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// Copyright 2023 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
//
// http://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.
// Normal include guard.
#ifndef THIRD_PARTY_GEMMA_CPP_COMPRESSION_NUQ_INL_H_
#define THIRD_PARTY_GEMMA_CPP_COMPRESSION_NUQ_INL_H_
#include <stddef.h>
#include <stdint.h>
#include "compression/nuq.h"
#include "compression/sfp.h"
#include "hwy/base.h"
#endif // THIRD_PARTY_GEMMA_CPP_COMPRESSION_NUQ_INL_H_
// Actual per-target include guard.
#if defined(THIRD_PARTY_GEMMA_CPP_COMPRESSION_NUQ_INL_TOGGLE) == \
defined(HWY_TARGET_TOGGLE)
#ifdef THIRD_PARTY_GEMMA_CPP_COMPRESSION_NUQ_INL_TOGGLE
#undef THIRD_PARTY_GEMMA_CPP_COMPRESSION_NUQ_INL_TOGGLE
#else
#define THIRD_PARTY_GEMMA_CPP_COMPRESSION_NUQ_INL_TOGGLE
#endif
#include "hwy/highway.h"
// After highway.h
#include "compression/sfp-inl.h"
#include "hwy/contrib/sort/vqsort-inl.h"
#ifndef HWY_IF_CONSTEXPR
#define HWY_IF_CONSTEXPR if
#endif
HWY_BEFORE_NAMESPACE();
namespace gcpp {
namespace HWY_NAMESPACE {
namespace hn = hwy::HWY_NAMESPACE;
// For internal use by NuqCodec.
class NuqClustering {
// To go from sorted order back to the original order in O(1), we store the
// original index in the lower bits of the float32 mantissa, which means they
// are sorted alongside the value.
struct FloatPayload {
// Resets payload to zero; useful for displaying the actual value.
static HWY_INLINE float Clear(float f) {
const uint32_t binary32 = hwy::BitCastScalar<uint32_t>(f);
return hwy::BitCastScalar<float>(binary32 &
~static_cast<uint32_t>(kGroupSize - 1));
}
// Sets payload to `bits`.
static HWY_INLINE float Set(float f, size_t bits) {
HWY_DASSERT(bits < kGroupSize);
const uint32_t binary32 = hwy::BitCastScalar<uint32_t>(Clear(f));
return hwy::BitCastScalar<float>(static_cast<uint32_t>(binary32 | bits));
}
// Obtains the payload (index) previously set by `Set`.
static HWY_INLINE size_t Get(float f) {
return hwy::BitCastScalar<uint32_t>(f) &
static_cast<uint32_t>(kGroupSize - 1);
}
};
// Cumulative sums for O(1) mean and interval sums.
class ClusterCost {
// Ensures it is safe to load a vector from the last element.
static constexpr size_t kMaxLanes = hn::MaxLanes(hn::ScalableTag<float>());
// Initialization value for table elements where `valid` is false.
static constexpr float kSentinel = -1.0f;
public:
explicit ClusterCost(const float* HWY_RESTRICT sorted) {
double cumsum = 0.0;
double cumsum2 = 0.0;
cumsum_[0] = cumsum2_[0] = 0.0;
for (size_t i = 0; i < kGroupSize; ++i) {
const float x = FloatPayload::Clear(sorted[i]);
cumsum += x;
cumsum2 += static_cast<double>(x) * x;
cumsum_[1 + i] = static_cast<float>(cumsum);
cumsum2_[1 + i] = static_cast<float>(cumsum2);
}
const hn::ScalableTag<float> df;
using VF = hn::Vec<decltype(df)>;
const VF k1 = hn::Set(df, 1.0f);
const size_t N = hn::Lanes(df);
HWY_DASSERT(kGroupSize % N == 0);
// Precomputed length and reciprocal.
for (size_t len = 0; len < kGroupSize; len += N) {
const VF vlen = hn::Iota(df, static_cast<int32_t>(len));
hn::StoreU(vlen, df, len_ + kMaxLanes + len);
hn::StoreU(hn::Div(k1, vlen), df, inv_len_ + kMaxLanes + len);
}
// len = kGroupSize is legitimate, e.g., for all-equal weights.
len_[kMaxLanes + kGroupSize] = static_cast<float>(kGroupSize);
inv_len_[kMaxLanes + kGroupSize] = 1.0f / static_cast<float>(kGroupSize);
// len = 0 can happen, but valid is false for that lane.
len_[kMaxLanes + 0] = kSentinel;
inv_len_[kMaxLanes + 0] = kSentinel;
// Ensure it is safe to load a vector from the last element.
for (size_t i = 0; i < kMaxLanes; ++i) {
constexpr size_t kEnd = kGroupSize + 1;
cumsum_[kEnd + i] = cumsum_[kGroupSize];
cumsum2_[kEnd + i] = cumsum2_[kGroupSize];
len_[kMaxLanes + kEnd + i] = len_[kMaxLanes + kGroupSize];
inv_len_[kMaxLanes + kEnd + i] = inv_len_[kMaxLanes + kGroupSize];
}
// For inv_len_ we also prepend MaxLanes in case first > last.
for (size_t i = 0; i < kMaxLanes; ++i) {
len_[i] = kSentinel;
inv_len_[i] = kSentinel;
}
}
// Returns cost (L2 norm) for a single cluster, used for backtracking.
float SumOfSorted(size_t first, size_t last) const {
return cumsum_[last + 1] - cumsum_[first];
}
// Returns vector of costs of clustering first..last + i with their means.
// O(1) thanks to cumulative sums, which works for Lp-norms with p > 1; we
// choose p=2 for simplicity (fewer terms). Caller ignores lanes where
// `!valid[i]`, i.e. `first > last + i`.
template <class DF, class MF = hn::Mask<DF>, class VF = hn::Vec<DF>>
VF SumCosts(DF df, size_t first, size_t last, MF valid) const {
HWY_DASSERT(first < kGroupSize);
HWY_DASSERT(last < kGroupSize);
VF inv_len;
const VF vlen = Lengths(df, first, last, valid, inv_len);
const VF u_lo = hn::Set(df, cumsum_[first]);
const VF u_lo2 = hn::Set(df, cumsum2_[first]);
const VF hi = hn::LoadU(df, cumsum_ + last + 1);
const VF hi2 = hn::LoadU(df, cumsum2_ + last + 1);
const VF sum = hn::Sub(hi, u_lo);
const VF sum2 = hn::Sub(hi2, u_lo2);
// Sum of L2 over i in [first, last] = (x[i] - mu)^2. `sum` and `sum2` are
// the cumulative sums of x and x^2, so expand to `sum x^2 + sum x * -2 *
// mu + sum mu^2`. The last term is the sum of a constant, hence `mu^2 *
// len`. Thus we have: `sum2 + mu * (-2 * sum + mu * len)`. We avoid a
// (-)2 constant by adding.
const VF mu = hn::Mul(sum, inv_len); // mean := sum[i] / len[i]
const VF two_sum = hn::Add(sum, sum);
const VF l2 = hn::MulAdd(mu, hn::MulSub(mu, vlen, two_sum), sum2);
// mu can have some roundoff error. To avoid multiple redundant clusters,
// clamp to zero.
return hn::ZeroIfNegative(l2);
}
private:
// Returns precomputed lengths of [first, last + i] and their reciprocals.
template <class DF, class VF = hn::Vec<DF>, class MF = hn::Mask<DF>>
VF Lengths(DF df, size_t first, size_t last, MF valid, VF& inv_len) const {
const int len = static_cast<int>(last) - static_cast<int>(first) + 1;
HWY_DASSERT(kMaxLanes + len >= 0);
HWY_DASSERT(len <= static_cast<int>(kGroupSize));
// last + i are contiguous, hence single loads instead of gather.
const VF vlen = hn::LoadU(df, len_ + kMaxLanes + len);
inv_len = hn::LoadU(df, inv_len_ + kMaxLanes + len);
if constexpr (HWY_IS_DEBUG_BUILD) {
// Sanity check: no valid lanes are sentinels, all invalid are.
const VF sentinel = hn::Set(df, kSentinel);
const MF bad = hn::Eq(vlen, sentinel);
const MF inv_bad = hn::Eq(inv_len, sentinel);
HWY_DASSERT(hn::AllFalse(df, hn::And(valid, bad)));
HWY_DASSERT(hn::AllFalse(df, hn::And(valid, inv_bad)));
HWY_DASSERT(hn::AllTrue(df, hn::Or(valid, bad)));
HWY_DASSERT(hn::AllTrue(df, hn::Or(valid, inv_bad)));
}
return vlen;
}
// Float has enough precision for our relatively small kGroupSize (256).
// Element i = sums of [0..i-1].
float cumsum_[kGroupSize + 1 + kMaxLanes];
float cumsum2_[kGroupSize + 1 + kMaxLanes];
float len_[kMaxLanes + kGroupSize + 1 + kMaxLanes]; // = vlen[i]
float inv_len_[kMaxLanes + kGroupSize + 1 + kMaxLanes]; // = 1 / vlen[i]
};
// Dynamic programming step: returns costs of clustering 0..last+i, where the
// rightmost clusters start at `first`. Called for each `idx_cluster`,
// `first`, and `last`; vectorized across `last`. `first` may be greater than
// `last`. `valid[i]` is `first <= last + i`.
template <class DF, class VF = hn::Vec<DF>, class MF = hn::Mask<DF>>
static HWY_INLINE VF ClusterDynProg(DF df, const AlignedMatrix<float>& D,
const ClusterCost& cc,
const size_t idx_cluster,
const size_t first, const size_t last,
const MF valid) {
HWY_DASSERT(idx_cluster != 0);
HWY_DASSERT(0 != first && first < kGroupSize);
HWY_DASSERT(last < kGroupSize);
HWY_DASSERT(last % hn::Lanes(df) == 0); // Called in steps of N
// Cost of clustering 0..first-1 with one fewer cluster than now.
const VF prev = hn::Set(df, D(idx_cluster - 1, first - 1));
// Eq2: add to that the cost of another cluster from first..last.
return hn::Add(prev, cc.SumCosts(df, first, last, valid));
}
public:
// Clusters `num <= kGroupSize` values in `x`, which need not be sorted
// already nor aligned, by choosing and filling `centers` (size `kClusters`,
// ascending order, not necessarily equal to one of the `x`). Fills `indices`
// with the index of the cluster to which each `x` belongs (16-bit for
// bit-packing). `buf` is per-thread.
//
// Returns the number of unused clusters, i.e., the starting index within
// `centers`; prior centers are zero-initialized.
//
// O(kClusters * kGroupSize * kGroupSize), but the constant factors are so low
// that this is about 5 times as fast as the O(kClusters * kGroupSize) SMAWK
// as implemented in FAISS, for our kGroupSize of 256.
template <class DF>
static HWY_NOINLINE size_t ClusterExactL2(DF df, const float* HWY_RESTRICT x,
size_t num, ClusterBuf& buf,
float* HWY_RESTRICT centers,
uint16_t* HWY_RESTRICT indices) {
HWY_DASSERT(num <= kGroupSize);
const hn::RebindToSigned<decltype(df)> di;
using VF = hn::Vec<decltype(df)>;
using MF = hn::Mask<decltype(df)>;
using VI = hn::Vec<decltype(di)>;
const VI k1 = hn::Set(di, 1);
const size_t N = hn::Lanes(df);
HWY_DASSERT(kGroupSize % N == 0);
HWY_ALIGN float sorted_and_i[kGroupSize];
for (size_t i = 0; i < num; ++i) {
sorted_and_i[i] = FloatPayload::Set(x[i], i);
}
if (num != kGroupSize) {
// Initialize the rest of the group. Use an existing value so we do not
// waste a cluster on a sentinel value. Arbitrarily choose the largest.
float max = -1E38f;
for (size_t i = 0; i < num; ++i) {
max = HWY_MAX(max, x[i]);
}
for (size_t i = num; i < kGroupSize; ++i) {
sorted_and_i[i] = FloatPayload::Set(max, i);
}
}
hn::VQSortStatic(sorted_and_i, kGroupSize, hwy::SortAscending());
ClusterCost cc(sorted_and_i); // ignores payload bits.
// Reference: https://arxiv.org/abs/1701.07204
// D[k-1][m] is the lowest cost of clustering x1..m into k clusters.
AlignedMatrix<float>& D = buf.d;
// T[k][m] is the starting index within sorted_and_i[] of the k-th cluster.
AlignedMatrix<int32_t>& T = buf.t;
// Fill first row of `D` and `T`: single cluster, iterate over all `last`.
{
const size_t cluster_idx = 0;
const size_t first = 0;
const VI vfirst = hn::Set(di, static_cast<int32_t>(first));
const MF all_valid = hn::FirstN(df, N); // first <= last is always true
for (size_t last = 0; last < kGroupSize; last += N) {
const VF costs = cc.SumCosts(df, first, last, all_valid);
hn::Store(costs, df, &D(cluster_idx, last));
hn::Store(vfirst, di, &T(cluster_idx, last));
}
}
for (size_t cluster_idx = 1; cluster_idx < kClusters; ++cluster_idx) {
// For vectors of `last + i` with `i < N`:
for (size_t last = 0; last < kGroupSize; last += N) {
const VI vlast = hn::Iota(di, static_cast<int32_t>(last));
const VF prev_cost = hn::LoadU(df, &D(cluster_idx - 1, last));
VF min = prev_cost;
VI arg = hn::LoadU(di, &T(cluster_idx - 1, last));
// For each `first` (j), which is the start of the rightmost of at least
// two clusters, hence never zero. `first` also continues past `last`
// because the last `vlast` lane is `last + N - 1`.
for (size_t first = 1; first < last + N; ++first) {
const VI vfirst = hn::Set(di, static_cast<int32_t>(first));
const MF valid = hn::RebindMask(df, hn::Le(vfirst, vlast));
const VF c =
ClusterDynProg(df, D, cc, cluster_idx, first, last, valid);
// Retain the min cost and the `first` that caused it.
const MF less = hn::And(valid, hn::Lt(c, min));
min = hn::IfThenElse(less, c, min);
arg = hn::IfThenElse(RebindMask(di, less), vfirst, arg);
}
HWY_DASSERT(hn::AllTrue(df, hn::Le(min, prev_cost)));
hn::Store(min, df, &D(cluster_idx, last));
hn::Store(arg, di, &T(cluster_idx, last));
}
}
// Backtrack to find centers. Clusters are [T(k, last), last].
size_t last = kGroupSize - 1;
size_t unused_clusters = 0;
for (size_t k = kClusters - 1; k < kClusters; --k) {
const size_t start = static_cast<size_t>(T(k, last));
// Center = mean, O(1) thanks to cumulative sums.
const float sum = cc.SumOfSorted(start, last);
const int size = static_cast<int>(last) - static_cast<int>(start) + 1;
HWY_DASSERT(0 < size && size <= static_cast<int>(kGroupSize));
centers[k] = sum / static_cast<float>(size);
// We know the range inside sorted_and_i[]; translate to original indices,
// which are stored inside each of the sorted_and_i mantissas.
for (size_t i = start; i <= last; ++i) {
const size_t idx_x = FloatPayload::Get(sorted_and_i[i]);
HWY_DASSERT(idx_x < kGroupSize);
indices[idx_x] = static_cast<uint16_t>(k);
}
// Not using all clusters. Avoid out of bounds accesses by stopping early.
if (start == 0) {
unused_clusters = k;
for (size_t cluster = 0; cluster < unused_clusters; ++cluster) {
centers[cluster] = 0.0f;
}
break;
}
last = start - 1;
HWY_DASSERT(last < kGroupSize);
}
if (HWY_IS_DEBUG_BUILD) {
// Centers are in ascending order.
for (size_t i = unused_clusters + 1; i < kClusters; ++i) {
HWY_DASSERT(centers[i] >= centers[i - 1]);
}
}
return unused_clusters;
}
}; // NuqClustering
// Bit-packing 4-bit values is trivial if we have 2 or 4 independent vectors:
// simply shift+OR them together into a full vector of 8 or 16-bit lanes.
// However, the order then depends on the vector length, which is unacceptable
// because we may store the encoding to disk and decode on another CPU.
//
// The dependency on vector length could be removed by introducing fixed-size
// packets and loading the next vector from a fixed offset known to be at
// least the vector length. However, this may require packets that are larger
// than the seek granularity of the application (e.g. matrix rows).
//
// We instead choose a continuous stream layout, which seems to entail the
// nibbles being stored and decoded in-order. This involves nontrivial shuffle
// operations which benefit from special-casing for target and vector length.
class NibbleCodec {
public:
// Packs four u16 vectors' lanes to nibbles within one vector, in order, and
// stores that vector to `out`.
template <class D16, class V16 = hn::Vec<D16>>
static HWY_INLINE void OrderedPackU16(D16 d16, V16 in0, V16 in1, V16 in2,
V16 in3, uint8_t* HWY_RESTRICT out) {
const hn::Repartition<uint8_t, D16> d8;
const hn::Repartition<uint32_t, D16> d32;
const hn::Repartition<uint64_t, D16> d64;
using V8 = hn::Vec<decltype(d8)>;
// Pairwise compaction of a single vector so nibbles are packed in-order.
// v16 lanes hold a 4-bit value; OR together adjacent pairs into the lower
// byte of *even* u16.
const auto combine_u16_pair_to_8 = [d16, d32](V16 v16) HWY_ATTR {
return hn::Xor(
v16, hn::BitCast(d16, hn::ShiftRight<12>(hn::BitCast(d32, v16))));
};
const V16 u8_0 = combine_u16_pair_to_8(in0);
const V16 u8_1 = combine_u16_pair_to_8(in1);
const V16 u8_2 = combine_u16_pair_to_8(in2);
const V16 u8_3 = combine_u16_pair_to_8(in3);
V8 packed;
if constexpr (HWY_TARGET <= HWY_AVX3_DL || !HWY_ARCH_X86) {
// 8-bit ConcatEven is efficient. Let digits denote eight u8 lanes
// of u8_1/0: ?d?3 ?c?2 / ?b?1 ?a?0. 8-bit ConcatEven = d3c2 b1a0, and
// again with the second x2_1 gives 7654 3210.
const V8 x2_0 = hn::ConcatEven(d8, BitCast(d8, u8_1), BitCast(d8, u8_0));
const V8 x2_1 = hn::ConcatEven(d8, BitCast(d8, u8_3), BitCast(d8, u8_2));
packed = hn::ConcatEven(d8, x2_1, x2_0);
} else {
// To avoid expensive 8-bit ConcatEven, compact pairs of u32 into the
// lower 16 bits in each u64, with other bits undefined.
const auto combine_u32_pair_to_16 = [d16, d64](V16 v16) HWY_ATTR {
return hn::Xor(
v16, hn::BitCast(d16, hn::ShiftRight<24>(hn::BitCast(d64, v16))));
};
const V16 u16_0 = combine_u32_pair_to_16(u8_0);
const V16 u16_1 = combine_u32_pair_to_16(u8_1);
const V16 u16_2 = combine_u32_pair_to_16(u8_2);
const V16 u16_3 = combine_u32_pair_to_16(u8_3);
// In-order compaction of four vectors into one, keeping only the low
// u16 of every u64. This is the same as above but with 16-bit Concat.
const V16 x2_0 = hn::ConcatEven(d16, u16_1, u16_0);
const V16 x2_1 = hn::ConcatEven(d16, u16_3, u16_2);
packed = hn::BitCast(d8, hn::ConcatEven(d16, x2_1, x2_0));
}
hn::StoreU(packed, d8, out);
}
// Unpacks `Lanes(d16)` nibbles to u16 lanes. The first comes from the low
// nibble of packed[0], then its high nibble, then the next low nibble, etc.
template <class D16, class V16 = hn::Vec<D16>>
static HWY_INLINE V16 OrderedUnpackU16(D16 d16, const uint8_t* packed) {
const hn::Repartition<uint8_t, D16> d8;
using V8 = hn::Vec<decltype(d8)>;
const hn::CappedTag<uint8_t, d16.MaxBytes() / 4> d_load;
// We replicate each byte 4x, so that its two nibbles propagate to both
// u16 lanes that they will initialize. The only performance-portable op to
// replicate bytes is TableLookupBytes, which shuffles 128-bit blocks
// independently. Thus each block receives 4 packed bytes, replicates them
// 4x, shifts/masks, and casts to 8 u16 lanes.
//
// Loading 16 bytes via LoadDup128 only works on AVX3; for smaller vectors,
// it may trigger asan errors from overrunning the end. We thus special-case
// vector lengths, handling any non-constexpr, and constexpr <= 512 bit.
V8 rep4;
if constexpr (HWY_HAVE_SCALABLE) {
// Non constexpr length: 4 per whole block equals size/4.
const size_t num_bytes = HWY_MAX(1, hn::Lanes(d8) / 4);
const V8 bytes = hn::LoadN(d8, packed, num_bytes);
// Replicate bytes 4x: lowest 4 = 0, next 4 = 1 etc.
const V8 idx = hn::ShiftRight<2>(hn::Iota(d8, 0));
rep4 = hn::TableLookupLanes(bytes, hn::IndicesFromVec(d8, idx));
} else if (hn::MaxLanes(d16) <= 8) { // <= 128-bit
const V8 bytes = hn::ResizeBitCast(d8, hn::LoadU(d_load, packed));
alignas(16) static constexpr uint8_t kRep4[16] = {
HWY_REP4(0), HWY_REP4(1), HWY_REP4(2), HWY_REP4(3)};
rep4 = hn::TableLookupBytes(bytes, hn::Load(d8, kRep4));
} else if (HWY_TARGET <= HWY_AVX3_DL || !HWY_ARCH_X86) {
// Plain load, can do 256..512-bit permute across blocks.
const V8 bytes = hn::ResizeBitCast(d8, hn::LoadU(d_load, packed));
alignas(64) static constexpr uint8_t kRep4[64] = {
HWY_REP4(0), HWY_REP4(1), HWY_REP4(2), HWY_REP4(3),
HWY_REP4(4), HWY_REP4(5), HWY_REP4(6), HWY_REP4(7),
HWY_REP4(8), HWY_REP4(9), HWY_REP4(10), HWY_REP4(11),
HWY_REP4(12), HWY_REP4(13), HWY_REP4(14), HWY_REP4(15)};
rep4 = hn::TableLookupLanes(bytes, hn::SetTableIndices(d8, kRep4));
} else if (hn::MaxLanes(d16) == 16) { // 256-bit
const V8 bytes = hn::ResizeBitCast(d8, hn::LoadU(d_load, packed));
// First copy to upper block for TableLookupBytes. This is slightly
// faster than 64-bit BroadcastLane.
const V8 bcast = hn::ConcatLowerLower(d8, bytes, bytes);
alignas(32) static constexpr uint8_t kRep4[32] = {
HWY_REP4(0), HWY_REP4(1), HWY_REP4(2), HWY_REP4(3),
HWY_REP4(4), HWY_REP4(5), HWY_REP4(6), HWY_REP4(7)};
rep4 = hn::TableLookupBytes(bcast, hn::Load(d8, kRep4));
} else if (hn::MaxLanes(d16) == 32) { // 512-bit
const V8 bytes = hn::LoadDup128(d8, packed);
alignas(64) static constexpr uint8_t kRep4[64] = {
HWY_REP4(0), HWY_REP4(1), HWY_REP4(2), HWY_REP4(3),
HWY_REP4(4), HWY_REP4(5), HWY_REP4(6), HWY_REP4(7),
HWY_REP4(8), HWY_REP4(9), HWY_REP4(10), HWY_REP4(11),
HWY_REP4(12), HWY_REP4(13), HWY_REP4(14), HWY_REP4(15)};
rep4 = hn::TableLookupBytes(bytes, hn::Load(d8, kRep4));
} else {
HWY_DASSERT(false);
}
const V16 mask4 = hn::Set(d16, 0xF);
const V16 u16 = BitCast(d16, rep4);
// In-order unpack. Right-shift odd u16 by 4. Example with two packed
// bytes, one digit representing a nibble:
// 32 32 32 32 | 10 10 10 10 u16
// z3 23 32 32 | z1 01 10 10 OddEven+ShiftRight
// zz z3 zz z2 | zz z1 zz z0 And (unpacked result)
return hn::And(mask4, hn::OddEven(hn::ShiftRight<4>(u16), u16));
}
};
// Encode/decode functions.
class NuqCodec {
// 256-bit vectors can hold 16 bf16, otherwise we require 2x128-bit.
template <class DU>
static constexpr size_t NumTables(DU du) {
return (!HWY_HAVE_SCALABLE && du.MaxBytes() >= 32) ? 1 : 2;
}
// Unpacks `centers` from SFP into bf16 and loads them into one or two vectors
// for use by [Two]TableLookups. Returns as u16 because TableLookupLanes might
// not be available for bf16.
template <class DU, HWY_IF_U16_D(DU)>
static HWY_INLINE hn::Vec<DU> LoadTable(DU du, const uint8_t* centers,
hn::Vec<DU>* HWY_RESTRICT tbl1) {
// Cap to the table size (kClusters) for decoding SFP - sufficient, and may
// be faster than a large vector.
const hn::CappedTag<hwy::bfloat16_t, kClusters> d_table;
// We ResizeCast tables to DU: if DU is bigger, table lookups will only
// access lanes < kClusters. If DU is smaller (128-bit), we have 2 tables.
HWY_DASSERT(hn::Lanes(du) >= hn::Lanes(d_table) || NumTables(du) == 2);
HWY_ALIGN hwy::bfloat16_t table[kClusters];
SfpCodec::Dec(d_table, reinterpret_cast<const SfpStream*>(centers),
kClusters, table);
// If we assume >= 128-bit vectors, we can use [Two]TableLookupLanes
// instead of TableLookupBytes, which requires extra interleaving of lo/hi.
HWY_DASSERT(hn::Lanes(du) >= 8);
HWY_IF_CONSTEXPR(NumTables(du) == 2) {
// Reduce cap for second half to avoid loading past the end of the table.
const hn::CappedTag<hwy::bfloat16_t, kClusters / 2> d_table2;
*tbl1 = hn::ResizeBitCast(du, hn::LoadU(d_table2, table + kClusters / 2));
}
return hn::ResizeBitCast(du, hn::Load(d_table, table));
}
// Unpacks per-weight indices and sets c0/c1 to the corresponding centers.
template <class DU>
static HWY_INLINE void TableLookups(DU du, hn::Vec<DU> tbl0, hn::Vec<DU> tbl1,
const uint8_t* packed, hn::Vec<DU>& c0,
hn::Vec<DU>& c1) {
using V16 = hn::Vec<decltype(du)>;
const size_t N16 = hn::Lanes(du);
const V16 idx0 = NibbleCodec::OrderedUnpackU16(du, packed);
const V16 idx1 = NibbleCodec::OrderedUnpackU16(du, packed + N16 / 2);
const auto indices0 = hn::IndicesFromVec(du, idx0);
const auto indices1 = hn::IndicesFromVec(du, idx1);
HWY_IF_CONSTEXPR(NumTables(du) == 1) {
(void)tbl1;
c0 = hn::TableLookupLanes(tbl0, indices0);
c1 = hn::TableLookupLanes(tbl0, indices1);
}
HWY_IF_CONSTEXPR(NumTables(du) == 2) { // `else` is poorly formatted.
c0 = hn::TwoTablesLookupLanes(du, tbl0, tbl1, indices0);
c1 = hn::TwoTablesLookupLanes(du, tbl0, tbl1, indices1);
}
}
public:
// Encodes `num` floats starting from `in`. `out` points to compressed
// storage for `out_capacity` values and `out_ofs` indicates the destination
// offset within it, in units of float values, for parallel encoding by
// multiple threads. `num`, `out_capacity`, and `out_ofs` must all be
// multiples of `kGroupSize`. Returns the total number of unused clusters,
// which is expected to be zero.
template <class DF, HWY_IF_F32_D(DF)>
static HWY_INLINE size_t Enc(DF df, const float* const in, const size_t num,
ClusterBuf& buf, const size_t out_capacity,
NuqStream* const out, const size_t out_ofs) {
const hn::Repartition<uint16_t, DF> d16;
using V16 = hn::Vec<decltype(d16)>;
const size_t N16 = hn::Lanes(d16);
HWY_ASSERT(kGroupSize >= 4 * N16);
HWY_ASSERT(out_ofs + num <= out_capacity);
buf.Resize(num);
HWY_ASSERT(num % kGroupSize == 0);
HWY_ASSERT(out_capacity % kGroupSize == 0);
HWY_ASSERT(out_ofs % kGroupSize == 0);
const size_t num_groups = num / kGroupSize;
const size_t ofs_groups = out_ofs / kGroupSize;
size_t unused_clusters = 0;
for (size_t g = 0; g < num_groups; ++g) {
const float* HWY_RESTRICT g_in = in + g * kGroupSize;
float* HWY_RESTRICT g_centers = buf.centers.get() + g * kClusters;
uint16_t* HWY_RESTRICT g_idx = buf.idx.get() + g * kGroupSize;
unused_clusters += NuqClustering::ClusterExactL2(df, g_in, kGroupSize,
buf, g_centers, g_idx);
}
uint8_t* centers = &out->byte + ofs_groups * kClusters;
SfpCodec::Enc(df, buf.centers.get(), num_groups * kClusters,
reinterpret_cast<SfpStream*>(centers));
uint8_t* packed_start = &out->byte + NuqStream::PackedStart(out_capacity) +
ofs_groups * kGroupSize / 2;
HWY_UNROLL(1)
for (size_t g = 0; g < num_groups; ++g) {
const uint16_t* HWY_RESTRICT g_idx = buf.idx.get() + g * kGroupSize;
uint8_t* HWY_RESTRICT g_packed = packed_start + g * kGroupSize / 2;
HWY_UNROLL(1)
for (size_t i = 0; i < kGroupSize; i += 4 * N16) {
const V16 idx0 = hn::LoadU(d16, g_idx + i + N16 * 0);
const V16 idx1 = hn::LoadU(d16, g_idx + i + N16 * 1);
const V16 idx2 = hn::LoadU(d16, g_idx + i + N16 * 2);
const V16 idx3 = hn::LoadU(d16, g_idx + i + N16 * 3);
NibbleCodec::OrderedPackU16(d16, idx0, idx1, idx2, idx3,
g_packed + i / 2);
}
}
return unused_clusters;
}
// Decodes `num` values from the stream `in`, starting at the offset `in_ofs`
// (in units of values), to bf16 in `out`. `in_capacity`, `in_ofs` and `num`
// must all be multiples of `kGroupSize`.
template <class DBF, HWY_IF_BF16_D(DBF)>
static HWY_INLINE void Dec(DBF dbf, const size_t in_capacity,
const NuqStream* const in, const size_t in_ofs,
hwy::bfloat16_t* const out, const size_t num) {
const hn::RebindToUnsigned<decltype(dbf)> d16;
using V16 = hn::Vec<decltype(d16)>;
const size_t N16 = hn::Lanes(d16);
HWY_DASSERT(kGroupSize >= 4 * N16);
HWY_DASSERT(in_ofs + num <= in_capacity);
HWY_DASSERT(in_capacity % kGroupSize == 0);
HWY_DASSERT(in_ofs % kGroupSize == 0);
HWY_DASSERT(num % kGroupSize == 0);
const size_t num_groups = num / kGroupSize;
const size_t ofs_groups = in_ofs / kGroupSize;
const uint8_t* tables = &in->byte + ofs_groups * kClusters;
const uint8_t* packed_start = &in->byte +
NuqStream::PackedStart(in_capacity) +
ofs_groups * kGroupSize / 2;
HWY_UNROLL(1)
for (size_t g = 0; g < num_groups; ++g) {
const uint8_t* g_centers = tables + g * kClusters;
const uint8_t* HWY_RESTRICT g_packed = packed_start + g * kGroupSize / 2;
hwy::bfloat16_t* HWY_RESTRICT g_out = out + g * kGroupSize;
V16 tbl1 = Zero(d16);
const V16 tbl0 = LoadTable(d16, g_centers, &tbl1);
HWY_UNROLL(1)
for (size_t i = 0; i < kGroupSize; i += 2 * N16) {
V16 c0, c1;
TableLookups(d16, tbl0, tbl1, g_packed + i / 2, c0, c1);
hn::StoreU(BitCast(dbf, c0), dbf, g_out + i + N16 * 0);
hn::StoreU(BitCast(dbf, c1), dbf, g_out + i + N16 * 1);
}
}
}
// Decodes `num` values from the stream `in`, starting at the offset
// `in_ofs` (in units of values), to f32 in `out`. `in_capacity`,
// `in_ofs` and `num` must all be multiples of `kGroupSize`.
template <class DF, HWY_IF_F32_D(DF)>
static HWY_INLINE void Dec(DF df, const size_t in_capacity,
const NuqStream* const in, const size_t in_ofs,
float* const out, const size_t num) {
const hn::Repartition<hwy::bfloat16_t, DF> dbf;
const hn::RebindToUnsigned<decltype(dbf)> d16;
using V16 = hn::Vec<decltype(d16)>;
using VF = hn::Vec<DF>;
const size_t NF = hn::Lanes(df);
HWY_DASSERT(kGroupSize >= 4 * NF);
HWY_DASSERT(in_ofs + num <= in_capacity);
HWY_DASSERT(in_capacity % kGroupSize == 0);
HWY_DASSERT(in_ofs % kGroupSize == 0);
HWY_DASSERT(num % kGroupSize == 0);
const size_t ofs_groups = in_ofs / kGroupSize;
const size_t num_groups = num / kGroupSize;
const uint8_t* tables = &in->byte + ofs_groups * kClusters;
const uint8_t* packed_start = &in->byte +
NuqStream::PackedStart(in_capacity) +
ofs_groups * kGroupSize / 2;
HWY_UNROLL(1)
for (size_t g = 0; g < num_groups; ++g) {
const uint8_t* g_centers = tables + g * kClusters;
const uint8_t* HWY_RESTRICT g_packed = packed_start + g * kGroupSize / 2;
float* HWY_RESTRICT g_out = out + g * kGroupSize;
V16 tbl1 = Zero(d16);
const V16 tbl0 = LoadTable(d16, g_centers, &tbl1);
HWY_UNROLL(1)
for (size_t i = 0; i < kGroupSize; i += 4 * NF) {
V16 c0, c1;
TableLookups(d16, tbl0, tbl1, g_packed + i / 2, c0, c1);
const VF f0 = hn::PromoteLowerTo(df, BitCast(dbf, c0));
const VF f1 = hn::PromoteUpperTo(df, BitCast(dbf, c0));
const VF f2 = hn::PromoteLowerTo(df, BitCast(dbf, c1));
const VF f3 = hn::PromoteUpperTo(df, BitCast(dbf, c1));
hn::StoreU(f0, df, g_out + i + NF * 0);
hn::StoreU(f1, df, g_out + i + NF * 1);
hn::StoreU(f2, df, g_out + i + NF * 2);
hn::StoreU(f3, df, g_out + i + NF * 3);
}
}
}
// Accumulates into `sum0..3` dot products of decoded values with `num` bf16
// from `vec_aligned`. DF is f32 because sum0..3 are also f32. `in_capacity`,
// `in_ofs` and `num` must all be multiples of `kGroupSize`.
template <class DF, HWY_IF_F32_D(DF)>
static HWY_INLINE void Dot(DF df, const size_t in_capacity,
const NuqStream* const in, const size_t in_ofs,
const hwy::bfloat16_t* const vec_aligned,
const size_t num, hn::Vec<DF>& sum0,
hn::Vec<DF>& sum1, hn::Vec<DF>& sum2,
hn::Vec<DF>& sum3) {
const hn::Repartition<hwy::bfloat16_t, DF> dbf;
const hn::RebindToUnsigned<decltype(dbf)> d16;
using VBF = hn::Vec<decltype(dbf)>;
using V16 = hn::Vec<decltype(d16)>;
const size_t N16 = hn::Lanes(d16);
HWY_DASSERT(kGroupSize >= 4 * N16);
HWY_DASSERT(in_ofs + num <= in_capacity);
HWY_DASSERT(in_capacity % kGroupSize == 0);
HWY_DASSERT(in_ofs % kGroupSize == 0);
HWY_DASSERT(num % kGroupSize == 0);
const size_t ofs_groups = in_ofs / kGroupSize;
const size_t num_groups = num / kGroupSize;
const uint8_t* tables = &in->byte + ofs_groups * kClusters;
const uint8_t* packed_start = &in->byte +
NuqStream::PackedStart(in_capacity) +
ofs_groups * kGroupSize / 2;
HWY_UNROLL(1)
for (size_t g = 0; g < num_groups; ++g) {
const uint8_t* g_centers = tables + g * kClusters;
const uint8_t* HWY_RESTRICT g_packed = packed_start + g * kGroupSize / 2;
const hwy::bfloat16_t* HWY_RESTRICT g_in = vec_aligned + g * kGroupSize;
V16 tbl1 = Zero(d16);
const V16 tbl0 = LoadTable(d16, g_centers, &tbl1);
HWY_UNROLL(1)
for (size_t i = 0; i < kGroupSize; i += 2 * N16) {
V16 c0, c1;
TableLookups(d16, tbl0, tbl1, g_packed + i / 2, c0, c1);
const VBF in0 = hn::Load(dbf, g_in + i + N16 * 0);
const VBF in1 = hn::Load(dbf, g_in + i + N16 * 1);
sum0 = hn::ReorderWidenMulAccumulate(df, in0, BitCast(dbf, c0), sum0,
sum1);
sum2 = hn::ReorderWidenMulAccumulate(df, in1, BitCast(dbf, c1), sum2,
sum3);
}
}
}
// Accumulates into `sum0..3` dot products of decoded values with `num` f32
// from `vec_aligned`. `in_capacity`, `in_ofs` and `num` must all be
// multiples of `kGroupSize`.
template <class DF, HWY_IF_F32_D(DF)>
static HWY_INLINE void Dot(DF df, const size_t in_capacity,
const NuqStream* const in, const size_t in_ofs,
const float* const vec_aligned, const size_t num,
hn::Vec<DF>& sum0, hn::Vec<DF>& sum1,
hn::Vec<DF>& sum2, hn::Vec<DF>& sum3) {
const hn::Repartition<hwy::bfloat16_t, DF> dbf;
const hn::RebindToUnsigned<decltype(dbf)> d16;
using VF = hn::Vec<decltype(df)>;
using V16 = hn::Vec<decltype(d16)>;
const size_t NF = hn::Lanes(df);
HWY_DASSERT(kGroupSize >= 4 * NF);
HWY_DASSERT(in_ofs + num <= in_capacity);
HWY_DASSERT(in_capacity % kGroupSize == 0);
HWY_DASSERT(in_ofs % kGroupSize == 0);
HWY_DASSERT(num % kGroupSize == 0);
const size_t ofs_groups = in_ofs / kGroupSize;
const size_t num_groups = num / kGroupSize;
const uint8_t* tables = &in->byte + ofs_groups * kClusters;
const uint8_t* packed_start = &in->byte +
NuqStream::PackedStart(in_capacity) +
ofs_groups * kGroupSize / 2;
HWY_UNROLL(1)
for (size_t g = 0; g < num_groups; ++g) {
const uint8_t* g_centers = tables + g * kClusters;
const uint8_t* HWY_RESTRICT g_packed = packed_start + g * kGroupSize / 2;
const float* HWY_RESTRICT g_in = vec_aligned + g * kGroupSize;
V16 tbl1 = Zero(d16);
const V16 tbl0 = LoadTable(d16, g_centers, &tbl1);
HWY_UNROLL(1)
for (size_t i = 0; i < kGroupSize; i += 4 * NF) {
V16 c0, c1;
TableLookups(d16, tbl0, tbl1, g_packed + i / 2, c0, c1);
const VF in0 = hn::LoadU(df, g_in + i + NF * 0);
const VF in1 = hn::LoadU(df, g_in + i + NF * 1);
const VF in2 = hn::LoadU(df, g_in + i + NF * 2);
const VF in3 = hn::LoadU(df, g_in + i + NF * 3);
const VF f0 = hn::PromoteLowerTo(df, BitCast(dbf, c0));
const VF f1 = hn::PromoteUpperTo(df, BitCast(dbf, c0));
const VF f2 = hn::PromoteLowerTo(df, BitCast(dbf, c1));
const VF f3 = hn::PromoteUpperTo(df, BitCast(dbf, c1));
sum0 = hn::MulAdd(in0, f0, sum0);
sum1 = hn::MulAdd(in1, f1, sum1);
sum2 = hn::MulAdd(in2, f2, sum2);
sum3 = hn::MulAdd(in3, f3, sum3);
}
}
}
}; // NuqCodec
// NOLINTNEXTLINE(google-readability-namespace-comments)
} // namespace HWY_NAMESPACE
} // namespace gcpp
HWY_AFTER_NAMESPACE();
#endif // THIRD_PARTY_GEMMA_CPP_COMPRESSION_NUQ_INL_H_
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