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[Ck tile] support rmsnorm and related fusion (#1605)

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* Add reduce2d new api

* Prevent user use cross warp reduction

* Fix bug of std caculation

* Add rmsnorm2d

* Add rmsnorm small example

* Remove static assert to prevent compile fail

* Add script to test performance and correctness

* Add missing cmake change

* refine naming

* refine example of rmsnorm

* Fix bug of rmsnorm

* Refine naming

* Fix cmake

* clang format

* Refine pipeline name

* Add add_rmsnorm2d_rdquant kernel

* Add reduce op

* host verification

* Fix bug of one pass pipeline

* Refine tile size

* Add two pass pipeline

* Rename two pass to three pass

* Fix bug of kSaveX == false

* Add instance library

* Add test script

* Fix bug of x verification

* Add save_x to trait

* Add README

* Move reduce2d into reduce folder

* Fix bug of welford when number of m warp > 1

* remove reduncant comment

* 1. move 06_rmsnorm2d to 10_rmsnorm2d
2. move 07_add_rmsnorm2d_rdquant to 11_add_rmsnorm2d_rdquant

* clang format and add missing header

* Add host validation of add + layernorm2d + rsquant

* Revert "Add host validation of add + layernorm2d + rsquant"

This reverts commit 936cb45797.

* Remove deprecated flag
This commit is contained in:
rocking
2024-10-30 15:22:56 +08:00
committed by GitHub
parent 8632221814
commit 3d60953477
90 changed files with 4674 additions and 128 deletions
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17
include/ck_tile/host/reference/reference_reduce.hpp
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@@ -9,24 +9,25 @@
namespace ck_tile {
template <typename ADataType, typename AccDataType, typename BDataType>
CK_TILE_HOST void reference_reduce(const HostTensor<ADataType>& a_m_n, HostTensor<BDataType>& b_m)
template <typename XDataType, typename ComputeDataType, typename YDataType, typename ReduceOp>
CK_TILE_HOST void
reference_reduce(const HostTensor<XDataType>& x_m_n, HostTensor<YDataType>& y_m, ReduceOp reduce_op)
{
auto f = [&](auto m) {
const int N = a_m_n.mDesc.get_lengths()[1];
const int N = x_m_n.mDesc.get_lengths()[1];
AccDataType v_acc = 0;
ComputeDataType v_acc = reduce_op.template GetIdentityValue<ComputeDataType>();
for(int n = 0; n < N; ++n)
{
const ADataType v_a = a_m_n(m, n);
const ComputeDataType v_a = type_convert<ComputeDataType>(x_m_n(m, n));
v_acc += v_a;
v_acc = reduce_op(v_acc, v_a);
}
b_m(m) = ck_tile::type_convert<BDataType>(v_acc);
y_m(m) = ck_tile::type_convert<YDataType>(v_acc);
};
make_ParallelTensorFunctor(f, b_m.mDesc.get_lengths()[0])(std::thread::hardware_concurrency());
make_ParallelTensorFunctor(f, y_m.mDesc.get_lengths()[0])(std::thread::hardware_concurrency());
}
} // namespace ck_tile