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Commit 9d2280d6 authored by rocking's avatar rocking
Browse files

1. Rename AccDatatype in normalization to computeData 

2. Rename AccElementwiseOperation to YElementwiseOperation in normalization
parent 1a38e362
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Showing with 109 additions and 107 deletions
client_example/05_layernorm/layernorm2d.cpp
View file @ 9d2280d6
......@@ -12,12 +12,12 @@
#include "ck/library/tensor_operation_instance/gpu/normalization.hpp"
using XDataType = ck::half_t;
using GammaDataType = ck::half_t;
using BetaDataType = ck::half_t;
using YDataType = ck::half_t;
using AccDataType = float;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using XDataType = ck::half_t;
using GammaDataType = ck::half_t;
using BetaDataType = ck::half_t;
using YDataType = ck::half_t;
using ComputeDataType = float;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
constexpr int Rank = 2;
constexpr int NumReduceDim = 1;
......@@ -54,7 +54,7 @@ int main(int argc, char* argv[])
using DeviceOp = ck::tensor_operation::device::DeviceNormalization<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
ComputeDataType,
YDataType,
PassThrough,
Rank,
......
example/27_layernorm/layernorm_blockwise.cpp
View file @ 9d2280d6
......@@ -20,12 +20,12 @@
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_layernorm.hpp"
using XDataType = ck::half_t;
using GammaDataType = ck::half_t;
using BetaDataType = ck::half_t;
using YDataType = ck::half_t;
using AccDataType = float;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using XDataType = ck::half_t;
using GammaDataType = ck::half_t;
using BetaDataType = ck::half_t;
using YDataType = ck::half_t;
using ConputeDataType = float;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
constexpr int Rank = 2;
constexpr int NumReduceDim = 1;
......@@ -34,7 +34,7 @@ using DeviceInstance =
ck::tensor_operation::device::DeviceNormalizationImpl<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
ConputeDataType,
YDataType,
PassThrough,
Rank,
......@@ -121,7 +121,7 @@ int main()
GammaDataType,
BetaDataType,
YDataType,
AccDataType,
ConputeDataType,
PassThrough,
Rank,
NumReduceDim>;
......
example/42_groupnorm/groupnorm_sigmoid_fp16.cpp
View file @ 9d2280d6
......@@ -23,11 +23,11 @@
constexpr int Rank = 5;
constexpr int NumReduceDim = 3;
using XDataType = ck::half_t;
using GammaDataType = ck::half_t;
using BetaDataType = ck::half_t;
using YDataType = ck::half_t;
using AccDataType = float;
using XDataType = ck::half_t;
using GammaDataType = ck::half_t;
using BetaDataType = ck::half_t;
using YDataType = ck::half_t;
using ConputeDataType = float;
struct YElementOp
{
......@@ -50,7 +50,7 @@ using DeviceInstance =
ck::tensor_operation::device::DeviceNormalizationImpl<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
ConputeDataType,
YDataType,
YElementOp,
Rank,
......@@ -157,7 +157,7 @@ int main(int argc, char* argv[])
GammaDataType,
BetaDataType,
YDataType,
AccDataType,
ConputeDataType,
YElementOp>;
ReferenceInstance ref;
......
include/ck/tensor_operation/gpu/device/device_normalization.hpp
View file @ 9d2280d6
......@@ -14,9 +14,9 @@ namespace device {
template <typename XDataType,
typename GammaDataType,
typename BetaDataType,
typename AccDataType,
typename ConputeDataType,
typename YDataType,
typename AccElementwiseOperation,
typename YElementwiseOperation,
index_t Rank,
index_t NumReduceDim>
struct DeviceNormalization : public BaseOperator
......@@ -35,7 +35,7 @@ struct DeviceNormalization : public BaseOperator
void* p_y,
void* p_savedMean,
void* p_savedInvVar,
AccElementwiseOperation acc_elementwise_op) = 0;
YElementwiseOperation y_elementwise_op) = 0;
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
};
......@@ -43,17 +43,17 @@ struct DeviceNormalization : public BaseOperator
template <typename XDataType,
typename GammaDataType,
typename BetaDataType,
typename AccDataType,
typename ConputeDataType,
typename YDataType,
typename AccElementwiseOperation,
typename YElementwiseOperation,
index_t Rank,
index_t NumReduceDim>
using DeviceNormalizationPtr = std::unique_ptr<DeviceNormalization<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
ConputeDataType,
YDataType,
AccElementwiseOperation,
YElementwiseOperation,
Rank,
NumReduceDim>>;
......
include/ck/tensor_operation/gpu/device/impl/device_normalization_impl.hpp
View file @ 9d2280d6
......@@ -21,20 +21,20 @@ template <typename GridwiseReduction,
typename GammaDataType,
typename BetaDataType,
typename YDataType,
typename AccDataType,
typename AccElementwiseOperation,
typename ConputeDataType,
typename YElementwiseOperation,
typename GridDesc_M_K>
__global__ void kernel_normalization(const GridDesc_M_K x_grid_desc_m_k,
const GridDesc_M_K gamma_grid_desc_m_k,
const GridDesc_M_K beta_grid_desc_m_k,
const GridDesc_M_K y_grid_desc_m_k,
index_t num_k_block_tile_iteration,
AccDataType epsilon,
ConputeDataType epsilon,
const XDataType* const __restrict__ p_x_global,
const GammaDataType* const __restrict__ p_gamma_global,
const BetaDataType* const __restrict__ p_beta_global,
YDataType* const __restrict__ p_y_global,
const AccElementwiseOperation acc_elementwise_op)
const YElementwiseOperation y_elementwise_op)
{
GridwiseReduction::Run(x_grid_desc_m_k,
gamma_grid_desc_m_k,
......@@ -46,7 +46,7 @@ __global__ void kernel_normalization(const GridDesc_M_K x_grid_desc_m_k,
p_gamma_global,
p_beta_global,
p_y_global,
acc_elementwise_op);
y_elementwise_op);
};
} // namespace ck
......@@ -58,9 +58,9 @@ namespace device {
template <typename XDataType,
typename GammaDataType,
typename BetaDataType,
typename AccDataType,
typename ConputeDataType,
typename YDataType,
typename AccElementwiseOperation,
typename YElementwiseOperation,
index_t Rank,
index_t NumReduceDim,
index_t BlockSize,
......@@ -78,9 +78,9 @@ template <typename XDataType,
struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
ConputeDataType,
YDataType,
AccElementwiseOperation,
YElementwiseOperation,
Rank,
NumReduceDim>
{
......@@ -172,8 +172,8 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
GammaDataType,
BetaDataType,
YDataType,
AccDataType,
AccElementwiseOperation,
ConputeDataType,
YElementwiseOperation,
GridDesc_M_K,
BlockSize,
MThreadClusterSize,
......@@ -194,8 +194,8 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
GammaDataType,
BetaDataType,
YDataType,
AccDataType,
AccElementwiseOperation,
ConputeDataType,
YElementwiseOperation,
GridDesc_M_K,
BlockSize,
MThreadClusterSize,
......@@ -220,7 +220,7 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
const std::vector<index_t> betaStrides,
const std::vector<index_t> yStrides,
const std::vector<index_t> reduceDims,
AccElementwiseOperation acc_elementwise_op,
YElementwiseOperation y_elementwise_op,
double epsilon,
const XDataType* p_x,
const GammaDataType* p_gamma,
......@@ -230,9 +230,9 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
p_gamma_(p_gamma),
p_beta_(p_beta),
p_y_(p_y),
acc_elementwise_op_(acc_elementwise_op)
y_elementwise_op_(y_elementwise_op)
{
epsilon_ = static_cast<AccDataType>(epsilon);
epsilon_ = static_cast<ConputeDataType>(epsilon);
Lengths_ = shuffle_tensor_dimensions<Rank, NumReduceDim>(lengths, reduceDims);
xStrides_ = shuffle_tensor_dimensions<Rank, NumReduceDim>(xStrides, reduceDims);
......@@ -265,7 +265,7 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
x_grid_desc_m_k_.GetLength(Number<1>{}) <= KThreadClusterSize * KThreadSliceSize;
}
AccDataType epsilon_;
ConputeDataType epsilon_;
const XDataType* p_x_;
const GammaDataType* p_gamma_;
......@@ -278,7 +278,7 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
std::vector<index_t> betaStrides_;
std::vector<index_t> yStrides_;
AccElementwiseOperation acc_elementwise_op_;
YElementwiseOperation y_elementwise_op_;
int blkGroupSize_;
int numBlockTileIteration_;
......@@ -301,16 +301,16 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
GammaDataType,
BetaDataType,
YDataType,
AccDataType,
AccElementwiseOperation,
ConputeDataType,
YElementwiseOperation,
GridDesc_M_K>
: kernel_normalization<GridwiseReduceLayernormGeneric,
XDataType,
GammaDataType,
BetaDataType,
YDataType,
AccDataType,
AccElementwiseOperation,
ConputeDataType,
YElementwiseOperation,
GridDesc_M_K>;
float avg_time = 0;
......@@ -329,7 +329,7 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
arg.p_gamma_,
arg.p_beta_,
arg.p_y_,
arg.acc_elementwise_op_);
arg.y_elementwise_op_);
return (avg_time);
};
......@@ -429,7 +429,7 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
void* p_y,
void* p_saveMean,
void* p_saveInvVar,
AccElementwiseOperation acc_elementwise_op) override
YElementwiseOperation y_elementwise_op) override
{
// TODO
// Optional cache of the intermediate results (mean and InvVariance) during the
......@@ -443,7 +443,7 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
betaStrides,
yStrides,
reduceDims,
acc_elementwise_op,
y_elementwise_op,
epsilon,
static_cast<const XDataType*>(p_x),
static_cast<const GammaDataType*>(p_gamma),
......
include/ck/tensor_operation/gpu/grid/gridwise_normalization_welford_variance.hpp
View file @ 9d2280d6
......@@ -16,8 +16,8 @@ template <typename XDataType,
typename GammaDataType,
typename BetaDataType,
typename YDataType,
typename AccDataType,
typename AccElementwiseOperation,
typename ComputeDataType,
typename YElementwiseOperation,
typename GridDesc_M_K,
index_t BlockSize,
index_t MThreadClusterSize,
......@@ -70,9 +70,9 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
decltype(make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{})));
using ThreadwiseWelford =
ThreadwiseWelford<AccDataType, ThreadReduceSrcDesc_M_K, ThreadReduceDstDesc_M>;
ThreadwiseWelford<ComputeDataType, ThreadReduceSrcDesc_M_K, ThreadReduceDstDesc_M>;
using BlockwiseWelford = BlockwiseWelford<AccDataType,
using BlockwiseWelford = BlockwiseWelford<ComputeDataType,
BlockSize,
ThreadClusterLengths_M_K,
ThreadClusterArrangeOrder>;
......@@ -115,12 +115,12 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
const GridDesc_M_K& beta_grid_desc_m_k,
const GridDesc_M_K& y_grid_desc_m_k,
index_t num_k_block_tile_iteration,
AccDataType epsilon,
ComputeDataType epsilon,
const XDataType* const __restrict__ p_x_global,
const GammaDataType* const __restrict__ p_gamma_global,
const BetaDataType* const __restrict__ p_beta_global,
YDataType* const __restrict__ p_y_global,
const AccElementwiseOperation acc_elementwise_op)
const YElementwiseOperation y_elementwise_op)
{
if constexpr(SweepOnce)
{
......@@ -133,7 +133,7 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
auto x_thread_buf = generate_tuple(
[&](auto) {
return StaticBuffer<AddressSpaceEnum::Vgpr,
AccDataType,
ComputeDataType,
MThreadSliceSize * XSrcVectorSize,
true>{};
},
......@@ -142,7 +142,7 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
auto gamma_thread_buf = generate_tuple(
[&](auto) {
return StaticBuffer<AddressSpaceEnum::Vgpr,
AccDataType,
ComputeDataType,
MThreadSliceSize * GammaSrcVectorSize,
true>{};
},
......@@ -151,7 +151,7 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
auto beta_thread_buf = generate_tuple(
[&](auto) {
return StaticBuffer<AddressSpaceEnum::Vgpr,
AccDataType,
ComputeDataType,
MThreadSliceSize * BetaSrcVectorSize,
true>{};
},
......@@ -160,14 +160,16 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
auto y_thread_buf = generate_tuple(
[&](auto) {
return StaticBuffer<AddressSpaceEnum::Vgpr,
AccDataType,
ComputeDataType,
MThreadSliceSize * YDstVectorSize,
true>{};
},
Number<ThreadBufferNumber>{});
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true> mean_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true> var_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, ComputeDataType, MThreadSliceSize, true>
mean_thread_buf;
StaticBuffer<AddressSpaceEnum::Vgpr, ComputeDataType, MThreadSliceSize, true>
var_thread_buf;
const index_t thread_local_id = get_thread_local_1d_id();
const index_t block_global_id = get_block_1d_id();
......@@ -179,7 +181,7 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
const auto thread_k_cluster_id = thread_cluster_idx[I1];
auto threadwise_x_load = ThreadwiseTensorSliceTransfer_v2<XDataType,
AccDataType,
ComputeDataType,
GridDesc_M_K,
decltype(thread_buffer_desc_m_k),
ThreadBufferLengths_M_K,
......@@ -195,7 +197,7 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
auto threadwise_gamma_load =
ThreadwiseTensorSliceTransfer_v2<GammaDataType,
AccDataType,
ComputeDataType,
GridDesc_M_K,
decltype(thread_buffer_desc_m_k),
ThreadBufferLengths_M_K,
......@@ -211,7 +213,7 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
auto threadwise_beta_load =
ThreadwiseTensorSliceTransfer_v2<BetaDataType,
AccDataType,
ComputeDataType,
GridDesc_M_K,
decltype(thread_buffer_desc_m_k),
ThreadBufferLengths_M_K,
......@@ -226,11 +228,11 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
thread_k_cluster_id * BetaSrcVectorSize));
auto threadwise_y_store =
ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
ThreadwiseTensorSliceTransfer_v1r3<ComputeDataType,
YDataType,
decltype(thread_buffer_desc_m_k),
GridDesc_M_K,
AccElementwiseOperation,
YElementwiseOperation,
ThreadBufferLengths_M_K,
ThreadBufferDimAccessOrder,
YDstVectorDim,
......@@ -242,7 +244,7 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
make_multi_index(block_global_id * M_BlockTileSize +
thread_m_cluster_id * MThreadSliceSize,
thread_k_cluster_id * YDstVectorSize),
acc_elementwise_op);
y_elementwise_op);
constexpr auto thread_copy_fwd_step_m_k = make_multi_index(0, K_BlockTileStepSize);
constexpr auto thread_copy_bwd_step_m_k =
......@@ -261,8 +263,8 @@ struct GridwiseNormalizationWelfordVariance_mk_to_mk
threadwise_welford.max_count_ = GetKPerThread(x_grid_desc_m_k, thread_k_cluster_id);
static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
mean_thread_buf(I) = type_convert<AccDataType>(0.0f);
var_thread_buf(I) = type_convert<AccDataType>(0.0f);
mean_thread_buf(I) = type_convert<ComputeDataType>(0.0f);
var_thread_buf(I) = type_convert<ComputeDataType>(0.0f);
});
// Separate sweep once and sweep twice pipeline
......
library/src/tensor_operation_instance/gpu/normalization/device_normalization_f16_instance.cpp
View file @ 9d2280d6
......@@ -21,7 +21,7 @@ template <typename OutElementwise, index_t Rank, index_t Reduce>
// clang-format off
using device_normalization_f16_instances =
std::tuple <
// XDataType, GammaDataType, BetaDataType, AccDataType, YDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorDim, GammaSrcVectorSize, BetaSrcVectorDim, BetaSrcVectorSize, YDstVectorSize>
// XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorDim, GammaSrcVectorSize, BetaSrcVectorDim, BetaSrcVectorSize, YDstVectorSize>
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, OutElementwise, Rank, Reduce, 256, 1, 256, 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
......
library/src/tensor_operation_instance/gpu/normalization/device_normalization_f32_instance.cpp
View file @ 9d2280d6
......@@ -19,7 +19,7 @@ using Pass = ck::tensor_operation::element_wise::PassThrough;
template <typename OutElementwise, index_t Rank, index_t Reduce>
using device_layernorm_f32_instances = std::tuple<
// clang-format off
// XDataType, GammaDataType, BetaDataType, AccDataType, YDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorSize, BetaSrcVectorSize, YDstVectorSize>
// XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorSize, BetaSrcVectorSize, YDstVectorSize>
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, OutElementwise, Rank, Reduce, 256, 1, 256, 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
......
profiler/include/profiler/profile_layernorm_impl.hpp
View file @ 9d2280d6
......@@ -19,7 +19,7 @@ namespace profiler {
template <typename XDataType,
typename GammaDataType,
typename BetaDataType,
typename AccDataType,
typename ComputeDataType,
typename YDataType,
index_t Rank>
bool profile_layernorm_impl(int do_verification,
......@@ -86,7 +86,7 @@ bool profile_layernorm_impl(int do_verification,
using DeviceOp = ck::tensor_operation::device::DeviceNormalization<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
ComputeDataType,
YDataType,
PassThrough,
Rank,
......@@ -109,7 +109,7 @@ bool profile_layernorm_impl(int do_verification,
GammaDataType,
BetaDataType,
YDataType,
AccDataType,
ComputeDataType,
PassThrough,
Rank,
NumReduceDim>;
......
test/normalization/test_groupnorm_fp16.cpp
View file @ 9d2280d6
......@@ -12,11 +12,11 @@ template <typename Tuple>
class TestGroupnorm : public ::testing::Test
{
protected:
using XDataType = std::tuple_element_t<0, Tuple>;
using GammaDataType = std::tuple_element_t<1, Tuple>;
using BetaDataType = std::tuple_element_t<2, Tuple>;
using AccDataType = std::tuple_element_t<3, Tuple>;
using YDataType = std::tuple_element_t<4, Tuple>;
using XDataType = std::tuple_element_t<0, Tuple>;
using GammaDataType = std::tuple_element_t<1, Tuple>;
using BetaDataType = std::tuple_element_t<2, Tuple>;
using ComputeDataType = std::tuple_element_t<3, Tuple>;
using YDataType = std::tuple_element_t<4, Tuple>;
void Run()
{
......@@ -36,7 +36,7 @@ class TestGroupnorm : public ::testing::Test
ck::profiler::profile_groupnorm_impl<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
ComputeDataType,
YDataType>(true, 2, false, false, length);
EXPECT_TRUE(success);
}
......@@ -44,7 +44,7 @@ class TestGroupnorm : public ::testing::Test
};
using KernelTypes = ::testing::Types<
// XDataType, GammaDataType, BetaDataType, AccDataType, YDataType>
// XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType>
std::tuple<F16, F16, F16, F32, F16>>;
TYPED_TEST_SUITE(TestGroupnorm, KernelTypes);
......
test/normalization/test_groupnorm_fp32.cpp
View file @ 9d2280d6
......@@ -12,11 +12,11 @@ template <typename Tuple>
class TestGroupnorm : public ::testing::Test
{
protected:
using XDataType = std::tuple_element_t<0, Tuple>;
using GammaDataType = std::tuple_element_t<1, Tuple>;
using BetaDataType = std::tuple_element_t<2, Tuple>;
using AccDataType = std::tuple_element_t<3, Tuple>;
using YDataType = std::tuple_element_t<4, Tuple>;
using XDataType = std::tuple_element_t<0, Tuple>;
using GammaDataType = std::tuple_element_t<1, Tuple>;
using BetaDataType = std::tuple_element_t<2, Tuple>;
using ComputeDataType = std::tuple_element_t<3, Tuple>;
using YDataType = std::tuple_element_t<4, Tuple>;
void Run()
{
......@@ -34,7 +34,7 @@ class TestGroupnorm : public ::testing::Test
ck::profiler::profile_groupnorm_impl<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
ComputeDataType,
YDataType>(true, 2, false, false, length);
EXPECT_TRUE(success);
}
......@@ -42,7 +42,7 @@ class TestGroupnorm : public ::testing::Test
};
using KernelTypes = ::testing::Types<
// XDataType, GammaDataType, BetaDataType, AccDataType, YDataType>
// XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType>
std::tuple<F32, F32, F32, F32, F32>>;
TYPED_TEST_SUITE(TestGroupnorm, KernelTypes);
......
test/normalization/test_layernorm2d_fp16.cpp
View file @ 9d2280d6
......@@ -12,11 +12,11 @@ template <typename Tuple>
class TestLayernorm2d : public ::testing::Test
{
protected:
using XDataType = std::tuple_element_t<0, Tuple>;
using GammaDataType = std::tuple_element_t<1, Tuple>;
using BetaDataType = std::tuple_element_t<2, Tuple>;
using AccDataType = std::tuple_element_t<3, Tuple>;
using YDataType = std::tuple_element_t<4, Tuple>;
using XDataType = std::tuple_element_t<0, Tuple>;
using GammaDataType = std::tuple_element_t<1, Tuple>;
using BetaDataType = std::tuple_element_t<2, Tuple>;
using ComputeDataType = std::tuple_element_t<3, Tuple>;
using YDataType = std::tuple_element_t<4, Tuple>;
void Run()
{
......@@ -29,7 +29,7 @@ class TestLayernorm2d : public ::testing::Test
bool success = ck::profiler::profile_layernorm_impl<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
ComputeDataType,
YDataType,
2>(true, 2, false, false, length);
EXPECT_TRUE(success);
......@@ -38,7 +38,7 @@ class TestLayernorm2d : public ::testing::Test
};
using KernelTypes = ::testing::Types<
// XDataType, GammaDataType, BetaDataType, AccDataType, YDataType>
// XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType>
std::tuple<F16, F16, F16, F32, F16>>;
TYPED_TEST_SUITE(TestLayernorm2d, KernelTypes);
......
test/normalization/test_layernorm2d_fp32.cpp
View file @ 9d2280d6
......@@ -12,11 +12,11 @@ template <typename Tuple>
class TestLayernorm2d : public ::testing::Test
{
protected:
using XDataType = std::tuple_element_t<0, Tuple>;
using GammaDataType = std::tuple_element_t<1, Tuple>;
using BetaDataType = std::tuple_element_t<2, Tuple>;
using AccDataType = std::tuple_element_t<3, Tuple>;
using YDataType = std::tuple_element_t<4, Tuple>;
using XDataType = std::tuple_element_t<0, Tuple>;
using GammaDataType = std::tuple_element_t<1, Tuple>;
using BetaDataType = std::tuple_element_t<2, Tuple>;
using ComputeDataType = std::tuple_element_t<3, Tuple>;
using YDataType = std::tuple_element_t<4, Tuple>;
void Run()
{
......@@ -29,7 +29,7 @@ class TestLayernorm2d : public ::testing::Test
bool success = ck::profiler::profile_layernorm_impl<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
ComputeDataType,
YDataType,
2>(true, 2, false, false, length);
EXPECT_TRUE(success);
......@@ -38,7 +38,7 @@ class TestLayernorm2d : public ::testing::Test
};
using KernelTypes = ::testing::Types<
// XDataType, GammaDataType, BetaDataType, AccDataType, YDataType>
// XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType>
std::tuple<F32, F32, F32, F32, F32>>;
TYPED_TEST_SUITE(TestLayernorm2d, KernelTypes);
......
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