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Commit 7df98b04 authored by rocking's avatar rocking
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Change save mean var type from f32 to f16 in f16 mode

parent de1cb1ed
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Showing with 58 additions and 58 deletions
client_example/05_layernorm/layernorm2d_fwd.cpp
View file @ 7df98b04
...@@ -16,7 +16,7 @@ using XDataType = ck::half_t; ...@@ -16,7 +16,7 @@ using XDataType = ck::half_t;
using GammaDataType = ck::half_t; using GammaDataType = ck::half_t;
using BetaDataType = ck::half_t; using BetaDataType = ck::half_t;
using YDataType = ck::half_t; using YDataType = ck::half_t;
using SaveMeanInvStdDataType = float; using SaveMeanInvStdDataType = ck::half_t;
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using PassThrough = ck::tensor_operation::element_wise::PassThrough;
#define SAVE_MEAN_INV_STD #define SAVE_MEAN_INV_STD
......
client_example/05_layernorm/layernorm4d_fwd.cpp
View file @ 7df98b04
...@@ -16,7 +16,7 @@ using XDataType = ck::half_t; ...@@ -16,7 +16,7 @@ using XDataType = ck::half_t;
using GammaDataType = ck::half_t; using GammaDataType = ck::half_t;
using BetaDataType = ck::half_t; using BetaDataType = ck::half_t;
using YDataType = ck::half_t; using YDataType = ck::half_t;
using SaveMeanInvStdDataType = float; using SaveMeanInvStdDataType = ck::half_t;
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using PassThrough = ck::tensor_operation::element_wise::PassThrough;
#define SAVE_MEAN_INV_STD #define SAVE_MEAN_INV_STD
......
library/include/ck/library/tensor_operation_instance/gpu/normalization_fwd.hpp
View file @ 7df98b04
...@@ -20,15 +20,15 @@ namespace instance { ...@@ -20,15 +20,15 @@ namespace instance {
// FP16 // FP16
void add_device_normalization_fwd_rank_2_1_f16_instances( void add_device_normalization_fwd_rank_2_1_f16_instances(
std::vector< std::vector<
std::unique_ptr<DeviceNormalizationFwd<F16, F16, F16, F16, F32, PassThrough, 2, 1>>>&); std::unique_ptr<DeviceNormalizationFwd<F16, F16, F16, F16, F16, PassThrough, 2, 1>>>&);
void add_device_normalization_fwd_rank_4_3_f16_instances( void add_device_normalization_fwd_rank_4_3_f16_instances(
std::vector< std::vector<
std::unique_ptr<DeviceNormalizationFwd<F16, F16, F16, F16, F32, PassThrough, 4, 3>>>&); std::unique_ptr<DeviceNormalizationFwd<F16, F16, F16, F16, F16, PassThrough, 4, 3>>>&);
void add_device_normalization_fwd_rank_5_3_f16_instances( void add_device_normalization_fwd_rank_5_3_f16_instances(
std::vector< std::vector<
std::unique_ptr<DeviceNormalizationFwd<F16, F16, F16, F16, F32, PassThrough, 5, 3>>>&); std::unique_ptr<DeviceNormalizationFwd<F16, F16, F16, F16, F16, PassThrough, 5, 3>>>&);
#endif #endif
#ifdef CK_ENABLE_FP32 #ifdef CK_ENABLE_FP32
// FP32 // FP32
...@@ -76,7 +76,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceNormal ...@@ -76,7 +76,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceNormal
#ifdef CK_ENABLE_FP16 #ifdef CK_ENABLE_FP16
if constexpr(is_same_v<XDataType, F16> && is_same_v<GammaDataType, F16> && if constexpr(is_same_v<XDataType, F16> && is_same_v<GammaDataType, F16> &&
is_same_v<BetaDataType, F16> && is_same_v<YDataType, F16> && is_same_v<BetaDataType, F16> && is_same_v<YDataType, F16> &&
is_same_v<SaveMeanInvStdDataType, F32>) is_same_v<SaveMeanInvStdDataType, F16>)
{ {
if constexpr(Rank == 2 && NumReduceDim == 1) if constexpr(Rank == 2 && NumReduceDim == 1)
{ {
......
library/include/ck/library/tensor_operation_instance/gpu/normalization_fwd_swish.hpp
View file @ 7df98b04
...@@ -19,7 +19,7 @@ namespace instance { ...@@ -19,7 +19,7 @@ namespace instance {
// FP16 // FP16
void add_device_normalization_fwd_rank_5_3_swish_f16_instances( void add_device_normalization_fwd_rank_5_3_swish_f16_instances(
std::vector<std::unique_ptr<DeviceNormalizationFwd<F16, F16, F16, F16, F32, Swish, 5, 3>>>&); std::vector<std::unique_ptr<DeviceNormalizationFwd<F16, F16, F16, F16, F16, Swish, 5, 3>>>&);
// FP32 // FP32
void add_device_normalization_fwd_rank_5_3_swish_f32_instances( void add_device_normalization_fwd_rank_5_3_swish_f32_instances(
...@@ -61,7 +61,7 @@ struct DeviceOperationInstanceFactory< ...@@ -61,7 +61,7 @@ struct DeviceOperationInstanceFactory<
if constexpr(is_same_v<XDataType, F16> && is_same_v<GammaDataType, F16> && if constexpr(is_same_v<XDataType, F16> && is_same_v<GammaDataType, F16> &&
is_same_v<BetaDataType, F16> && is_same_v<YDataType, F16> && is_same_v<BetaDataType, F16> && is_same_v<YDataType, F16> &&
is_same_v<SaveMeanInvStdDataType, F32>) is_same_v<SaveMeanInvStdDataType, F16>)
{ {
if constexpr(Rank == 5 && NumReduceDim == 3) if constexpr(Rank == 5 && NumReduceDim == 3)
{ {
......
library/src/tensor_operation_instance/gpu/normalization_fwd/device_groupnorm_fwd_f16_instance.cpp
View file @ 7df98b04
...@@ -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_fwd_rank_5_3_f16_instances( void add_device_normalization_fwd_rank_5_3_f16_instances(
std::vector<std::unique_ptr<DeviceNormalizationFwd<F16, F16, F16, F16, F32, Pass, 5, 3>>>& std::vector<std::unique_ptr<DeviceNormalizationFwd<F16, F16, F16, F16, F16, Pass, 5, 3>>>&
instances) instances)
{ {
add_device_operation_instances(instances, add_device_operation_instances(instances,
......
library/src/tensor_operation_instance/gpu/normalization_fwd/device_groupnorm_fwd_swish_f16_instance.cpp
View file @ 7df98b04
...@@ -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_fwd_rank_5_3_swish_f16_instances( void add_device_normalization_fwd_rank_5_3_swish_f16_instances(
std::vector<std::unique_ptr<DeviceNormalizationFwd<F16, F16, F16, F16, F32, Swish, 5, 3>>>& std::vector<std::unique_ptr<DeviceNormalizationFwd<F16, F16, F16, F16, F16, Swish, 5, 3>>>&
instances) instances)
{ {
add_device_operation_instances(instances, add_device_operation_instances(instances,
......
library/src/tensor_operation_instance/gpu/normalization_fwd/device_layernorm2d_fwd_f16_instance.cpp
View file @ 7df98b04
...@@ -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_fwd_rank_2_1_f16_instances( void add_device_normalization_fwd_rank_2_1_f16_instances(
std::vector<std::unique_ptr<DeviceNormalizationFwd<F16, F16, F16, F16, F32, Pass, 2, 1>>>& std::vector<std::unique_ptr<DeviceNormalizationFwd<F16, F16, F16, F16, F16, Pass, 2, 1>>>&
instances) instances)
{ {
add_device_operation_instances(instances, add_device_operation_instances(instances,
......
library/src/tensor_operation_instance/gpu/normalization_fwd/device_layernorm4d_fwd_f16_instance.cpp
View file @ 7df98b04
...@@ -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_fwd_rank_4_3_f16_instances( void add_device_normalization_fwd_rank_4_3_f16_instances(
std::vector<std::unique_ptr<DeviceNormalizationFwd<F16, F16, F16, F16, F32, Pass, 4, 3>>>& std::vector<std::unique_ptr<DeviceNormalizationFwd<F16, F16, F16, F16, F16, Pass, 4, 3>>>&
instances) instances)
{ {
add_device_operation_instances(instances, add_device_operation_instances(instances,
......
library/src/tensor_operation_instance/gpu/normalization_fwd/normalization_fwd_instance_common.hpp
View file @ 7df98b04
...@@ -23,24 +23,24 @@ using device_normalization_f16_instances = ...@@ -23,24 +23,24 @@ using device_normalization_f16_instances =
// clang-format off // clang-format off
std::tuple < std::tuple <
// XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, SaveMeanInvStdDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorDim, GammaSrcVectorSize, BetaSrcVectorDim, BetaSrcVectorSize, YDstVectorSize, SaveMeanInvStdScalarPerVector> // XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, SaveMeanInvStdDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorDim, GammaSrcVectorSize, BetaSrcVectorDim, BetaSrcVectorSize, YDstVectorSize, SaveMeanInvStdScalarPerVector>
DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1>, // irregular size DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1>, // irregular size
DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 4, 1, 4, 1, 4, 1, 4, 4, 1>, // irregular size DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 4, 1, 4, 1, 4, 1, 4, 4, 1>, // irregular size
DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 64, 1, 64, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>, DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 64, 1, 64, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>, DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 16, 1, 8, 1, 8, 1, 8, 8, 1>, DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 16, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 32, 1, 8, 1, 8, 1, 8, 8, 1>, DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 32, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>, DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 16, 1, 8, 1, 8, 1, 8, 8, 1>, DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 16, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 2, 16, 1, 8, 1, 8, 1, 8, 8, 2>, DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 2, 16, 1, 8, 1, 8, 1, 8, 8, 2>,
DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 32, 1, 8, 1, 8, 1, 8, 8, 1>, DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 32, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>, DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 16, 1, 8, 1, 8, 1, 8, 8, 1>, DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 16, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>, DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 16, 1, 8, 1, 8, 1, 8, 8, 1> DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 16, 1, 8, 1, 8, 1, 8, 8, 1>
// clang-format on // clang-format on
>; >;
...@@ -49,31 +49,31 @@ using device_normalization_splitk_f16_instances = ...@@ -49,31 +49,31 @@ using device_normalization_splitk_f16_instances =
// clang-format off // clang-format off
std::tuple < std::tuple <
// XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, SaveMeanInvStdDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorDim, GammaSrcVectorSize, BetaSrcVectorDim, BetaSrcVectorSize, YDstVectorSize, SaveMeanInvStdScalarPerVector> // XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, SaveMeanInvStdDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorDim, GammaSrcVectorSize, BetaSrcVectorDim, BetaSrcVectorSize, YDstVectorSize, SaveMeanInvStdScalarPerVector>
DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1>, // irregular size DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1>, // irregular size
DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 4, 1, 4, 1, 4, 1, 4, 4, 1>, // irregular size DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 4, 1, 4, 1, 4, 1, 4, 4, 1>, // irregular size
DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 64, 1, 64, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>, DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 64, 1, 64, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>, DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 16, 1, 8, 1, 8, 1, 8, 8, 1>, DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 16, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 32, 1, 8, 1, 8, 1, 8, 8, 1>, DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 32, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>, DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 16, 1, 8, 1, 8, 1, 8, 8, 1>, DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 16, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 2, 16, 1, 8, 1, 8, 1, 8, 8, 2>, DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 2, 16, 1, 8, 1, 8, 1, 8, 8, 2>,
DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 32, 1, 8, 1, 8, 1, 8, 8, 1>, DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 32, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>, DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 16, 1, 8, 1, 8, 1, 8, 8, 1>, DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 16, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>, DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 8, 1, 8, 1, 8, 1, 8, 8, 1>,
DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 16, 1, 8, 1, 8, 1, 8, 8, 1> DeviceNormalizationFwdSplitKImpl<F16, F16, F16, F32, F16, F16, 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
DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F32, OutElementwise, Rank, Reduce, 64, 1, 64, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1> DeviceNormalizationFwdImpl<F16, F16, F16, F32, F16, F16, OutElementwise, Rank, Reduce, 64, 1, 64, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1>
// clang-format on // clang-format on
>; >;
......
profiler/src/profile_groupnorm_fwd.cpp
View file @ 7df98b04
...@@ -98,7 +98,7 @@ int profile_groupnorm(int argc, char* argv[]) ...@@ -98,7 +98,7 @@ int profile_groupnorm(int argc, char* argv[])
} }
else if(data_type == ck::DataTypeEnum::Half) else if(data_type == ck::DataTypeEnum::Half)
{ {
ck::profiler::profile_groupnorm_impl<F16, F16, F16, F32, F16, F32, false>( ck::profiler::profile_groupnorm_impl<F16, F16, F16, F32, F16, F16, false>(
do_verification, init_method, do_log, time_kernel, length); do_verification, init_method, do_log, time_kernel, length);
} }
else else
......
profiler/src/profile_layernorm_fwd.cpp
View file @ 7df98b04
...@@ -104,7 +104,7 @@ int profile_layernorm(int argc, char* argv[]) ...@@ -104,7 +104,7 @@ int profile_layernorm(int argc, char* argv[])
if(data_type == ck::DataTypeEnum::Half) if(data_type == ck::DataTypeEnum::Half)
{ {
ck::profiler::profile_layernorm_impl<F16, F16, F16, F32, F16, F32, false, rank>( ck::profiler::profile_layernorm_impl<F16, F16, F16, F32, F16, F16, false, rank>(
do_verification, init_method, do_log, time_kernel, length); do_verification, init_method, do_log, time_kernel, length);
} }
else if(data_type == ck::DataTypeEnum::Float) else if(data_type == ck::DataTypeEnum::Float)
......
test/normalization_fwd/test_groupnorm_fwd_fp16.cpp
View file @ 7df98b04
...@@ -47,8 +47,8 @@ class TestGroupnorm : public ::testing::Test ...@@ -47,8 +47,8 @@ class TestGroupnorm : public ::testing::Test
}; };
using KernelTypes = ::testing::Types< using KernelTypes = ::testing::Types<
// XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType> // XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, SaveMeanInvStdDataType>
std::tuple<F16, F16, F16, F32, F16, F32>>; std::tuple<F16, F16, F16, F32, F16, F16>>;
TYPED_TEST_SUITE(TestGroupnorm, KernelTypes); TYPED_TEST_SUITE(TestGroupnorm, KernelTypes);
TYPED_TEST(TestGroupnorm, Test_FP16) { this->Run(); } TYPED_TEST(TestGroupnorm, Test_FP16) { this->Run(); }
test/normalization_fwd/test_groupnorm_fwd_fp32.cpp
View file @ 7df98b04
...@@ -45,7 +45,7 @@ class TestGroupnorm : public ::testing::Test ...@@ -45,7 +45,7 @@ class TestGroupnorm : public ::testing::Test
}; };
using KernelTypes = ::testing::Types< using KernelTypes = ::testing::Types<
// XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType> // XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, SaveMeanInvStdDataType>
std::tuple<F32, F32, F32, F32, F32, F32>>; std::tuple<F32, F32, F32, F32, F32, F32>>;
TYPED_TEST_SUITE(TestGroupnorm, KernelTypes); TYPED_TEST_SUITE(TestGroupnorm, KernelTypes);
......
test/normalization_fwd/test_layernorm2d_fwd_fp16.cpp
View file @ 7df98b04
...@@ -41,8 +41,8 @@ class TestLayernorm2d : public ::testing::Test ...@@ -41,8 +41,8 @@ class TestLayernorm2d : public ::testing::Test
}; };
using KernelTypes = ::testing::Types< using KernelTypes = ::testing::Types<
// XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType> // XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, SaveMeanInvStdDataType>
std::tuple<F16, F16, F16, F32, F16, F32>>; std::tuple<F16, F16, F16, F32, F16, F16>>;
TYPED_TEST_SUITE(TestLayernorm2d, KernelTypes); TYPED_TEST_SUITE(TestLayernorm2d, KernelTypes);
TYPED_TEST(TestLayernorm2d, Test_FP16) { this->Run(); } TYPED_TEST(TestLayernorm2d, Test_FP16) { this->Run(); }
test/normalization_fwd/test_layernorm4d_fwd_fp16.cpp
View file @ 7df98b04
...@@ -41,8 +41,8 @@ class TestLayernorm4d : public ::testing::Test ...@@ -41,8 +41,8 @@ class TestLayernorm4d : public ::testing::Test
}; };
using KernelTypes = ::testing::Types< using KernelTypes = ::testing::Types<
// XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType> // XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, SaveMeanInvStdDataType>
std::tuple<F16, F16, F16, F32, F16, F32>>; std::tuple<F16, F16, F16, F32, F16, F16>>;
TYPED_TEST_SUITE(TestLayernorm4d, KernelTypes); TYPED_TEST_SUITE(TestLayernorm4d, KernelTypes);
TYPED_TEST(TestLayernorm4d, Test_FP16) { this->Run(); } TYPED_TEST(TestLayernorm4d, Test_FP16) { this->Run(); }
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