Skip to content

GitLab

Commit 11279540 authored by Astha Rai's avatar Astha Rai
Browse files

Merge branch 'transpose_5d' of github.com:ROCmSoftwarePlatform/composable_kernel into transpose_5d

parents 14daa201 33e78b9a
Hide whitespace changes
Inline Side-by-side
Showing with 843 additions and 420 deletions
library/src/tensor_operation_instance/gpu/normalization/device_groupnorm_f16_instance.cpp
View file @ 11279540
...@@ -11,7 +11,7 @@ namespace instance { ...@@ -11,7 +11,7 @@ namespace instance {
using Pass = ck::tensor_operation::element_wise::PassThrough; using Pass = ck::tensor_operation::element_wise::PassThrough;
void add_device_normalization_rank_5_3_f16_instances( void add_device_normalization_rank_5_3_f16_instances(
std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F32, F16, Pass, 5, 3>>>& std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F16, F32, Pass, 5, 3>>>&
instances) instances)
{ {
add_device_operation_instances(instances, add_device_operation_instances(instances,
......
library/src/tensor_operation_instance/gpu/normalization/device_groupnorm_swish_f16_f32_f32_f16_instance.cpp
View file @ 11279540
...@@ -11,7 +11,7 @@ namespace instance { ...@@ -11,7 +11,7 @@ namespace instance {
using Swish = ck::tensor_operation::element_wise::Swish; using Swish = ck::tensor_operation::element_wise::Swish;
void add_device_normalization_rank_5_3_swish_f16_f32_f32_f16_instances( void add_device_normalization_rank_5_3_swish_f16_f32_f32_f16_instances(
std::vector<std::unique_ptr<DeviceNormalization<F16, F32, F32, F32, F16, Swish, 5, 3>>>& std::vector<std::unique_ptr<DeviceNormalization<F16, F32, F32, F16, F32, Swish, 5, 3>>>&
instances) instances)
{ {
add_device_operation_instances( add_device_operation_instances(
......
library/src/tensor_operation_instance/gpu/normalization/device_groupnorm_swish_f16_instance.cpp
View file @ 11279540
...@@ -11,7 +11,7 @@ namespace instance { ...@@ -11,7 +11,7 @@ namespace instance {
using Swish = ck::tensor_operation::element_wise::Swish; using Swish = ck::tensor_operation::element_wise::Swish;
void add_device_normalization_rank_5_3_swish_f16_instances( void add_device_normalization_rank_5_3_swish_f16_instances(
std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F32, F16, Swish, 5, 3>>>& std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F16, F32, Swish, 5, 3>>>&
instances) instances)
{ {
add_device_operation_instances(instances, add_device_operation_instances(instances,
......
library/src/tensor_operation_instance/gpu/normalization/device_layernorm2d_f16_instance.cpp
View file @ 11279540
...@@ -11,7 +11,7 @@ namespace instance { ...@@ -11,7 +11,7 @@ namespace instance {
using Pass = ck::tensor_operation::element_wise::PassThrough; using Pass = ck::tensor_operation::element_wise::PassThrough;
void add_device_normalization_rank_2_1_f16_instances( void add_device_normalization_rank_2_1_f16_instances(
std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F32, F16, Pass, 2, 1>>>& std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F16, F32, Pass, 2, 1>>>&
instances) instances)
{ {
add_device_operation_instances(instances, add_device_operation_instances(instances,
......
library/src/tensor_operation_instance/gpu/normalization/device_layernorm4d_f16_instance.cpp
View file @ 11279540
...@@ -11,7 +11,7 @@ namespace instance { ...@@ -11,7 +11,7 @@ namespace instance {
using Pass = ck::tensor_operation::element_wise::PassThrough; using Pass = ck::tensor_operation::element_wise::PassThrough;
void add_device_normalization_rank_4_3_f16_instances( void add_device_normalization_rank_4_3_f16_instances(
std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F32, F16, Pass, 4, 3>>>& std::vector<std::unique_ptr<DeviceNormalization<F16, F16, F16, F16, F32, Pass, 4, 3>>>&
instances) instances)
{ {
add_device_operation_instances(instances, add_device_operation_instances(instances,
......
library/src/tensor_operation_instance/gpu/normalization/normalization_instance_common.hpp
View file @ 11279540
...@@ -22,25 +22,25 @@ template <typename OutElementwise, index_t Rank, index_t Reduce> ...@@ -22,25 +22,25 @@ template <typename OutElementwise, index_t Rank, index_t Reduce>
using device_normalization_f16_instances = using device_normalization_f16_instances =
// clang-format off // clang-format off
std::tuple < std::tuple <
// XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorDim, GammaSrcVectorSize, BetaSrcVectorDim, BetaSrcVectorSize, YDstVectorSize> // XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, SaveMeanInvStdDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorDim, GammaSrcVectorSize, BetaSrcVectorDim, BetaSrcVectorSize, YDstVectorSize, SaveMeanInvStdScalarPerVector>
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 2, 1, 2, 1, 2, 1, 2, 2>, // irregular size DeviceNormalizationImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1>, // irregular size
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 4, 1, 4, 1, 4, 1, 4, 4>, // irregular size DeviceNormalizationImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 4, 1, 4, 1, 4, 1, 4, 4, 1>, // irregular size
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 64, 1, 64, 1, 8, 1, 8, 1, 8, 1, 8, 8>, DeviceNormalizationImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 64, 1, 64, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 8, 1, 8, 1, 8, 1, 8, 8>, DeviceNormalizationImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 16, 1, 8, 1, 8, 1, 8, 8>, DeviceNormalizationImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 16, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 32, 1, 8, 1, 8, 1, 8, 8>, DeviceNormalizationImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 32, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 8, 1, 8, 1, 8, 1, 8, 8>, DeviceNormalizationImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 16, 1, 8, 1, 8, 1, 8, 8>, DeviceNormalizationImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 16, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 2, 16, 1, 8, 1, 8, 1, 8, 8>, DeviceNormalizationImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 2, 16, 1, 8, 1, 8, 1, 8, 8, 2>,
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 32, 1, 8, 1, 8, 1, 8, 8>, DeviceNormalizationImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 32, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 8, 1, 8, 1, 8, 1, 8, 8>, DeviceNormalizationImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 16, 1, 8, 1, 8, 1, 8, 8>, DeviceNormalizationImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 16, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 8, 1, 8, 1, 8, 1, 8, 8>, DeviceNormalizationImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 16, 1, 8, 1, 8, 1, 8, 8> DeviceNormalizationImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 16, 1, 8, 1, 8, 1, 8, 8, 1>
// clang-format on // clang-format on
>; >;
...@@ -48,150 +48,150 @@ template <typename OutElementwise, index_t Rank, index_t Reduce> ...@@ -48,150 +48,150 @@ template <typename OutElementwise, index_t Rank, index_t Reduce>
using device_normalization_splitk_f16_instances = using device_normalization_splitk_f16_instances =
// clang-format off // clang-format off
std::tuple < std::tuple <
// XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorDim, GammaSrcVectorSize, BetaSrcVectorDim, BetaSrcVectorSize, YDstVectorSize> // XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, SaveMeanInvStdDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorDim, GammaSrcVectorSize, BetaSrcVectorDim, BetaSrcVectorSize, YDstVectorSize, SaveMeanInvStdScalarPerVector>
DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 2, 1, 2, 1, 2, 1, 2, 2>, // irregular size DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1>, // irregular size
DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 4, 1, 4, 1, 4, 1, 4, 4>, // irregular size DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 4, 1, 4, 1, 4, 1, 4, 4, 1>, // irregular size
DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 64, 1, 64, 1, 8, 1, 8, 1, 8, 1, 8, 8>, DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 64, 1, 64, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 8, 1, 8, 1, 8, 1, 8, 8>, DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 16, 1, 8, 1, 8, 1, 8, 8>, DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 16, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 32, 1, 8, 1, 8, 1, 8, 8>, DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 32, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 8, 1, 8, 1, 8, 1, 8, 8>, DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 16, 1, 8, 1, 8, 1, 8, 8>, DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 16, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 2, 16, 1, 8, 1, 8, 1, 8, 8>, DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 2, 16, 1, 8, 1, 8, 1, 8, 8, 2>,
DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 32, 1, 8, 1, 8, 1, 8, 8>, DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 32, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 8, 1, 8, 1, 8, 1, 8, 8>, DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 16, 1, 8, 1, 8, 1, 8, 8>, DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 16, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 8, 1, 8, 1, 8, 1, 8, 8>, DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 16, 1, 8, 1, 8, 1, 8, 8> DeviceNormalizationSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 16, 1, 8, 1, 8, 1, 8, 8, 1>
// clang-format on // clang-format on
>; >;
template <typename OutElementwise, index_t Rank, index_t Reduce> template <typename OutElementwise, index_t Rank, index_t Reduce>
using device_normalization_f16_generic_instance = std::tuple< using device_normalization_f16_generic_instance = std::tuple<
// clang-format off // clang-format off
DeviceNormalizationImpl<F16, F16, F16, F32, F16, OutElementwise, Rank, Reduce, 64, 1, 64, 1, 1, 1, 1, 1, 1, 1, 1, 1> DeviceNormalizationImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 64, 1, 64, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>
// clang-format on // clang-format on
>; >;
template <typename OutElementwise, index_t Rank, index_t Reduce> template <typename OutElementwise, index_t Rank, index_t Reduce>
using device_normalization_f32_instances = std::tuple< using device_normalization_f32_instances = std::tuple<
// clang-format off // clang-format off
// XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorSize, BetaSrcVectorSize, YDstVectorSize> // XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, SaveMeanInvStdDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorDim, GammaSrcVectorSize, BetaSrcVectorDim, BetaSrcVectorSize, YDstVectorSize, SaveMeanInvStdScalarPerVector>
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 2, 1, 2, 1, 2, 1, 2, 2>, // irregular size DeviceNormalizationImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1>, // irregular size
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 4, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 4, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 8, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 8, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 16, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 16, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 32, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 32, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 4, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 4, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 8, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 8, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 16, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 16, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 2, 16, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 2, 16, 1, 4, 1, 4, 1, 4, 4, 2>,
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 32, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 32, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 4, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 4, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 8, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 8, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 2, 8, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 2, 8, 1, 4, 1, 4, 1, 4, 4, 2>,
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 4, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 4, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 8, 1, 4, 1, 4, 1, 4, 4> DeviceNormalizationImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 8, 1, 4, 1, 4, 1, 4, 4, 1>
// clang-format on // clang-format on
>; >;
template <typename OutElementwise, index_t Rank, index_t Reduce> template <typename OutElementwise, index_t Rank, index_t Reduce>
using device_normalization_splitk_f32_instances = std::tuple< using device_normalization_splitk_f32_instances = std::tuple<
// clang-format off // clang-format off
// XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorSize, BetaSrcVectorSize, YDstVectorSize> // XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, SaveMeanInvStdDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorDim, GammaSrcVectorSize, BetaSrcVectorDim, BetaSrcVectorSize, YDstVectorSize, SaveMeanInvStdScalarPerVector>
DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 2, 1, 2, 1, 2, 1, 2, 2>, // irregular size DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1>, // irregular size
DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 4, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 4, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 8, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 8, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 16, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 16, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 32, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 32, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 4, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 4, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 8, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 8, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 16, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 16, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 2, 16, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 2, 16, 1, 4, 1, 4, 1, 4, 4, 2>,
DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 32, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 32, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 4, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 4, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 8, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 8, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 2, 8, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 2, 8, 1, 4, 1, 4, 1, 4, 4, 2>,
DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 4, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 4, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 8, 1, 4, 1, 4, 1, 4, 4> DeviceNormalizationSplitKImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 8, 1, 4, 1, 4, 1, 4, 4, 1>
// clang-format on // clang-format on
>; >;
template <typename OutElementwise, index_t Rank, index_t Reduce> template <typename OutElementwise, index_t Rank, index_t Reduce>
using device_normalization_f32_generic_instance = std::tuple< using device_normalization_f32_generic_instance = std::tuple<
// clang-format off // clang-format off
DeviceNormalizationImpl<F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 64, 1, 64, 1, 1, 1, 1, 1, 1, 1, 1, 1> DeviceNormalizationImpl<F32, F32, F32, F32, F32, F32, OutElementwise, Rank, Reduce, 64, 1, 64, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>
// clang-format on // clang-format on
>; >;
template <typename OutElementwise, index_t Rank, index_t Reduce> template <typename OutElementwise, index_t Rank, index_t Reduce>
using device_normalization_f16_f32_f32_f16_instances = std::tuple< using device_normalization_f16_f32_f32_f16_instances = std::tuple<
// clang-format off // clang-format off
// XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorSize, BetaSrcVectorSize, YDstVectorSize> // XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, SaveMeanInvStdDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorDim, GammaSrcVectorSize, BetaSrcVectorDim, BetaSrcVectorSize, YDstVectorSize, SaveMeanInvStdScalarPerVector>
DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 2, 1, 2, 1, 2, 1, 2, 2>, // irregular size DeviceNormalizationImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1>, // irregular size
DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 4, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 4, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 8, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 8, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 16, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 16, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 32, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 32, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 4, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 4, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 8, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 8, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 16, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 16, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 2, 16, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 2, 16, 1, 4, 1, 4, 1, 4, 4, 2>,
DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 32, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 32, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 4, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 4, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 8, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 8, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 512, 1, 512, 2, 8, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 2, 8, 1, 4, 1, 4, 1, 4, 4, 2>,
DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 4, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 4, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 8, 1, 4, 1, 4, 1, 4, 4> DeviceNormalizationImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 8, 1, 4, 1, 4, 1, 4, 4, 1>
// clang-format on // clang-format on
>; >;
template <typename OutElementwise, index_t Rank, index_t Reduce> template <typename OutElementwise, index_t Rank, index_t Reduce>
using device_normalization_splitk_f16_f32_f32_f16_instances = std::tuple< using device_normalization_splitk_f16_f32_f32_f16_instances = std::tuple<
// clang-format off // clang-format off
// XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorSize, BetaSrcVectorSize, YDstVectorSize> // XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, SaveMeanInvStdDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorDim, GammaSrcVectorSize, BetaSrcVectorDim, BetaSrcVectorSize, YDstVectorSize, SaveMeanInvStdScalarPerVector>
DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 2, 1, 2, 1, 2, 1, 2, 2>, // irregular size DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1>, // irregular size
DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 4, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 4, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 8, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 8, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 16, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 16, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 32, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 32, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 4, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 4, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 8, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 8, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 16, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 16, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 2, 16, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 2, 16, 1, 4, 1, 4, 1, 4, 4, 2>,
DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 32, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 32, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 4, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 4, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 8, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 8, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 512, 1, 512, 2, 8, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 2, 8, 1, 4, 1, 4, 1, 4, 4, 2>,
DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 4, 1, 4, 1, 4, 1, 4, 4>, DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 4, 1, 4, 1, 4, 1, 4, 4, 1>,
DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 8, 1, 4, 1, 4, 1, 4, 4> DeviceNormalizationSplitKImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 8, 1, 4, 1, 4, 1, 4, 4, 1>
// clang-format on // clang-format on
>; >;
template <typename OutElementwise, index_t Rank, index_t Reduce> template <typename OutElementwise, index_t Rank, index_t Reduce>
using device_normalization_f16_f32_f32_f16_generic_instance = std::tuple< using device_normalization_f16_f32_f32_f16_generic_instance = std::tuple<
// clang-format off // clang-format off
DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 64, 1, 64, 1, 1, 1, 1, 1, 1, 1, 1, 1> DeviceNormalizationImpl<F16, F32, F32, F32, F16, F32, OutElementwise, Rank, Reduce, 64, 1, 64, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>
// clang-format on // clang-format on
>; >;
......
profiler/README.md
View file @ 11279540
...@@ -22,7 +22,7 @@ c_m_n: dim 2, lengths {3840, 4096}, strides {4096, 1} ...@@ -22,7 +22,7 @@ c_m_n: dim 2, lengths {3840, 4096}, strides {4096, 1}
Best Perf: 1.1933 ms, 107.977 TFlops, 79.0848 GB/s Best Perf: 1.1933 ms, 107.977 TFlops, 79.0848 GB/s
``` ```
## Profile 2d forward convolution kernels ## Profile 2D forward convolution kernels
```bash ```bash
#arg1: tensor operation (conv=Convolution) #arg1: tensor operation (conv=Convolution)
#arg2: data type (0=fp32, 1=fp16) #arg2: data type (0=fp32, 1=fp16)
...@@ -50,21 +50,23 @@ Best Perf: 1.42509 ms, 102.988 TFlops, 234.086 GB/s ...@@ -50,21 +50,23 @@ Best Perf: 1.42509 ms, 102.988 TFlops, 234.086 GB/s
## Profile contraction kernels ## Profile contraction kernels
```bash ```bash
#arg1: tensor operation (contraction_bilinear=CONTRACTION+Bilinear) #arg1: tensor operation (contraction_bilinear=CONTRACTION+Bilinear)
#arg2: data type (0: fp32; 1: f64)\n" #arg2: data type (0: fp32; 1: f64; 2: f16; 3: bf16)
#arg3: matrix layout (0: A[m0, m1, k0, k1] * B[k0, k1, n0, n1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1]; #arg3: compute data type (0: fp32; 1: f64; 2: f16; 3: bf16)
#arg4: matrix layout (0: A[m0, m1, k0, k1] * B[k0, k1, n0, n1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1];
# 1: A[m0, m1, k0, k1] * B[n0, n1, k0, k1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1]; # 1: A[m0, m1, k0, k1] * B[n0, n1, k0, k1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1];
# 2: A[k0, k1, m0, m1] * B[k0, k1, n0, n1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1]; # 2: A[k0, k1, m0, m1] * B[k0, k1, n0, n1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1];
# 3: A[k0, k1, m0, m1] * B[n0, n1, k0, k1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1]) # 3: A[k0, k1, m0, m1] * B[n0, n1, k0, k1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1])
#arg4: verification (0: no; 1: yes) #arg5: verification (0: no; 1: yes)
#arg5: initialization (0: no init; 1: integer value; 2: decimal value) #arg6: initialization (0: no init; 1: integer value; 2: decimal value)
#arg6: print tensor value (0: no; 1: yes) #arg7: print tensor value (0: no; 1: yes)
#arg7: time kernel (0: no, 1: yes) #arg8: time kernel (0: no, 1: yes)
#arg8 and arg9: alpha and beta #arg9: alpha
#arg10 to 15: M0, M1, N0, N1, K0, K1 #arg10: beta
#arg16 to 31: Strides for A, B, D and E (skip for default) #arg11 to 16: M0, M1, N0, N1, K0, K1
#arg17 to 32: Strides for A, B, D and E (skip for default)
################ op datatype layout verify init log time alpha beta M0 M1 N0 N1 K0 K1
./bin/ckProfiler contraction_bilinear 0 1 0 0 0 1 1.0 1.0 128 128 128 128 128 128 ################ op datatype compute_datatype layout verify init log time alpha beta M0 M1 N0 N1 K0 K1
./bin/ckProfiler contraction_bilinear 0 0 1 0 0 0 1 1.0 1.0 128 128 128 128 128 128
``` ```
Result (MI100) Result (MI100)
...@@ -115,7 +117,7 @@ Best Perf: 58.0306 ms, 37.8942 TFlops, 27.7545 GB/s ...@@ -115,7 +117,7 @@ Best Perf: 58.0306 ms, 37.8942 TFlops, 27.7545 GB/s
# arg6: print tensor value (0: no; 1: yes) # arg6: print tensor value (0: no; 1: yes)
# arg7: time kernel (0: no, 1: yes) # arg7: time kernel (0: no, 1: yes)
# Following arguments (depending on number of spatial dims): # Following arguments (depending on number of spatial dims):
# Number of spatial dimensions (1=Conv1d, 2=Conv2d, 3=Conv3d) # Number of spatial dimensions (1=Conv1D, 2=Conv2D, 3=Conv3D)
# G, N, K, C, # G, N, K, C,
# <filter spatial dimensions>, (ie Y, X for 2D) # <filter spatial dimensions>, (ie Y, X for 2D)
# <input image spatial dimensions>, (ie Hi, Wi for 2D) # <input image spatial dimensions>, (ie Hi, Wi for 2D)
...@@ -147,7 +149,9 @@ GB/s: 127.947 ...@@ -147,7 +149,9 @@ GB/s: 127.947
# arg1: tensor operation (grouped_conv_bwd_weight: Grouped Convolution Backward Weight) # arg1: tensor operation (grouped_conv_bwd_weight: Grouped Convolution Backward Weight)
# arg2: data type (0: Input fp32, Weight fp32, Output fp32 # arg2: data type (0: Input fp32, Weight fp32, Output fp32
# 1: Input fp16, Weight fp16, Output fp16 # 1: Input fp16, Weight fp16, Output fp16
# 2: Input bf16, Weight fp32, Output bf16) # 2: Input bf16, Weight fp32, Output bf16
# 3: Input fp16, Weight fp16, Output fp16, Gemm bf8@fp8
# 4: Input int8, Weight int8, Output int8)
# arg3: tensor layout (0: Input[G, N, C, Hi, Wi], Weight[G, K, C, Y, X], Output[G, N, K, Ho, Wo] # arg3: tensor layout (0: Input[G, N, C, Hi, Wi], Weight[G, K, C, Y, X], Output[G, N, K, Ho, Wo]
# 1: Input[G, N, Hi, Wi, C], Weight[G, K, Y, X, C], Output[G, N, Ho, Wo, K] # 1: Input[G, N, Hi, Wi, C], Weight[G, K, Y, X, C], Output[G, N, Ho, Wo, K]
# 2: Input[N, Hi, Wi, G, C], Weight[G, K, Y, X, C], Output[N, Ho, Wo, G, K] # 2: Input[N, Hi, Wi, G, C], Weight[G, K, Y, X, C], Output[N, Ho, Wo, G, K]
...@@ -156,7 +160,7 @@ GB/s: 127.947 ...@@ -156,7 +160,7 @@ GB/s: 127.947
# arg6: print tensor value (0: no; 1: yes) # arg6: print tensor value (0: no; 1: yes)
# arg7: time kernel (0: no, 1: yes) # arg7: time kernel (0: no, 1: yes)
# Following arguments (depending on number of spatial dims): # Following arguments (depending on number of spatial dims):
# Number of spatial dimensions (1=Conv1d, 2=Conv2d, 3=Conv3d) # Number of spatial dimensions (1=Conv1D, 2=Conv2D, 3=Conv3D)
# G, N, K, C, # G, N, K, C,
# <filter spatial dimensions>, (ie Y, X for 2D) # <filter spatial dimensions>, (ie Y, X for 2D)
# <input image spatial dimensions>, (ie Hi, Wi for 2D) # <input image spatial dimensions>, (ie Hi, Wi for 2D)
...@@ -167,7 +171,7 @@ GB/s: 127.947 ...@@ -167,7 +171,7 @@ GB/s: 127.947
# SplitK # SplitK
################ op datatype layout verify init log time Ndims G N K C Y X Hi Wi Sy Sx Dy Dx LeftPy LeftPx RightPy RightPx SplitK ################ op datatype layout verify init log time Ndims G N K C Y X Hi Wi Sy Sx Dy Dx LeftPy LeftPx RightPy RightPx SplitK
./bin/ckProfiler grouped_conv_bwd_weight 1 0 1 1 0 1 2 32 256 256 512 3 3 28 28 1 1 1 1 1 0 0 0 1 ./bin/ckProfiler grouped_conv_bwd_weight 1 1 0 1 0 1 2 32 256 256 512 3 3 28 28 1 1 1 1 1 0 0 0 1
``` ```
...@@ -192,14 +196,15 @@ Note: This kernel use atomic add, this will cause output buffer to be accumulate ...@@ -192,14 +196,15 @@ Note: This kernel use atomic add, this will cause output buffer to be accumulate
# 1: Input fp16, Weight fp16, Output fp16 # 1: Input fp16, Weight fp16, Output fp16
# 2: Input bf16, Weight bf16, Output bf16 # 2: Input bf16, Weight bf16, Output bf16
# 3: Input int8, Weight int8, Output int8) # 3: Input int8, Weight int8, Output int8)
# arg3: tensor layout (0: Input[N, Hi, Wi, C], Output[N * Ho * Wo, Y * X * C]) # arg3: tensor layout (0: Input[G, N, Hi, Wi, C], Output[G * N * Ho * Wo, Y * X * C],
# 1: Input[N, Hi, Wi, G, C], Output[N * Ho * Wo * G, Y * X * C])
# arg4: verification (0: no, 1: yes) # arg4: verification (0: no, 1: yes)
# arg5: initialization (0: no init, 1: integer value, 2: decimal value) # arg5: initialization (0: no init, 1: integer value, 2: decimal value)
# arg6: print tensor value (0: no; 1: yes) # arg6: print tensor value (0: no; 1: yes)
# arg7: time kernel (0: no, 1: yes) # arg7: time kernel (0: no, 1: yes)
# arg8: operation type (0: ImageToColumn, 1: ColumnToImage) # arg8: operation type (0: ImageToColumn, 1: ColumnToImage)
# Following arguments (depending on number of spatial dims): # Following arguments (depending on number of spatial dims):
# Number of spatial dimensions (1=Conv1d, 2=Conv2d, 3=Conv3d) # Number of spatial dimensions (1=Conv1D, 2=Conv2D, 3=Conv3D)
# G, N, K, C, # G, N, K, C,
# <filter spatial dimensions>, (ie Y, X for 2D) # <filter spatial dimensions>, (ie Y, X for 2D)
# <input image spatial dimensions>, (ie Hi, Wi for 2D) # <input image spatial dimensions>, (ie Hi, Wi for 2D)
......
profiler/include/profiler/profile_contraction_impl.hpp
View file @ 11279540
...@@ -31,10 +31,14 @@ namespace profiler { ...@@ -31,10 +31,14 @@ namespace profiler {
using Bilinear = ck::tensor_operation::element_wise::Bilinear; using Bilinear = ck::tensor_operation::element_wise::Bilinear;
using Scale = ck::tensor_operation::element_wise::Scale; using Scale = ck::tensor_operation::element_wise::Scale;
using F32 = float;
using F64 = double;
template <typename ALayout, template <typename ALayout,
typename BLayout, typename BLayout,
typename CDELayout, typename CDELayout,
typename DataType, typename DataType,
typename ComputeDataType,
typename DTupleDataType, typename DTupleDataType,
typename CDElementOp> typename CDElementOp>
int profile_contraction_impl(ck::index_t do_verification, int profile_contraction_impl(ck::index_t do_verification,
...@@ -45,10 +49,10 @@ int profile_contraction_impl(ck::index_t do_verification, ...@@ -45,10 +49,10 @@ int profile_contraction_impl(ck::index_t do_verification,
const std::vector<ck::index_t>& M, const std::vector<ck::index_t>& M,
const std::vector<ck::index_t>& N, const std::vector<ck::index_t>& N,
const std::vector<ck::index_t>& K, const std::vector<ck::index_t>& K,
const std::vector<ck::index_t>& StridesA, const std::vector<ck::index_t>& StridesA, // [M0, M1, K0, K1]
const std::vector<ck::index_t>& StridesB, const std::vector<ck::index_t>& StridesB, // [N0, N1, K0, K1]
const std::vector<ck::index_t>& StridesE, const std::vector<ck::index_t>& StridesE, // [M0, M1, N0, N1]
const std::vector<ck::index_t>& StridesD) const std::vector<ck::index_t>& StridesD) // [M0, M1, N0, N1]
{ {
bool pass = true; bool pass = true;
...@@ -63,13 +67,13 @@ int profile_contraction_impl(ck::index_t do_verification, ...@@ -63,13 +67,13 @@ int profile_contraction_impl(ck::index_t do_verification,
}; };
Tensor<DataType> a_m_k(f_host_tensor_descriptor(M, K, StridesA)); Tensor<DataType> a_m_k(f_host_tensor_descriptor(M, K, StridesA));
Tensor<DataType> b_k_n(f_host_tensor_descriptor(K, N, StridesB)); Tensor<DataType> b_n_k(f_host_tensor_descriptor(N, K, StridesB));
Tensor<DataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StridesE)); Tensor<DataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StridesE));
Tensor<DataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StridesE)); Tensor<DataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StridesE));
Tensor<DataType> d_m_n(f_host_tensor_descriptor(M, N, StridesD)); Tensor<DataType> d_m_n(f_host_tensor_descriptor(M, N, StridesD));
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl; std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl; std::cout << "b_n_k: " << b_n_k.mDesc << std::endl;
std::cout << "d_m_n: " << d_m_n.mDesc << std::endl; std::cout << "d_m_n: " << d_m_n.mDesc << std::endl;
std::cout << "e_m_n: " << e_m_n_device_result.mDesc << std::endl; std::cout << "e_m_n: " << e_m_n_device_result.mDesc << std::endl;
...@@ -78,12 +82,12 @@ int profile_contraction_impl(ck::index_t do_verification, ...@@ -78,12 +82,12 @@ int profile_contraction_impl(ck::index_t do_verification,
case 0: break; case 0: break;
case 1: case 1:
a_m_k.GenerateTensorValue(GeneratorTensor_2<DataType>{-5, 5}); a_m_k.GenerateTensorValue(GeneratorTensor_2<DataType>{-5, 5});
b_k_n.GenerateTensorValue(GeneratorTensor_2<DataType>{-5, 5}); b_n_k.GenerateTensorValue(GeneratorTensor_2<DataType>{-5, 5});
d_m_n.GenerateTensorValue(GeneratorTensor_2<DataType>{-5, 5}); d_m_n.GenerateTensorValue(GeneratorTensor_2<DataType>{-5, 5});
break; break;
default: default:
a_m_k.GenerateTensorValue(GeneratorTensor_3<DataType>{0.0, 1.0}); a_m_k.GenerateTensorValue(GeneratorTensor_3<DataType>{0.0, 1.0});
b_k_n.GenerateTensorValue(GeneratorTensor_3<DataType>{-0.5, 0.5}); b_n_k.GenerateTensorValue(GeneratorTensor_3<DataType>{-0.5, 0.5});
d_m_n.GenerateTensorValue(GeneratorTensor_3<DataType>{-0.5, 0.5}); d_m_n.GenerateTensorValue(GeneratorTensor_3<DataType>{-0.5, 0.5});
} }
...@@ -91,12 +95,12 @@ int profile_contraction_impl(ck::index_t do_verification, ...@@ -91,12 +95,12 @@ int profile_contraction_impl(ck::index_t do_verification,
using BElementOp = ck::tensor_operation::element_wise::PassThrough; using BElementOp = ck::tensor_operation::element_wise::PassThrough;
DeviceMem a_device_buf(sizeof(DataType) * a_m_k.mDesc.GetElementSpaceSize()); DeviceMem a_device_buf(sizeof(DataType) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf(sizeof(DataType) * b_k_n.mDesc.GetElementSpaceSize()); DeviceMem b_device_buf(sizeof(DataType) * b_n_k.mDesc.GetElementSpaceSize());
DeviceMem e_device_buf(sizeof(DataType) * e_m_n_device_result.mDesc.GetElementSpaceSize()); DeviceMem e_device_buf(sizeof(DataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
DeviceMem d_device_buf(sizeof(DataType) * d_m_n.mDesc.GetElementSpaceSize()); DeviceMem d_device_buf(sizeof(DataType) * d_m_n.mDesc.GetElementSpaceSize());
a_device_buf.ToDevice(a_m_k.mData.data()); a_device_buf.ToDevice(a_m_k.mData.data());
b_device_buf.ToDevice(b_k_n.mData.data()); b_device_buf.ToDevice(b_n_k.mData.data());
e_device_buf.SetZero(); e_device_buf.SetZero();
d_device_buf.ToDevice(d_m_n.mData.data()); d_device_buf.ToDevice(d_m_n.mData.data());
...@@ -118,7 +122,8 @@ int profile_contraction_impl(ck::index_t do_verification, ...@@ -118,7 +122,8 @@ int profile_contraction_impl(ck::index_t do_verification,
DataType, DataType,
AElementOp, AElementOp,
BElementOp, BElementOp,
CDElementOp>; CDElementOp,
ComputeDataType>;
// get device op instances // get device op instances
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory< const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
...@@ -126,6 +131,9 @@ int profile_contraction_impl(ck::index_t do_verification, ...@@ -126,6 +131,9 @@ int profile_contraction_impl(ck::index_t do_verification,
std::cout << "found " << op_ptrs.size() << " instances" << std::endl; std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
using AccDataType =
typename std::conditional<std::is_same<ComputeDataType, F64>::value, F64, F32>::type;
// Run reference op // Run reference op
if(do_verification) if(do_verification)
{ {
...@@ -136,7 +144,8 @@ int profile_contraction_impl(ck::index_t do_verification, ...@@ -136,7 +144,8 @@ int profile_contraction_impl(ck::index_t do_verification,
DataType, DataType,
DataType, DataType,
DataType, DataType,
DataType, AccDataType,
ComputeDataType,
AElementOp, AElementOp,
BElementOp>; BElementOp>;
...@@ -146,7 +155,7 @@ int profile_contraction_impl(ck::index_t do_verification, ...@@ -146,7 +155,7 @@ int profile_contraction_impl(ck::index_t do_verification,
Tensor<DataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StridesE)); Tensor<DataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StridesE));
auto ref_argument = auto ref_argument =
ref_op.MakeArgument(a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op); ref_op.MakeArgument(a_m_k, b_n_k, c_m_n_host_result, a_element_op, b_element_op);
ref_invoker.Run(ref_argument); ref_invoker.Run(ref_argument);
...@@ -272,8 +281,29 @@ int profile_contraction_impl(ck::index_t do_verification, ...@@ -272,8 +281,29 @@ int profile_contraction_impl(ck::index_t do_verification,
{ {
e_device_buf.FromDevice(e_m_n_device_result.mData.data()); e_device_buf.FromDevice(e_m_n_device_result.mData.data());
float threshold = // Both the kernel and the reference use `AccDataType`, so an absolute error of both
static_cast<DataType>(nelems_k) * std::numeric_limits<DataType>::epsilon(); // of them is bounded by `nelems_k * std::numeric_limits<AccDataType>::epsilon()`.
// Comparing one to another can result in an absolute error as high as twice that
// value.
double threshold = 2 * nelems_k * std::numeric_limits<AccDataType>::epsilon();
// Handle the possible casting error of either AccDataType -> DataType or
// DataType -> ComputeDataType.
// TODO: Add a generic solution for calculating thresholds in CK.
if constexpr(ck::is_same_v<DataType, ck::bhalf_t> ||
ck::is_same_v<ComputeDataType, ck::bhalf_t>)
{
const double epsilon = std::pow(2, -7);
// Maximum relative casting error when rounding to zero.
threshold += epsilon * 2;
}
else if constexpr(ck::is_same_v<DataType, ck::half_t> ||
ck::is_same_v<ComputeDataType, ck::half_t>)
{
const double epsilon = std::pow(2, -10);
// Maximum relative casting error when rounding to zero.
threshold += epsilon * 2;
}
pass = pass & ck::utils::check_err(e_m_n_device_result, pass = pass & ck::utils::check_err(e_m_n_device_result,
e_m_n_host_result, e_m_n_host_result,
"Error: incorrect results!", "Error: incorrect results!",
...@@ -283,7 +313,7 @@ int profile_contraction_impl(ck::index_t do_verification, ...@@ -283,7 +313,7 @@ int profile_contraction_impl(ck::index_t do_verification,
if(do_log) if(do_log)
{ {
LogRangeAsType<float>(std::cout << "a : ", a_m_k.mData, ",") << std::endl; LogRangeAsType<float>(std::cout << "a : ", a_m_k.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "b: ", b_k_n.mData, ",") << std::endl; LogRangeAsType<float>(std::cout << "b: ", b_n_k.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "c_host : ", e_m_n_host_result.mData, ",") LogRangeAsType<float>(std::cout << "c_host : ", e_m_n_host_result.mData, ",")
<< std::endl; << std::endl;
LogRangeAsType<float>(std::cout << "c_device: ", e_m_n_device_result.mData, ",") LogRangeAsType<float>(std::cout << "c_device: ", e_m_n_device_result.mData, ",")
......
profiler/include/profiler/profile_contraction_utils.hpp
View file @ 11279540
...@@ -23,8 +23,18 @@ enum struct ContractionMatrixLayout ...@@ -23,8 +23,18 @@ enum struct ContractionMatrixLayout
enum struct ContractionDataType enum struct ContractionDataType
{ {
F32_F32_F32_F32, // 0 F32_F32_F32_F32, // 0
F64_F64_F64_F64, // 1 F64_F64_F64_F64, // 1
F16_F16_F16_F16, // 2
BF16_BF16_BF16_BF16, // 3
};
enum struct ContractionComputeDataType
{
F32 = 0,
F64,
F16,
BF16,
}; };
inline void collect_index_params(char* argv[], inline void collect_index_params(char* argv[],
......
profiler/include/profiler/profile_conv_tensor_rearrange_impl.hpp
View file @ 11279540
...@@ -93,6 +93,26 @@ static auto make_ref_op() ...@@ -93,6 +93,26 @@ static auto make_ref_op()
} }
} }
template <typename InputLayout>
static auto create_gemm_desc(const ck::index_t G, const ck::index_t NDoHoWo, const ck::index_t CZYX)
{
using namespace ck::tensor_layout::convolution;
if constexpr(std::is_same_v<InputLayout, GNWC> || std::is_same_v<InputLayout, GNHWC> ||
std::is_same_v<InputLayout, GNDHWC>)
{
return HostTensorDescriptor({G, NDoHoWo, CZYX});
}
else if constexpr(std::is_same_v<InputLayout, NWGC> || std::is_same_v<InputLayout, NHWGC> ||
std::is_same_v<InputLayout, NDHWGC>)
{
return HostTensorDescriptor({G, NDoHoWo, CZYX}, {CZYX, CZYX * G, 1});
}
else
{
throw std::runtime_error("Unsupported layout!");
}
}
template <index_t NDimSpatial, template <index_t NDimSpatial,
typename InputLayout, typename InputLayout,
typename InputDataType, typename InputDataType,
...@@ -116,13 +136,13 @@ bool profile_conv_tensor_rearrange_impl(int do_verification, ...@@ -116,13 +136,13 @@ bool profile_conv_tensor_rearrange_impl(int do_verification,
const auto image_desc = const auto image_desc =
ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InputLayout>( ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InputLayout>(
conv_param); conv_param);
const auto gemm_desc = HostTensorDescriptor({NDoHoWo, CZYX}); const auto gemm_desc = create_gemm_desc<InputLayout>(conv_param.G_, NDoHoWo, CZYX);
std::array<ck::index_t, NDimSpatial> input_spatial_lengths{}; std::array<ck::index_t, NDimSpatial> input_spatial_lengths{};
std::array<ck::index_t, NDimSpatial> filter_spatial_lengths{}; std::array<ck::index_t, NDimSpatial> filter_spatial_lengths{};
std::array<ck::index_t, NDimSpatial> output_spatial_lengths{}; std::array<ck::index_t, NDimSpatial> output_spatial_lengths{};
std::array<ck::index_t, NDimSpatial + 3> image_g_n_c_wis_strides{}; std::array<ck::index_t, NDimSpatial + 3> image_g_n_c_wis_strides{};
std::array<ck::index_t, 2> gemm_m_k_strides{}; std::array<ck::index_t, 3> gemm_g_m_k_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_strides{}; std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_dilations{}; std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
std::array<ck::index_t, NDimSpatial> input_left_pads{}; std::array<ck::index_t, NDimSpatial> input_left_pads{};
...@@ -134,7 +154,7 @@ bool profile_conv_tensor_rearrange_impl(int do_verification, ...@@ -134,7 +154,7 @@ bool profile_conv_tensor_rearrange_impl(int do_verification,
copy(conv_param.filter_spatial_lengths_, filter_spatial_lengths); copy(conv_param.filter_spatial_lengths_, filter_spatial_lengths);
copy(conv_param.output_spatial_lengths_, output_spatial_lengths); copy(conv_param.output_spatial_lengths_, output_spatial_lengths);
copy(image_desc.GetStrides(), image_g_n_c_wis_strides); copy(image_desc.GetStrides(), image_g_n_c_wis_strides);
copy(gemm_desc.GetStrides(), gemm_m_k_strides); copy(gemm_desc.GetStrides(), gemm_g_m_k_strides);
copy(conv_param.conv_filter_strides_, conv_filter_strides); copy(conv_param.conv_filter_strides_, conv_filter_strides);
copy(conv_param.conv_filter_dilations_, conv_filter_dilations); copy(conv_param.conv_filter_dilations_, conv_filter_dilations);
copy(conv_param.input_left_pads_, input_left_pads); copy(conv_param.input_left_pads_, input_left_pads);
...@@ -212,13 +232,14 @@ bool profile_conv_tensor_rearrange_impl(int do_verification, ...@@ -212,13 +232,14 @@ bool profile_conv_tensor_rearrange_impl(int do_verification,
auto argument_ptr = op_ptr->MakeArgumentPointer( auto argument_ptr = op_ptr->MakeArgumentPointer(
static_cast<InputDataType*>(in_device_buf.GetDeviceBuffer()), static_cast<InputDataType*>(in_device_buf.GetDeviceBuffer()),
static_cast<OutputDataType*>(out_device_buf.GetDeviceBuffer()), static_cast<OutputDataType*>(out_device_buf.GetDeviceBuffer()),
conv_param.G_,
conv_param.N_, conv_param.N_,
conv_param.C_, conv_param.C_,
input_spatial_lengths, input_spatial_lengths,
filter_spatial_lengths, filter_spatial_lengths,
output_spatial_lengths, output_spatial_lengths,
image_g_n_c_wis_strides, image_g_n_c_wis_strides,
gemm_m_k_strides, gemm_g_m_k_strides,
conv_filter_strides, conv_filter_strides,
conv_filter_dilations, conv_filter_dilations,
input_left_pads, input_left_pads,
...@@ -234,7 +255,7 @@ bool profile_conv_tensor_rearrange_impl(int do_verification, ...@@ -234,7 +255,7 @@ bool profile_conv_tensor_rearrange_impl(int do_verification,
float avg_time = float avg_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel}); invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::size_t num_btype = std::size_t num_btype =
NDoHoWo * CZYX * (sizeof(OutputDataType) + sizeof(InputDataType)); conv_param.G_ * NDoHoWo * CZYX * (sizeof(OutputDataType) + sizeof(InputDataType));
float gb_per_sec = num_btype / 1.E6 / avg_time; float gb_per_sec = num_btype / 1.E6 / avg_time;
std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << gb_per_sec << " GB/s, " std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << gb_per_sec << " GB/s, "
<< op_name << std::endl; << op_name << std::endl;
......
profiler/include/profiler/profile_elementwise_layernorm_impl.hpp
View file @ 11279540
...@@ -80,6 +80,8 @@ bool profile_elementwise_layernorm_impl(int do_verification, ...@@ -80,6 +80,8 @@ bool profile_elementwise_layernorm_impl(int do_verification,
Tensor<BetaDataType> beta(gammaBetaLength); Tensor<BetaDataType> beta(gammaBetaLength);
Tensor<YDataType> y(length); Tensor<YDataType> y(length);
Tensor<YDataType> host_y(length); Tensor<YDataType> host_y(length);
Tensor<AccDataType> host_save_mean({M});
Tensor<AccDataType> host_save_inv_std({M});
switch(init_method) switch(init_method)
{ {
...@@ -152,14 +154,23 @@ bool profile_elementwise_layernorm_impl(int do_verification, ...@@ -152,14 +154,23 @@ bool profile_elementwise_layernorm_impl(int do_verification,
BetaDataType, BetaDataType,
YDataType, YDataType,
AccDataType, AccDataType,
AccDataType,
PassThrough, PassThrough,
Rank, Rank,
NumReduceDim>; NumReduceDim>;
ReferenceInstance ref; ReferenceInstance ref;
auto ref_argument = auto ref_argument = ref.MakeArgument(x,
ref.MakeArgument(x, gamma, beta, host_y, PassThrough{}, {M, N}, {1}, 1e-4); gamma,
auto ref_invoker = ref.MakeInvoker(); beta,
host_y,
host_save_mean,
host_save_inv_std,
PassThrough{},
{M, N},
{1},
1e-4);
auto ref_invoker = ref.MakeInvoker();
ref_invoker.Run(ref_argument); ref_invoker.Run(ref_argument);
} }
......
profiler/include/profiler/profile_gemm_add_relu_add_layernorm_impl.hpp
View file @ 11279540
...@@ -66,12 +66,15 @@ void host_gemm_layernorm(Tensor<HDataType>& h_m_n, ...@@ -66,12 +66,15 @@ void host_gemm_layernorm(Tensor<HDataType>& h_m_n,
BetaDataType, BetaDataType,
HDataType, HDataType,
AccDataType, AccDataType,
AccDataType,
HElementOp, HElementOp,
2, 2,
1>; 1>;
Tensor<EMeanVarDataType> e_m_n(HostTensorDescriptor{M, N}); Tensor<EMeanVarDataType> e_m_n(HostTensorDescriptor{M, N});
Tensor<AccDataType> c_m_n(HostTensorDescriptor{M, N}); Tensor<AccDataType> c_m_n(HostTensorDescriptor{M, N});
Tensor<AccDataType> save_mean({M});
Tensor<AccDataType> save_inv_std({M});
auto ref_gemm = ReferenceGemm{}; auto ref_gemm = ReferenceGemm{};
auto ref_gemm_invoker = ref_gemm.MakeInvoker(); auto ref_gemm_invoker = ref_gemm.MakeInvoker();
...@@ -97,7 +100,7 @@ void host_gemm_layernorm(Tensor<HDataType>& h_m_n, ...@@ -97,7 +100,7 @@ void host_gemm_layernorm(Tensor<HDataType>& h_m_n,
auto ref_layernorm_invoker = ref_layernorm.MakeInvoker(); auto ref_layernorm_invoker = ref_layernorm.MakeInvoker();
auto ref_layernorm_argument = ref_layernorm.MakeArgument( auto ref_layernorm_argument = ref_layernorm.MakeArgument(
e_m_n, gamma_n, beta_n, h_m_n, h_element_op, {M, N}, {1}, epsilon); e_m_n, gamma_n, beta_n, h_m_n, save_mean, save_inv_std, h_element_op, {M, N}, {1}, epsilon);
ref_layernorm_invoker.Run(ref_layernorm_argument); ref_layernorm_invoker.Run(ref_layernorm_argument);
} }
......
profiler/include/profiler/profile_gemm_impl.hpp
View file @ 11279540
...@@ -75,8 +75,8 @@ int profile_gemm_impl(int do_verification, ...@@ -75,8 +75,8 @@ int profile_gemm_impl(int do_verification,
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5}); b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
break; break;
default: default:
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0}); a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 0.1});
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}); b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.05, 0.05});
} }
using AElementOp = ck::tensor_operation::element_wise::PassThrough; using AElementOp = ck::tensor_operation::element_wise::PassThrough;
......
profiler/include/profiler/profile_gemm_splitk_impl.hpp
View file @ 11279540
...@@ -143,8 +143,7 @@ bool profile_gemm_splitk_impl(int do_verification, ...@@ -143,8 +143,7 @@ bool profile_gemm_splitk_impl(int do_verification,
// profile device GEMM instances // profile device GEMM instances
for(auto& op_ptr : op_ptrs) for(auto& op_ptr : op_ptrs)
{ {
std::vector<int> kbatch_list = {1, 2, 4, 8, 12, 16, 20, 24, 32, 36, 40, 60, std::vector<int> kbatch_list = {1, 2, 4, 8, 12, 16, 20, 32, 36, 40, 64, 96, 128};
64, 72, 80, 88, 96, 128, 144, 160, 176, 192, 256};
if(KBatch > 0) if(KBatch > 0)
{ {
......
profiler/include/profiler/profile_groupnorm_impl.hpp
View file @ 11279540
...@@ -21,8 +21,10 @@ namespace profiler { ...@@ -21,8 +21,10 @@ namespace profiler {
template <typename XDataType, template <typename XDataType,
typename GammaDataType, typename GammaDataType,
typename BetaDataType, typename BetaDataType,
typename AccDataType, typename ComputeDataType,
typename YDataType> typename YDataType,
typename SaveMeanInvStdDataType,
bool SaveMeanInvStd>
bool profile_groupnorm_impl(int do_verification, bool profile_groupnorm_impl(int do_verification,
int init_method, int init_method,
bool do_log, bool do_log,
...@@ -34,6 +36,7 @@ bool profile_groupnorm_impl(int do_verification, ...@@ -34,6 +36,7 @@ bool profile_groupnorm_impl(int do_verification,
if(length.size() != 5) if(length.size() != 5)
return false; return false;
index_t N = length[0];
index_t G = length[3]; index_t G = length[3];
index_t C = length[4]; index_t C = length[4];
...@@ -45,7 +48,14 @@ bool profile_groupnorm_impl(int do_verification, ...@@ -45,7 +48,14 @@ bool profile_groupnorm_impl(int do_verification,
Tensor<GammaDataType> gamma(gammaBetaLength); Tensor<GammaDataType> gamma(gammaBetaLength);
Tensor<BetaDataType> beta(gammaBetaLength); Tensor<BetaDataType> beta(gammaBetaLength);
Tensor<YDataType> y(length); Tensor<YDataType> y(length);
Tensor<SaveMeanInvStdDataType> save_mean({N, G});
Tensor<SaveMeanInvStdDataType> save_inv_std({N, G});
Tensor<YDataType> host_y(length); Tensor<YDataType> host_y(length);
Tensor<SaveMeanInvStdDataType> host_save_mean({N, G});
Tensor<SaveMeanInvStdDataType> host_save_inv_std({N, G});
std::vector<index_t> strideSaveMeanInvStd = {1};
switch(init_method) switch(init_method)
{ {
...@@ -69,6 +79,9 @@ bool profile_groupnorm_impl(int do_verification, ...@@ -69,6 +79,9 @@ bool profile_groupnorm_impl(int do_verification,
DeviceMem gamma_dev(sizeof(GammaDataType) * gamma.mDesc.GetElementSpaceSize()); DeviceMem gamma_dev(sizeof(GammaDataType) * gamma.mDesc.GetElementSpaceSize());
DeviceMem beta_dev(sizeof(BetaDataType) * beta.mDesc.GetElementSpaceSize()); DeviceMem beta_dev(sizeof(BetaDataType) * beta.mDesc.GetElementSpaceSize());
DeviceMem y_dev(sizeof(YDataType) * y.mDesc.GetElementSpaceSize()); DeviceMem y_dev(sizeof(YDataType) * y.mDesc.GetElementSpaceSize());
DeviceMem save_mean_dev(sizeof(SaveMeanInvStdDataType) * save_mean.mDesc.GetElementSpaceSize());
DeviceMem save_inv_std_dev(sizeof(SaveMeanInvStdDataType) *
save_inv_std.mDesc.GetElementSpaceSize());
x_dev.ToDevice(x.mData.data()); x_dev.ToDevice(x.mData.data());
gamma_dev.ToDevice(gamma.mData.data()); gamma_dev.ToDevice(gamma.mData.data());
...@@ -78,8 +91,8 @@ bool profile_groupnorm_impl(int do_verification, ...@@ -78,8 +91,8 @@ bool profile_groupnorm_impl(int do_verification,
using DeviceOp = ck::tensor_operation::device::DeviceNormalization<XDataType, using DeviceOp = ck::tensor_operation::device::DeviceNormalization<XDataType,
GammaDataType, GammaDataType,
BetaDataType, BetaDataType,
AccDataType,
YDataType, YDataType,
SaveMeanInvStdDataType,
PassThrough, PassThrough,
5, 5,
3>; 3>;
...@@ -97,38 +110,70 @@ bool profile_groupnorm_impl(int do_verification, ...@@ -97,38 +110,70 @@ bool profile_groupnorm_impl(int do_verification,
if(do_verification) if(do_verification)
{ {
using ReferenceInstance = ck::tensor_operation::host::ReferenceGroupnorm<XDataType, using ReferenceInstance =
GammaDataType, ck::tensor_operation::host::ReferenceGroupnorm<XDataType,
BetaDataType, GammaDataType,
YDataType, BetaDataType,
AccDataType, YDataType,
PassThrough>; SaveMeanInvStdDataType,
ComputeDataType,
PassThrough>;
ReferenceInstance ref; ReferenceInstance ref;
auto ref_argument = ref.MakeArgument(x, gamma, beta, host_y, PassThrough{}, length, 1e-6); auto ref_argument = ref.MakeArgument(
auto ref_invoker = ref.MakeInvoker(); x, gamma, beta, host_y, host_save_mean, host_save_inv_std, PassThrough{}, length, 1e-6);
auto ref_invoker = ref.MakeInvoker();
ref_invoker.Run(ref_argument); ref_invoker.Run(ref_argument);
} }
int num_kernel = 0; int num_kernel = 0;
auto f_get_argument = [&](auto& inst_ptr) {
if constexpr(SaveMeanInvStd)
return inst_ptr->MakeArgumentPointer(
length,
std::vector<ck::index_t>{x.mDesc.GetStrides().begin(), x.mDesc.GetStrides().end()},
gammaBetaStride,
gammaBetaStride,
std::vector<ck::index_t>{y.mDesc.GetStrides().begin(), y.mDesc.GetStrides().end()},
std::vector<ck::index_t>{save_mean.mDesc.GetStrides().begin(),
save_mean.mDesc.GetStrides().end()},
std::vector<ck::index_t>{save_inv_std.mDesc.GetStrides().begin(),
save_inv_std.mDesc.GetStrides().end()},
reduce_dim,
1e-6,
x_dev.GetDeviceBuffer(),
gamma_dev.GetDeviceBuffer(),
beta_dev.GetDeviceBuffer(),
y_dev.GetDeviceBuffer(),
save_mean_dev.GetDeviceBuffer(),
save_inv_std_dev.GetDeviceBuffer(),
PassThrough{});
else
return inst_ptr->MakeArgumentPointer(
length,
std::vector<ck::index_t>{x.mDesc.GetStrides().begin(), x.mDesc.GetStrides().end()},
gammaBetaStride,
gammaBetaStride,
std::vector<ck::index_t>{y.mDesc.GetStrides().begin(), y.mDesc.GetStrides().end()},
std::vector<ck::index_t>{save_mean.mDesc.GetStrides().begin(),
save_mean.mDesc.GetStrides().end()},
std::vector<ck::index_t>{save_inv_std.mDesc.GetStrides().begin(),
save_inv_std.mDesc.GetStrides().end()},
reduce_dim,
1e-6,
x_dev.GetDeviceBuffer(),
gamma_dev.GetDeviceBuffer(),
beta_dev.GetDeviceBuffer(),
y_dev.GetDeviceBuffer(),
nullptr,
nullptr,
PassThrough{});
};
for(auto& inst_ptr : instance_ptrs) for(auto& inst_ptr : instance_ptrs)
{ {
auto argument_ptr = inst_ptr->MakeArgumentPointer( auto argument_ptr = f_get_argument(inst_ptr);
length,
std::vector<ck::index_t>{x.mDesc.GetStrides().begin(), x.mDesc.GetStrides().end()},
gammaBetaStride,
gammaBetaStride,
std::vector<ck::index_t>{y.mDesc.GetStrides().begin(), y.mDesc.GetStrides().end()},
reduce_dim,
1e-6,
x_dev.GetDeviceBuffer(),
gamma_dev.GetDeviceBuffer(),
beta_dev.GetDeviceBuffer(),
y_dev.GetDeviceBuffer(),
nullptr,
nullptr,
PassThrough{});
if(inst_ptr->IsSupportedArgument(argument_ptr.get())) if(inst_ptr->IsSupportedArgument(argument_ptr.get()))
{ {
...@@ -152,6 +197,10 @@ bool profile_groupnorm_impl(int do_verification, ...@@ -152,6 +197,10 @@ bool profile_groupnorm_impl(int do_verification,
beta.mDesc.GetElementSize() * sizeof(BetaDataType) + beta.mDesc.GetElementSize() * sizeof(BetaDataType) +
y.mDesc.GetElementSize() * sizeof(YDataType); y.mDesc.GetElementSize() * sizeof(YDataType);
if constexpr(SaveMeanInvStd)
num_bytes += save_mean.mDesc.GetElementSpaceSize() * sizeof(SaveMeanInvStdDataType) +
save_inv_std.mDesc.GetElementSpaceSize() * sizeof(SaveMeanInvStdDataType);
float gb_per_sec = num_bytes / 1.E6 / avg_time; float gb_per_sec = num_bytes / 1.E6 / avg_time;
if(time_kernel) if(time_kernel)
...@@ -168,9 +217,22 @@ bool profile_groupnorm_impl(int do_verification, ...@@ -168,9 +217,22 @@ bool profile_groupnorm_impl(int do_verification,
if(do_verification) if(do_verification)
{ {
y_dev.FromDevice(y.mData.data()); y_dev.FromDevice(y.mData.data());
bool pass = ck::utils::check_err(y, host_y, "Error: Incorrect results", 1e-3, 1e-3); bool pass = ck::utils::check_err(y, host_y, "Error: Incorrect results", 1e-3, 1e-3);
if constexpr(SaveMeanInvStd)
{
save_mean_dev.FromDevice(save_mean.mData.data());
pass &= ck::utils::check_err(
save_mean.mData, host_save_mean.mData, "Error: Incorrect results", 1e-3, 1e-3);
save_inv_std_dev.FromDevice(save_inv_std.mData.data());
pass &= ck::utils::check_err(save_inv_std.mData,
host_save_inv_std.mData,
"Error: Incorrect results",
1e-3,
1e-3);
}
if(do_log) if(do_log)
{ {
LogRangeAsType<float>(std::cout << "x : ", x.mData, ",") << std::endl; LogRangeAsType<float>(std::cout << "x : ", x.mData, ",") << std::endl;
......
profiler/include/profiler/profile_layernorm_impl.hpp
View file @ 11279540
...@@ -21,6 +21,8 @@ template <typename XDataType, ...@@ -21,6 +21,8 @@ template <typename XDataType,
typename BetaDataType, typename BetaDataType,
typename ComputeDataType, typename ComputeDataType,
typename YDataType, typename YDataType,
typename SaveMeanInvStdDataType,
bool SaveMeanInvStd,
index_t Rank> index_t Rank>
bool profile_layernorm_impl(int do_verification, bool profile_layernorm_impl(int do_verification,
int init_method, int init_method,
...@@ -43,13 +45,19 @@ bool profile_layernorm_impl(int do_verification, ...@@ -43,13 +45,19 @@ bool profile_layernorm_impl(int do_verification,
Tensor<GammaDataType> gamma(reduce_length); Tensor<GammaDataType> gamma(reduce_length);
Tensor<BetaDataType> beta(reduce_length); Tensor<BetaDataType> beta(reduce_length);
Tensor<YDataType> y(length); Tensor<YDataType> y(length);
Tensor<SaveMeanInvStdDataType> save_mean({length[0]});
Tensor<SaveMeanInvStdDataType> save_inv_std({length[0]});
Tensor<YDataType> host_y(length); Tensor<YDataType> host_y(length);
Tensor<SaveMeanInvStdDataType> host_save_mean({length[0]});
Tensor<SaveMeanInvStdDataType> host_save_inv_std({length[0]});
std::vector<index_t> strideXY = std::vector<index_t> strideXY =
std::vector<ck::index_t>{x.mDesc.GetStrides().begin(), x.mDesc.GetStrides().end()}; std::vector<ck::index_t>{x.mDesc.GetStrides().begin(), x.mDesc.GetStrides().end()};
std::vector<index_t> strideGammaBeta = strideXY; std::vector<index_t> strideGammaBeta = strideXY;
strideGammaBeta[0] = 0; strideGammaBeta[0] = 0;
std::vector<index_t> strideSaveMeanInvStd = {1};
switch(init_method) switch(init_method)
{ {
case 0: case 0:
...@@ -75,6 +83,9 @@ bool profile_layernorm_impl(int do_verification, ...@@ -75,6 +83,9 @@ bool profile_layernorm_impl(int do_verification,
DeviceMem gamma_dev(sizeof(GammaDataType) * gamma.mDesc.GetElementSpaceSize()); DeviceMem gamma_dev(sizeof(GammaDataType) * gamma.mDesc.GetElementSpaceSize());
DeviceMem beta_dev(sizeof(BetaDataType) * beta.mDesc.GetElementSpaceSize()); DeviceMem beta_dev(sizeof(BetaDataType) * beta.mDesc.GetElementSpaceSize());
DeviceMem y_dev(sizeof(YDataType) * y.mDesc.GetElementSpaceSize()); DeviceMem y_dev(sizeof(YDataType) * y.mDesc.GetElementSpaceSize());
DeviceMem save_mean_dev(sizeof(SaveMeanInvStdDataType) * save_mean.mDesc.GetElementSpaceSize());
DeviceMem save_inv_std_dev(sizeof(SaveMeanInvStdDataType) *
save_inv_std.mDesc.GetElementSpaceSize());
x_dev.ToDevice(x.mData.data()); x_dev.ToDevice(x.mData.data());
gamma_dev.ToDevice(gamma.mData.data()); gamma_dev.ToDevice(gamma.mData.data());
...@@ -86,8 +97,8 @@ bool profile_layernorm_impl(int do_verification, ...@@ -86,8 +97,8 @@ bool profile_layernorm_impl(int do_verification,
using DeviceOp = ck::tensor_operation::device::DeviceNormalization<XDataType, using DeviceOp = ck::tensor_operation::device::DeviceNormalization<XDataType,
GammaDataType, GammaDataType,
BetaDataType, BetaDataType,
ComputeDataType,
YDataType, YDataType,
SaveMeanInvStdDataType,
PassThrough, PassThrough,
Rank, Rank,
NumReduceDim>; NumReduceDim>;
...@@ -105,40 +116,74 @@ bool profile_layernorm_impl(int do_verification, ...@@ -105,40 +116,74 @@ bool profile_layernorm_impl(int do_verification,
if(do_verification) if(do_verification)
{ {
using ReferenceInstance = ck::tensor_operation::host::ReferenceLayernorm<XDataType, using ReferenceInstance =
GammaDataType, ck::tensor_operation::host::ReferenceLayernorm<XDataType,
BetaDataType, GammaDataType,
YDataType, BetaDataType,
ComputeDataType, YDataType,
PassThrough, SaveMeanInvStdDataType,
Rank, ComputeDataType,
NumReduceDim>; PassThrough,
Rank,
NumReduceDim>;
ReferenceInstance ref; ReferenceInstance ref;
auto ref_argument = auto ref_argument = ref.MakeArgument(x,
ref.MakeArgument(x, gamma, beta, host_y, PassThrough{}, length, reduce_dim, 1e-4); gamma,
auto ref_invoker = ref.MakeInvoker(); beta,
host_y,
host_save_mean,
host_save_inv_std,
PassThrough{},
length,
reduce_dim,
1e-4);
auto ref_invoker = ref.MakeInvoker();
ref_invoker.Run(ref_argument); ref_invoker.Run(ref_argument);
} }
int num_kernel = 0; int num_kernel = 0;
auto f_get_argument = [&](auto& inst_ptr) {
if constexpr(SaveMeanInvStd)
return inst_ptr->MakeArgumentPointer(length,
strideXY,
strideGammaBeta,
strideGammaBeta,
strideXY,
strideSaveMeanInvStd,
strideSaveMeanInvStd,
reduce_dim,
1e-4,
x_dev.GetDeviceBuffer(),
gamma_dev.GetDeviceBuffer(),
beta_dev.GetDeviceBuffer(),
y_dev.GetDeviceBuffer(),
save_mean_dev.GetDeviceBuffer(),
save_inv_std_dev.GetDeviceBuffer(),
PassThrough{});
else
return inst_ptr->MakeArgumentPointer(length,
strideXY,
strideGammaBeta,
strideGammaBeta,
strideXY,
strideSaveMeanInvStd,
strideSaveMeanInvStd,
reduce_dim,
1e-4,
x_dev.GetDeviceBuffer(),
gamma_dev.GetDeviceBuffer(),
beta_dev.GetDeviceBuffer(),
y_dev.GetDeviceBuffer(),
nullptr,
nullptr,
PassThrough{});
};
for(auto& inst_ptr : instance_ptrs) for(auto& inst_ptr : instance_ptrs)
{ {
auto argument_ptr = inst_ptr->MakeArgumentPointer(length, auto argument_ptr = f_get_argument(inst_ptr);
strideXY,
strideGammaBeta,
strideGammaBeta,
strideXY,
reduce_dim,
1e-4,
x_dev.GetDeviceBuffer(),
gamma_dev.GetDeviceBuffer(),
beta_dev.GetDeviceBuffer(),
y_dev.GetDeviceBuffer(),
nullptr,
nullptr,
PassThrough{});
if(inst_ptr->IsSupportedArgument(argument_ptr.get())) if(inst_ptr->IsSupportedArgument(argument_ptr.get()))
{ {
...@@ -168,6 +213,10 @@ bool profile_layernorm_impl(int do_verification, ...@@ -168,6 +213,10 @@ bool profile_layernorm_impl(int do_verification,
beta.mDesc.GetElementSize() * sizeof(BetaDataType) + beta.mDesc.GetElementSize() * sizeof(BetaDataType) +
y.mDesc.GetElementSize() * sizeof(YDataType); y.mDesc.GetElementSize() * sizeof(YDataType);
if constexpr(SaveMeanInvStd)
num_bytes += save_mean.mDesc.GetElementSpaceSize() * sizeof(SaveMeanInvStdDataType) +
save_inv_std.mDesc.GetElementSpaceSize() * sizeof(SaveMeanInvStdDataType);
float gb_per_sec = num_bytes / 1.E6 / avg_time; float gb_per_sec = num_bytes / 1.E6 / avg_time;
if(time_kernel) if(time_kernel)
...@@ -184,10 +233,23 @@ bool profile_layernorm_impl(int do_verification, ...@@ -184,10 +233,23 @@ bool profile_layernorm_impl(int do_verification,
if(do_verification) if(do_verification)
{ {
y_dev.FromDevice(y.mData.data()); y_dev.FromDevice(y.mData.data());
bool pass = bool pass =
ck::utils::check_err(y.mData, host_y.mData, "Error: Incorrect results", 1e-3, 1e-3); ck::utils::check_err(y.mData, host_y.mData, "Error: Incorrect results", 1e-3, 1e-3);
if constexpr(SaveMeanInvStd)
{
save_mean_dev.FromDevice(save_mean.mData.data());
pass &= ck::utils::check_err(
save_mean.mData, host_save_mean.mData, "Error: Incorrect results", 1e-3, 1e-3);
save_inv_std_dev.FromDevice(save_inv_std.mData.data());
pass &= ck::utils::check_err(save_inv_std.mData,
host_save_inv_std.mData,
"Error: Incorrect results",
1e-3,
1e-3);
}
if(do_log) if(do_log)
{ {
LogRangeAsType<float>(std::cout << "x : ", x.mData, ",") << std::endl; LogRangeAsType<float>(std::cout << "x : ", x.mData, ",") << std::endl;
......
profiler/src/CMakeLists.txt
View file @ 11279540
...@@ -25,14 +25,14 @@ set(PROFILER_SOURCES ...@@ -25,14 +25,14 @@ set(PROFILER_SOURCES
profile_batchnorm_fwd.cpp profile_batchnorm_fwd.cpp
profile_batchnorm_bwd.cpp profile_batchnorm_bwd.cpp
profile_batchnorm_infer.cpp profile_batchnorm_infer.cpp
profile_contraction_bilinear.cpp
profile_contraction_scale.cpp
profile_grouped_conv_bwd_data.cpp profile_grouped_conv_bwd_data.cpp
profile_conv_tensor_rearrange.cpp profile_conv_tensor_rearrange.cpp
) )
if(DL_KERNELS) if(DL_KERNELS)
list(APPEND PROFILER_SOURCES profile_batched_gemm_multi_d.cpp) list(APPEND PROFILER_SOURCES profile_batched_gemm_multi_d.cpp)
endif() endif()
if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES) if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
list(APPEND PROFILER_SOURCES profile_batched_gemm_gemm.cpp) list(APPEND PROFILER_SOURCES profile_batched_gemm_gemm.cpp)
list(APPEND PROFILER_SOURCES profile_gemm_fastgelu.cpp) list(APPEND PROFILER_SOURCES profile_gemm_fastgelu.cpp)
...@@ -46,6 +46,11 @@ if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES) ...@@ -46,6 +46,11 @@ if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
list(APPEND PROFILER_SOURCES profile_grouped_gemm_fastgelu.cpp) list(APPEND PROFILER_SOURCES profile_grouped_gemm_fastgelu.cpp)
endif() endif()
if(DTYPES MATCHES "fp32" OR DTYPES MATCHES "fp64" OR NOT DEFINED DTYPES)
list(APPEND PROFILER_SOURCES profile_contraction_bilinear.cpp)
list(APPEND PROFILER_SOURCES profile_contraction_scale.cpp)
endif()
set(PROFILER_EXECUTABLE ckProfiler) set(PROFILER_EXECUTABLE ckProfiler)
add_executable(${PROFILER_EXECUTABLE} ${PROFILER_SOURCES}) add_executable(${PROFILER_EXECUTABLE} ${PROFILER_SOURCES})
...@@ -76,8 +81,6 @@ target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_normalization_instan ...@@ -76,8 +81,6 @@ target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_normalization_instan
target_link_libraries(${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(${PROFILER_EXECUTABLE} PRIVATE device_batchnorm_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_contraction_bilinear_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_contraction_scale_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_pool3d_fwd_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_pool3d_fwd_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_avg_pool3d_bwd_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_avg_pool3d_bwd_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_max_pool_bwd_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_max_pool_bwd_instance)
...@@ -85,9 +88,18 @@ target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv2d_bwd_d ...@@ -85,9 +88,18 @@ target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv2d_bwd_d
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_bwd_data_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_bwd_data_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_image_to_column_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_image_to_column_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_column_to_image_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_column_to_image_instance)
if(DTYPES MATCHES "fp32" OR DTYPES MATCHES "fp64" OR NOT DEFINED DTYPES)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_contraction_bilinear_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_contraction_scale_instance)
endif()
if(DL_KERNELS) if(DL_KERNELS)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_multi_d_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_batched_gemm_multi_d_instance)
endif() endif()
if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES) if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_fastgelu_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_fastgelu_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_relu_add_layernorm_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_gemm_add_relu_add_layernorm_instance)
...@@ -100,4 +112,5 @@ if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES) ...@@ -100,4 +112,5 @@ if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_gemm_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_gemm_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_gemm_fastgelu_instance) target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_gemm_fastgelu_instance)
endif() endif()
rocm_install(TARGETS ${PROFILER_EXECUTABLE} COMPONENT profiler) rocm_install(TARGETS ${PROFILER_EXECUTABLE} COMPONENT profiler)
profiler/src/profile_contraction_bilinear.cpp
View file @ 11279540
...@@ -17,8 +17,9 @@ ...@@ -17,8 +17,9 @@
static void print_helper_msg() static void print_helper_msg()
{ {
std::cout << "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n" std::cout << "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n"
<< "arg2: data type (0: fp32; 1: f64)\n" << "arg2: data type (0: fp32; 1: f64; 2: f16; 3: bf16)\n"
<< "arg3: matrix layout (0: A[m0, m1, k0, k1] * B[k0, k1, n0, n1] + " << "arg3: compute data type (0: fp32; 1: f64; 2: f16; 3: bf16)\n"
<< "arg4: matrix layout (0: A[m0, m1, k0, k1] * B[k0, k1, n0, n1] + "
"D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n" "D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n"
<< " 1: A[m0, m1, k0, k1] * B[n0, n1, k0, k1] + " << " 1: A[m0, m1, k0, k1] * B[n0, n1, k0, k1] + "
"D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n" "D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n"
...@@ -26,40 +27,42 @@ static void print_helper_msg() ...@@ -26,40 +27,42 @@ static void print_helper_msg()
"D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n" "D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n"
<< " 3: A[k0, k1, m0, m1] * B[n0, n1, k0, k1] + " << " 3: A[k0, k1, m0, m1] * B[n0, n1, k0, k1] + "
"D[m0, m1, n0, n1] = E[m0, m1, n0, n1])\n" "D[m0, m1, n0, n1] = E[m0, m1, n0, n1])\n"
<< "arg4: verification (0: no; 1: yes)\n" << "arg5: verification (0: no; 1: yes)\n"
<< "arg5: initialization (0: no init; 1: integer value; 2: decimal " << "arg6: initialization (0: no init; 1: integer value; 2: decimal "
<< "value)\n" << "value)\n"
<< "arg6: print tensor value (0: no; 1: yes)\n" << "arg7: print tensor value (0: no; 1: yes)\n"
<< "arg7: time kernel (0: no, 1: yes)\n" << "arg8: time kernel (0: no, 1: yes)\n"
<< "arg8 and arg9: alpha and beta\n" << "arg9: alpha\n"
<< "arg10 to 15: M0, M1, N0, N1, K0, K1\n" << "arg10: beta\n"
<< "arg16 to 31: Strides for A, B, D and E (skip for default)\n" << "arg11 to 16: M0, M1, N0, N1, K0, K1\n"
<< "arg17 to 32: Strides for A, B, D and E (skip for default)\n"
<< std::endl; << std::endl;
} }
int profile_contraction_bilinear(int argc, char* argv[]) int profile_contraction_bilinear(int argc, char* argv[])
{ {
const bool default_strides = argc == 16; const bool default_strides = argc == 17;
if(argc != 32 && argc != 16) if(argc != 33 && argc != 17)
{ {
print_helper_msg(); print_helper_msg();
exit(1); exit(1);
} }
const auto data_type = static_cast<ContractionDataType>(std::stoi(argv[2])); const auto data_type = static_cast<ContractionDataType>(std::stoi(argv[2]));
const auto layout = static_cast<ContractionMatrixLayout>(std::stoi(argv[3])); const auto compute_data_type = static_cast<ContractionComputeDataType>(std::stoi(argv[3]));
const bool do_verification = std::stoi(argv[4]); const auto layout = static_cast<ContractionMatrixLayout>(std::stoi(argv[4]));
const ck::index_t init_method = std::stoi(argv[5]); const bool do_verification = std::stoi(argv[5]);
const bool do_log = std::stoi(argv[6]); const ck::index_t init_method = std::stoi(argv[6]);
const bool time_kernel = std::stoi(argv[7]); const bool do_log = std::stoi(argv[7]);
const float alpha = std::stof(argv[8]); const bool time_kernel = std::stoi(argv[8]);
const float beta = std::stof(argv[9]); const float alpha = std::stof(argv[9]);
const float beta = std::stof(argv[10]);
std::vector<ck::index_t> M; std::vector<ck::index_t> M;
std::vector<ck::index_t> N; std::vector<ck::index_t> N;
std::vector<ck::index_t> K; std::vector<ck::index_t> K;
const ck::index_t dims_arg_num = 10; const ck::index_t dims_arg_num = 11;
collect_index_params(argv, M, dims_arg_num, 2); collect_index_params(argv, M, dims_arg_num, 2);
collect_index_params(argv, N, dims_arg_num + 2, 2); collect_index_params(argv, N, dims_arg_num + 2, 2);
collect_index_params(argv, K, dims_arg_num + 4, 2); collect_index_params(argv, K, dims_arg_num + 4, 2);
...@@ -76,90 +79,130 @@ int profile_contraction_bilinear(int argc, char* argv[]) ...@@ -76,90 +79,130 @@ int profile_contraction_bilinear(int argc, char* argv[])
collect_index_params(argv, StridesD, dims_arg_num + 18, 4); collect_index_params(argv, StridesD, dims_arg_num + 18, 4);
} }
using F32 = float; using F16 = ck::half_t;
using F64 = double; using BF16 = ck::bhalf_t;
using F32 = float;
auto profile = [&](auto a_layout, auto b_layout, auto cde_layout, auto type) { using F64 = double;
using ALayout = decltype(a_layout);
using BLayout = decltype(b_layout); auto profile =
using CDELayout = decltype(cde_layout); [&](auto a_layout, auto b_layout, auto cde_layout, auto type, auto compute_type) {
using ALayout = decltype(a_layout);
using DataType = decltype(type); using BLayout = decltype(b_layout);
using CDELayout = decltype(cde_layout);
if(default_strides)
using DataType = decltype(type);
using ComputeDataType = decltype(compute_type);
if(default_strides)
{
assign_default_strides(a_layout, StridesA, {M[0], M[1], K[0], K[1]});
assign_default_strides(b_layout, StridesB, {N[0], N[1], K[0], K[1]});
assign_default_strides(cde_layout, StridesE, {M[0], M[1], N[0], N[1]});
assign_default_strides(cde_layout, StridesD, {M[0], M[1], N[0], N[1]});
}
bool pass = ck::profiler::profile_contraction_impl<ALayout,
BLayout,
CDELayout,
DataType,
ComputeDataType,
ck::Tuple<DataType>,
Bilinear>(do_verification,
init_method,
do_log,
time_kernel,
Bilinear{alpha, beta},
M,
N,
K,
StridesA,
StridesB,
StridesE,
StridesD);
return pass;
};
auto run_profile_for_datatype = [&](auto type, auto compute_type) {
if(layout == ContractionMatrixLayout::MK_KN_MN_MN)
{ {
assign_default_strides(a_layout, StridesA, {M[0], M[1], K[0], K[1]}); return profile(Row{}, Row{}, Row{}, type, compute_type);
assign_default_strides(b_layout, StridesB, {K[0], K[1], N[0], N[1]});
assign_default_strides(cde_layout, StridesE, {M[0], M[1], N[0], N[1]});
assign_default_strides(cde_layout, StridesD, {M[0], M[1], N[0], N[1]});
} }
bool pass = ck::profiler::profile_contraction_impl<ALayout, else if(layout == ContractionMatrixLayout::MK_NK_MN_MN)
BLayout, {
CDELayout, return profile(Row{}, Col{}, Row{}, type, compute_type);
DataType, }
ck::Tuple<DataType>, else if(layout == ContractionMatrixLayout::KM_KN_MN_MN)
Bilinear>(do_verification, {
init_method, return profile(Col{}, Row{}, Row{}, type, compute_type);
do_log, }
time_kernel, else if(layout == ContractionMatrixLayout::KM_NK_MN_MN)
Bilinear{alpha, beta}, {
M, return profile(Col{}, Col{}, Row{}, type, compute_type);
N, }
K, return false;
StridesA,
StridesB,
StridesE,
StridesD);
return pass;
}; };
if(data_type == ContractionDataType::F32_F32_F32_F32 && if(data_type == ContractionDataType::F32_F32_F32_F32)
layout == ContractionMatrixLayout::MK_KN_MN_MN)
{
return profile(Row{}, Row{}, Row{}, F32{});
}
else if(data_type == ContractionDataType::F32_F32_F32_F32 &&
layout == ContractionMatrixLayout::MK_NK_MN_MN)
{ {
return profile(Row{}, Col{}, Row{}, F32{}); if(compute_data_type == ContractionComputeDataType::F32)
} {
else if(data_type == ContractionDataType::F32_F32_F32_F32 && return run_profile_for_datatype(F32{}, F32{});
layout == ContractionMatrixLayout::KM_KN_MN_MN) }
{ else if(compute_data_type == ContractionComputeDataType::F16)
return profile(Col{}, Row{}, Row{}, F32{}); {
} return run_profile_for_datatype(F32{}, F16{});
else if(data_type == ContractionDataType::F32_F32_F32_F32 && }
layout == ContractionMatrixLayout::KM_NK_MN_MN) else if(compute_data_type == ContractionComputeDataType::BF16)
{ {
return profile(Col{}, Col{}, Row{}, F32{}); return run_profile_for_datatype(F32{}, BF16{});
} }
else if(data_type == ContractionDataType::F64_F64_F64_F64 && else
layout == ContractionMatrixLayout::MK_KN_MN_MN) {
{ std::cout << "Incorrect combination of data type and compute data type." << std::endl;
return profile(Row{}, Row{}, Row{}, F64{}); return 1;
} }
else if(data_type == ContractionDataType::F64_F64_F64_F64 &&
layout == ContractionMatrixLayout::MK_NK_MN_MN)
{
return profile(Row{}, Col{}, Row{}, F64{});
} }
else if(data_type == ContractionDataType::F64_F64_F64_F64 && else if(data_type == ContractionDataType::F64_F64_F64_F64)
layout == ContractionMatrixLayout::KM_KN_MN_MN)
{ {
return profile(Col{}, Row{}, Row{}, F64{}); if(compute_data_type == ContractionComputeDataType::F64)
{
return run_profile_for_datatype(F64{}, F64{});
}
else if(compute_data_type == ContractionComputeDataType::F32)
{
return run_profile_for_datatype(F64{}, F32{});
}
else
{
std::cout << "Incorrect combination of data type and compute data type." << std::endl;
return 1;
}
} }
else if(data_type == ContractionDataType::F64_F64_F64_F64 && else if(data_type == ContractionDataType::F16_F16_F16_F16)
layout == ContractionMatrixLayout::KM_NK_MN_MN)
{ {
return profile(Col{}, Col{}, Row{}, F64{}); if(compute_data_type == ContractionComputeDataType::F32)
{
return run_profile_for_datatype(F16{}, F32{});
}
else
{
std::cout << "Incorrect combination of data type and compute data type." << std::endl;
return 1;
}
} }
else else if(data_type == ContractionDataType::BF16_BF16_BF16_BF16)
{ {
std::cout << "this data_type & layout is not implemented" << std::endl; if(compute_data_type == ContractionComputeDataType::F32)
{
return 1; return run_profile_for_datatype(BF16{}, F32{});
}
else
{
std::cout << "Incorrect combination of data type and compute data type." << std::endl;
return 1;
}
} }
return 1;
} }
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_contraction_bilinear); REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_contraction_bilinear);
profiler/src/profile_contraction_scale.cpp
View file @ 11279540
...@@ -17,8 +17,9 @@ ...@@ -17,8 +17,9 @@
static void print_helper_msg() static void print_helper_msg()
{ {
std::cout << "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n" std::cout << "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n"
<< "arg2: data type (0: fp32; 1: f64)\n" << "arg2: data type (0: fp32; 1: f64; 2: f16; 3: bf16)\n"
<< "arg3: matrix layout (0: A[m0, m1, k0, k1] * B[k0, k1, n0, n1] + " << "arg3: compute data type (0: fp32; 1: f64; 2: f16; 3: bf16)\n"
<< "arg4: matrix layout (0: A[m0, m1, k0, k1] * B[k0, k1, n0, n1] + "
"D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n" "D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n"
<< " 1: A[m0, m1, k0, k1] * B[n0, n1, k0, k1] + " << " 1: A[m0, m1, k0, k1] * B[n0, n1, k0, k1] + "
"D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n" "D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n"
...@@ -26,39 +27,40 @@ static void print_helper_msg() ...@@ -26,39 +27,40 @@ static void print_helper_msg()
"D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n" "D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n"
<< " 3: A[k0, k1, m0, m1] * B[n0, n1, k0, k1] + " << " 3: A[k0, k1, m0, m1] * B[n0, n1, k0, k1] + "
"D[m0, m1, n0, n1] = E[m0, m1, n0, n1])\n" "D[m0, m1, n0, n1] = E[m0, m1, n0, n1])\n"
<< "arg4: verification (0: no; 1: yes)\n" << "arg5: verification (0: no; 1: yes)\n"
<< "arg5: initialization (0: no init; 1: integer value; 2: decimal " << "arg6: initialization (0: no init; 1: integer value; 2: decimal "
<< "value)\n" << "value)\n"
<< "arg6: print tensor value (0: no; 1: yes)\n" << "arg7: print tensor value (0: no; 1: yes)\n"
<< "arg7: time kernel (0: no, 1: yes)\n" << "arg8: time kernel (0: no, 1: yes)\n"
<< "arg8: alpha\n" << "arg9: alpha\n"
<< "arg9 to 14: M0, M1, N0, N1, K0, K1\n" << "arg10 to 15: M0, M1, N0, N1, K0, K1\n"
<< "arg15 to 30: Strides for A, B, D and E (skip for default)\n" << "arg16 to 31: Strides for A, B, D and E (skip for default)\n"
<< std::endl; << std::endl;
} }
int profile_contraction_scale(int argc, char* argv[]) int profile_contraction_scale(int argc, char* argv[])
{ {
const bool default_strides = argc == 15; const bool default_strides = argc == 16;
if(argc != 31 && argc != 15) if(argc != 32 && argc != 16)
{ {
print_helper_msg(); print_helper_msg();
exit(1); exit(1);
} }
const auto data_type = static_cast<ContractionDataType>(std::stoi(argv[2])); const auto data_type = static_cast<ContractionDataType>(std::stoi(argv[2]));
const auto layout = static_cast<ContractionMatrixLayout>(std::stoi(argv[3])); const auto compute_data_type = static_cast<ContractionComputeDataType>(std::stoi(argv[3]));
const bool do_verification = std::stoi(argv[4]); const auto layout = static_cast<ContractionMatrixLayout>(std::stoi(argv[4]));
const ck::index_t init_method = std::stoi(argv[5]); const bool do_verification = std::stoi(argv[5]);
const bool do_log = std::stoi(argv[6]); const ck::index_t init_method = std::stoi(argv[6]);
const bool time_kernel = std::stoi(argv[7]); const bool do_log = std::stoi(argv[7]);
const float alpha = std::stof(argv[8]); const bool time_kernel = std::stoi(argv[8]);
const float alpha = std::stof(argv[9]);
std::vector<ck::index_t> M; std::vector<ck::index_t> M;
std::vector<ck::index_t> N; std::vector<ck::index_t> N;
std::vector<ck::index_t> K; std::vector<ck::index_t> K;
const ck::index_t dims_arg_num = 9; const ck::index_t dims_arg_num = 10;
collect_index_params(argv, M, dims_arg_num, 2); collect_index_params(argv, M, dims_arg_num, 2);
collect_index_params(argv, N, dims_arg_num + 2, 2); collect_index_params(argv, N, dims_arg_num + 2, 2);
collect_index_params(argv, K, dims_arg_num + 4, 2); collect_index_params(argv, K, dims_arg_num + 4, 2);
...@@ -75,88 +77,131 @@ int profile_contraction_scale(int argc, char* argv[]) ...@@ -75,88 +77,131 @@ int profile_contraction_scale(int argc, char* argv[])
collect_index_params(argv, StridesD, dims_arg_num + 18, 4); collect_index_params(argv, StridesD, dims_arg_num + 18, 4);
} }
using F32 = float; using F16 = ck::half_t;
using F64 = double; using BF16 = ck::bhalf_t;
using F32 = float;
auto profile = [&](auto a_layout, auto b_layout, auto cde_layout, auto type) { using F64 = double;
using ALayout = decltype(a_layout);
using BLayout = decltype(b_layout); auto profile =
using CDELayout = decltype(cde_layout); [&](auto a_layout, auto b_layout, auto cde_layout, auto type, auto compute_type) {
using ALayout = decltype(a_layout);
using DataType = decltype(type); using BLayout = decltype(b_layout);
using CDELayout = decltype(cde_layout);
if(default_strides)
using DataType = decltype(type);
using ComputeDataType = decltype(compute_type);
if(default_strides)
{
assign_default_strides(a_layout, StridesA, {M[0], M[1], K[0], K[1]});
assign_default_strides(b_layout, StridesB, {N[0], N[1], K[0], K[1]});
assign_default_strides(cde_layout, StridesE, {M[0], M[1], N[0], N[1]});
assign_default_strides(cde_layout, StridesD, {M[0], M[1], N[0], N[1]});
}
bool pass = ck::profiler::profile_contraction_impl<ALayout,
BLayout,
CDELayout,
DataType,
ComputeDataType,
ck::Tuple<>,
Scale>(do_verification,
init_method,
do_log,
time_kernel,
Scale{alpha},
M,
N,
K,
StridesA,
StridesB,
StridesE,
StridesD);
return pass;
};
auto run_profile_for_datatype = [&](auto type, auto compute_type) {
if(layout == ContractionMatrixLayout::MK_KN_MN_MN)
{ {
assign_default_strides(a_layout, StridesA, {M[0], M[1], K[0], K[1]}); return profile(Row{}, Row{}, Row{}, type, compute_type);
assign_default_strides(b_layout, StridesB, {K[0], K[1], N[0], N[1]});
assign_default_strides(cde_layout, StridesE, {M[0], M[1], N[0], N[1]});
assign_default_strides(cde_layout, StridesD, {M[0], M[1], N[0], N[1]});
} }
else if(layout == ContractionMatrixLayout::MK_NK_MN_MN)
bool pass = ck::profiler:: {
profile_contraction_impl<ALayout, BLayout, CDELayout, DataType, ck::Tuple<>, Scale>( return profile(Row{}, Col{}, Row{}, type, compute_type);
do_verification, }
init_method, else if(layout == ContractionMatrixLayout::KM_KN_MN_MN)
do_log, {
time_kernel, return profile(Col{}, Row{}, Row{}, type, compute_type);
Scale{alpha}, }
M, else if(layout == ContractionMatrixLayout::KM_NK_MN_MN)
N, {
K, return profile(Col{}, Col{}, Row{}, type, compute_type);
StridesA, }
StridesB, return false;
StridesE,
StridesD);
return pass;
}; };
if(data_type == ContractionDataType::F32_F32_F32_F32 && if(data_type == ContractionDataType::F32_F32_F32_F32)
layout == ContractionMatrixLayout::MK_KN_MN_MN)
{
return profile(Row{}, Row{}, Row{}, F32{});
}
else if(data_type == ContractionDataType::F32_F32_F32_F32 &&
layout == ContractionMatrixLayout::MK_NK_MN_MN)
{
return profile(Row{}, Col{}, Row{}, F32{});
}
else if(data_type == ContractionDataType::F32_F32_F32_F32 &&
layout == ContractionMatrixLayout::KM_KN_MN_MN)
{ {
return profile(Col{}, Row{}, Row{}, F32{}); if(compute_data_type == ContractionComputeDataType::F32)
} {
else if(data_type == ContractionDataType::F32_F32_F32_F32 && return run_profile_for_datatype(F32{}, F32{});
layout == ContractionMatrixLayout::KM_NK_MN_MN) }
{ else if(compute_data_type == ContractionComputeDataType::F16)
return profile(Col{}, Col{}, Row{}, F32{}); {
} return run_profile_for_datatype(F32{}, F16{});
else if(data_type == ContractionDataType::F64_F64_F64_F64 && }
layout == ContractionMatrixLayout::MK_KN_MN_MN) else if(compute_data_type == ContractionComputeDataType::BF16)
{ {
return profile(Row{}, Row{}, Row{}, F64{}); return run_profile_for_datatype(F32{}, BF16{});
} }
else if(data_type == ContractionDataType::F64_F64_F64_F64 && else
layout == ContractionMatrixLayout::MK_NK_MN_MN) {
{ std::cout << "Incorrect combination of data type and compute data type." << std::endl;
return profile(Row{}, Col{}, Row{}, F64{}); return 1;
}
} }
else if(data_type == ContractionDataType::F64_F64_F64_F64 && else if(data_type == ContractionDataType::F64_F64_F64_F64)
layout == ContractionMatrixLayout::KM_KN_MN_MN)
{ {
return profile(Col{}, Row{}, Row{}, F64{}); if(compute_data_type == ContractionComputeDataType::F64)
{
return run_profile_for_datatype(F64{}, F64{});
}
else if(compute_data_type == ContractionComputeDataType::F32)
{
return run_profile_for_datatype(F64{}, F32{});
}
else
{
std::cout << "Incorrect combination of data type and compute data type." << std::endl;
return 1;
}
} }
else if(data_type == ContractionDataType::F64_F64_F64_F64 && else if(data_type == ContractionDataType::F16_F16_F16_F16)
layout == ContractionMatrixLayout::KM_NK_MN_MN)
{ {
return profile(Col{}, Col{}, Row{}, F64{}); if(compute_data_type == ContractionComputeDataType::F32)
{
return run_profile_for_datatype(F16{}, F32{});
}
else
{
std::cout << "Incorrect combination of data type and compute data type." << std::endl;
return 1;
}
} }
else else if(data_type == ContractionDataType::BF16_BF16_BF16_BF16)
{ {
std::cout << "this data_type & layout is not implemented" << std::endl; if(compute_data_type == ContractionComputeDataType::F32)
{
return 1; return run_profile_for_datatype(BF16{}, F32{});
}
else
{
std::cout << "Incorrect combination of data type and compute data type." << std::endl;
return 1;
}
} }
return 1;
} }
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_contraction_scale); REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_contraction_scale);
profiler/src/profile_conv_tensor_rearrange.cpp
View file @ 11279540
...@@ -19,7 +19,8 @@ enum struct RearrangeOp ...@@ -19,7 +19,8 @@ enum struct RearrangeOp
enum struct ConvLayout enum struct ConvLayout
{ {
NHWC, // 0 GNHWC, // 0
NHWGC, // 1
}; };
enum struct DataType enum struct DataType
...@@ -42,7 +43,8 @@ static void print_helper_msg() ...@@ -42,7 +43,8 @@ static void print_helper_msg()
<< " 1: Input fp16, Weight fp16, Output fp16\n" << " 1: Input fp16, Weight fp16, Output fp16\n"
<< " 2: Input bf16, Weight bf16, Output bf16\n" << " 2: Input bf16, Weight bf16, Output bf16\n"
<< " 3: Input int8, Weight int8, Output int8)\n" << " 3: Input int8, Weight int8, Output int8)\n"
<< "arg3: tensor layout (0: Input[N, Hi, Wi, C], Output[N * Ho * Wo, Y * X * C])\n" << "arg3: tensor layout (0: Input[G, N, Hi, Wi, C], Output[G * N * Ho * Wo, Y * X * C],\n"
<< " 1: Input[N, Hi, Wi, G, C], Output[N * Ho * Wo * G, Y * X * C])\n"
<< "arg4: verification (0: no, 1: yes)\n" << "arg4: verification (0: no, 1: yes)\n"
<< "arg5: initialization (0: no init, 1: integer value, 2: decimal value)\n" << "arg5: initialization (0: no init, 1: integer value, 2: decimal value)\n"
<< "arg6: print tensor value (0: no; 1: yes)\n" << "arg6: print tensor value (0: no; 1: yes)\n"
...@@ -114,11 +116,9 @@ int profile_conv_tensor_rearrange(int argc, char* argv[]) ...@@ -114,11 +116,9 @@ int profile_conv_tensor_rearrange(int argc, char* argv[])
return pass ? 0 : 1; return pass ? 0 : 1;
}; };
// Image To Column
if(rearrange_op == RearrangeOp::ImageToColumn) if(rearrange_op == RearrangeOp::ImageToColumn)
{ {
// NHWC if(layout == ConvLayout::GNHWC)
if(layout == ConvLayout::NHWC)
{ {
if(num_dim_spatial == 1) if(num_dim_spatial == 1)
{ {
...@@ -178,11 +178,70 @@ int profile_conv_tensor_rearrange(int argc, char* argv[]) ...@@ -178,11 +178,70 @@ int profile_conv_tensor_rearrange(int argc, char* argv[])
} }
} }
} }
else if(layout == ConvLayout::NHWGC)
{
if(num_dim_spatial == 1)
{
if(data_type == DataType::F32_F32)
{
return profile(I1, NWGC{}, F32{}, F32{}, ImageToColumn{});
}
else if(data_type == DataType::F16_F16)
{
return profile(I1, NWGC{}, F16{}, F16{}, ImageToColumn{});
}
else if(data_type == DataType::BF16_BF16)
{
return profile(I1, NWGC{}, BF16{}, BF16{}, ImageToColumn{});
}
else if(data_type == DataType::INT8_INT8)
{
return profile(I1, NWGC{}, INT8{}, INT8{}, ImageToColumn{});
}
}
else if(num_dim_spatial == 2)
{
if(data_type == DataType::F32_F32)
{
return profile(I2, NHWGC{}, F32{}, F32{}, ImageToColumn{});
}
else if(data_type == DataType::F16_F16)
{
return profile(I2, NHWGC{}, F16{}, F16{}, ImageToColumn{});
}
else if(data_type == DataType::BF16_BF16)
{
return profile(I2, NHWGC{}, BF16{}, BF16{}, ImageToColumn{});
}
else if(data_type == DataType::INT8_INT8)
{
return profile(I2, NHWGC{}, INT8{}, INT8{}, ImageToColumn{});
}
}
else if(num_dim_spatial == 3)
{
if(data_type == DataType::F32_F32)
{
return profile(I3, NDHWGC{}, F32{}, F32{}, ImageToColumn{});
}
else if(data_type == DataType::F16_F16)
{
return profile(I3, NDHWGC{}, F16{}, F16{}, ImageToColumn{});
}
else if(data_type == DataType::BF16_BF16)
{
return profile(I3, NDHWGC{}, BF16{}, BF16{}, ImageToColumn{});
}
else if(data_type == DataType::INT8_INT8)
{
return profile(I3, NDHWGC{}, INT8{}, INT8{}, ImageToColumn{});
}
}
}
} }
else if(rearrange_op == RearrangeOp::ColumnToImage) else if(rearrange_op == RearrangeOp::ColumnToImage)
{ {
// NHWC if(layout == ConvLayout::GNHWC)
if(layout == ConvLayout::NHWC)
{ {
if(num_dim_spatial == 1) if(num_dim_spatial == 1)
{ {
...@@ -242,6 +301,66 @@ int profile_conv_tensor_rearrange(int argc, char* argv[]) ...@@ -242,6 +301,66 @@ int profile_conv_tensor_rearrange(int argc, char* argv[])
} }
} }
} }
else if(layout == ConvLayout::NHWGC)
{
if(num_dim_spatial == 1)
{
if(data_type == DataType::F32_F32)
{
return profile(I1, NWGC{}, F32{}, F32{}, ColumnToImage{});
}
else if(data_type == DataType::F16_F16)
{
return profile(I1, NWGC{}, F16{}, F16{}, ColumnToImage{});
}
else if(data_type == DataType::BF16_BF16)
{
return profile(I1, NWGC{}, BF16{}, BF16{}, ColumnToImage{});
}
else if(data_type == DataType::INT8_INT8)
{
return profile(I1, NWGC{}, INT8{}, INT8{}, ColumnToImage{});
}
}
else if(num_dim_spatial == 2)
{
if(data_type == DataType::F32_F32)
{
return profile(I2, NHWGC{}, F32{}, F32{}, ColumnToImage{});
}
else if(data_type == DataType::F16_F16)
{
return profile(I2, NHWGC{}, F16{}, F16{}, ColumnToImage{});
}
else if(data_type == DataType::BF16_BF16)
{
return profile(I2, NHWGC{}, BF16{}, BF16{}, ColumnToImage{});
}
else if(data_type == DataType::INT8_INT8)
{
return profile(I2, NHWGC{}, INT8{}, INT8{}, ColumnToImage{});
}
}
else if(num_dim_spatial == 3)
{
if(data_type == DataType::F32_F32)
{
return profile(I3, NDHWGC{}, F32{}, F32{}, ColumnToImage{});
}
else if(data_type == DataType::F16_F16)
{
return profile(I3, NDHWGC{}, F16{}, F16{}, ColumnToImage{});
}
else if(data_type == DataType::BF16_BF16)
{
return profile(I3, NDHWGC{}, BF16{}, BF16{}, ColumnToImage{});
}
else if(data_type == DataType::INT8_INT8)
{
return profile(I3, NDHWGC{}, INT8{}, INT8{}, ColumnToImage{});
}
}
}
} }
std::cout << "this data_type & layout is not implemented" << std::endl; std::cout << "this data_type & layout is not implemented" << std::endl;
......
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