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// Copyright 2024 Google LLC
// SPDX-License-Identifier: Apache-2.0
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// https://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// Include guard for non-SIMD code.
#ifndef THIRD_PARTY_GEMMA_CPP_GEMMA_OPS_H_
#define THIRD_PARTY_GEMMA_CPP_GEMMA_OPS_H_
#include <stddef.h>
#include <stdint.h>
#include <array>
#include <cmath>
#include <cstdio>
#include <random>
#include <type_traits> // std::enable_if_t
#include "compression/sfp.h"
#include "hwy/base.h"
#include "hwy/contrib/thread_pool/thread_pool.h"
#include "hwy/profiler.h"
namespace gcpp {
// __builtin_sqrt is not constexpr as of Clang 17.
#if HWY_COMPILER_GCC_ACTUAL
#define GEMMA_CONSTEXPR_SQRT constexpr
static GEMMA_CONSTEXPR_SQRT HWY_INLINE float Sqrt(float x) {
return __builtin_sqrt(x);
}
#else
#define GEMMA_CONSTEXPR_SQRT
static GEMMA_CONSTEXPR_SQRT HWY_INLINE float Sqrt(float x) { return sqrtf(x); }
#endif
} // namespace gcpp
#endif // THIRD_PARTY_GEMMA_CPP_GEMMA_OPS_H_
// Include guard for (potentially) SIMD code.
#if defined(THIRD_PARTY_GEMMA_CPP_OPS_TOGGLE) == defined(HWY_TARGET_TOGGLE)
#ifdef THIRD_PARTY_GEMMA_CPP_OPS_TOGGLE
#undef THIRD_PARTY_GEMMA_CPP_OPS_TOGGLE
#else
#define THIRD_PARTY_GEMMA_CPP_OPS_TOGGLE
#endif
#include "compression/compress-inl.h"
#include "hwy/contrib/algo/transform-inl.h"
#include "hwy/contrib/dot/dot-inl.h"
#include "hwy/contrib/math/math-inl.h"
#include "hwy/contrib/matvec/matvec-inl.h"
HWY_BEFORE_NAMESPACE();
namespace gcpp {
namespace HWY_NAMESPACE {
namespace hn = hwy::HWY_NAMESPACE;
HWY_INLINE constexpr size_t MaxCols() {
// Vec + mat rows should fit into 32 KiB L1.
return 2048;
}
template <typename To, typename From>
HWY_INLINE constexpr std::enable_if_t<
std::is_arithmetic_v<To> && std::is_arithmetic_v<From>, To>
StaticCast(From from) noexcept {
if constexpr (std::is_unsigned_v<From> && std::is_floating_point_v<To>)
return static_cast<To>(
static_cast<hwy::SignedFromSize<sizeof(From)>>(from));
else
return static_cast<To>(from);
}
template <size_t kOuter>
HWY_INLINE constexpr size_t RowsPerStrip() {
// Aim for 128 work items to reduce pool overhead. Must be at least one
// vector; prefer a power of two for faster division.
constexpr size_t kLanes = hn::ScalableTag<float>().MaxLanes();
constexpr size_t kRowsPerStrip =
kOuter < 128 ? kLanes
: HWY_MAX(kLanes, 1ULL << hwy::FloorLog2(kOuter / 128));
return kRowsPerStrip;
}
// Processes a single 4x4 tile of A x B. Shared between static and dynamic
// versions.
template <typename MatT, size_t kColsA>
HWY_INLINE void GEMM_4x4_Tile(const MatT* HWY_RESTRICT A,
const MatT* HWY_RESTRICT B, MatT* HWY_RESTRICT C,
size_t tile_num, const int xtiles, const int lda,
const int ldb, const int ldc) {
constexpr int RM = 4;
constexpr int RN = 4;
// Calculate chunk start coords.
int ii = tile_num / xtiles * RM;
int jj = tile_num % xtiles * RN;
const hn::ScalableTag<MatT> d;
const size_t N = Lanes(d);
using V = hn::Vec<decltype(d)>;
V c00 = hn::Zero(d);
V c01 = hn::Zero(d);
V c02 = hn::Zero(d);
V c03 = hn::Zero(d);
V c10 = hn::Zero(d);
V c11 = hn::Zero(d);
V c12 = hn::Zero(d);
V c13 = hn::Zero(d);
V c20 = hn::Zero(d);
V c21 = hn::Zero(d);
V c22 = hn::Zero(d);
V c23 = hn::Zero(d);
V c30 = hn::Zero(d);
V c31 = hn::Zero(d);
V c32 = hn::Zero(d);
V c33 = hn::Zero(d);
// Steps down the rows of A and B, and across width (kN) in steps of
// N (Lanes()). Accumulates into the cache vectors. hn::ReduceSum() is
// called on each of the cache vectors to sum the partial sums into C.
for (size_t l = 0; l < kColsA; l += N) {
V k0 = hn::LoadU(d, B + ldb * (jj + 0) + l);
V k1 = hn::LoadU(d, B + ldb * (jj + 1) + l);
V k2 = hn::LoadU(d, B + ldb * (jj + 2) + l);
V k3 = hn::LoadU(d, B + ldb * (jj + 3) + l);
V a0 = hn::LoadU(d, A + lda * (ii + 0) + l);
c00 = hn::MulAdd(a0, k0, c00);
c01 = hn::MulAdd(a0, k1, c01);
c02 = hn::MulAdd(a0, k2, c02);
c03 = hn::MulAdd(a0, k3, c03);
V a1 = hn::LoadU(d, A + lda * (ii + 1) + l);
c10 = hn::MulAdd(a1, k0, c10);
c11 = hn::MulAdd(a1, k1, c11);
c12 = hn::MulAdd(a1, k2, c12);
c13 = hn::MulAdd(a1, k3, c13);
V a2 = hn::LoadU(d, A + lda * (ii + 2) + l);
c20 = hn::MulAdd(a2, k0, c20);
c21 = hn::MulAdd(a2, k1, c21);
c22 = hn::MulAdd(a2, k2, c22);
c23 = hn::MulAdd(a2, k3, c23);
V a3 = hn::LoadU(d, A + lda * (ii + 3) + l);
c30 = hn::MulAdd(a3, k0, c30);
c31 = hn::MulAdd(a3, k1, c31);
c32 = hn::MulAdd(a3, k2, c32);
c33 = hn::MulAdd(a3, k3, c33);
}
C[ldc * (ii + 0) + (jj + 0)] = hn::ReduceSum(d, c00);
C[ldc * (ii + 0) + (jj + 1)] = hn::ReduceSum(d, c01);
C[ldc * (ii + 0) + (jj + 2)] = hn::ReduceSum(d, c02);
C[ldc * (ii + 0) + (jj + 3)] = hn::ReduceSum(d, c03);
C[ldc * (ii + 1) + (jj + 0)] = hn::ReduceSum(d, c10);
C[ldc * (ii + 1) + (jj + 1)] = hn::ReduceSum(d, c11);
C[ldc * (ii + 1) + (jj + 2)] = hn::ReduceSum(d, c12);
C[ldc * (ii + 1) + (jj + 3)] = hn::ReduceSum(d, c13);
C[ldc * (ii + 2) + (jj + 0)] = hn::ReduceSum(d, c20);
C[ldc * (ii + 2) + (jj + 1)] = hn::ReduceSum(d, c21);
C[ldc * (ii + 2) + (jj + 2)] = hn::ReduceSum(d, c22);
C[ldc * (ii + 2) + (jj + 3)] = hn::ReduceSum(d, c23);
C[ldc * (ii + 3) + (jj + 0)] = hn::ReduceSum(d, c30);
C[ldc * (ii + 3) + (jj + 1)] = hn::ReduceSum(d, c31);
C[ldc * (ii + 3) + (jj + 2)] = hn::ReduceSum(d, c32);
C[ldc * (ii + 3) + (jj + 3)] = hn::ReduceSum(d, c33);
}
// Tiled 4x4 GEMM. Covers primary M =4..512, k = 3k/24k, n = 24k/3k use case.
// This version uses tiling suitable for static scheduling.
// Note: expects transposed / shuffled B.
template <size_t kM, size_t kColsA, size_t kK, typename MatT>
void GEMM_4x4_Static(const MatT* HWY_RESTRICT A, const MatT* HWY_RESTRICT B,
MatT* HWY_RESTRICT C) {
const hn::ScalableTag<MatT> d;
const size_t N = hn::Lanes(d); // column step size
constexpr int RM = 4; // tile height
constexpr int RN = 4; // tile width
static_assert(kM % RM == 0);
static_assert(kColsA % N == 0);
static_assert(kColsA % RN == 0);
int lda = kColsA;
int ldb = kColsA; // n instead of k because we're transposing
int ldc = kK;
int ytiles = (kM) / RM;
int xtiles = (kK) / RN; // k instead of n because we're transposing
int tiles = xtiles * ytiles;
for (int job = 0; job < tiles; ++job) {
GEMM_4x4_Tile<MatT, kColsA>(A, B, C, job, xtiles, lda, ldb, ldc);
}
}
// Tiled 4x4 GEMM. Covers primary M =4..512, k = 3k/24k, n = 24k/3k use case.
// This version uses tiling and pooled threads.
// Note: expects transposed / shuffled B.
template <size_t kM, size_t kColsA, size_t kK, typename MatT>
HWY_NOINLINE void MatMul_4x4_Impl(const MatT* HWY_RESTRICT A,
const MatT* HWY_RESTRICT B,
MatT* HWY_RESTRICT C, hwy::ThreadPool& pool) {
// Process 4x4 chunks of C in parallel. Each pool thread handles a single A x
// B tile. Note that C is being addressed directly without a buffer, and that
// the cache vectors (c00, c01, etc.) are being summed directly into C. There
// may be additional stability / speed gains to be made by using a buffer.
const hn::ScalableTag<MatT> d;
const size_t N = Lanes(d);
const int lda = kColsA;
const int ldb = kColsA; // n instead of k because we're transposing
const int ldc = kK;
// 4x4
const int RM = 4;
const int RN = 4;
const int ytiles = (kM) / RM;
const int xtiles = (kK) / RN; // k instead of n because we're transposing
const int tiles = xtiles * ytiles;
// 4x4 case requires kM % 4 == 0, kN % N == 0, kK % 4 == 0
static_assert(kM % RM == 0);
static_assert(kColsA % N == 0);
static_assert(kColsA % RN == 0);
static_assert(kK % RN == 0);
static_assert(kColsA >= N);
// Handles a single 4x4 chunk, which is completed and then written into C.
pool.Run(0, tiles, [&](const uint64_t chunk, size_t /*thread*/) HWY_ATTR {
GEMM_4x4_Tile<MatT, kColsA>(A, B, C, chunk, xtiles, lda, ldb, ldc);
});
}
// Requires m % 4 == 0, n % Lanes() == 0, k % 4 == 0
template <size_t kM, size_t kN, size_t kK, typename MatT>
HWY_INLINE void MatMul_4x4(const MatT* HWY_RESTRICT a,
const MatT* HWY_RESTRICT b, MatT* HWY_RESTRICT out,
hwy::ThreadPool& pool) {
return MatMul_4x4_Impl<kM, kN, kK, MatT>(a, b, out, pool);
}
// Largely unoptimized; reordered innermost loops nets ~5-10X speedup on
// ops_test across instruction sets.
template <size_t kM, size_t kN, size_t kK, typename MatT>
HWY_INLINE void MatMul(const MatT* HWY_RESTRICT a, const MatT* HWY_RESTRICT b,
MatT* HWY_RESTRICT out) {
int i, j, k;
for (i = 0; i < kM; ++i) {
for (k = 0; k < kN; ++k) {
for (j = 0; j < kK; ++j) {
out[i * kK + j] += a[i * kN + k] * b[k * kK + j];
}
}
}
}
HWY_INLINE void ToEvenOddF32(const hwy::bfloat16_t* HWY_RESTRICT vec_aligned,
const size_t size, float* HWY_RESTRICT out) {
const hn::ScalableTag<float> df;
const hn::Repartition<hwy::bfloat16_t, decltype(df)> dbf16;
HWY_DASSERT(size % hn::Lanes(dbf16) == 0);
HWY_DASSERT(hn::IsAligned(df, vec_aligned));
for (size_t i = 0; i < size; i += hn::Lanes(dbf16)) {
const auto interleaved = hn::LoadU(dbf16, vec_aligned + i);
hn::Store(hn::PromoteEvenTo(df, interleaved), df, out + i);
hn::Store(hn::PromoteOddTo(df, interleaved), df, out + i + hn::Lanes(df));
}
}
HWY_INLINE void ToEvenOddF32(const float* HWY_RESTRICT vec_aligned,
const size_t size, float* HWY_RESTRICT out) {
const hn::ScalableTag<float> df;
using VF = hn::Vec<decltype(df)>;
HWY_DASSERT(size % (hn::Lanes(df) * 2) == 0);
HWY_DASSERT(hn::IsAligned(df, vec_aligned));
VF vec0, vec1;
for (size_t i = 0; i < size; i += hn::Lanes(df) * 2) {
hn::LoadInterleaved2(df, vec_aligned + i, vec0, vec1);
hn::Store(vec0, df, out + i);
hn::Store(vec1, df, out + i + hn::Lanes(df));
}
}
// Simple version without tiling nor threading, but two offsets/outputs and
// always with addition.
template <size_t kOuter, size_t kInner, typename ArrayT, typename VecT,
typename AddT>
HWY_INLINE void TwoOfsMatVecAddLoop(const ArrayT& mat, const size_t mat_ofs0,
const size_t mat_ofs1,
const VecT* HWY_RESTRICT vec_aligned,
const AddT* HWY_RESTRICT add0,
const AddT* HWY_RESTRICT add1,
float* HWY_RESTRICT out0,
float* HWY_RESTRICT out1) {
PROFILER_ZONE("TwoOfsMatVecAddLoop");
constexpr bool kVecEO = false;
const hn::ScalableTag<float> df;
for (size_t idx_row = 0; idx_row < kOuter; ++idx_row) {
const size_t row_ofs0 = mat_ofs0 + (idx_row)*kInner;
const size_t row_ofs1 = mat_ofs1 + (idx_row)*kInner;
out0[idx_row] = hwy::ConvertScalarTo<float>(add0[idx_row]) +
Dot<kVecEO>(df, mat, row_ofs0, vec_aligned, kInner);
out1[idx_row] = hwy::ConvertScalarTo<float>(add1[idx_row]) +
Dot<kVecEO>(df, mat, row_ofs1, vec_aligned, kInner);
}
}
namespace detail {
// For each i = [0, num_rows), compute partial (length `num_cols`) dot product
// of row i with `vec_aligned` and add into `out[i]`. The upper-left
// coordinate of the tile is r0, c0.
template <bool kVecEO, class DF, typename ArrayT, typename VecT>
HWY_INLINE void AccumulatePartialDotProducts(
DF df, const ArrayT& mat, size_t mat_ofs, size_t mat_stride, size_t r0,
size_t c0, size_t num_rows, size_t num_cols,
const VecT* HWY_RESTRICT vec_aligned, float* HWY_RESTRICT out) {
for (size_t idx_row = 0; idx_row < num_rows; ++idx_row) {
const size_t row_ofs = mat_ofs + (r0 + idx_row) * mat_stride;
out[idx_row] +=
Dot<kVecEO>(df, mat, row_ofs + c0, vec_aligned + c0, num_cols);
}
}
// Same as AccumulatePartialDotProducts, but sets out[i] to the first partial
// dot product + init (if kInit), which avoids having to zero-initialize and
// accumulate.
template <bool kVecEO, bool kInit, class DF, typename ArrayT, typename VecT,
typename InitT>
HWY_INLINE void SetFirstPartialDotProducts(DF df, const ArrayT& mat,
size_t mat_ofs, size_t mat_stride,
size_t r0, size_t c0,
size_t num_rows, size_t num_cols,
const VecT* HWY_RESTRICT vec_aligned,
const InitT* HWY_RESTRICT init,
float* HWY_RESTRICT out) {
for (size_t idx_row = 0; idx_row < num_rows; ++idx_row) {
const size_t row_ofs = mat_ofs + (r0 + idx_row) * mat_stride;
if constexpr (kInit) {
out[idx_row] =
hwy::ConvertScalarTo<float>(init[idx_row + r0]) +
Dot<kVecEO>(df, mat, row_ofs + c0, vec_aligned + c0, num_cols);
} else {
out[idx_row] =
Dot<kVecEO>(df, mat, row_ofs + c0, vec_aligned + c0, num_cols);
}
}
}
// Adds together partial dot products for all tiles with the same r0 (a
// horizontal strip of the entire matrix); the result is the full dot product
// for rows r in [r0, r0 + num_rows) + optionally the add vector, which we
// store into in out[r - r0].
template <bool kVecEO, bool kAdd, class DF, typename ArrayT, typename VecT,
typename AddT>
HWY_INLINE void FullDotProductsForStrip(DF df, const ArrayT& mat,
size_t mat_ofs, size_t mat_stride,
size_t r0, size_t num_rows,
const VecT* HWY_RESTRICT vec_aligned,
const AddT* HWY_RESTRICT add,
float* HWY_RESTRICT out) {
// Tall and skinny: set `out` to the single dot product.
if (mat_stride < MaxCols()) {
SetFirstPartialDotProducts<kVecEO, kAdd>(df, mat, mat_ofs, mat_stride, r0,
0, num_rows, mat_stride,
vec_aligned, add, out);
return;
}
// We have at least MaxCols, so start by setting `out` to that:
SetFirstPartialDotProducts<kVecEO, kAdd>(df, mat, mat_ofs, mat_stride, r0, 0,
num_rows, MaxCols(), vec_aligned,
add, out);
// For further multiples of MaxCols, accumulate. Remainders handled below.
size_t c0 = MaxCols();
for (; c0 <= mat_stride - MaxCols(); c0 += MaxCols()) {
AccumulatePartialDotProducts<kVecEO>(df, mat, mat_ofs, mat_stride, r0, c0,
num_rows, MaxCols(), vec_aligned, out);
}
if (c0 < mat_stride) { // Final cols
AccumulatePartialDotProducts<kVecEO>(df, mat, mat_ofs, mat_stride, r0, c0,
num_rows, mat_stride - c0, vec_aligned,
out);
}
}
template <bool kVecIsEvenOdd, bool kAdd, size_t kOuter, size_t kInner,
typename ArrayT, typename VecT, typename AddT>
HWY_INLINE void MatVecAddInner(const ArrayT& mat, const size_t mat_ofs,
const VecT* HWY_RESTRICT const vec_aligned,
const AddT* HWY_RESTRICT const add,
float* HWY_RESTRICT out, hwy::ThreadPool& pool) {
const hn::ScalableTag<float> df;
constexpr size_t kRowsPerStrip = RowsPerStrip<kOuter>();
constexpr size_t kNumStrips = kOuter / kRowsPerStrip;
// For each entire strip.
pool.Run(0, kNumStrips, [&](const uint64_t strip, size_t thread) HWY_ATTR {
PROFILER_ZONE("MatVec.lambda");
const size_t r0 = strip * kRowsPerStrip;
detail::FullDotProductsForStrip<kVecIsEvenOdd, kAdd>(
df, mat, mat_ofs, kInner, r0, kRowsPerStrip, vec_aligned, add,
out + r0);
});
// Remaining rows
const size_t r0 = kNumStrips * kRowsPerStrip;
if (r0 < kOuter) {
PROFILER_ZONE("MatVec remainder");
const size_t num_rows = kOuter - r0;
detail::FullDotProductsForStrip<kVecIsEvenOdd, kAdd>(
df, mat, mat_ofs, kInner, r0, num_rows, vec_aligned, add, out + r0);
}
}
} // namespace detail
// Stores dot products of rows with `vec_aligned` + add the values from `add`
// (if kAdd), then stores them to `out`.
template <bool kAdd, size_t kOuter, size_t kInner, typename ArrayT,
typename VecT, typename AddT>
HWY_INLINE void MatVecT(const ArrayT& mat, const size_t mat_ofs,
const VecT* HWY_RESTRICT const vec_aligned,
const AddT* HWY_RESTRICT const add,
float* HWY_RESTRICT even_odd, float* HWY_RESTRICT out,
hwy::ThreadPool& pool) {
PROFILER_ZONE("MatVecAdd");
#if !defined(HWY_NATIVE_DOT_BF16) || !HWY_NATIVE_DOT_BF16
using MatT = typename ArrayT::value_type;
// Sfp -> float does not benefit enough to recoup the cost of ToEvenOddF32.
if constexpr (CompressTraits<MatT>::kSupportsEvenOdd &&
hwy::IsSameEither<VecT, float, hwy::bfloat16_t>() &&
!(hwy::IsSame<MatT, SfpStream>() &&
hwy::IsSame<VecT, float>())) {
ToEvenOddF32(vec_aligned, kInner, even_odd);
detail::MatVecAddInner</*kVecIsEvenOdd=*/true, kAdd, kOuter, kInner>(
mat, mat_ofs, even_odd, add, out, pool);
return;
}
#else
(void)even_odd;
#endif
detail::MatVecAddInner</*kVecIsEvenOdd=*/false, kAdd, kOuter, kInner>(
mat, mat_ofs, vec_aligned, add, out, pool);
}
// With addition
template <size_t kOuter, size_t kInner, typename ArrayT, typename VecT,
typename AddT>
HWY_INLINE void MatVecAdd(const ArrayT& mat, const size_t mat_ofs,
const VecT* HWY_RESTRICT const vec_aligned,
const AddT* HWY_RESTRICT const add,
float* HWY_RESTRICT even_odd, float* HWY_RESTRICT out,
hwy::ThreadPool& pool) {
return MatVecT</*kAdd=*/true, kOuter, kInner>(mat, mat_ofs, vec_aligned, add,
even_odd, out, pool);
}
// Without addition
template <size_t kOuter, size_t kInner, typename ArrayT, typename VecT>
HWY_INLINE void MatVec(const ArrayT& mat, const size_t mat_ofs,
const VecT* HWY_RESTRICT const vec_aligned,
float* HWY_RESTRICT even_odd, float* HWY_RESTRICT out,
hwy::ThreadPool& pool) {
MatVecT</*kAdd=*/false, kOuter, kInner>(mat, mat_ofs, vec_aligned,
/*add=*/static_cast<VecT*>(nullptr),
even_odd, out, pool);
}
template <class D, HWY_IF_F32_D(D)>
static HWY_INLINE hn::Vec<D> Gelu(D d, hn::Vec<D> v) {
const hn::Vec<D> kMul = hn::Set(d, 0.044715f);
const hn::Vec<D> kSqrt2OverPi = hn::Set(d, 0.797884560804236f);
const hn::Vec<D> kHalf = hn::Set(d, 0.5f);
// tanh approximation matches training.
const hn::Vec<D> v3 = hn::Mul(hn::Mul(v, v), v);
const hn::Vec<D> arg = hn::Mul(kSqrt2OverPi, hn::MulAdd(kMul, v3, v));
// 0.5 * (1 + tan) = MulAdd(0.5, tan, 0.5).
const hn::Vec<D> cdf = hn::MulAdd(kHalf, hn::Tanh(d, arg), kHalf);
return hn::Mul(v, cdf);
}
static HWY_NOINLINE HWY_MAYBE_UNUSED void Gelu(float* HWY_RESTRICT x,
size_t size) {
namespace hn = hwy::HWY_NAMESPACE;
using D = hn::ScalableTag<float>;
hn::Transform(D(), x, size,
[](D d, hn::Vec<D> v) HWY_ATTR { return Gelu(d, v); });
}
// out[i] = BF(mul[i] * Gelu(gelu_in[i]))
static HWY_NOINLINE HWY_MAYBE_UNUSED void GeluMulToBF16(
const float* HWY_RESTRICT gelu_in, const float* HWY_RESTRICT mul,
hwy::bfloat16_t* HWY_RESTRICT out, size_t size) {
namespace hn = hwy::HWY_NAMESPACE;
const hn::ScalableTag<float> df;
const hn::Repartition<hwy::bfloat16_t, decltype(df)> dbf;
const size_t NF = hn::Lanes(df);
using VF = hn::Vec<decltype(df)>;
size_t i = 0;
if (size >= 2 * NF) {
for (; i <= size - 2 * NF; i += 2 * NF) {
const VF mul0 = hn::LoadU(df, mul + i);
const VF mul1 = hn::LoadU(df, mul + i + NF);
const VF g0 = hn::Mul(mul0, Gelu(df, hn::LoadU(df, gelu_in + i)));
const VF g1 = hn::Mul(mul1, Gelu(df, hn::LoadU(df, gelu_in + i + NF)));
const hn::Vec<decltype(dbf)> bf = hn::OrderedDemote2To(dbf, g0, g1);
hn::StoreU(bf, dbf, out + i);
}
}
if (i != size) {
const size_t remaining = size - i;
const VF mul0 = hn::LoadN(df, mul + i, remaining);
const VF g0 =
hn::Mul(mul0, Gelu(df, hn::LoadN(df, gelu_in + i, remaining)));
const hn::Half<decltype(dbf)> dbfh;
const hn::Vec<decltype(dbfh)> bfh = hn::DemoteTo(dbfh, g0);
hn::StoreN(bfh, dbfh, out + i, remaining);
}
}
template <class D, HWY_IF_F32_D(D)>
static HWY_INLINE hn::Vec<D> Sigmoid(D d, hn::Vec<D> v) {
using VF = hn::Vec<D>;
// Chebyshev polynomial coefficients for rational approximation
const VF c0 = hn::Set(d, 0.00949107017368078f);
const VF c1 = hn::Set(d, 0.0654858946800232f);
const VF c2 = hn::Set(d, 0.231547489762306f - 0.00949107017368078f);
const VF c3 = hn::Set(d, 0.530778527259827f);
const VF c4 = hn::Set(d, 0.855334937572479f);
const VF c5 = hn::Set(d, 0.500000894069672f);
const VF d0 = hn::Set(d, 0.130970627069473f);
const VF d1 = hn::Set(d, 3.99615288415589e-07f);
const VF d2 = hn::Set(d, 1.06155431270599f - 0.130970627069473f);
const VF d3 = hn::Set(d, 1.35144250634767e-06f);
const VF d4 = hn::Set(d, 1);
// The approximation works in range -12..12, but the input value is clamped
// in -11.5..11.5 since the approximation slightly overshoots after that.
// The function is nearly 0 for input values below -11.5 and nearly 1 for
// input values above 11.5.
const VF invtwelve = hn::Set(d, 1.0f / 12.0f);
const VF lo = hn::Set(d, -11.5f);
const VF hi = hn::Set(d, 11.5f);
VF f = hn::Clamp(v, lo, hi);
f = hn::Mul(f, invtwelve);
VF f2 = hn::Add(f, f);
VF a1 = hn::MulAdd(f2, c0, c1);
VF a2 = hn::MulAdd(f2, a1, c2);
VF a3 = hn::Sub(hn::MulAdd(f2, a2, c3), a1);
VF a4 = hn::Sub(hn::MulAdd(f2, a3, c4), a2);
VF f0 = hn::Sub(hn::MulAdd(f, a4, c5), a3);
VF b1 = hn::MulAdd(f2, d0, d1);
VF b2 = hn::MulAdd(f2, b1, d2);
VF b3 = hn::Sub(hn::MulAdd(f2, b2, d3), b1);
VF f1 = hn::Sub(hn::MulAdd(f, b3, d4), b2);
return hn::Div(f0, f1);
}
// Sigmoid using the logistic function 1 / (1 + exp(-x[i]))
static HWY_NOINLINE HWY_MAYBE_UNUSED void Sigmoid(float* HWY_RESTRICT x,
size_t size) {
namespace hn = hwy::HWY_NAMESPACE;
using D = hn::ScalableTag<float>;
hn::Transform(D(), x, size,
[](D d, hn::Vec<D> v) HWY_ATTR { return Sigmoid(d, v); });
}
// Two matrices, same vector
template <bool kAdd, size_t kOuter, size_t kInner, typename ArrayT,
typename VecT, typename AddT>
HWY_NOINLINE void TwoMatVecT(const ArrayT& mat0, const ArrayT& mat1,
const size_t mat_ofs,
const VecT* HWY_RESTRICT vec_aligned,
const AddT* HWY_RESTRICT add0,
const AddT* HWY_RESTRICT add1,
float* HWY_RESTRICT out0, float* HWY_RESTRICT out1,
hwy::ThreadPool& pool) {
PROFILER_ZONE("TwoMatVecAdd");
const hn::ScalableTag<float> df;
constexpr size_t kRowsPerStrip = RowsPerStrip<kOuter>();
constexpr size_t kNumStrips = kOuter / kRowsPerStrip;
constexpr bool kVecIsEvenOdd = false;
// For each entire strip.
pool.Run(0, kNumStrips, [&](const uint64_t strip, size_t thread) HWY_ATTR {
PROFILER_ZONE("TwoMatVec.lambda");
const size_t r0 = strip * kRowsPerStrip;
detail::FullDotProductsForStrip<kVecIsEvenOdd, kAdd>(
df, mat0, mat_ofs, kInner, r0, kRowsPerStrip, vec_aligned, add0,
out0 + r0);
detail::FullDotProductsForStrip<kVecIsEvenOdd, kAdd>(
df, mat1, mat_ofs, kInner, r0, kRowsPerStrip, vec_aligned, add1,
out1 + r0);
});
// Remaining rows
const size_t r0 = kNumStrips * kRowsPerStrip;
if (r0 < kOuter) {
PROFILER_ZONE("TwoMatVec remainder");
const size_t num_rows = kOuter - r0;
detail::FullDotProductsForStrip<kVecIsEvenOdd, kAdd>(
df, mat0, mat_ofs, kInner, r0, num_rows, vec_aligned, add0, out0 + r0);
detail::FullDotProductsForStrip<kVecIsEvenOdd, kAdd>(
df, mat1, mat_ofs, kInner, r0, num_rows, vec_aligned, add1, out1 + r0);
}
}
// With addition
template <size_t kOuter, size_t kInner, typename ArrayT, typename VecT,
typename AddT>
HWY_NOINLINE void TwoMatVecAdd(
const ArrayT& mat0, const ArrayT& mat1, const size_t mat_ofs,
const VecT* HWY_RESTRICT vec_aligned, const AddT* HWY_RESTRICT add0,
const AddT* HWY_RESTRICT add1, float* HWY_RESTRICT out0,
float* HWY_RESTRICT out1, hwy::ThreadPool& pool) {
return TwoMatVecT</*kAdd=*/true, kOuter, kInner>(
mat0, mat1, mat_ofs, vec_aligned, add0, add1, out0, out1, pool);
}
// Without addition
template <size_t kOuter, size_t kInner, typename ArrayT, typename VecT>
HWY_NOINLINE void TwoMatVec(const ArrayT& mat0, const ArrayT& mat1,
const size_t mat_ofs,
const VecT* HWY_RESTRICT vec_aligned,
float* HWY_RESTRICT out0, float* HWY_RESTRICT out1,
hwy::ThreadPool& pool) {
TwoMatVecT</*kAdd=*/false, kOuter, kInner, ArrayT, VecT, VecT>(
mat0, mat1, mat_ofs, vec_aligned, /*add0=*/nullptr, /*add1=*/nullptr,
out0, out1, pool);
}
static HWY_NOINLINE HWY_MAYBE_UNUSED float Dot(const float* HWY_RESTRICT a,
const float* HWY_RESTRICT b,
size_t size) {
const hn::ScalableTag<float> d;
HWY_DASSERT(size >= hn::Lanes(d));
HWY_DASSERT(size % hn::Lanes(d) == 0);
constexpr int kAssumptions =
hn::Dot::kAtLeastOneVector | hn::Dot::kMultipleOfVector;
return hn::Dot::Compute<kAssumptions>(d, a, b, size);
}
// = Dot(a, a, size), but that is not allowed due to HWY_RESTRICT.
static HWY_NOINLINE HWY_MAYBE_UNUSED float SquaredL2(
const float* HWY_RESTRICT a, size_t size) {
const hn::ScalableTag<float> d;
using V = hn::Vec<decltype(d)>;
const size_t N = hn::Lanes(d);
HWY_DASSERT(size >= 2 * N);
HWY_DASSERT(size % (2 * N) == 0);
V sum0 = hn::Zero(d);
V sum1 = hn::Zero(d);
for (size_t i = 0; i <= size - 2 * N; i += 2 * N) {
const V a0 = hn::LoadU(d, a + i);
sum0 = hn::MulAdd(a0, a0, sum0);
const V a1 = hn::LoadU(d, a + i + N);
sum1 = hn::MulAdd(a1, a1, sum1);
}
return hn::ReduceSum(d, hn::Add(sum0, sum1));
}
static HWY_NOINLINE HWY_MAYBE_UNUSED void RMSNorm(
const float* HWY_RESTRICT x, const float* HWY_RESTRICT weight,
float* HWY_RESTRICT out, size_t size) {
constexpr float eps = 1e-6f;
float ss = SquaredL2(x, size);
ss = 1.0f / sqrtf(ss / StaticCast<float>(size) + eps);
for (size_t j = 0; j < size; j++) {
// Note 1.0f centering here
out[j] = (1.0f + weight[j]) * (ss * x[j]);
}
}
static HWY_NOINLINE HWY_MAYBE_UNUSED void RMSNorm(
const float* HWY_RESTRICT x, const hwy::bfloat16_t* HWY_RESTRICT weight,
float* HWY_RESTRICT out, size_t size) {
namespace hn = hwy::HWY_NAMESPACE;
constexpr float kEps = 1e-6f;
constexpr size_t kUnrollSize = 2;
const hn::ScalableTag<hwy::bfloat16_t> dbf;
const hn::Repartition<float, decltype(dbf)> df32;
const size_t N32 = hn::Lanes(df32);
const float ss = SquaredL2(x, size);
const auto vss =
hn::Set(df32, 1.0f / sqrtf(ss / StaticCast<float>(size) + kEps));
HWY_DASSERT(size % (kUnrollSize * MaxLanes(df32)) == 0);
for (size_t i = 0; i < size; i += kUnrollSize * N32) {
const hn::Vec<decltype(dbf)> w16 = hn::LoadU(dbf, weight + i);
const auto w0 = hn::PromoteLowerTo(df32, w16);
const auto w1 = hn::PromoteUpperTo(df32, w16);
const auto m0 = hn::Mul(vss, hn::LoadU(df32, x + i));
const auto m1 = hn::Mul(vss, hn::LoadU(df32, x + i + N32));
// (1+weight) * m = m + weight*m = one FMA.
hn::StoreU(hn::MulAdd(m0, w0, m0), df32, out + i);
hn::StoreU(hn::MulAdd(m1, w1, m1), df32, out + i + N32);
}
}
static HWY_NOINLINE HWY_MAYBE_UNUSED void RMSNormInplace(
const float* HWY_RESTRICT weight, float* HWY_RESTRICT inout, size_t size) {
constexpr float eps = 1e-6f;
float ss = SquaredL2(inout, size);
ss = 1.0f / sqrtf(ss / StaticCast<float>(size) + eps);
for (size_t j = 0; j < size; j++) {
// Note 1.0f centering here
inout[j] = (1.0f + weight[j]) * (ss * inout[j]);
}
}
// w=bf16 -> f
static HWY_NOINLINE HWY_MAYBE_UNUSED void RMSNormInplace(
const hwy::bfloat16_t* HWY_RESTRICT weight, float* HWY_RESTRICT inout,
const size_t size) {
namespace hn = hwy::HWY_NAMESPACE;
const hn::ScalableTag<hwy::bfloat16_t> dbf;
const hn::Repartition<float, decltype(dbf)> df32;
using VF = hn::Vec<decltype(df32)>;
const size_t N32 = hn::Lanes(df32);
constexpr float eps = 1e-6f;
const float ss = SquaredL2(inout, size);
const VF vss =
hn::Set(df32, 1.0f / sqrtf(ss / StaticCast<float>(size) + eps));
HWY_DASSERT(size % (2 * MaxLanes(df32)) == 0);
for (size_t i = 0; i < size; i += 2 * N32) {
const hn::Vec<decltype(dbf)> w16 = hn::LoadU(dbf, weight + i);
const VF w0 = hn::PromoteLowerTo(df32, w16);
const VF w1 = hn::PromoteUpperTo(df32, w16);
const VF m0 = hn::Mul(vss, hn::LoadU(df32, inout + i));
const VF m1 = hn::Mul(vss, hn::LoadU(df32, inout + i + N32));
// (1+weight) * m = m + weight*m = one FMA.
hn::StoreU(hn::MulAdd(m0, w0, m0), df32, inout + i);
hn::StoreU(hn::MulAdd(m1, w1, m1), df32, inout + i + N32);
}
}
// f, f -> bf
// TODO(janwas): consider generic function with adapter for loading bf16/f32
static HWY_NOINLINE HWY_MAYBE_UNUSED void RMSNorm(
const float* HWY_RESTRICT x, const float* HWY_RESTRICT weight,
hwy::bfloat16_t* HWY_RESTRICT out, const size_t size) {
namespace hn = hwy::HWY_NAMESPACE;
const hn::ScalableTag<hwy::bfloat16_t> dbf;
const hn::Repartition<float, decltype(dbf)> df32;
using VF = hn::Vec<decltype(df32)>;
const size_t N32 = hn::Lanes(df32);
constexpr float eps = 1e-6f;
const float ss = SquaredL2(x, size);
const VF vss =
hn::Set(df32, 1.0f / sqrtf(ss / StaticCast<float>(size) + eps));
HWY_DASSERT(size % (2 * MaxLanes(df32)) == 0);
for (size_t i = 0; i < size; i += 2 * N32) {
const VF w0 = hn::LoadU(df32, weight + i);
const VF w1 = hn::LoadU(df32, weight + i + N32);
const VF m0 = hn::Mul(vss, hn::LoadU(df32, x + i));
const VF m1 = hn::Mul(vss, hn::LoadU(df32, x + i + N32));
// (1+weight) * m = m + weight*m = one FMA.
const VF out0 = hn::MulAdd(m0, w0, m0);
const VF out1 = hn::MulAdd(m1, w1, m1);
hn::StoreU(hn::OrderedDemote2To(dbf, out0, out1), dbf, out + i);
}
}
// x=f, w=bf16 -> bf16 to enable W16A16 MatVec.
static HWY_NOINLINE HWY_MAYBE_UNUSED void RMSNorm(
const float* HWY_RESTRICT x, const hwy::bfloat16_t* HWY_RESTRICT weight,
hwy::bfloat16_t* HWY_RESTRICT out, const size_t size) {
namespace hn = hwy::HWY_NAMESPACE;
const hn::ScalableTag<hwy::bfloat16_t> dbf;
const hn::Repartition<float, decltype(dbf)> df32;
using VF = hn::Vec<decltype(df32)>;
const size_t N32 = hn::Lanes(df32);
constexpr float eps = 1e-6f;
const float ss = SquaredL2(x, size);
const VF vss =
hn::Set(df32, 1.0f / sqrtf(ss / StaticCast<float>(size) + eps));
HWY_DASSERT(size % (2 * MaxLanes(df32)) == 0);
for (size_t i = 0; i < size; i += 2 * N32) {
const hn::Vec<decltype(dbf)> w16 = hn::LoadU(dbf, weight + i);
const VF w0 = hn::PromoteLowerTo(df32, w16);
const VF w1 = hn::PromoteUpperTo(df32, w16);
const VF m0 = hn::Mul(vss, hn::LoadU(df32, x + i));
const VF m1 = hn::Mul(vss, hn::LoadU(df32, x + i + N32));
// (1+weight) * m = m + weight*m = one FMA.
const VF out0 = hn::MulAdd(m0, w0, m0);
const VF out1 = hn::MulAdd(m1, w1, m1);
hn::StoreU(hn::OrderedDemote2To(dbf, out0, out1), dbf, out + i);
}
}
static HWY_NOINLINE HWY_MAYBE_UNUSED void AddAbsolutePositionalEmbeddings(
float* HWY_RESTRICT x, size_t dim_model, size_t pos) {
const size_t num_timescales = dim_model / 2;
const float log_timescale_increment =
logf(10000.0f) /
(num_timescales != 0 ? StaticCast<float>(num_timescales - 1) : 1.0f);
for (size_t dim = 0; dim < num_timescales; ++dim) {
const float inv_timescale =
expf(StaticCast<float>(dim) * -log_timescale_increment);
x[dim] += sinf(StaticCast<float>(pos) * inv_timescale);
x[num_timescales + dim] += cosf(StaticCast<float>(pos) * inv_timescale);
}
}
/* RoPE as in Rotary Position Embeddings from the RoFormer paper
(https://arxiv.org/abs/2104.09864v5). The query and key vectors are rotated
as a function of their absolute position using the rotation matrix R before
the self-attention operation. R is a d x d matrix.
R = cos(m*theta_1) -sin(m*theta_1) ... 0 0
sin(m*theta_1) cos(m*theta_1)
0 0 ... 0 0
0 0 ... 0 0
...
0 0 ... cos(m*theta_{d/2}) sin(m*theta_{d/2})
0 0 ... sin(m*theta_{d/2}) cos(m*theta_{d/2})
Here theta_i = 10000^(-2(i-1)/d), where d is the dimension of the vector and
i is the ith index of the vector.
Applying the rotation matrix R to a vector v is equivalent to rotating every
consecutive pair of dimensions of v i.e. v_{2i} and v_{2i+1} by an angle
m*theta_i. However in the Gemma implementation we choose to rotate
the pairs of dimensions v_{i} and v_{i + d//2} instead.
pos parameter is deliberately an int because in the backward pass we
call this with negative values (for the VJP calculation we need the transpose
of this rotation matrix which is simply the same matrix with -pos parameter)
*/
static HWY_NOINLINE HWY_MAYBE_UNUSED void Rope(float* HWY_RESTRICT x,
size_t dim_qkv, int pos) {
HWY_DASSERT(dim_qkv % 2 == 0);
const size_t half_dim_qkv = dim_qkv / 2;
for (size_t dim = 0; dim < half_dim_qkv; ++dim) {
const float freq_exponents =
StaticCast<float>(2 * dim) / StaticCast<float>(dim_qkv);
// Replacing with expf(ln(1E4) * freq_exponents) changes results noticeably.
const float timescale = powf(10000.0f, freq_exponents);
const float theta = StaticCast<float>(pos) / timescale;
const float cos_val = cosf(theta);
const float sin_val = sinf(theta);
const float x0 = x[dim];
const float x1 = x[dim + half_dim_qkv];
x[dim] = x0 * cos_val - x1 * sin_val;
x[dim + half_dim_qkv] = x0 * sin_val + x1 * cos_val;
}
}
static HWY_NOINLINE HWY_MAYBE_UNUSED void RopeAndMulBy(const float mul,
float* HWY_RESTRICT x,
size_t dim_qkv,
int pos) {
HWY_DASSERT(dim_qkv % 2 == 0);
const size_t half_dim_qkv = dim_qkv / 2;
for (size_t dim = 0; dim < half_dim_qkv; ++dim) {
const float freq_exponents =
StaticCast<float>(2 * dim) / StaticCast<float>(dim_qkv);
// Replacing with expf(ln(1E4) * freq_exponents) changes results noticeably.
const float timescale = powf(10000.0f, freq_exponents);
const float theta = StaticCast<float>(pos) / timescale;
const float cos_val = cosf(theta);
const float sin_val = sinf(theta);
const float x0 = x[dim];
const float x1 = x[dim + half_dim_qkv];
x[dim] = mul * (x0 * cos_val - x1 * sin_val);
x[dim + half_dim_qkv] = mul * (x0 * sin_val + x1 * cos_val);
}
}
static HWY_NOINLINE HWY_MAYBE_UNUSED void AddFrom(
const float* HWY_RESTRICT other, float* HWY_RESTRICT x, const size_t size) {
namespace hn = hwy::HWY_NAMESPACE;
using D = hn::ScalableTag<float>;
const D d;
hn::Transform1(d, x, size, other,
[](const auto d, const auto x, const auto other)
HWY_ATTR { return hn::Add(x, other); });
}
static HWY_NOINLINE void MulBy(const float* HWY_RESTRICT other,
float* HWY_RESTRICT x, const size_t size,
const size_t max_pos) {
HWY_DASSERT(max_pos <= size);
namespace hn = hwy::HWY_NAMESPACE;
using D = hn::ScalableTag<float>;
const D d;
hn::Transform1(d, x, max_pos, other,
[](const auto d, const auto x, const auto other)
HWY_ATTR { return hn::Mul(x, other); });
}
static HWY_INLINE HWY_MAYBE_UNUSED void MulBy(const float* HWY_RESTRICT other,
float* HWY_RESTRICT x,
const size_t size) {
return MulBy(other, x, size, size);
}
static HWY_NOINLINE void MulByConst(const float c, float* HWY_RESTRICT x,
const size_t size, const size_t max_pos) {
HWY_DASSERT(max_pos <= size);
namespace hn = hwy::HWY_NAMESPACE;
using D = hn::ScalableTag<float>;
const D d;
const auto constant = hn::Set(d, c);
hn::Transform(d, x, max_pos,
[&constant](const auto d, const auto x)
HWY_ATTR { return hn::Mul(x, constant); });
}
static HWY_INLINE HWY_MAYBE_UNUSED void MulByConst(const float c,
float* HWY_RESTRICT x,
const size_t size) {
MulByConst(c, x, size, size);
}
static HWY_NOINLINE void MulByConstAndAdd(const float c,
const float* HWY_RESTRICT x,
float* HWY_RESTRICT out,
const size_t size,
const size_t max_pos) {
namespace hn = hwy::HWY_NAMESPACE;
using D = hn::ScalableTag<float>;
const D d;
const auto constant = hn::Set(d, c);
hn::Transform1(
d, out, max_pos, x,
[&constant](const auto d, const auto out_element, const auto x_element)
HWY_ATTR { return hn::MulAdd(x_element, constant, out_element); });
}
static HWY_INLINE HWY_MAYBE_UNUSED void MulByConstAndAdd(
float c, const float* HWY_RESTRICT x, float* HWY_RESTRICT out,
size_t size) {
MulByConstAndAdd(c, x, out, size, size);
}
static HWY_NOINLINE void Softmax(float* HWY_RESTRICT x, const size_t size,
const size_t mask_pos) {
HWY_DASSERT(size != 0);
HWY_DASSERT(mask_pos <= size);
namespace hn = hwy::HWY_NAMESPACE;
using D = hn::ScalableTag<float>;
const D d;
const auto vmin = hn::Set(d, hwy::LowestValue<float>());
auto vmax = vmin;
Foreach(d, x, mask_pos, vmin,
[&vmax](const auto d, const auto value)
HWY_ATTR { vmax = hn::Max(vmax, value); });
vmax = hn::MaxOfLanes(d, vmax);
// Subtract max (avoid precision loss for large exponents) and exponentiate.
hn::Transform(d, x, mask_pos,
[&vmax](const auto d, const auto value) HWY_ATTR {
return hn::Exp(d, hn::Sub(value, vmax));
});
auto sum = hn::Zero(d);
Foreach(d, x, mask_pos, sum,
[&sum](const auto d, const auto value)
HWY_ATTR { sum = hn::Add(sum, value); });
// Normalize to probability distribution
const float mul = 1.0f / hn::ReduceSum(d, sum);
MulByConst(mul, x, size, mask_pos);
}
static HWY_INLINE HWY_MAYBE_UNUSED void Softmax(float* HWY_RESTRICT x,
const size_t size) {
Softmax(x, size, size);
}
static HWY_NOINLINE void LogitsSoftCap(const float cap, float* HWY_RESTRICT x,
const size_t size,
const size_t max_pos) {
HWY_DASSERT(max_pos <= size);
namespace hn = hwy::HWY_NAMESPACE;
using D = hn::ScalableTag<float>;
const D d;
const auto vcap = hn::Set(d, cap);
const auto vinv_cap = hn::Div(hn::Set(d, 1.0f), vcap);
hn::Transform(d, x, size, [&vcap, &vinv_cap](D d, hn::Vec<D> v) HWY_ATTR {
return hn::Mul(vcap, hn::Tanh(d, hn::Mul(v, vinv_cap)));
});
}
static HWY_INLINE HWY_MAYBE_UNUSED void LogitsSoftCap(const float cap,
float* HWY_RESTRICT x,
const size_t size) {
LogitsSoftCap(cap, x, size, size);
}
static HWY_NOINLINE HWY_MAYBE_UNUSED size_t
SampleArgmax(const float* probabilities, size_t vocab_size) {
size_t max_index = 0;
float max_prob = probabilities[0];
for (size_t i = 1; i < vocab_size; ++i) {
if (probabilities[i] > max_prob) {
max_index = i;
max_prob = probabilities[i];
}
}
return max_index;
}
template <size_t k>
static HWY_NOINLINE HWY_MAYBE_UNUSED std::discrete_distribution<int>
create_distribution(std::array<float, k>& top_k, float temperature) {
// re-normalize distribution
namespace hn = hwy::HWY_NAMESPACE;
using D = hn::ScalableTag<float>;
const D d;
const auto temperature_inv =
hn::Div(hn::Set(d, 1.0f), hn::Set(d, temperature));
hn::Transform(d, top_k.data(), top_k.size(),
[&temperature_inv](D d, hn::Vec<D> v) HWY_ATTR {
return hn::Exp(d, hn::Mul(hn::Log(d, v), temperature_inv));
});
return std::discrete_distribution<int>(std::begin(top_k), std::end(top_k));
}
template <size_t k, typename TAcceptToken>
static HWY_NOINLINE HWY_MAYBE_UNUSED int SampleTopK(
const float* HWY_RESTRICT probabilities, size_t vocab_size,
std::mt19937& gen, float temperature, TAcceptToken& accept_token) {
static_assert(k != 0, "");
// TODO: Optimize, potentially using new VQSort PartialSort.
std::array<float, k> top_k{}; // sorted from highest [0], to lowest [k-1]
std::array<int, k> indices{};
for (size_t i = 0; i < vocab_size; ++i) {
if (probabilities[i] < top_k[k - 1] && accept_token(StaticCast<int>(i))) {
continue;
}
for (size_t j = 0; j < k; ++j) {
if (probabilities[i] > top_k[j] && accept_token(StaticCast<int>(i))) {
// shift elements by 1, insert the new value, move on to next value
for (size_t idx = k - 1; idx > j; --idx) {
top_k[idx] = top_k[idx - 1];
indices[idx] = indices[idx - 1];
}
top_k[j] = probabilities[i];
indices[j] = StaticCast<int>(i);
break;
}
}
}
return indices[create_distribution<k>(top_k, temperature)(gen)];
}
// NOLINTNEXTLINE(google-readability-namespace-comments)
} // namespace HWY_NAMESPACE
} // namespace gcpp
HWY_AFTER_NAMESPACE();
#endif // NOLINT
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