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composable_kernel/include/ck_tile/host/reference/reference_batched_softmax.hpp

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2.3 KiB
C++

// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#pragma once
#include "ck_tile/core.hpp"
#include "ck_tile/host/host_tensor.hpp"
#include <thread>
namespace ck_tile {
template <typename ADataType,
typename CompDataType,
typename BDataType,
typename CompElementOp = ck_tile::identity>
CK_TILE_HOST void reference_batched_softmax(
const HostTensor<ADataType>& a_b_m_n,
HostTensor<BDataType>& b_b_m_n,
const CompElementOp& comp_element_op = {},
std::optional<std::reference_wrapper<HostTensor<CompDataType>>> lse_b_m = std::nullopt)
{
const int N = a_b_m_n.mDesc.get_lengths()[2];
auto f = [&](auto batch, auto m) {
CompDataType v_max = -ck_tile::numeric<CompDataType>::infinity();
// max
for(int n = 0; n < N; ++n)
{
const CompDataType v_a = ck_tile::type_convert<CompDataType>(a_b_m_n(batch, m, n));
v_max = v_max < v_a ? v_a : v_max;
}
CompDataType v_exp_sum = 0;
// validate v_max if all the elements within a row are -INF
if(std::isinf(v_max) && v_max < 0)
{
v_max = ck_tile::type_convert<CompDataType>(0.f);
}
// sum
for(int n = 0; n < N; ++n)
{
const CompDataType v_a = ck_tile::type_convert<CompDataType>(a_b_m_n(batch, m, n));
v_exp_sum += ck_tile::exp(v_a - v_max);
}
// if sum is zero(masked), or nan/inf(other computation error), don't do divide
CompDataType inv_sum = (v_exp_sum == 0.f ? 1.f : 1.f / v_exp_sum);
// elementwise
for(int n = 0; n < N; ++n)
{
const CompDataType v_a = ck_tile::type_convert<CompDataType>(a_b_m_n(batch, m, n));
const CompDataType v_b = ck_tile::exp(v_a - v_max) * inv_sum;
b_b_m_n(batch, m, n) = ck_tile::type_convert<BDataType>(comp_element_op(v_b));
}
// lse
if(lse_b_m)
{
lse_b_m->get()(batch, m) = v_max + ck_tile::log(v_exp_sum);
}
};
make_ParallelTensorFunctor(f, b_b_m_n.mDesc.get_lengths()[0], b_b_m_n.mDesc.get_lengths()[1])(
std::thread::hardware_concurrency());
}
} // namespace ck_tile