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Commit c7ba4159 authored by Jing Zhang's avatar Jing Zhang
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add mnk padding, support m=0

parent d072790f
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Showing with 125 additions and 51 deletions
include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_xdl.hpp
View file @ c7ba4159
...@@ -373,12 +373,20 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout, ...@@ -373,12 +373,20 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout,
gemm_desc_kernel_arg_.reserve(group_count_); gemm_desc_kernel_arg_.reserve(group_count_);
skipped_group_count_ = 0;
for(std::size_t i = 0; i < gemm_descs.size(); i++) for(std::size_t i = 0; i < gemm_descs.size(); i++)
{ {
const index_t M = gemm_descs[i].M_; const index_t M = gemm_descs[i].M_;
const index_t N = gemm_descs[i].N_; const index_t N = gemm_descs[i].N_;
const index_t K = gemm_descs[i].K_; const index_t K = gemm_descs[i].K_;
if(M == 0)
{
skipped_group_count_++;
continue;
}
const index_t StrideA = gemm_descs[i].stride_A_; const index_t StrideA = gemm_descs[i].stride_A_;
const index_t StrideB = gemm_descs[i].stride_B_; const index_t StrideB = gemm_descs[i].stride_B_;
const index_t StrideC = gemm_descs[i].stride_C_; const index_t StrideC = gemm_descs[i].stride_C_;
...@@ -470,6 +478,8 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout, ...@@ -470,6 +478,8 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout,
// private: // private:
index_t group_count_; index_t group_count_;
index_t skipped_group_count_;
AElementwiseOperation a_element_op_; AElementwiseOperation a_element_op_;
BElementwiseOperation b_element_op_; BElementwiseOperation b_element_op_;
CDEElementwiseOperation c_element_op_; CDEElementwiseOperation c_element_op_;
...@@ -581,7 +591,8 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout, ...@@ -581,7 +591,8 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout,
static bool IsSupportedArgument(const Argument& arg) static bool IsSupportedArgument(const Argument& arg)
{ {
if(ck::type_convert<ck::index_t>(arg.gemm_desc_kernel_arg_.size()) != arg.group_count_) if((ck::type_convert<ck::index_t>(arg.gemm_desc_kernel_arg_.size()) +
arg.skipped_group_count_) != arg.group_count_)
{ {
return false; return false;
} }
......
library/src/tensor_operation_instance/gpu/grouped_gemm/device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instance.cpp
View file @ c7ba4159
...@@ -56,6 +56,19 @@ using device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances = std::tuple< ...@@ -56,6 +56,19 @@ using device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances = std::tuple<
// clang-format on // clang-format on
>; >;
static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
using device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_irregular_tile_instances = std::tuple<
// clang-format off
//###################| A| B| Ds| E| AData| BData| AccData| CShuffle| DsData| EData| A| B| C| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//###################| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//###################| | | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//###################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedGemm_Xdl< Col, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 64, 16, 16, 32, 8, 8, 16, 16, 1, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
DeviceGroupedGemm_Xdl< Col, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 16, 64, 32, 8, 8, 16, 16, 1, 1, S<4, 16, 4>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>
// clang-format on
>;
void add_device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances( void add_device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances(
std::vector<std::unique_ptr<DeviceGroupedGemm<Col, std::vector<std::unique_ptr<DeviceGroupedGemm<Col,
Row, Row,
...@@ -71,6 +84,8 @@ void add_device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances( ...@@ -71,6 +84,8 @@ void add_device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances(
{ {
add_device_operation_instances(instances, add_device_operation_instances(instances,
device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances{}); device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances{});
add_device_operation_instances(
instances, device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_irregular_tile_instances{});
} }
} // namespace instance } // namespace instance
......
library/src/tensor_operation_instance/gpu/grouped_gemm/device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instance.cpp
View file @ c7ba4159
...@@ -56,6 +56,19 @@ using device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances = std::tuple< ...@@ -56,6 +56,19 @@ using device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances = std::tuple<
// clang-format on // clang-format on
>; >;
static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
using device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_irregular_tile_instances = std::tuple<
// clang-format off
//###################| A| B| Ds| E| AData| BData| AccData| CShuffle| DsData| EData| A| B| C| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//###################| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//###################| | | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//###################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedGemm_Xdl< Col, Col, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 64, 16, 16, 32, 8, 8, 16, 16, 1, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
DeviceGroupedGemm_Xdl< Col, Col, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 16, 64, 32, 8, 8, 16, 16, 1, 1, S<4, 16, 4>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>
// clang-format on
>;
void add_device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances( void add_device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances(
std::vector<std::unique_ptr<DeviceGroupedGemm<Col, std::vector<std::unique_ptr<DeviceGroupedGemm<Col,
Col, Col,
...@@ -71,6 +84,8 @@ void add_device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances( ...@@ -71,6 +84,8 @@ void add_device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances(
{ {
add_device_operation_instances(instances, add_device_operation_instances(instances,
device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances{}); device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances{});
add_device_operation_instances(
instances, device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_irregular_tile_instances{});
} }
} // namespace instance } // namespace instance
......
library/src/tensor_operation_instance/gpu/grouped_gemm/device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instance.cpp
View file @ c7ba4159
...@@ -56,6 +56,19 @@ using device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instances = std::tuple< ...@@ -56,6 +56,19 @@ using device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instances = std::tuple<
// clang-format on // clang-format on
>; >;
static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
using device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_irregular_tile_instances = std::tuple<
// clang-format off
//###################| A| B| Ds| E| AData| BData| AccData| CShuffle| DsData| EData| A| B| C| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//###################| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//###################| | | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//###################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedGemm_Xdl< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 64, 16, 16, 32, 8, 8, 16, 16, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
DeviceGroupedGemm_Xdl< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 16, 64, 32, 8, 8, 16, 16, 1, 1, S<4, 16, 4>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>
// clang-format on
>;
void add_device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instances( void add_device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instances(
std::vector<std::unique_ptr<DeviceGroupedGemm<Row, std::vector<std::unique_ptr<DeviceGroupedGemm<Row,
Row, Row,
...@@ -71,6 +84,8 @@ void add_device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instances( ...@@ -71,6 +84,8 @@ void add_device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instances(
{ {
add_device_operation_instances(instances, add_device_operation_instances(instances,
device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instances{}); device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instances{});
add_device_operation_instances(
instances, device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_irregular_tile_instances{});
} }
} // namespace instance } // namespace instance
......
library/src/tensor_operation_instance/gpu/grouped_gemm/device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instance.cpp
View file @ c7ba4159
...@@ -53,6 +53,19 @@ using device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instances = std::tuple< ...@@ -53,6 +53,19 @@ using device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instances = std::tuple<
// clang-format on // clang-format on
>; >;
static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
using device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_irregular_tile_instances = std::tuple<
// clang-format off
//###################| A| B| Ds| E| AData| BData| AccData| CShuffle| DsData| EData| A| B| C| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//###################| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//###################| | | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//###################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedGemm_Xdl< Row, Col, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 64, 16, 16, 32, 8, 8, 16, 16, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>,
DeviceGroupedGemm_Xdl< Row, Col, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 16, 64, 32, 8, 8, 16, 16, 1, 1, S<4, 16, 4>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 8, 1, 1, 1, S<1, 16, 1, 4>, 1>
// clang-format on
>;
void add_device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instances( void add_device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(
std::vector<std::unique_ptr<DeviceGroupedGemm<Row, std::vector<std::unique_ptr<DeviceGroupedGemm<Row,
Col, Col,
...@@ -68,6 +81,8 @@ void add_device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instances( ...@@ -68,6 +81,8 @@ void add_device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(
{ {
add_device_operation_instances(instances, add_device_operation_instances(instances,
device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instances{}); device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instances{});
add_device_operation_instances(
instances, device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_irregular_tile_instances{});
} }
} // namespace instance } // namespace instance
......
library/src/utility/host_tensor.cpp
View file @ c7ba4159
...@@ -31,6 +31,9 @@ std::size_t HostTensorDescriptor::GetElementSpaceSize() const ...@@ -31,6 +31,9 @@ std::size_t HostTensorDescriptor::GetElementSpaceSize() const
std::size_t space = 1; std::size_t space = 1;
for(std::size_t i = 0; i < mLens.size(); ++i) for(std::size_t i = 0; i < mLens.size(); ++i)
{ {
if(mLens[i] == 0)
continue;
space += (mLens[i] - 1) * mStrides[i]; space += (mLens[i] - 1) * mStrides[i];
} }
return space; return space;
......
profiler/src/CMakeLists.txt
View file @ c7ba4159
# ckProfiler # ckProfiler
set(PROFILER_SOURCES set(PROFILER_SOURCES
profiler.cpp profiler.cpp
profile_gemm.cpp #profile_gemm.cpp
profile_gemm_splitk.cpp #profile_gemm_splitk.cpp
profile_gemm_bilinear.cpp #profile_gemm_bilinear.cpp
profile_gemm_bias_add_reduce.cpp #profile_gemm_bias_add_reduce.cpp
profile_gemm_add_add_fastgelu.cpp #profile_gemm_add_add_fastgelu.cpp
profile_gemm_reduce.cpp #profile_gemm_reduce.cpp
profile_batched_gemm.cpp #profile_batched_gemm.cpp
profile_batched_gemm_gemm.cpp #profile_batched_gemm_gemm.cpp
profile_batched_gemm_add_relu_gemm_add.cpp #profile_batched_gemm_add_relu_gemm_add.cpp
profile_batched_gemm_reduce.cpp #profile_batched_gemm_reduce.cpp
profile_grouped_gemm.cpp profile_grouped_gemm.cpp
profile_conv_fwd.cpp #profile_conv_fwd.cpp
profile_conv_fwd_bias_relu.cpp #profile_conv_fwd_bias_relu.cpp
profile_conv_fwd_bias_relu_add.cpp #profile_conv_fwd_bias_relu_add.cpp
profile_conv_bwd_data.cpp #profile_conv_bwd_data.cpp
profile_grouped_conv_fwd.cpp #profile_grouped_conv_fwd.cpp
profile_grouped_conv_bwd_weight.cpp #profile_grouped_conv_bwd_weight.cpp
profile_reduce.cpp #profile_reduce.cpp
profile_groupnorm.cpp #profile_groupnorm.cpp
profile_layernorm.cpp #profile_layernorm.cpp
profile_softmax.cpp #profile_softmax.cpp
profile_batchnorm_fwd.cpp #profile_batchnorm_fwd.cpp
profile_batchnorm_bwd.cpp #profile_batchnorm_bwd.cpp
) )
set(PROFILER_EXECUTABLE ckProfiler) set(PROFILER_EXECUTABLE ckProfiler)
...@@ -32,32 +32,32 @@ add_executable(${PROFILER_EXECUTABLE} ${PROFILER_SOURCES}) ...@@ -32,32 +32,32 @@ add_executable(${PROFILER_EXECUTABLE} ${PROFILER_SOURCES})
target_compile_options(${PROFILER_EXECUTABLE} PRIVATE -Wno-global-constructors) target_compile_options(${PROFILER_EXECUTABLE} PRIVATE -Wno-global-constructors)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE utility) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE utility)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_splitk_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_splitk_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_bilinear_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_bilinear_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_add_fastgelu_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_add_fastgelu_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_reduce_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_reduce_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_bias_add_reduce_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_bias_add_reduce_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_gemm_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_gemm_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_add_relu_gemm_add_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_add_relu_gemm_add_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_reduce_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_reduce_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_gemm_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_gemm_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv2d_fwd_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv2d_fwd_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv1d_fwd_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv1d_fwd_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv2d_fwd_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv2d_fwd_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_fwd_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_fwd_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv1d_bwd_data_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv1d_bwd_data_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv2d_bwd_data_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv2d_bwd_data_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv3d_bwd_data_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv3d_bwd_data_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv1d_bwd_weight_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv1d_bwd_weight_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv2d_bwd_weight_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv2d_bwd_weight_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_bwd_weight_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_bwd_weight_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv2d_fwd_bias_relu_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv2d_fwd_bias_relu_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv2d_fwd_bias_relu_add_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv2d_fwd_bias_relu_add_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_normalization_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_normalization_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_softmax_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_softmax_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_reduce_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_reduce_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batchnorm_instance) #target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batchnorm_instance)
rocm_install(TARGETS ${PROFILER_EXECUTABLE} COMPONENT profiler) rocm_install(TARGETS ${PROFILER_EXECUTABLE} COMPONENT profiler)
script/cmake-ck-dev.sh
View file @ c7ba4159
...@@ -10,8 +10,8 @@ cmake ...@@ -10,8 +10,8 @@ cmake
-D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc \ -D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc \
-D CMAKE_CXX_FLAGS="-O3 -ftemplate-backtrace-limit=0 -gline-tables-only -save-temps=$PWD" \ -D CMAKE_CXX_FLAGS="-O3 -ftemplate-backtrace-limit=0 -gline-tables-only -save-temps=$PWD" \
-D CMAKE_BUILD_TYPE=Release \ -D CMAKE_BUILD_TYPE=Release \
-D BUILD_DEV=ON \ -D BUILD_DEV=OFF \
-D GPU_TARGETS="gfx908;gfx90a" \ -D GPU_TARGETS="gfx908" \
-D CMAKE_VERBOSE_MAKEFILE:BOOL=ON \ -D CMAKE_VERBOSE_MAKEFILE:BOOL=ON \
-D USE_BITINT_EXTENSION_INT4=OFF \ -D USE_BITINT_EXTENSION_INT4=OFF \
${MY_PROJECT_SOURCE} ${MY_PROJECT_SOURCE}
......
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