mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-03 21:21:22 +00:00
Improve normalization (#580)
* Sync the order of type string with template parameter * Add more instances * Check the vector size and remove redundant var * Extract var to static, prepare to separate sweep once kernel * Separate sweeponce flow and optimize the flow * 1. Rename AccDatatype in normalization to computeData 2. Rename AccElementwiseOperation to YElementwiseOperation in normalization * Remove useless code * Update naive variance kernel * Refine string * Fix typo * Support naive variance for device_normalization * Check the blocksize * Share the VGPR of x and y * Share the VGPR of gamma and beta * Add more instances * Support fp16 sqrt for experiment * Add CHANGELOG * Fix typo * clang-format
This commit is contained in:
rocking5566
@@ -14,9 +14,9 @@ namespace device {
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template <typename XDataType,
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typename GammaDataType,
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typename BetaDataType,
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typename AccDataType,
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typename ComputeDataType,
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typename YDataType,
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typename AccElementwiseOperation,
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typename YElementwiseOperation,
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index_t Rank,
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index_t NumReduceDim>
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struct DeviceNormalization : public BaseOperator
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@@ -35,7 +35,7 @@ struct DeviceNormalization : public BaseOperator
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void* p_y,
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void* p_savedMean,
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void* p_savedInvVar,
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AccElementwiseOperation acc_elementwise_op) = 0;
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YElementwiseOperation y_elementwise_op) = 0;
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virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
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};
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@@ -43,17 +43,17 @@ struct DeviceNormalization : public BaseOperator
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template <typename XDataType,
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typename GammaDataType,
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typename BetaDataType,
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typename AccDataType,
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typename ComputeDataType,
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typename YDataType,
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typename AccElementwiseOperation,
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typename YElementwiseOperation,
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index_t Rank,
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index_t NumReduceDim>
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using DeviceNormalizationPtr = std::unique_ptr<DeviceNormalization<XDataType,
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GammaDataType,
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BetaDataType,
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AccDataType,
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ComputeDataType,
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YDataType,
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AccElementwiseOperation,
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YElementwiseOperation,
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Rank,
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NumReduceDim>>;
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@@ -10,46 +10,11 @@
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#include "ck/tensor_operation/gpu/device/device_normalization.hpp"
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#include "ck/tensor_operation/gpu/device/device_reduce.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_reduce_common.hpp"
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#include "ck/tensor_operation/gpu/grid/gridwise_normalization_welford_variance.hpp"
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#include "ck/tensor_operation/gpu/grid/gridwise_normalization_selector.hpp"
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#include "ck/tensor_operation/gpu/grid/gridwise_set_buffer_value.hpp"
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#include "ck/host_utility/device_prop.hpp"
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#include "ck/host_utility/kernel_launch.hpp"
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namespace ck {
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template <typename GridwiseReduction,
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typename XDataType,
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typename GammaDataType,
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typename BetaDataType,
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typename YDataType,
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typename AccDataType,
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typename AccElementwiseOperation,
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typename GridDesc_M_K>
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__global__ void kernel_normalization(const GridDesc_M_K x_grid_desc_m_k,
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const GridDesc_M_K gamma_grid_desc_m_k,
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const GridDesc_M_K beta_grid_desc_m_k,
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const GridDesc_M_K y_grid_desc_m_k,
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index_t num_k_block_tile_iteration,
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AccDataType epsilon,
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const XDataType* const __restrict__ p_x_global,
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const GammaDataType* const __restrict__ p_gamma_global,
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const BetaDataType* const __restrict__ p_beta_global,
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YDataType* const __restrict__ p_y_global,
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const AccElementwiseOperation acc_elementwise_op)
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{
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GridwiseReduction::Run(x_grid_desc_m_k,
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gamma_grid_desc_m_k,
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beta_grid_desc_m_k,
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y_grid_desc_m_k,
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num_k_block_tile_iteration,
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epsilon,
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p_x_global,
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p_gamma_global,
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p_beta_global,
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p_y_global,
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acc_elementwise_op);
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};
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} // namespace ck
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namespace ck {
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namespace tensor_operation {
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namespace device {
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@@ -58,9 +23,9 @@ namespace device {
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template <typename XDataType,
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typename GammaDataType,
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typename BetaDataType,
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typename AccDataType,
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typename ComputeDataType,
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typename YDataType,
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typename AccElementwiseOperation,
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typename YElementwiseOperation,
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index_t Rank,
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index_t NumReduceDim,
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index_t BlockSize,
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@@ -74,16 +39,18 @@ template <typename XDataType,
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index_t GammaSrcVectorSize,
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index_t BetaSrcVectorDim,
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index_t BetaSrcVectorSize,
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index_t YDstVectorSize>
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index_t YDstVectorSize,
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bool UseWelford = true>
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struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
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GammaDataType,
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BetaDataType,
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AccDataType,
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ComputeDataType,
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YDataType,
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AccElementwiseOperation,
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YElementwiseOperation,
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Rank,
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NumReduceDim>
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{
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static_assert(BlockSize == MThreadClusterSize * KThreadClusterSize);
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static_assert(
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((GammaSrcVectorDim == 0 && MThreadSliceSize % GammaSrcVectorSize == 0) ||
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(GammaSrcVectorDim == 1 && KThreadSliceSize % GammaSrcVectorSize == 0)),
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@@ -167,51 +134,6 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
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using GridDesc_M_K = decltype(MakeSrc2dDescriptor({1}, {1}, 1, 1));
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using GridwiseReduceLayernormGeneric =
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GridwiseNormalizationWelfordVariance_mk_to_mk<XDataType,
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GammaDataType,
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BetaDataType,
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YDataType,
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AccDataType,
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AccElementwiseOperation,
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GridDesc_M_K,
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BlockSize,
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MThreadClusterSize,
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KThreadClusterSize,
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MThreadSliceSize,
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KThreadSliceSize,
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XYSrcVectorDim,
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XSrcVectorSize,
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GammaSrcVectorDim,
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GammaSrcVectorSize,
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BetaSrcVectorDim,
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BetaSrcVectorSize,
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XYSrcVectorDim,
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YDstVectorSize,
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false>;
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using GridwiseNormalizationSweepOnce =
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GridwiseNormalizationWelfordVariance_mk_to_mk<XDataType,
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GammaDataType,
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BetaDataType,
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YDataType,
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AccDataType,
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AccElementwiseOperation,
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GridDesc_M_K,
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BlockSize,
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MThreadClusterSize,
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KThreadClusterSize,
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MThreadSliceSize,
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KThreadSliceSize,
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XYSrcVectorDim,
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XSrcVectorSize,
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GammaSrcVectorDim,
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GammaSrcVectorSize,
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BetaSrcVectorDim,
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BetaSrcVectorSize,
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XYSrcVectorDim,
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YDstVectorSize,
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true>;
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struct Argument : public BaseArgument
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{
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Argument(const std::vector<index_t> lengths,
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@@ -220,7 +142,7 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
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const std::vector<index_t> betaStrides,
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const std::vector<index_t> yStrides,
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const std::vector<index_t> reduceDims,
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AccElementwiseOperation acc_elementwise_op,
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YElementwiseOperation y_elementwise_op,
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double epsilon,
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const XDataType* p_x,
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const GammaDataType* p_gamma,
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@@ -230,9 +152,9 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
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p_gamma_(p_gamma),
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p_beta_(p_beta),
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p_y_(p_y),
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acc_elementwise_op_(acc_elementwise_op)
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y_elementwise_op_(y_elementwise_op)
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{
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epsilon_ = static_cast<AccDataType>(epsilon);
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epsilon_ = static_cast<ComputeDataType>(epsilon);
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Lengths_ = shuffle_tensor_dimensions<Rank, NumReduceDim>(lengths, reduceDims);
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xStrides_ = shuffle_tensor_dimensions<Rank, NumReduceDim>(xStrides, reduceDims);
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@@ -265,7 +187,7 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
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x_grid_desc_m_k_.GetLength(Number<1>{}) <= KThreadClusterSize * KThreadSliceSize;
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}
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AccDataType epsilon_;
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ComputeDataType epsilon_;
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const XDataType* p_x_;
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const GammaDataType* p_gamma_;
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@@ -278,7 +200,7 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
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std::vector<index_t> betaStrides_;
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std::vector<index_t> yStrides_;
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AccElementwiseOperation acc_elementwise_op_;
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YElementwiseOperation y_elementwise_op_;
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int blkGroupSize_;
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int numBlockTileIteration_;
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@@ -295,23 +217,27 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
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{
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float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
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{
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const auto kernel_main = arg.isSweeponce_
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? kernel_normalization<GridwiseNormalizationSweepOnce,
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XDataType,
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GammaDataType,
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BetaDataType,
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YDataType,
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AccDataType,
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AccElementwiseOperation,
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GridDesc_M_K>
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: kernel_normalization<GridwiseReduceLayernormGeneric,
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XDataType,
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GammaDataType,
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BetaDataType,
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YDataType,
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AccDataType,
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AccElementwiseOperation,
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GridDesc_M_K>;
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auto kernel_main = NormalizationKernelSelector<XDataType,
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GammaDataType,
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BetaDataType,
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YDataType,
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ComputeDataType,
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YElementwiseOperation,
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GridDesc_M_K,
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BlockSize,
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MThreadClusterSize,
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KThreadClusterSize,
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MThreadSliceSize,
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KThreadSliceSize,
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XYSrcVectorDim,
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XSrcVectorSize,
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GammaSrcVectorDim,
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GammaSrcVectorSize,
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BetaSrcVectorDim,
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BetaSrcVectorSize,
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XYSrcVectorDim,
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YDstVectorSize,
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UseWelford>(arg.isSweeponce_);
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float avg_time = 0;
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avg_time += launch_and_time_kernel(stream_config,
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@@ -329,7 +255,7 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
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arg.p_gamma_,
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arg.p_beta_,
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arg.p_y_,
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arg.acc_elementwise_op_);
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arg.y_elementwise_op_);
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return (avg_time);
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};
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@@ -429,7 +355,7 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
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void* p_y,
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void* p_saveMean,
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void* p_saveInvVar,
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AccElementwiseOperation acc_elementwise_op) override
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YElementwiseOperation y_elementwise_op) override
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{
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// TODO
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// Optional cache of the intermediate results (mean and InvVariance) during the
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@@ -443,7 +369,7 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
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betaStrides,
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yStrides,
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reduceDims,
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acc_elementwise_op,
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y_elementwise_op,
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epsilon,
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static_cast<const XDataType*>(p_x),
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static_cast<const GammaDataType*>(p_gamma),
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@@ -462,8 +388,8 @@ struct DeviceNormalizationImpl : public DeviceNormalization<XDataType,
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// clang-format off
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str << "DeviceNormalizationImpl<" << BlockSize << ",";
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str << "M_C" << MThreadClusterSize << "_S" << MThreadSliceSize << ",";
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str << "K_C" << KThreadClusterSize << "_S" << KThreadSliceSize << ",";
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str << "Cluster_MK_" << MThreadClusterSize << "_" << KThreadClusterSize << ",";
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str << "Slice_MK_" << MThreadSliceSize << "_" << KThreadSliceSize << ",";
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str << "XYSrcVectorDim_" << XYSrcVectorDim << ",";
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str << "VectorSize_X" << XSrcVectorSize << "_Gamma" << GammaSrcVectorSize << "_Beta" << BetaSrcVectorSize << "_Y" << YDstVectorSize << ">";
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// clang-format on
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