same<T, half_t>::value,
bool>::type
check_err(const std::vector<T>& out,
const std::vector<T>& ref,
const std::string& msg,
double rtol = 1e-5,
double atol = 1e-8)
const std::string& msg = "Error: Incorrect results!",
double rtol = 1e-5,
double atol = 1e-8)
{
if(out.size() != ref.size())
{
......@@ -60,13 +62,12 @@ check_err(const std::vector<T>& out,
}
template <typename T>
typename std::enable_if<std::is_same<T, ck::bhalf_t>::value || std::is_same<T, ck::half_t>::value,
bool>::type
typename std::enable_if<std::is_same<T, bhalf_t>::value, bool>::type
check_err(const std::vector<T>& out,
const std::vector<T>& ref,
const std::string& msg,
double rtol = 1e-5,
double atol = 1e-8)
const std::string& msg = "Error: Incorrect results!",
double rtol = 1e-3,
double atol = 1e-3)
{
if(out.size() != ref.size())
{
......@@ -77,14 +78,15 @@ check_err(const std::vector<T>& out,
}
bool res{true};
int err_count = 0;
double err = 0;
double max_err = ck::type_convert<float>(ck::NumericLimits<T>::Min());
int err_count = 0;
double err = 0;
// TODO: This is a hack. We should have proper specialization for bhalf_t data type.
double max_err = std::numeric_limits<float>::min();
for(std::size_t i = 0; i < ref.size(); ++i)
{
float o = ck::type_convert<float>(out[i]);
float r = ck::type_convert<float>(ref[i]);
err = std::abs(o - r);
double o = type_convert<float>(out[i]);
double r = type_convert<float>(ref[i]);
err = std::abs(o - r);
if(err > atol + rtol * std::abs(r) || !std::isfinite(o) || !std::isfinite(r))
{
max_err = err > max_err ? err : max_err;
......@@ -105,11 +107,14 @@ check_err(const std::vector<T>& out,
return res;
}
bool check_err(const std::vector<ck::half_t>& out,
const std::vector<ck::half_t>& ref,
const std::string& msg,
ck::half_t rtol = static_cast<ck::half_t>(1e-3f),
ck::half_t atol = static_cast<ck::half_t>(1e-3f))
template <typename T>
typename std::enable_if<std::is_same<T, half_t>::value || std::is_same<T, half_float::half>::value,
bool>::type
check_err(const std::vector<T>& out,
const std::vector<T>& ref,
const std::string& msg = "Error: Incorrect results!",
double rtol = 1e-3,
double atol = 1e-3)
{
if(out.size() != ref.size())
{
......@@ -122,20 +127,20 @@ bool check_err(const std::vector<ck::half_t>& out,
bool res{true};
int err_count = 0;
double err = 0;
double max_err = std::numeric_limits<ck::half_t>::min();
double max_err = std::numeric_limits<T>::min();
for(std::size_t i = 0; i < ref.size(); ++i)
{
double out_ = double(out[i]);
double ref_ = double(ref[i]);
err = std::abs(out_ - ref_);
if(err > atol + rtol * std::abs(ref_) || !std::isfinite(out_) || !std::isfinite(ref_))
double o = type_convert<float>(out[i]);
double r = type_convert<float>(ref[i]);
err = std::abs(o - r);
if(err > atol + rtol * std::abs(r) || !std::isfinite(o) || !std::isfinite(r))
{
max_err = err > max_err ? err : max_err;
err_count++;
if(err_count < 5)
{
std::cout << std::setw(12) << std::setprecision(7) << "out[" << i << "] != ref["
<< i << "]: " << out_ << "!=" << ref_ << std::endl
<< i << "]: " << o << " != " << r << std::endl
<< msg << std::endl;
}
res = false;
......@@ -149,13 +154,12 @@ bool check_err(const std::vector<ck::half_t>& out,
}
template <typename T>
typename std::enable_if<std::is_integral<T>::value && !std::is_same<T, ck::bhalf_t>::value,
bool>::type
typename std::enable_if<std::is_integral<T>::value && !std::is_same<T, bhalf_t>::value, bool>::type
check_err(const std::vector<T>& out,
const std::vector<T>& ref,
const std::string& msg,
double = 0,
double = 0)
const std::string& msg = "Error: Incorrect results!",
double = 0,
double = 0)
{
if(out.size() != ref.size())
{
......@@ -178,7 +182,8 @@ check_err(const std::vector<T>& out,
return true;
}
} // namespace test
} // namespace utils
} // namespace ck
template <typename T>
std::ostream& operator<<(std::ostream& os, const std::vector<T>& v)
......
include/ck/tensor_operation/gpu/device/conv_utils.hpp → library/include/ck/library/utility/conv_fwd_util.hpp
View file @ 07a673c6
#ifndef CONV_UTILS_HPP
#define CONV_UTILS_HPP
#include <cstdlib>
#include <functional>
#include <iterator>
#include <numeric>
#include <sstream>
#include <type_traits>
#include <vector>
#include "config.hpp"
#include "host_tensor.hpp"
#include "tensor_layout.hpp"
namespace ck {
namespace conv_util {
/**
* @brief Calculate number of FLOPs for Convolution
*
* @param[in] N Batch size.
* @param[in] C Number of input channels.
* @param[in] K Number of output channels.
* @param[in] filter_spatial_lengths Filter spatial dimensions lengths.
* @param[in] output_spatial_lengths Convolution output spatial dimensions
* lengths.
*
* @return The number of flops.
*/
std::size_t GetFlops(ck::index_t N,
ck::index_t C,
ck::index_t K,
const std::vector<ck::index_t>& filter_spatial_lengths,
const std::vector<ck::index_t>& output_spatial_lengths)
{
// 2 * N * K * <output spatial lengths product> * C * <filter spatial lengths product>
return static_cast<std::size_t>(2) * N * K *
std::accumulate(std::begin(output_spatial_lengths),
std::end(output_spatial_lengths),
static_cast<std::size_t>(1),
std::multiplies<std::size_t>()) *
C *
std::accumulate(std::begin(filter_spatial_lengths),
std::end(filter_spatial_lengths),
static_cast<std::size_t>(1),
std::multiplies<std::size_t>());
}
/**
* @brief Calculate number of bytes read/write by convolution algorithm.
*
* @param[in] N Batch size.
* @param[in] C Number of input channels.
* @param[in] K Number of output channels.
* @param[in] input_spatial_lengths Input spatial dimensions lengths.
* @param[in] filter_spatial_lengths Filter spatial dimensions lengths.
* @param[in] output_spatial_lengths Output spatial dimensions lengths
*
* @tparam InDataType Input tensor data type.
* @tparam WeiDataType Weights tensor data type.
* @tparam OutDataType Output tensor data type.
*
* @return The number of used bytes.
*/
template <typename InDataType = float,
typename WeiDataType = InDataType,
typename OutDataType = InDataType>
std::size_t GetBtype(ck::index_t N,
ck::index_t C,
ck::index_t K,
const std::vector<ck::index_t>& input_spatial_lengths,
const std::vector<ck::index_t>& filter_spatial_lengths,
const std::vector<ck::index_t>& output_spatial_lengths)
{
// sizeof(InDataType) * (N * C * <input spatial lengths product>) +
// sizeof(WeiDataType) * (K * C * <filter spatial lengths product>) +
// sizeof(OutDataType) * (N * K * <output spatial lengths product>);
return sizeof(InDataType) * (N * C *
std::accumulate(std::begin(input_spatial_lengths),
std::end(input_spatial_lengths),
static_cast<std::size_t>(1),
std::multiplies<std::size_t>())) +
sizeof(WeiDataType) * (K * C *
std::accumulate(std::begin(filter_spatial_lengths),
std::end(filter_spatial_lengths),
static_cast<std::size_t>(1),
std::multiplies<std::size_t>())) +
sizeof(OutDataType) * (N * K *
std::accumulate(std::begin(output_spatial_lengths),
std::end(output_spatial_lengths),
static_cast<std::size_t>(1),
std::multiplies<std::size_t>()));
}
struct ConvParams
{
ConvParams()
: num_dim_spatial(2),
N(128),
K(256),
C(192),
filter_spatial_lengths(2, 3),
input_spatial_lengths(2, 71),
conv_filter_strides(2, 2),
conv_filter_dilations(2, 1),
input_left_pads(2, 1),
input_right_pads(2, 1)
{
}
ConvParams(ck::index_t n_dim_spatial,
ck::index_t n,
ck::index_t k,
ck::index_t c,
std::vector<ck::index_t> filter_lengths,
std::vector<ck::index_t> input_lengths,
std::vector<ck::index_t> conv_strides,
std::vector<ck::index_t> conv_dilations,
std::vector<ck::index_t> left_pads,
std::vector<ck::index_t> right_pads)
: num_dim_spatial(n_dim_spatial),
N(n),
K(k),
C(c),
filter_spatial_lengths(filter_lengths),
input_spatial_lengths(input_lengths),
conv_filter_strides(conv_strides),
conv_filter_dilations(conv_dilations),
input_left_pads(left_pads),
input_right_pads(right_pads)
{
}
ck::index_t num_dim_spatial;
ck::index_t N;
ck::index_t K;
ck::index_t C;
std::vector<ck::index_t> filter_spatial_lengths;
std::vector<ck::index_t> input_spatial_lengths;
std::vector<ck::index_t> conv_filter_strides;
std::vector<ck::index_t> conv_filter_dilations;
std::vector<ck::index_t> input_left_pads;
std::vector<ck::index_t> input_right_pads;
std::vector<ck::index_t> GetOutputSpatialLengths() const
{
std::vector<ck::index_t> out_spatial_len(num_dim_spatial, 0);
for(ck::index_t i = 0; i < num_dim_spatial; ++i)
{
// XEff = (X - 1) * conv_dilation_w + 1;
// Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
const ck::index_t idx_eff =
(filter_spatial_lengths[i] - 1) * conv_filter_dilations[i] + 1;
out_spatial_len[i] =
(input_spatial_lengths[i] + input_left_pads[i] + input_right_pads[i] - idx_eff) /
conv_filter_strides[i] +
1;
}
return out_spatial_len;
}
};
/**
* @brief Gets the host tensor descriptor.
*
* @param[in] dims The tensor dimensions lengths. Always in NCHW format.
* @param[in] layout The tensor data layout.
*
* @tparam TensorLayout Layout type.
*
* @return The host tensor descriptor object.
*/
template <typename TensorLayout>
HostTensorDescriptor GetHostTensorDescriptor(const std::vector<std::size_t>& dims,
const TensorLayout& layout)
{
std::size_t C = dims[1];
// 1D
if constexpr(std::is_same<TensorLayout, ck::tensor_layout::convolution::NCW>::value ||
std::is_same<TensorLayout, ck::tensor_layout::convolution::KCX>::value ||
std::is_same<TensorLayout, ck::tensor_layout::convolution::NKW>::value)
{
return HostTensorDescriptor(dims, std::vector<std::size_t>({C * dims[2], dims[2], 1}));
}
else if constexpr(std::is_same<TensorLayout, ck::tensor_layout::convolution::NWC>::value ||
std::is_same<TensorLayout, ck::tensor_layout::convolution::KXC>::value ||
std::is_same<TensorLayout, ck::tensor_layout::convolution::NWK>::value)
{
return HostTensorDescriptor(dims, std::vector<std::size_t>({C * dims[2], 1, C}));
}
// 2D
else if constexpr(std::is_same<TensorLayout, ck::tensor_layout::convolution::NCHW>::value ||
std::is_same<TensorLayout, ck::tensor_layout::convolution::KCYX>::value ||
std::is_same<TensorLayout, ck::tensor_layout::convolution::NKHW>::value)
{
return HostTensorDescriptor(
dims, std::vector<std::size_t>{C * dims[2] * dims[3], dims[2] * dims[3], dims[3], 1});
}
else if constexpr(std::is_same<TensorLayout, ck::tensor_layout::convolution::NHWC>::value ||
std::is_same<TensorLayout, ck::tensor_layout::convolution::KYXC>::value ||
std::is_same<TensorLayout, ck::tensor_layout::convolution::NHWK>::value)
{
return HostTensorDescriptor(
dims, std::vector<std::size_t>{C * dims[2] * dims[3], 1, dims[3] * C, C});
}
// 3D
else if constexpr(std::is_same<TensorLayout, ck::tensor_layout::convolution::NCDHW>::value ||
std::is_same<TensorLayout, ck::tensor_layout::convolution::KCZYX>::value ||
std::is_same<TensorLayout, ck::tensor_layout::convolution::NKDHW>::value)
{
return HostTensorDescriptor(dims,
std::vector<std::size_t>{C * dims[2] * dims[3] * dims[4],
dims[2] * dims[3] * dims[4],
dims[3] * dims[4],
dims[4],
1});
}
else if constexpr(std::is_same<TensorLayout, ck::tensor_layout::convolution::NDHWC>::value ||
std::is_same<TensorLayout, ck::tensor_layout::convolution::KZYXC>::value ||
std::is_same<TensorLayout, ck::tensor_layout::convolution::NDHWK>::value)
{
return HostTensorDescriptor(
dims,
std::vector<std::size_t>{
C * dims[2] * dims[3] * dims[4], 1, dims[3] * dims[4] * C, dims[4] * C, C});
}
std::stringstream err_msg;
err_msg << "Unsupported data layout provided: " << layout << "!";
throw std::runtime_error(err_msg.str());
}
} // namespace conv_util
} // namespace ck
#endif
#ifndef CONV_FWD_UTIL_HPP
#define CONV_FWD_UTIL_HPP
#include <algorithm>
#include <cstdlib>
#include <functional>
#include <iterator>
#include <numeric>
#include <sstream>
#include <random>
#include <tuple>
#include <type_traits>
#include <vector>
#include "check_err.hpp"
#include "config.hpp"
#include "device.hpp"
#include "device_conv_fwd.hpp"
#include "device_tensor.hpp"
#include "element_wise_operation.hpp"
#include "host_tensor.hpp"
#include "reference_conv_fwd.hpp"
#include "tensor_layout.hpp"
namespace ck {
namespace utils {
namespace conv {
using DeviceConvFwdNoOpPtr =
ck::tensor_operation::device::DeviceConvFwdPtr<ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
/**
* @brief Calculate number of FLOPs for Convolution
*
* @param[in] N Batch size.
* @param[in] C Number of input channels.
* @param[in] K Number of output channels.
* @param[in] filter_spatial_lengths Filter spatial dimensions lengths.
* @param[in] output_spatial_lengths Convolution output spatial dimensions
* lengths.
*
* @return The number of flops.
*/
std::size_t get_flops(ck::index_t N,
ck::index_t C,
ck::index_t K,
const std::vector<ck::index_t>& filter_spatial_lengths,
const std::vector<ck::index_t>& output_spatial_lengths)
{
// 2 * N * K * <output spatial lengths product> * C * <filter spatial lengths product>
return static_cast<std::size_t>(2) * N * K *
std::accumulate(std::begin(output_spatial_lengths),
std::end(output_spatial_lengths),
static_cast<std::size_t>(1),
std::multiplies<std::size_t>()) *
C *
std::accumulate(std::begin(filter_spatial_lengths),
std::end(filter_spatial_lengths),
static_cast<std::size_t>(1),
std::multiplies<std::size_t>());
}
/**
* @brief Calculate number of bytes read/write by convolution algorithm.
*
* @param[in] N Batch size.
* @param[in] C Number of input channels.
* @param[in] K Number of output channels.
* @param[in] input_spatial_lengths Input spatial dimensions lengths.
* @param[in] filter_spatial_lengths Filter spatial dimensions lengths.
* @param[in] output_spatial_lengths Output spatial dimensions lengths
*
* @tparam InDataType Input tensor data type.
* @tparam WeiDataType Weights tensor data type.
* @tparam OutDataType Output tensor data type.
*
* @return The number of used bytes.
*/
template <typename InDataType = float,
typename WeiDataType = InDataType,
typename OutDataType = InDataType>
std::size_t get_btype(ck::index_t N,
ck::index_t C,
ck::index_t K,
const std::vector<ck::index_t>& input_spatial_lengths,
const std::vector<ck::index_t>& filter_spatial_lengths,
const std::vector<ck::index_t>& output_spatial_lengths)
{
// sizeof(InDataType) * (N * C * <input spatial lengths product>) +
// sizeof(WeiDataType) * (K * C * <filter spatial lengths product>) +
// sizeof(OutDataType) * (N * K * <output spatial lengths product>);
return sizeof(InDataType) * (N * C *
std::accumulate(std::begin(input_spatial_lengths),
std::end(input_spatial_lengths),
static_cast<std::size_t>(1),
std::multiplies<std::size_t>())) +
sizeof(WeiDataType) * (K * C *
std::accumulate(std::begin(filter_spatial_lengths),
std::end(filter_spatial_lengths),
static_cast<std::size_t>(1),
std::multiplies<std::size_t>())) +
sizeof(OutDataType) * (N * K *
std::accumulate(std::begin(output_spatial_lengths),
std::end(output_spatial_lengths),
static_cast<std::size_t>(1),
std::multiplies<std::size_t>()));
}
struct ConvParams
{
ConvParams()
: num_dim_spatial(2),
N(128),
K(256),
C(192),
filter_spatial_lengths(2, 3),
input_spatial_lengths(2, 71),
conv_filter_strides(2, 2),
conv_filter_dilations(2, 1),
input_left_pads(2, 1),
input_right_pads(2, 1)
{
}
ConvParams(ck::index_t n_dim,
ck::index_t n_batch,
ck::index_t n_out_channels,
ck::index_t n_in_channels,
const std::vector<ck::index_t>& filters_len,
const std::vector<ck::index_t>& input_len,
const std::vector<ck::index_t>& strides,
const std::vector<ck::index_t>& dilations,
const std::vector<ck::index_t>& left_pads,
const std::vector<ck::index_t>& right_pads)
: num_dim_spatial(n_dim),
N(n_batch),
K(n_out_channels),
C(n_in_channels),
filter_spatial_lengths(filters_len),
input_spatial_lengths(input_len),
conv_filter_strides(strides),
conv_filter_dilations(dilations),
input_left_pads(left_pads),
input_right_pads(right_pads)
{
if(filter_spatial_lengths.size() != num_dim_spatial ||
input_spatial_lengths.size() != num_dim_spatial ||
conv_filter_strides.size() != num_dim_spatial ||
conv_filter_dilations.size() != num_dim_spatial ||
input_left_pads.size() != num_dim_spatial || input_right_pads.size() != num_dim_spatial)
{
throw(std::runtime_error(
"ConvParams::GetOutputSpatialLengths: "
"parameter size is different from number of declared dimensions!"));
}
}
ck::index_t num_dim_spatial;
ck::index_t N;
ck::index_t K;
ck::index_t C;
std::vector<ck::index_t> filter_spatial_lengths;
std::vector<ck::index_t> input_spatial_lengths;
std::vector<ck::index_t> conv_filter_strides;
std::vector<ck::index_t> conv_filter_dilations;
std::vector<ck::index_t> input_left_pads;
std::vector<ck::index_t> input_right_pads;
std::vector<ck::index_t> GetOutputSpatialLengths() const
{
if(filter_spatial_lengths.size() != num_dim_spatial ||
input_spatial_lengths.size() != num_dim_spatial ||
conv_filter_strides.size() != num_dim_spatial ||
conv_filter_dilations.size() != num_dim_spatial ||
input_left_pads.size() != num_dim_spatial || input_right_pads.size() != num_dim_spatial)
{
throw(std::runtime_error(
"ConvParams::GetOutputSpatialLengths: "
"parameter size is different from number of declared dimensions!"));
}
std::vector<ck::index_t> out_spatial_len(num_dim_spatial, 0);
for(ck::index_t i = 0; i < num_dim_spatial; ++i)
{
// XEff = (X - 1) * conv_dilation_w + 1;
// Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
const ck::index_t idx_eff =
(filter_spatial_lengths[i] - 1) * conv_filter_dilations[i] + 1;
out_spatial_len[i] =
(input_spatial_lengths[i] + input_left_pads[i] + input_right_pads[i] - idx_eff) /
conv_filter_strides[i] +
1;
}
return out_spatial_len;
}
};
/**
* @brief Gets the host tensor descriptor.
*
* @param[in] dims The tensor dimensions lengths. Always in NCHW format.
* @param[in] layout The tensor data layout.
*
* @tparam TensorLayout Layout type.
*
* @return The host tensor descriptor object.
*/
template <typename TensorLayout>
HostTensorDescriptor get_host_tensor_descriptor(const std::vector<std::size_t>& dims,
const TensorLayout& layout)
{
std::size_t C = dims[1];
// 1D
if constexpr(std::is_same<TensorLayout, ck::tensor_layout::convolution::NCW>::value ||
std::is_same<TensorLayout, ck::tensor_layout::convolution::KCX>::value ||
std::is_same<TensorLayout, ck::tensor_layout::convolution::NKW>::value)
{
return HostTensorDescriptor(dims, std::vector<std::size_t>({C * dims[2], dims[2], 1}));
}
else if constexpr(std::is_same<TensorLayout, ck::tensor_layout::convolution::NWC>::value ||
std::is_same<TensorLayout, ck::tensor_layout::convolution::KXC>::value ||
std::is_same<TensorLayout, ck::tensor_layout::convolution::NWK>::value)
{
return HostTensorDescriptor(dims, std::vector<std::size_t>({C * dims[2], 1, C}));
}
// 2D
else if constexpr(std::is_same<TensorLayout, ck::tensor_layout::convolution::NCHW>::value ||
std::is_same<TensorLayout, ck::tensor_layout::convolution::KCYX>::value ||
std::is_same<TensorLayout, ck::tensor_layout::convolution::NKHW>::value)
{
return HostTensorDescriptor(
dims, std::vector<std::size_t>{C * dims[2] * dims[3], dims[2] * dims[3], dims[3], 1});
}
else if constexpr(std::is_same<TensorLayout, ck::tensor_layout::convolution::NHWC>::value ||
std::is_same<TensorLayout, ck::tensor_layout::convolution::KYXC>::value ||
std::is_same<TensorLayout, ck::tensor_layout::convolution::NHWK>::value)
{
return HostTensorDescriptor(
dims, std::vector<std::size_t>{C * dims[2] * dims[3], 1, dims[3] * C, C});
}
// 3D
else if constexpr(std::is_same<TensorLayout, ck::tensor_layout::convolution::NCDHW>::value ||
std::is_same<TensorLayout, ck::tensor_layout::convolution::KCZYX>::value ||
std::is_same<TensorLayout, ck::tensor_layout::convolution::NKDHW>::value)
{
return HostTensorDescriptor(dims,
std::vector<std::size_t>{C * dims[2] * dims[3] * dims[4],
dims[2] * dims[3] * dims[4],
dims[3] * dims[4],
dims[4],
1});
}
else if constexpr(std::is_same<TensorLayout, ck::tensor_layout::convolution::NDHWC>::value ||
std::is_same<TensorLayout, ck::tensor_layout::convolution::KZYXC>::value ||
std::is_same<TensorLayout, ck::tensor_layout::convolution::NDHWK>::value)
{
return HostTensorDescriptor(
dims,
std::vector<std::size_t>{
C * dims[2] * dims[3] * dims[4], 1, C * dims[3] * dims[4], C * dims[4], C});
}
std::stringstream err_msg;
err_msg << "Unsupported data layout provided: " << layout << "!";
throw std::runtime_error(err_msg.str());
}
template <typename InDataType = float,
typename WeiDataType = float,
typename OutDataType = float,
typename InLayout = ck::tensor_layout::convolution::NHWC,
typename WeiLayout = ck::tensor_layout::convolution::KYXC,
typename OutLayout = ck::tensor_layout::convolution::NHWK>
auto get_host_tensors(const ConvParams& params, bool init = true)
{
std::vector<std::size_t> input_dims{static_cast<std::size_t>(params.N),
static_cast<std::size_t>(params.C)};
input_dims.insert(std::end(input_dims),
std::begin(params.input_spatial_lengths),
std::end(params.input_spatial_lengths));
std::vector<std::size_t> filter_dims{static_cast<std::size_t>(params.K),
static_cast<std::size_t>(params.C)};
filter_dims.insert(std::end(filter_dims),
std::begin(params.filter_spatial_lengths),
std::end(params.filter_spatial_lengths));
const std::vector<ck::index_t>& output_spatial_lengths = params.GetOutputSpatialLengths();
std::vector<std::size_t> output_dims{static_cast<std::size_t>(params.N),
static_cast<std::size_t>(params.K)};
output_dims.insert(std::end(output_dims),
std::begin(output_spatial_lengths),
std::end(output_spatial_lengths));
Tensor<InDataType> input(ck::utils::conv::get_host_tensor_descriptor(input_dims, InLayout{}));
Tensor<WeiDataType> weights(
ck::utils::conv::get_host_tensor_descriptor(filter_dims, WeiLayout{}));
Tensor<OutDataType> host_output(
ck::utils::conv::get_host_tensor_descriptor(output_dims, OutLayout{}));
Tensor<OutDataType> device_output(
ck::utils::conv::get_host_tensor_descriptor(output_dims, OutLayout{}));
if(init)
{
std::mt19937 gen(11939);
if constexpr(std::is_same<InDataType, uint8_t>::value)
{
std::uniform_int_distribution<> dis(-5, 5);
std::generate(
input.begin(), input.end(), [&dis, &gen]() { return InDataType(dis(gen)); });
std::generate(
weights.begin(), weights.end(), [&dis, &gen]() { return WeiDataType(dis(gen)); });
}
else
{
std::uniform_real_distribution<> dis(0.f, 1.f);
std::generate(
input.begin(), input.end(), [&dis, &gen]() { return InDataType(dis(gen)); });
std::generate(
weights.begin(), weights.end(), [&dis, &gen]() { return WeiDataType(dis(gen)); });
}
std::fill(host_output.begin(), host_output.end(), OutDataType(0.f));
std::fill(device_output.begin(), device_output.end(), OutDataType(0.f));
}
return std::make_tuple(input, weights, host_output, device_output);
}
HostTensorDescriptor get_output_host_tensor_descriptor(const std::vector<std::size_t>& dims,
int num_dim_spatial = 2)
{
namespace tl = ck::tensor_layout::convolution;
switch(num_dim_spatial)
{
case 3: {
return ck::utils::conv::get_host_tensor_descriptor(dims, tl::NDHWK{});
}
case 2: {
return ck::utils::conv::get_host_tensor_descriptor(dims, tl::NHWK{});
}
case 1: {
return ck::utils::conv::get_host_tensor_descriptor(dims, tl::NWK{});
}
default: {
throw std::runtime_error("Unsupported number of spatial dimensions provided!");
}
}
}
HostTensorDescriptor get_filters_host_tensor_descriptor(const std::vector<std::size_t>& dims,
int num_dim_spatial = 2)
{
namespace tl = ck::tensor_layout::convolution;
switch(num_dim_spatial)
{
case 3: {
return ck::utils::conv::get_host_tensor_descriptor(dims, tl::KZYXC{});
}
case 2: {
return ck::utils::conv::get_host_tensor_descriptor(dims, tl::KYXC{});
}
case 1: {
return ck::utils::conv::get_host_tensor_descriptor(dims, tl::KXC{});
}
default: {
throw std::runtime_error("Unsupported number of spatial dimensions provided!");
}
}
}
HostTensorDescriptor get_input_host_tensor_descriptor(const std::vector<std::size_t>& dims,
int num_dim_spatial = 2)
{
namespace tl = ck::tensor_layout::convolution;
switch(num_dim_spatial)
{
case 3: {
return ck::utils::conv::get_host_tensor_descriptor(dims, tl::NDHWC{});
}
case 2: {
return ck::utils::conv::get_host_tensor_descriptor(dims, tl::NHWC{});
}
case 1: {
return ck::utils::conv::get_host_tensor_descriptor(dims, tl::NWC{});
}
default: {
throw std::runtime_error("Unsupported number of spatial dimensions provided!");
}
}
}
template <ck::index_t NDim,
typename InDataType = float,
typename WeiDataType = float,
typename OutDataType = float>
void run_reference_convolution_forward(const ConvParams& params,
const Tensor<InDataType>& input,
const Tensor<WeiDataType>& weights,
Tensor<OutDataType>& output)
{
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
auto ref_conv = ck::tensor_operation::host::ReferenceConvFwd<InDataType,
WeiDataType,
OutDataType,
PassThrough,
PassThrough,
PassThrough,
NDim>();
auto ref_invoker = ref_conv.MakeInvoker();
auto ref_argument = ref_conv.MakeArgument(input,
weights,
output,
params.conv_filter_strides,
params.conv_filter_dilations,
params.input_left_pads,
params.input_right_pads,
PassThrough{},
PassThrough{},
PassThrough{});
ref_invoker.Run(ref_argument);
}
template <ck::index_t NDim,
typename InDataType = float,
typename WeiDataType = float,
typename OutDataType = float,
template <ck::index_t, typename, typename, typename>
class DeviceConvNDFwdInstance>
void run_convolution_forward(const ConvParams& params,
const Tensor<InDataType>& input,
const Tensor<WeiDataType>& weights,
Tensor<OutDataType>& output)
{
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
DeviceMem in_device_buf(sizeof(InDataType) * input.mDesc.GetElementSpace());
DeviceMem wei_device_buf(sizeof(WeiDataType) * weights.mDesc.GetElementSpace());
DeviceMem out_device_buf(sizeof(OutDataType) * output.mDesc.GetElementSpace());
in_device_buf.ToDevice(input.mData.data());
wei_device_buf.ToDevice(weights.mData.data());
const std::vector<ck::index_t>& output_spatial_lengths = params.GetOutputSpatialLengths();
auto conv = DeviceConvNDFwdInstance<NDim, InDataType, WeiDataType, OutDataType>();
auto invoker = conv.MakeInvoker();
auto argument = conv.MakeArgument(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
params.N,
params.K,
params.C,
params.input_spatial_lengths,
params.filter_spatial_lengths,
output_spatial_lengths,
params.conv_filter_strides,
params.conv_filter_dilations,
params.input_left_pads,
params.input_right_pads,
PassThrough{},
PassThrough{},
PassThrough{});
if(!conv.IsSupportedArgument(argument))
{
throw std::runtime_error(
"Error! device_conv with the specified compilation parameters does "
"not support this Conv problem");
}
invoker.Run(argument);
out_device_buf.FromDevice(output.mData.data());
}
template <ck::index_t NDim,
typename InDataType = float,
typename WeiDataType = float,
typename OutDataType = float>
bool run_convolution_forward_instances(const ConvParams& params,
const std::vector<DeviceConvFwdNoOpPtr>& conv_ptrs,
const Tensor<InDataType>& input,
const Tensor<WeiDataType>& weights,
Tensor<OutDataType>& output,
const Tensor<OutDataType>& host_output)
{
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
DeviceMem in_device_buf(sizeof(InDataType) * input.mDesc.GetElementSpace());
DeviceMem wei_device_buf(sizeof(WeiDataType) * weights.mDesc.GetElementSpace());
DeviceMem out_device_buf(sizeof(OutDataType) * output.mDesc.GetElementSpace());
in_device_buf.ToDevice(input.mData.data());
wei_device_buf.ToDevice(weights.mData.data());
const std::vector<ck::index_t>& output_spatial_lengths = params.GetOutputSpatialLengths();
bool res{true};
for(auto& conv_ptr : conv_ptrs)
{
auto invoker = conv_ptr->MakeInvokerPointer();
auto argument = conv_ptr->MakeArgumentPointer(
static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
params.N,
params.K,
params.C,
params.input_spatial_lengths,
params.filter_spatial_lengths,
output_spatial_lengths,
params.conv_filter_strides,
params.conv_filter_dilations,
params.input_left_pads,
params.input_right_pads,
PassThrough{},
PassThrough{},
PassThrough{});
if(conv_ptr->IsSupportedArgument(argument.get()))
{
float atol{1e-5f};
float rtol{1e-4f};
if constexpr(std::is_same_v<InDataType, ck::half_t>)
{
atol = 1e-4f;
rtol = 2.5e-3f;
}
invoker->Run(argument.get());
out_device_buf.FromDevice(output.mData.data());
res = res &&
ck::utils::check_err(
output.mData, host_output.mData, "Error: incorrect results!", atol, rtol);
hipGetErrorString(
hipMemset(out_device_buf.GetDeviceBuffer(), 0, out_device_buf.mMemSize));
}
}
return res;
}
} // namespace conv
} // namespace utils
} // namespace ck
#endif
library/src/host_tensor/host_tensor.cpp
View file @ 07a673c6
......@@ -65,21 +65,10 @@ void ostream_HostTensorDescriptor(const HostTensorDescriptor& desc, std::ostream
}
#if 1
// FIXME: remove
float bf16_to_f32_(ck::bhalf_t src_val)
{
union
{
uint32_t int32;
float fp32;
} u = {uint32_t(src_val) << 16};
return u.fp32;
}
// FIXME: remove
void bf16_to_f32_(const Tensor<ck::bhalf_t>& src, Tensor<float>& dst)
{
for(int i = 0; i < src.mData.size(); ++i)
dst.mData[i] = bf16_to_f32_(src.mData[i]);
dst.mData[i] = ck::type_convert<float>(src.mData[i]);
}
#endif
library/src/obselete_driver_offline/conv_add_fwd_driver_offline_nchwc.cpp
View file @ 07a673c6
......@@ -4,6 +4,8 @@
#include <cstdlib>
#include <stdlib.h>
#include <half.hpp>
#include "check_err.hpp"
#include "config.hpp"
#include "debug.hpp"
#include "print.hpp"
......@@ -39,7 +41,7 @@ void host_direct_convolution_add_nchwc(const Tensor<TIn>& in,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads&,
const ck::ActivTypeEnum_t activ_type)
const ck::ActivTypeEnum activ_type)
{
using namespace ck;
......@@ -117,7 +119,7 @@ int main(int argc, char* argv[])
exit(1);
}
constexpr ck::ActivTypeEnum_t activ_type = ActivTypeEnum_t::LeakyRelu;
constexpr ck::ActivTypeEnum activ_type = ActivTypeEnum::LeakyRelu;
const ConvForwardAlgo algo = static_cast<ConvForwardAlgo>(std::stoi(argv[1]));
const bool do_verification = std::stoi(argv[2]);
......@@ -167,7 +169,7 @@ int main(int argc, char* argv[])
const bool do_log = std::stoi(argv[4]);
const int nrepeat = std::stoi(argv[5]);
constexpr ck::ActivTypeEnum_t activ_type = ActivTypeEnum_t::LeakyRelu;
constexpr ck::ActivTypeEnum activ_type = ActivTypeEnum::LeakyRelu;
#if 0
constexpr auto N = Number<1>{};
......@@ -401,7 +403,7 @@ int main(int argc, char* argv[])
make_tuple(in_right_pad_h, in_right_pad_w),
activ_type);
check_error(add_host, add_device);
ck::utils::check_err(add_device.mData, add_host.mData);
if(do_log)
{
......
library/src/obselete_driver_offline/conv_bwd_driver_offline.cpp
View file @ 07a673c6
......@@ -4,6 +4,8 @@
#include <cstdlib>
#include <stdlib.h>
#include <half.hpp>
#include "check_err.hpp"
#include "config.hpp"
#include "debug.hpp"
#include "print.hpp"
......@@ -473,7 +475,7 @@ int main(int argc, char* argv[])
make_tuple(in_right_pad_h, in_right_pad_w),
layout);
check_error(in_host, in_device);
ck::utils::check_err(in_device.mData, in_host.mData);
if(do_log)
{
......
library/src/obselete_driver_offline/conv_fwd_driver_offline.cpp
View file @ 07a673c6
......@@ -4,6 +4,8 @@
#include <cstdlib>
#include <stdlib.h>
#include <half.hpp>
#include "check_err.hpp"
#include "config.hpp"
#include "debug.hpp"
#include "print.hpp"
......@@ -534,7 +536,7 @@ int main(int argc, char* argv[])
make_tuple(in_right_pad_h, in_right_pad_w),
layout);
check_error(out_host, out_device);
ck::utils::check_err(out_device.mData, out_host.mData);
if(do_log)
{
......
library/src/obselete_driver_offline/conv_fwd_driver_offline_nchwc.cpp
View file @ 07a673c6
......@@ -4,6 +4,8 @@
#include <cstdlib>
#include <stdlib.h>
#include <half.hpp>
#include "check_err.hpp"
#include "config.hpp"
#include "debug.hpp"
#include "print.hpp"
......@@ -37,7 +39,7 @@ void host_direct_convolution_nchwc(const Tensor<TIn>& in,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads&,
const ck::ActivTypeEnum_t activ_type)
const ck::ActivTypeEnum activ_type)
{
using namespace ck;
......@@ -102,7 +104,7 @@ int main(int argc, char* argv[])
exit(1);
}
constexpr ck::ActivTypeEnum_t activ_type = ActivTypeEnum_t::LeakyRelu;
constexpr ck::ActivTypeEnum activ_type = ActivTypeEnum::LeakyRelu;
const ConvForwardAlgo algo = static_cast<ConvForwardAlgo>(std::stoi(argv[1]));
const bool do_verification = std::stoi(argv[2]);
......@@ -149,8 +151,8 @@ int main(int argc, char* argv[])
const bool do_log = std::stoi(argv[4]);
const int nrepeat = std::stoi(argv[5]);
// constexpr ck::ActivTypeEnum_t activ_type = ActivTypeEnum_t::Sigmoid;
constexpr ck::ActivTypeEnum_t activ_type = ActivTypeEnum_t::LeakyRelu;
// constexpr ck::ActivTypeEnum activ_type = ActivTypeEnum::Sigmoid;
constexpr ck::ActivTypeEnum activ_type = ActivTypeEnum::LeakyRelu;
#if 0
constexpr auto N = Number<1>{};
......@@ -377,7 +379,7 @@ int main(int argc, char* argv[])
make_tuple(in_right_pad_h, in_right_pad_w),
activ_type);
check_error(out_host, out_device);
ck::utils::check_err(out_device.mData, out_host.mData);
if(do_log)
{
......
library/src/obselete_driver_offline/conv_maxpool_fwd_driver_offline_nchwc.cpp
View file @ 07a673c6
......@@ -4,6 +4,8 @@
#include <cstdlib>
#include <stdlib.h>
#include <half.hpp>
#include "check_err.hpp"
#include "config.hpp"
#include "debug.hpp"
#include "print.hpp"
......@@ -38,7 +40,7 @@ void host_direct_convolution_maxpool_nchwc(const Tensor<TIn>& in,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads&,
const ck::ActivTypeEnum_t activ_type)
const ck::ActivTypeEnum activ_type)
{
using namespace ck;
......@@ -126,7 +128,7 @@ int main(int argc, char* argv[])
exit(1);
}
constexpr ck::ActivTypeEnum_t activ_type = ActivTypeEnum_t::LeakyRelu;
constexpr ck::ActivTypeEnum activ_type = ActivTypeEnum::LeakyRelu;
const ConvForwardAlgo algo = static_cast<ConvForwardAlgo>(std::stoi(argv[1]));
const bool do_verification = std::stoi(argv[2]);
......@@ -176,18 +178,18 @@ int main(int argc, char* argv[])
const bool do_log = std::stoi(argv[4]);
const int nrepeat = std::stoi(argv[5]);
constexpr ck::ActivTypeEnum_t activ_type = ActivTypeEnum_t::LeakyRelu;
constexpr ck::ActivTypeEnum activ_type = ActivTypeEnum::LeakyRelu;
#if 1
constexpr auto N = Number<1>{};
constexpr auto Hi = Number<1080>{};
constexpr auto Wi = Number<1920>{};
constexpr auto Y = Number<3>{};
constexpr auto X = Number<3>{};
constexpr auto C0 = Number<2>{};
constexpr auto C1 = Number<8>{};
constexpr auto K0 = Number<2>{};
constexpr auto K1 = Number<8>{};
constexpr auto N = Number<1>{};
constexpr auto Hi = Number<1080>{};
constexpr auto Wi = Number<1920>{};
constexpr auto Y = Number<3>{};
constexpr auto X = Number<3>{};
constexpr auto C0 = Number<2>{};
constexpr auto C1 = Number<8>{};
constexpr auto K0 = Number<2>{};
constexpr auto K1 = Number<8>{};
#elif 0
constexpr auto N = Number<1>{};
constexpr auto Hi = Number<1080>{};
......@@ -397,8 +399,8 @@ int main(int argc, char* argv[])
make_tuple(in_right_pad_h, in_right_pad_w),
activ_type);
check_error(out_host, out_device);
check_error(max_host, max_device);
ck::utils::check_err(out_device.mData, out_host.mData);
ck::utils::check_err(max_device.mData, max_host.mData);
if(do_log)
{
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment