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gemma.cpp/ops/matmul-inl.h

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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 <stddef.h>
#include <stdint.h>
#include <stdio.h>
#include <vector>
#include "compression/types.h"
#include "ops/matmul.h" // IWYU pragma: export
#include "util/allocator.h" // CacheInfo
#include "util/basics.h"
#include "util/mat.h"
#include "util/threading_context.h"
#include "hwy/base.h"
#include "hwy/profiler.h"
#include "hwy/timer.h"
// Include guard for (potentially) SIMD code.
#if defined(THIRD_PARTY_GEMMA_CPP_MATMUL_TOGGLE) == defined(HWY_TARGET_TOGGLE)
#ifdef THIRD_PARTY_GEMMA_CPP_MATMUL_TOGGLE
#undef THIRD_PARTY_GEMMA_CPP_MATMUL_TOGGLE
#else
#define THIRD_PARTY_GEMMA_CPP_MATMUL_TOGGLE
#endif
#include "hwy/highway.h"
// After highway.h
#include "compression/compress-inl.h"
HWY_BEFORE_NAMESPACE();
namespace gcpp {
namespace HWY_NAMESPACE {
namespace hn = hwy::HWY_NAMESPACE;
// Like hn::PromoteOddTo, but uses assembly to avoid an extra vector register.
template <class DF, class DBF = hn::Repartition<BF16, DF>>
static hn::VFromD<DF> FastPromoteOddTo(DF df, hn::VFromD<DBF> vbf) {
// Promoting odd means clearing the lower 16 bits. Doing this via AND
// requires a second input vector, which we prefer to avoid due to high
// register pressure. Unfortunately `hn::IfThenElseZero` and
// `IfThenZeroElse` are 'optimized' back to AND, hence resort to assembly.
// Note that SVE also has separate mask registers, but it anyway uses the
// native BF16 dot product code path.
#if HWY_TARGET < HWY_AVX2
const hn::Repartition<uint16_t, decltype(df)> du16;
const auto odd = static_cast<__mmask32>(0xAAAAAAAAu); // 10..10 (32 lanes)
// In-out because this is called after PromoteEvenTo, when we can clobber
// the original bf16 input.
auto u16 = hn::BitCast(du16, vbf).raw;
// Odd u16 lanes are set to the input and even lanes are zero.
asm("vmovdqu16 %[U16], %[U16]%{%[ODD]%}%{z%};"
: [U16] "+v"(u16) // AVX-512 reg
: [ODD] "Yk"(odd)); // mask reg except k0 (not writable)
return hn::BitCast(df, hn::VFromD<decltype(du16)>{u16});
#else
return hn::PromoteOddTo(df, vbf);
#endif
}
// Converts from float intermediate to/from MatMul output type `TC`.
template <class DC, HWY_IF_F32_D(DC)>
hn::Vec<DC> TCFromF32(DC /*dc*/, hn::Vec<DC> vf) {
return vf;
}
template <class DC, class DF = hn::Rebind<float, DC>, HWY_IF_BF16_D(DC)>
hn::Vec<DC> TCFromF32(DC dc, hn::Vec<DF> vf) {
return hn::DemoteTo(dc, vf);
}
template <class DC, HWY_IF_F32_D(DC)>
hn::Vec<DC> F32FromTC(DC /*dc*/, hn::Vec<DC> vc) {
return vc;
}
template <class DC, class DF = hn::Rebind<float, DC>, HWY_IF_BF16_D(DC)>
hn::Vec<DF> F32FromTC(DC dc, hn::Vec<DC> vc) {
return hn::PromoteTo(DF(), vc);
}
// Tag classes, passed to `MMKernel::A2C0` to choose between writing one
// (all-K) result to C via `MMStoreHorizontalSumsIntoC`, or accumulating the
// next kc result into `C`.
struct MMSetC {};
struct MMAddC {};
// Stores horizontal sums of up to 16 vectors via transpose.
template <size_t kRowsAC>
class MMStoreHorizontalSumsIntoC {
public:
static_assert(kNR == 4); // for `StoreInterleaved4`
// Given 16 (`kRowsAC x kNR`) full vectors of 32-bit float, returns four
// 4-wide float vectors with their horizontal sums.
// `Crc` are the 16 combinations of an A row vector indexed by `r`, times a
// transposed B row vector indexed by `c`. Their elements are thus a subset
// of the terms of the dot product constituting the final `C[r, c]` result.
// Thus we compute the horizontal sums of each `Crc`. The elements may be
// permuted because we multiply bf16 via `ReorderWidenMulAccumulate`, but
// this does not change their horizontal sum.
template <class DF, class VF = hn::Vec<DF>, class D4 = hn::Full128<float>,
class V4 = hn::Vec<D4>>
HWY_INLINE void Reduce4x4(DF df, //
VF C00, VF C01, VF C02, VF C03, //
VF C10, VF C11, VF C12, VF C13, //
VF C20, VF C21, VF C22, VF C23, //
VF C30, VF C31, VF C32, VF C33, //
V4& sum0, V4& sum1, V4& sum2, V4& sum3) {
HWY_ALIGN float buf[16 * hn::MaxLanes(df)];
HWY_LANES_CONSTEXPR const size_t N = hn::Lanes(df);
// Horizontal reductions (`ReduceSum`) are rather expensive, entailing
// log(N) operations for vectors of length N. Because `kNR` == 4, we
// instead use `StoreInterleaved4` for a vector length-agnostic
// 'transpose': `buf[0, 4 * N)` holds `C00[0], C01[0], C02[0], C03[0],
// C00[1], C01[1], C02[1], C03[1] .. C00[N-1], C01[N-1], C02[N-1],
// C03[N-1]`.
MaybeStoreInterleaved4<0>(df, N, C00, C01, C02, C03, buf);
MaybeStoreInterleaved4<1>(df, N, C10, C11, C12, C13, buf);
MaybeStoreInterleaved4<2>(df, N, C20, C21, C22, C23, buf);
MaybeStoreInterleaved4<3>(df, N, C30, C31, C32, C33, buf);
// Adding N consecutive V4 yields horizontal sums of Cr0, Cr1, Cr2, Cr3 in
// the elements of one V4. We have four independent rows `r`, hence the
// code is effectively unrolled, which increases throughput.
// Store to four elements per row of `C`.
// No loop is required because vectors are at least 4*32 bits.
const D4 d4;
sum0 = MaybeLoad<0>(d4, N, buf);
sum1 = MaybeLoad<1>(d4, N, buf);
sum2 = MaybeLoad<2>(d4, N, buf);
sum3 = MaybeLoad<3>(d4, N, buf);
for (size_t lane = 1; lane < N; ++lane) {
sum0 = MaybeAdd<0>(d4, N, sum0, buf + kNR * lane);
sum1 = MaybeAdd<1>(d4, N, sum1, buf + kNR * lane);
sum2 = MaybeAdd<2>(d4, N, sum2, buf + kNR * lane);
sum3 = MaybeAdd<3>(d4, N, sum3, buf + kNR * lane);
}
}
// Scales the dot-product terms plus `add` (if non-null) and stores the four
// 4-wide vectors to `C` starting at row 0, column 0. If `tag` is `MMSetC`,
// the vectors are written as-is (first call, or small K). Otherwise, they
// are partial sums and are accumulated into C.
template <class D4, class V4 = hn::Vec<D4>, class Tag, class CView>
HWY_INLINE void Store(D4 d4, V4 sum0, V4 sum1, V4 sum2, V4 sum3,
const float scale, const float* HWY_RESTRICT add,
const size_t imc, Tag tag, CView C_MC_NR) const {
const V4 vscale = hn::Set(d4, scale);
HWY_ALIGN static constexpr float kZero[4] = {};
const V4 vadd = hn::Load(d4, add ? add : kZero);
MaybeScaleAndStore<0>(d4, sum0, vscale, vadd, tag, imc, C_MC_NR);
MaybeScaleAndStore<1>(d4, sum1, vscale, vadd, tag, imc, C_MC_NR);
MaybeScaleAndStore<2>(d4, sum2, vscale, vadd, tag, imc, C_MC_NR);
MaybeScaleAndStore<3>(d4, sum3, vscale, vadd, tag, imc, C_MC_NR);
}
private:
// These helper functions hoist if() out of the main code below. They have
// no effect if kRow >= kRowsAC.
template <size_t kRow, class DD, class VD = hn::Vec<DD>>
static HWY_INLINE void MaybeStoreInterleaved4(DD dd, size_t N, VD Cr0, VD Cr1,
VD Cr2, VD Cr3,
float* HWY_RESTRICT buf) {
if constexpr (kRow < kRowsAC) {
hn::StoreInterleaved4(Cr0, Cr1, Cr2, Cr3, dd, buf + 4 * kRow * N);
}
}
// Note: N is the number of lanes in the StoreInterleaved4 vectors, not V4.
template <size_t kRow, class DF4, class VF4 = hn::Vec<DF4>>
static HWY_INLINE VF4 MaybeLoad(DF4 df4, size_t N,
const float* HWY_RESTRICT buf) {
if constexpr (kRow < kRowsAC) {
return hn::Load(df4, buf + 4 * kRow * N);
} else {
return hn::Zero(df4);
}
}
template <size_t kRow, class DF4, class VF4 = hn::Vec<DF4>>
static HWY_INLINE VF4 MaybeAdd(DF4 df4, size_t N, VF4 sum,
const float* HWY_RESTRICT buf) {
if constexpr (kRow < kRowsAC) {
return hn::Add(sum, hn::Load(df4, buf + 4 * kRow * N));
} else {
return sum;
}
}
template <size_t kRow, /*deduced:*/ class DF4, class VF4 = hn::Vec<DF4>,
class Tag, class CView>
static HWY_INLINE void MaybeScaleAndStore(DF4 df4, VF4 sum, VF4 vscale,
VF4 vadd, Tag, const size_t imc,
CView C_MC_NR) {
if constexpr (kRow < kRowsAC) {
using TC = hwy::RemoveCvRef<decltype(C_MC_NR.Row(0)[0])>;
TC* HWY_RESTRICT pos = C_MC_NR.Row(imc + kRow);
const hn::Rebind<TC, DF4> dc4;
if constexpr (hwy::IsSame<Tag, MMAddC>()) {
vadd = F32FromTC(dc4, hn::Load(dc4, pos)); // load prior value
} else {
static_assert(hwy::IsSame<Tag, MMSetC>());
// vadd remains the bias (added once, the first time we store to C)
}
const VF4 out = hn::MulAdd(sum, vscale, vadd);
hn::Store(TCFromF32(dc4, out), dc4, pos);
}
}
}; // MMStoreHorizontalSumsIntoC
// Stateless, wraps member functions.
class MMDecompress {
public:
// Decompresses `kNR x kc` from `B[row_b, range_kc.begin()]` to row 0,
// col 0 of `B_view`. Decompressing SFP is relatively cheap on `AVX3_DL`
// thanks to its large table lookups, and less so on other targets.
template <typename TB>
static StridedViewBF DecompressB(const MatPtrT<TB>& B, const size_t row_b,
const IndexRange& range_kc,
const StridedViewBF B_view) {
const hn::ScalableTag<BF16> dbf;
HWY_LANES_CONSTEXPR const size_t NBF = hn::Lanes(dbf);
// Neither A nor B require padding because `LoopKC` handles remainders.
if constexpr (hwy::IsSame<TB, BF16>()) {
return StridedViewBF(B, row_b, range_kc.begin(), range_kc.Num());
}
const PackedSpan<const TB> B_span = B.PaddedSpan();
const size_t kc = range_kc.Num();
const size_t col0 = range_kc.begin();
for (size_t r = 0; r < kNR; ++r) {
const size_t packed_ofs = (row_b + r) * B.Stride() + col0;
BF16* HWY_RESTRICT to = B_view.Row(r);
DecompressAndZeroPad(dbf, B_span, packed_ofs, to, kc);
// Verify that we zero-padded.
if constexpr (HWY_IS_DEBUG_BUILD) {
for (size_t i = kc; i < hwy::RoundUpTo(kc, NBF); ++i) {
HWY_DASSERT(hwy::ConvertScalarTo<float>(to[i]) == 0.0f);
}
}
}
return B_view;
}
template <typename TA>
static HWY_INLINE StridedViewBF MaybeDecompressA(const MatPtrT<TA>& A,
MMAutoTune<MMParA>& autotune,
const MatMulEnv& env,
MMOptions options) {
if constexpr (IsBF16<TA>()) {
// We can use a view, regardless of columns/padding, because
// `MMKernel::LoopKC` supports non-vector multiples.
return StridedViewBF(A, 0, 0, A.Cols());
} else {
// Always decompress. To reduce code size/compile time, we no longer
// support a separate F32 kernel; most A are already BF16. We also only
// have a single MMEntireA.
HWY_ASSERT(options.cluster_idx == 0);
const StridedViewBF A_view = env.A_BF.A(A.Extents());
AutotuneDecompressA(A, A_view, autotune, env, options);
return A_view;
}
}
private:
// Decompresses all `M x K` from `A` into padded BF16 `A_view`.
static HWY_NOINLINE void DecompressA(const MatPtrT<float>& A,
const StridedViewBF A_view,
MMAutoTune<MMParA>& autotune,
MMParA par_a, const MatMulEnv& env,
const MMOptions& options) {
const IndexRange all_M(0, A.Rows());
const IndexRange all_K(0, A.Cols());
HWY_DASSERT(all_K.Num() == A_view.Cols());
const hn::ScalableTag<BF16> dbf;
const size_t NBF = hn::Lanes(dbf);
static const auto zone = env.ctx.profiler.AddZone("MM.DecompressA");
const auto do_range =
[&](const IndexRange& range_M, const IndexRange& range_K, size_t worker)
HWY_ATTR {
MMZone mm_zone;
mm_zone.MaybeEnter(worker, zone, env, &autotune);
const size_t col0 = range_K.begin();
const size_t cols = range_K.Num();
// Must be a vector multiple, or the last range before row
// padding, otherwise `DecompressAndZeroPad` overwrites neighbors.
HWY_DASSERT(cols % NBF == 0 || range_K.end() == A.Cols());
for (size_t row_a : range_M) {
const PackedSpan<const float> from =
MakeSpan(A.Row(row_a) + col0, cols);
BF16* HWY_RESTRICT to = A_view.Row(row_a) + col0;
DecompressAndZeroPad(dbf, from, 0, to, cols);
// Verify that we zero-padded.
if constexpr (HWY_IS_DEBUG_BUILD) {
for (size_t i = cols; i < hwy::RoundUpTo(cols, NBF); ++i) {
HWY_DASSERT(hwy::ConvertScalarTo<float>(to[i]) == 0.0f);
}
}
}
};
switch (par_a) {
case MMParA::kNone:
do_range(all_M, all_K, env.ctx.Worker(options.cluster_idx));
break;
case MMParA::kK1:
case MMParA::kK2:
case MMParA::kK4: {
const size_t inner_tasks = static_cast<size_t>(par_a);
// At least one vector, otherwise DecompressAndZeroPad will add
// padding, which might overwrite neighboring tasks. Also a whole cache
// line to avoid false sharing.
const size_t multiple_K =
HWY_MAX(NBF, env.ctx.cache_info.LineBytes() / sizeof(BF16));
DispatchParallelism(options.parallelism, [&](const auto& parallel) {
parallel.ForN(env.ctx, all_K, multiple_K, inner_tasks,
options.cluster_idx,
[&](const IndexRange& range_K, size_t worker) {
do_range(all_M, range_K, worker);
});
});
break;
}
case MMParA::kM:
DispatchParallelism(options.parallelism, [&](const auto& parallel) {
parallel.ForRangeMC(env.ctx, all_M, options.cluster_idx,
[&](size_t row_a, size_t worker) {
do_range(IndexRange(row_a, row_a + 1), all_K,
worker);
});
});
break;
}
}
// Autotuning wrapper for `DoDecompressA`.
static HWY_INLINE void AutotuneDecompressA(const MatPtrT<float>& A,
const StridedViewBF A_view,
MMAutoTune<MMParA>& autotune,
const MatMulEnv& env,
const MMOptions& options) {
if (HWY_LIKELY(autotune.Best())) {
return DecompressA(A, A_view, autotune, *autotune.Best(), env, options);
}
// First call: generate candidates.
if (HWY_UNLIKELY(!autotune.HasCandidates())) {
const MMParA other = (A.Rows() == 1) ? MMParA::kNone : MMParA::kM;
std::vector<MMParA> candidates = {MMParA::kK1, MMParA::kK2, MMParA::kK4,
other};
autotune.SetCandidates(candidates);
}
const MMParA& par_a = autotune.NextConfig();
const uint64_t t0 = hwy::timer::Start();
DecompressA(A, A_view, autotune, par_a, env, options);
const uint64_t t1 =
env.have_timer_stop ? hwy::timer::Stop() : hwy::timer::Start();
const uint64_t min_elapsed = autotune.NotifyTicks(t1 - t0);
if (HWY_UNLIKELY(env.print_measurement && autotune.ShouldPrint())) {
fprintf(stderr, "%s,%7.3f\n", StringFromParA(par_a),
static_cast<double>(min_elapsed) /
hwy::platform::InvariantTicksPerSecond() * 1E6);
}
}
}; // MMDecompress
// Stateless, wraps member functions. Contains the innermost 2-4 loops.
class MMKernel {
public:
// Loop over NC/MC/KC, called from the outer loops. The MOMMS B3A2C0 reads
// `mc x kc` of A, `nc x kc` of B, and updates the `mc x nc` `C_MC_NC`.
// `CView` is either `RowPtrs<TC>` or `StridedView<TC>`.
template <typename TB, typename Tag, class CView>
static void B3A2C0(const StridedViewBF A, const MatPtrT<TB>& B,
const IndexRange& range_mc, const IndexRange& range_kc,
const IndexRange& range_nc, const MMArgs& args,
Tag out_tag, CView C_MC_NC) {
const size_t kc = range_kc.Num();
const StridedViewBF A_view = A.View(range_mc.begin(), range_kc.begin(), kc);
// Upper bound on per-worker storage for `kNR` row ranges of B. Stack
// allocation avoids passing a worker index.
constexpr size_t B_stride_max =
kMaxKC + 2 * CacheInfo::MaxLineBytes() / sizeof(BF16);
HWY_ALIGN BF16 B_storage[kNR * B_stride_max];
const size_t B_stride =
Stride(MatPadding::kOdd, kc, sizeof(BF16), args.line_bytes);
const StridedViewBF B_storage_view(B_storage, kc, B_stride);
const float scale = args.scale_A * B.Scale();
for (size_t inc = 0; inc < range_nc.Num(); inc += kNR) {
// For `add` and `B`, which are global, unlike `C_MC_NC`.
const size_t row_b = range_nc.begin() + inc;
const StridedViewBF B_view =
MMDecompress::DecompressB(B, row_b, range_kc, B_storage_view);
const CView C_MC_NR = C_MC_NC.View(0, inc, kNR);
const float* HWY_RESTRICT add = args.add ? args.add + row_b : nullptr;
A2C0(A_view, B_view, args.mr, range_mc, kc, scale, add, out_tag, C_MC_NR);
}
}
template <typename TB, class CView>
static void ForeachKC(const StridedViewBF A, const MatPtrT<TB>& B,
const IndexRange& range_mc,
const IndexRangePartition& ranges_kc,
const IndexRange& range_nc, const MMArgs& args,
CView C_MC_NC) {
// Peel off the first iteration of the kc loop: avoid zero-initializing `C`
// by writing directly into it, and later accumulating into it.
ranges_kc.VisitFirst([&](const IndexRange& range_kc) {
B3A2C0(A, B, range_mc, range_kc, range_nc, args, MMSetC(), C_MC_NC);
});
ranges_kc.VisitRemaining([&](const IndexRange& range_kc) {
B3A2C0(A, B, range_mc, range_kc, range_nc, args, MMAddC(), C_MC_NC);
});
}
private:
// Element-wise multiplies a vector from one row of A with `kNR` vectors,
// each from a row of transposed B, and adds them to `kNR` fp32 `Cc`
// vectors. The lanes of `Cc` are thus a subset of the terms of the dot
// product which is the MatMul result at column `c`.
//
// Why elementwise, when most MatMul instead broadcast one element from A and
// multiply with one element from kr columns in B to obtain kr columns of C?
// We double the compute throughput on NEON_BF16/SVE/AVX3_ZEN4 by using the
// bf16 * bf16 + f32 `ReorderWidenMulAccumulate`. However, this involves
// pairwise adds, whereas the kr-column approach requires that lanes remain
// separate. Our elementwise approach is fine with pairwise adds because they
// do not change the horizontal sum. However, horizontal sums can be costly,
// so we introduce a fast and new(?) vector-length agnostic 'transpose', see
// `MMAddHorizontalSumsIntoPartial`.
template <class DBF, class VBF = hn::Vec<DBF>,
class DF = hn::Repartition<float, DBF>, class VF = hn::Vec<DF>>
static HWY_INLINE void ElementwiseMulAccNativeBF(DBF dbf, VBF a, VBF b0,
VBF b1, VBF b2, VBF b3,
VF& C0, VF& C1, VF& C2,
VF& C3) {
// This handles a single row of A, so the horizontal sums of `C0..3` are the
// (partial) dot products for 4 consecutive values in one row of C.
static_assert(kNR == 4);
HWY_DASSERT(HWY_NATIVE_DOT_BF16);
const DF df;
VF unused_sum1 = hn::Zero(df);
// When implemented natively, this op includes 'free' f32 accumulation.
C0 = hn::ReorderWidenMulAccumulate(df, a, b0, C0, unused_sum1);
C1 = hn::ReorderWidenMulAccumulate(df, a, b1, C1, unused_sum1);
C2 = hn::ReorderWidenMulAccumulate(df, a, b2, C2, unused_sum1);
C3 = hn::ReorderWidenMulAccumulate(df, a, b3, C3, unused_sum1);
// Ensure unused_sum1 was indeed unused.
HWY_DASSERT(hn::AllTrue(df, hn::Eq(unused_sum1, hn::Zero(df))));
}
// Like `ElementwiseMulAccNativeBF`, but splits BF16 inputs into odd and even
// f32 for use with FMA. Also handles two rows at a time to hide the FMA
// latency (we assume 4 cycles and dual-issue) before writing `C00` again.
template <class DBF, class VBF = hn::Vec<DBF>,
class DF = hn::Repartition<float, DBF>, class VF = hn::Vec<DF>>
static HWY_INLINE void ElementwiseMulAccEmuBF(DBF dbf, VBF a0, VBF a1, VF b0o,
VF b0e, VF b1o, VF b1e, VF b2o,
VF b2e, VF b3o, VF b3e, VF& C00,
VF& C01, VF& C02, VF& C03,
VF& C10, VF& C11, VF& C12,
VF& C13) {
const DF df;
HWY_DASSERT(!HWY_NATIVE_DOT_BF16);
// Avoid `ReorderWidenMulAccumulate` because it requires extra adds for
// the two outputs, and `WidenMulPairwiseAdd` because it wastes an
// opportunity for a free f32 add via FMA, and `MulOddAdd` because we want
// to avoid an extra register for a constant. Use scoping to reduce register
// pressure and avoid spills on 32-register targets. Register usage:
// 4 for a0, a1, a0e, a1e; 8 for `b*`, 16 for `C*` = 28.
{
const VF a0e = hn::PromoteEvenTo(df, a0);
C00 = hn::MulAdd(a0e, b0e, C00);
C01 = hn::MulAdd(a0e, b1e, C01);
C02 = hn::MulAdd(a0e, b2e, C02);
C03 = hn::MulAdd(a0e, b3e, C03);
}
{
const VF a1e = hn::PromoteEvenTo(df, a1);
C10 = hn::MulAdd(a1e, b0e, C10);
C11 = hn::MulAdd(a1e, b1e, C11);
C12 = hn::MulAdd(a1e, b2e, C12);
C13 = hn::MulAdd(a1e, b3e, C13);
}
{
const VF a0o = FastPromoteOddTo(df, a0);
C00 = hn::MulAdd(a0o, b0o, C00);
C01 = hn::MulAdd(a0o, b1o, C01);
C02 = hn::MulAdd(a0o, b2o, C02);
C03 = hn::MulAdd(a0o, b3o, C03);
}
{
const VF a1o = FastPromoteOddTo(df, a1);
C10 = hn::MulAdd(a1o, b0o, C10);
C11 = hn::MulAdd(a1o, b1o, C11);
C12 = hn::MulAdd(a1o, b2o, C12);
C13 = hn::MulAdd(a1o, b3o, C13);
}
}
// Innermost loop over `kc` columns (typically 1024-4096, not necessarily a
// multiple of `NBF`) in steps of one vector, for `kRowsAC` rows of `A_view`
// from range_mc-relative `imc` and `B_view` from row 0 (both at column 0).
// Updates a `kRowsAC x kNR` tile in `C_MC_NR` starting at row `imc`, column
// 0. `A` and `B` are always BF16, `C` can be F32 or BF16. `add` is also
// relative to the C column.
template <size_t kRowsAC, /*deduced:*/ class Tag, class CView>
static HWY_INLINE void LoopKC(const StridedViewBF A_view,
const StridedViewBF B_view, size_t imc,
size_t kc, const float scale,
const float* HWY_RESTRICT add, Tag tag,
CView C_MC_NR) {
const hn::ScalableTag<BF16> dbf;
using VBF = hn::Vec<decltype(dbf)>;
HWY_LANES_CONSTEXPR const size_t NBF = hn::Lanes(dbf);
HWY_DASSERT(kRowsAC <= kMaxMR);
// Rows are aligned to `kMaxMR`, except for the last tile of A.
// `kRowsAC` rows of A (null for the rest) and `kNR` rows of B.
static_assert(kNR == 4);
const BF16* HWY_RESTRICT ar0 = A_view.Row(imc + 0);
const BF16* HWY_RESTRICT ar1 = kRowsAC > 1 ? A_view.Row(imc + 1) : nullptr;
const BF16* HWY_RESTRICT ar2 = kRowsAC > 2 ? A_view.Row(imc + 2) : nullptr;
const BF16* HWY_RESTRICT ar3 = kRowsAC > 3 ? A_view.Row(imc + 3) : nullptr;
const BF16* HWY_RESTRICT br0 = B_view.Row(0);
const BF16* HWY_RESTRICT br1 = B_view.Row(1);
const BF16* HWY_RESTRICT br2 = B_view.Row(2);
const BF16* HWY_RESTRICT br3 = B_view.Row(3);
// Neither A nor B are guaranteed to be zero-padded: they might be a view
// into the left half.
// Accumulate into f32.
const hn::Repartition<float, decltype(dbf)> df;
using VF = hn::Vec<decltype(df)>;
VF C00 = hn::Zero(df), C01 = hn::Zero(df), C02 = hn::Zero(df),
C03 = hn::Zero(df), C10 = hn::Zero(df), C11 = hn::Zero(df),
C12 = hn::Zero(df), C13 = hn::Zero(df), C20 = hn::Zero(df),
C21 = hn::Zero(df), C22 = hn::Zero(df), C23 = hn::Zero(df),
C30 = hn::Zero(df), C31 = hn::Zero(df), C32 = hn::Zero(df),
C33 = hn::Zero(df);
size_t ikc = 0;
const HWY_LANES_CONSTEXPR size_t kc_step = NBF;
if (kc >= kc_step) {
HWY_UNROLL(1)
for (; ikc <= kc - kc_step; ikc += kc_step) {
if constexpr (HWY_NATIVE_DOT_BF16) {
// NOTE: matmul_test has packed B so that it can call Span. The test
// cases with non-vector-multiple K require unaligned loads here.
// However, in actual usage, we should always have padded and thus
// aligned A and B.
const VBF b0 = hn::LoadU(dbf, br0 + ikc);
const VBF b1 = hn::LoadU(dbf, br1 + ikc);
const VBF b2 = hn::LoadU(dbf, br2 + ikc);
const VBF b3 = hn::LoadU(dbf, br3 + ikc);
{
const VBF a0 = hn::Load(dbf, ar0 + ikc);
ElementwiseMulAccNativeBF(dbf, a0, b0, b1, b2, b3, C00, C01, C02,
C03);
}
if constexpr (kRowsAC > 1) {
const VBF a1 = hn::Load(dbf, ar1 + ikc);
ElementwiseMulAccNativeBF(dbf, a1, b0, b1, b2, b3, C10, C11, C12,
C13);
}
if constexpr (kRowsAC > 2) {
const VBF a2 = hn::Load(dbf, ar2 + ikc);
ElementwiseMulAccNativeBF(dbf, a2, b0, b1, b2, b3, C20, C21, C22,
C23);
}
if constexpr (kRowsAC > 3) {
const VBF a3 = hn::Load(dbf, ar3 + ikc);
ElementwiseMulAccNativeBF(dbf, a3, b0, b1, b2, b3, C30, C31, C32,
C33);
}
} else { // !HWY_NATIVE_DOT_BF16
// When both are BF16, it is better to load promote odd/even,
// because lane-crossing promotion for both might be bottlenecked on
// shuffles.
VF b0e, b1e, b2e, b3e, b0o, b1o, b2o, b3o;
{
const VBF b0 = hn::LoadU(dbf, br0 + ikc);
const VBF b1 = hn::LoadU(dbf, br1 + ikc);
const VBF b2 = hn::LoadU(dbf, br2 + ikc);
const VBF b3 = hn::LoadU(dbf, br3 + ikc);
b0e = hn::PromoteEvenTo(df, b0);
b1e = hn::PromoteEvenTo(df, b1);
b2e = hn::PromoteEvenTo(df, b2);
b3e = hn::PromoteEvenTo(df, b3);
b0o = FastPromoteOddTo(df, b0);
b1o = FastPromoteOddTo(df, b1);
b2o = FastPromoteOddTo(df, b2);
b3o = FastPromoteOddTo(df, b3);
}
// Two rows at a time so we have 8 separate dependency chains,
// sufficient for IPC=2 and 4-cycle latency.
{
const VBF a0 = hn::Load(dbf, ar0 + ikc);
const VBF a1 = kRowsAC > 1 ? hn::Load(dbf, ar1 + ikc) : a0;
ElementwiseMulAccEmuBF(dbf, a0, a1, b0o, b0e, b1o, b1e, b2o, b2e,
b3o, b3e, C00, C01, C02, C03, C10, C11, C12,
C13);
}
if constexpr (kRowsAC > 2) {
const VBF a2 = hn::Load(dbf, ar2 + ikc);
const VBF a3 = kRowsAC > 3 ? hn::Load(dbf, ar3 + ikc) : a2;
ElementwiseMulAccEmuBF(dbf, a2, a3, b0o, b0e, b1o, b1e, b2o, b2e,
b3o, b3e, C20, C21, C22, C23, C30, C31, C32,
C33);
}
}
}
}
// Always handle remainders: even though A and B are generally padded, we
// might have a view into the left half of A and/or B.
const size_t remaining_kc = kc - ikc;
HWY_DASSERT(remaining_kc < kc_step);
if (HWY_UNLIKELY(remaining_kc != 0)) {
if constexpr (HWY_NATIVE_DOT_BF16) {
const VBF b0 = hn::LoadN(dbf, br0 + ikc, remaining_kc);
const VBF b1 = hn::LoadN(dbf, br1 + ikc, remaining_kc);
const VBF b2 = hn::LoadN(dbf, br2 + ikc, remaining_kc);
const VBF b3 = hn::LoadN(dbf, br3 + ikc, remaining_kc);
{
const VBF a0 = hn::LoadN(dbf, ar0 + ikc, remaining_kc);
ElementwiseMulAccNativeBF(dbf, a0, b0, b1, b2, b3, C00, C01, C02,
C03);
}
if constexpr (kRowsAC > 1) {
const VBF a1 = hn::LoadN(dbf, ar1 + ikc, remaining_kc);
ElementwiseMulAccNativeBF(dbf, a1, b0, b1, b2, b3, C10, C11, C12,
C13);
}
if constexpr (kRowsAC > 2) {
const VBF a2 = hn::LoadN(dbf, ar2 + ikc, remaining_kc);
ElementwiseMulAccNativeBF(dbf, a2, b0, b1, b2, b3, C20, C21, C22,
C23);
}
if constexpr (kRowsAC > 3) {
const VBF a3 = hn::LoadN(dbf, ar3 + ikc, remaining_kc);
ElementwiseMulAccNativeBF(dbf, a3, b0, b1, b2, b3, C30, C31, C32,
C33);
}
} else { // !HWY_NATIVE_DOT_BF16
// When both are BF16, it is better to load promote odd/even, because
// lane-crossing promotion for both might be bottlenecked on shuffles.
VF b0e, b1e, b2e, b3e, b0o, b1o, b2o, b3o;
{
const VBF b0 = hn::LoadN(dbf, br0 + ikc, remaining_kc);
const VBF b1 = hn::LoadN(dbf, br1 + ikc, remaining_kc);
const VBF b2 = hn::LoadN(dbf, br2 + ikc, remaining_kc);
const VBF b3 = hn::LoadN(dbf, br3 + ikc, remaining_kc);
b0e = hn::PromoteEvenTo(df, b0);
b1e = hn::PromoteEvenTo(df, b1);
b2e = hn::PromoteEvenTo(df, b2);
b3e = hn::PromoteEvenTo(df, b3);
b0o = FastPromoteOddTo(df, b0);
b1o = FastPromoteOddTo(df, b1);
b2o = FastPromoteOddTo(df, b2);
b3o = FastPromoteOddTo(df, b3);
}
// Two rows at a time so we have 8 separate dependency chains,
// sufficient for IPC=2 and 4-cycle latency.
{
const VBF a0 = hn::LoadN(dbf, ar0 + ikc, remaining_kc);
const VBF a1 =
kRowsAC > 1 ? hn::LoadN(dbf, ar1 + ikc, remaining_kc) : a0;
ElementwiseMulAccEmuBF(dbf, a0, a1, b0o, b0e, b1o, b1e, b2o, b2e, b3o,
b3e, C00, C01, C02, C03, C10, C11, C12, C13);
}
if constexpr (kRowsAC > 2) {
const VBF a2 = hn::LoadN(dbf, ar2 + ikc, remaining_kc);
const VBF a3 =
kRowsAC > 3 ? hn::LoadN(dbf, ar3 + ikc, remaining_kc) : a2;
ElementwiseMulAccEmuBF(dbf, a2, a3, b0o, b0e, b1o, b1e, b2o, b2e, b3o,
b3e, C20, C21, C22, C23, C30, C31, C32, C33);
}
}
} // remaining_kc != 0
// This is a substantial fraction (about 1/3) of the total time, but is
// called frequently, so do not add a profiler zone.
MMStoreHorizontalSumsIntoC<kRowsAC> horz;
const hn::Full128<float> d4;
hn::Vec<decltype(d4)> sum0, sum1, sum2, sum3;
horz.Reduce4x4(df, C00, C01, C02, C03, C10, C11, C12, C13, C20, C21, C22,
C23, C30, C31, C32, C33, sum0, sum1, sum2, sum3);
horz.Store(d4, sum0, sum1, sum2, sum3, scale, add, imc, tag, C_MC_NR);
}
// A2C0 in MOMMS terminology updates a `mc x kNR` slice of the output.
// Calls `LoopKC` for each of `mc` rows of A in steps of `mr`. `A_view` is
// `mc x kc` and `B_view` is `(kNR x kc)`. All views, including `add`, start
// at row/col 0.
template <class Tag, class CView>
static HWY_INLINE void A2C0(const StridedViewBF A_view,
const StridedViewBF B_view, size_t mr,
const IndexRange& range_mc, size_t kc,
const float scale, const float* HWY_RESTRICT add,
Tag tag, CView C_MC_NR) {
HWY_DASSERT(1 <= mr && mr <= kMaxMR);
const size_t mc = range_mc.Num();
size_t imc = 0;
// M == 1, or x86 with 8 SIMD registers:
if (HWY_UNLIKELY(mr == 1)) {
for (; imc < mc; ++imc) {
LoopKC<1>(A_view, B_view, imc, kc, scale, add, tag, C_MC_NR);
}
return;
}
// AVX2 (16 registers)
if (HWY_UNLIKELY(mr == 2)) {
if (HWY_LIKELY(mc >= 2)) {
for (; imc <= mc - 2; imc += 2) {
LoopKC<2>(A_view, B_view, imc, kc, scale, add, tag, C_MC_NR);
}
}
if (HWY_UNLIKELY(imc != mc)) {
LoopKC<1>(A_view, B_view, imc, kc, scale, add, tag, C_MC_NR);
}
return;
}
HWY_DASSERT(mr == 4);
if (HWY_LIKELY(mc >= 4)) {
for (; imc <= mc - 4; imc += 4) {
LoopKC<4>(A_view, B_view, imc, kc, scale, add, tag, C_MC_NR);
}
}
const size_t remainder_mc = mc - imc;
HWY_DASSERT(remainder_mc < 4);
if (HWY_UNLIKELY(remainder_mc & 2)) {
LoopKC<2>(A_view, B_view, imc, kc, scale, add, tag, C_MC_NR);
imc += 2;
}
if (HWY_UNLIKELY(remainder_mc & 1)) {
LoopKC<1>(A_view, B_view, imc, kc, scale, add, tag, C_MC_NR);
imc += 1;
}
HWY_DASSERT(imc == mc);
}
};
// Miscellaneous stateless helper functions.
class MMImpl {
// Returns existing entry for the given key or -1.
static HWY_INLINE intptr_t IndexOfKey(MMKeys::Key key, const MMKeys& keys) {
const hwy::Span<const uint64_t> all_keys = keys.Keys();
const hn::ScalableTag<uint64_t> d;
using V = hn::Vec<decltype(d)>;
const V broadcasted = Set(d, key);
const size_t N = hn::Lanes(d);
size_t i = 0;
if (all_keys.size() >= N) {
for (; i <= all_keys.size() - N; i += N) {
const intptr_t pos = hn::FindFirstTrue(
d, hn::Eq(broadcasted, hn::LoadU(d, &all_keys[i])));
if (pos >= 0) return static_cast<intptr_t>(i) + pos;
}
}
const size_t remaining = all_keys.size() - i;
if (HWY_LIKELY(remaining > 0)) {
HWY_DASSERT(remaining < N);
const V v = hn::LoadN(d, &all_keys[i], remaining);
const intptr_t pos = hn::FindFirstTrue(d, hn::Eq(broadcasted, v));
if (pos >= 0) return static_cast<intptr_t>(i) + pos;
}
return -1;
}
public:
static MMPerKey& FindOrAddPerKey(size_t M, size_t K, size_t N, size_t num_B,
size_t vector_bytes,
MatMulEnv::PerCluster& per_cluster) {
const MMKeys::Key key = MMKeys::KeyFromDims(M, K, N, num_B);
intptr_t index = IndexOfKey(key, per_cluster.keys);
// First time we see this shape/key.
if (HWY_UNLIKELY(index < 0)) {
per_cluster.keys.Append(key, vector_bytes);
// Invalidates `MMAutoTune::Best()`.
std::vector<MMPerKey>& per_keys = per_cluster.per_key;
index = per_keys.size();
per_keys.push_back(MMPerKey());
}
return per_cluster.per_key[index];
}
static void NotifyAutotuneResult(MatMulEnv& env, size_t M, size_t K, size_t N,
size_t num_B, double t0,
MMAutoTune<MMConfig>& tuner,
const MMConfig& cfg) {
const uint64_t t1 =
env.have_timer_stop ? hwy::timer::Stop() : hwy::timer::Start();
const double min_elapsed = static_cast<double>(tuner.NotifyTicks(t1 - t0)) /
hwy::platform::InvariantTicksPerSecond();
const double flops = 2 * M * K * N * num_B / min_elapsed; // * 2 for FMA
if (HWY_UNLIKELY(env.print_measurement && tuner.ShouldPrint())) {
fprintf(stderr, "%zu,%zu,%zu,%zu,%7.1f,%.2f,%zu,%4zu,%4zu,%5zu,%s,%zu\n",
M, K, N, num_B, flops * 1E-9, min_elapsed * 1E3, cfg.MR(),
cfg.MC(), cfg.KC(), cfg.NC(), StringFromOrder(cfg.Order()),
cfg.InnerTasks());
}
if (HWY_UNLIKELY(env.print_best && tuner.Best())) {
const auto ratio = [&tuner](uint64_t ticks) -> double {
return static_cast<double>(ticks) /
static_cast<double>(tuner.BestTicks());
};
const MMConfig& best = *tuner.Best();
fprintf(
stderr,
"\n%zu,%zu,%zu,%zu,%7.1f,%.2f,%zu,%4zu,%4zu,%5zu,%s,%zu,%.2f,%.2f\n",
M, K, N, num_B, flops * 1E-9, min_elapsed * 1E3, best.MR(), best.MC(),
best.KC(), best.NC(), StringFromOrder(best.Order()),
best.InnerTasks(), ratio(tuner.WorstMinTicks()),
ratio(tuner.FirstConfigTicks()));
}
}
static void EnsureAligned(const MatPtr& A, const size_t vector_bytes) {
// Ensure A rows are vector-aligned. Neither `Stride` nor `IsPacked` are
// reliable: the latter returns true for single rows, and the former may
// match `Cols` if the width matches the padding.
// Note that B is packed in matmul_test, but otherwise generally padded.
HWY_ASSERT(hwy::IsAligned(A.RowBytes(0), vector_bytes));
if (A.Rows() > 1) {
HWY_ASSERT(hwy::IsAligned(A.RowBytes(1), vector_bytes));
}
}
};
// Defines several variants of the outer M/N/K loops (see `MMOrder`).
class MMLoops {
public:
// Called from `MatMul` from two places: either with the next autotune config,
// or with the best config. `B2` is null unless called from `TwoMatMul`.
template <typename TB, typename TC>
static HWY_NOINLINE void Dispatch(const StridedViewBF A, const MatPtrT<TB>& B,
const MatPtrT<TB>* B2, RowPtrs<TC> C,
const MMArgs& args) {
static const auto zone = args.env.ctx.profiler.AddZone("MM.Dispatch");
PROFILER_ZONE3(args.env.ctx.profiler,
args.env.ctx.Worker(args.options.cluster_idx), zone);
DispatchParallelism(
args.options.parallelism, [&](const auto& parallel) HWY_ATTR {
DispatchOrder(args.order, [&](const auto& order) HWY_ATTR {
Loop(order, parallel, A, B, B2, C, args);
});
});
}
private:
// Granularity of `ForN`. B rows produce C columns, so we
// want a multiple of the line size to prevent false sharing.
static size_t MultipleN(size_t sizeof_TC, size_t line_bytes) {
return HWY_MAX(kNR, line_bytes / sizeof_TC);
}
// Single M and K ranges, parallel N.
template <typename TB, typename TC, class Parallel>
static HWY_INLINE void Loop(MMOrderNT, Parallel parallel,
const StridedViewBF A, const MatPtrT<TB>& B,
const MatPtrT<TB>* B2, RowPtrs<TC> C,
const MMArgs& args) {
static const auto zone = args.env.ctx.profiler.AddZone("MM.NT");
HWY_DASSERT(args.ranges_mc.NumTasks() == 1);
HWY_DASSERT(args.ranges_kc.NumTasks() == 1);
const IndexRange& range_mc = args.ranges_mc.Range(0);
const IndexRange& range_kc = args.ranges_kc.Range(0);
parallel.ForN(
args.env.ctx, args.range_n, MultipleN(sizeof(TC), args.line_bytes),
args.inner_tasks, args.options.cluster_idx,
[&](const IndexRange& range_nc, size_t worker) HWY_ATTR {
MMZone mm_zone;
mm_zone.MaybeEnter(worker, zone, args.env, &args.autotune);
MMKernel::B3A2C0(A, B, range_mc, range_kc, range_nc, args, MMSetC(),
C.View(0, range_nc.begin(), range_nc.Num()));
const StridedViewBF C2 = args.env.C_tiles.C(
Extents2D(range_mc.Num(), range_nc.Num()), worker);
if (B2 != nullptr) {
MMKernel::B3A2C0(A, *B2, range_mc, range_kc, range_nc, args,
MMSetC(), C2);
}
if constexpr (IsBF16<TC>()) {
args.options.MaybeCallFunc(C, range_mc, range_nc, C2, worker);
}
});
}
// Single M range, parallel N, sequential K. Sets C, then accumulates.
template <typename TB, typename TC, class Parallel>
static HWY_INLINE void Loop(MMOrderNT_K, Parallel parallel,
const StridedViewBF A, const MatPtrT<TB>& B,
const MatPtrT<TB>* B2, RowPtrs<TC> C,
const MMArgs& args) {
static const auto zone = args.env.ctx.profiler.AddZone("MM.NT_K");
HWY_DASSERT(args.ranges_mc.NumTasks() == 1);
const IndexRange& range_mc = args.ranges_mc.Range(0);
parallel.ForN(args.env.ctx, args.range_n,
MultipleN(sizeof(TC), args.line_bytes), args.inner_tasks,
args.options.cluster_idx,
[&](const IndexRange& range_nc, size_t worker) HWY_ATTR {
MMZone mm_zone;
mm_zone.MaybeEnter(worker, zone, args.env, &args.autotune);
MMKernel::ForeachKC(
A, B, range_mc, args.ranges_kc, range_nc, args,
C.View(0, range_nc.begin(), range_nc.Num()));
const StridedViewBF C2 = args.env.C_tiles.C(
Extents2D(range_mc.Num(), range_nc.Num()), worker);
if (B2 != nullptr) {
MMKernel::ForeachKC(A, *B2, range_mc, args.ranges_kc,
range_nc, args, C2);
}
if constexpr (IsBF16<TC>()) {
args.options.MaybeCallFunc(C, range_mc, range_nc, C2,
worker);
}
});
}
// Parallel loops over mc/nc blocks of M/range_n, single K.
// Fills `mc x nc` sections of C.
template <typename TB, typename TC, class Parallel>
static HWY_INLINE void Loop(MMOrderNT_MT, Parallel parallel,
const StridedViewBF A, const MatPtrT<TB>& B,
const MatPtrT<TB>* B2, RowPtrs<TC> C,
const MMArgs& args) {
static const auto zone = args.env.ctx.profiler.AddZone("MM.NT_MT");
HWY_DASSERT(args.ranges_kc.NumTasks() == 1);
const IndexRange& range_kc = args.ranges_kc.Range(0);
parallel.ForRangesMC_NC(
args.env.ctx, args.ranges_mc, args.ranges_nc, args.options.cluster_idx,
[&](const IndexRange& range_mc, const IndexRange& range_nc,
size_t worker) HWY_ATTR {
MMZone mm_zone;
mm_zone.MaybeEnter(worker, zone, args.env, &args.autotune);
MMKernel::B3A2C0(
A, B, range_mc, range_kc, range_nc, args, MMSetC(),
C.View(range_mc.begin(), range_nc.begin(), range_nc.Num()));
const StridedViewBF C2 = args.env.C_tiles.C(
Extents2D(range_mc.Num(), range_nc.Num()), worker);
if (B2 != nullptr) {
MMKernel::B3A2C0(A, *B2, range_mc, range_kc, range_nc, args,
MMSetC(), C2);
}
if constexpr (IsBF16<TC>()) {
args.options.MaybeCallFunc(C, range_mc, range_nc, C2, worker);
}
});
}
// Parallel loops over mc/nc blocks of M/range_n, sequential K.
// Accumulates into `mc x nc` sections of `C`.
template <typename TB, typename TC, class Parallel>
static HWY_INLINE void Loop(MMOrderNT_MT_K, Parallel parallel,
const StridedViewBF A, const MatPtrT<TB>& B,
const MatPtrT<TB>* B2, RowPtrs<TC> C,
const MMArgs& args) {
static const auto zone = args.env.ctx.profiler.AddZone("MM.NT_MT_K");
parallel.ForRangesMC_NC(
args.env.ctx, args.ranges_mc, args.ranges_nc, args.options.cluster_idx,
[&](const IndexRange& range_mc, const IndexRange& range_nc,
size_t worker) HWY_ATTR {
MMZone mm_zone;
mm_zone.MaybeEnter(worker, zone, args.env, &args.autotune);
MMKernel::ForeachKC(
A, B, range_mc, args.ranges_kc, range_nc, args,
C.View(range_mc.begin(), range_nc.begin(), range_nc.Num()));
const StridedViewBF C2 = args.env.C_tiles.C(
Extents2D(range_mc.Num(), range_nc.Num()), worker);
if (B2 != nullptr) {
MMKernel::ForeachKC(A, *B2, range_mc, args.ranges_kc, range_nc,
args, C2);
}
if constexpr (IsBF16<TC>()) {
args.options.MaybeCallFunc(C, range_mc, range_nc, C2, worker);
}
});
}
}; // MMLoops
// Computes the matrix product `A * B * scale [+ add]` and stores it in `C`.
//
// `A` is a row-major matrix with `M` rows and `B` is transposed. The latter's
// `K = B.Cols()`, which must match `A.Cols()`, is the number
// of rows in the original B. `N = C.Cols()` must be a multiple of 4. There
// are no other restrictions on shape, though performance is better when `M % 4
// == 0` or `M <= 4`.
//
// If `add` is non-null, the row-vector `add` is added to each of the `M` rows
// of `C`, which is a row-major matrix with arbitrary stride. A scale for
// `add` is not supported, so make sure its scale is 1.
//
// Must not be called concurrently with the same `env`. The first few calls
// for a given shape will try different configs. The best is recorded in `env`
// and will be used for subsequent calls with that shape.
//
// Returns the (autotuning) state for the current shape. This pointer may be
// invalidated by the next call to `MatMul`.
//
// Uses considerable stack space: at least 40 KiB per thread.
template <typename TA, typename TB, typename TC>
HWY_NOINLINE MMPerKey* MatMul(const MatPtrT<TA>& A, const MatPtrT<TB>& B,
const float* HWY_RESTRICT add, MatMulEnv& env,
MatPtrT<TC>& C, MMOptions options = MMOptions()) {
static const auto zone = env.ctx.profiler.AddZone("MM.MatMul");
const size_t cluster_idx = options.cluster_idx;
HWY_DASSERT(cluster_idx < env.row_ptrs.size());
PROFILER_ZONE3(env.ctx.profiler, env.ctx.Worker(cluster_idx), zone);
RowPtrs<TC> C_rows = GetOrSetTempRowPtrs(C, env.row_ptrs[cluster_idx]);
const size_t M = A.Rows();
const size_t K = A.Cols();
const size_t N = B.Rows();
const size_t num_B = 1;
const CacheInfo& cache = env.ctx.cache_info;
MMPerKey& per_key = MMImpl::FindOrAddPerKey(
M, K, N, num_B, cache.VectorBytes(), env.per_cluster[cluster_idx]);
// (Also auto-tunes, hence outside the timed section to prevent interference.)
const StridedViewBF A_view =
MMDecompress::MaybeDecompressA(A, per_key.autotune_par_a, env, options);
MatPtrT<TB>* B2 = nullptr; // required for type matching
MMAutoTune<MMConfig>& tuner = per_key.autotune;
if (HWY_LIKELY(tuner.Best())) {
const MMArgs args(env, M, K, N, A.Scale(), add, options, tuner,
*tuner.Best());
MMLoops::Dispatch(A_view, B, B2, C_rows, args);
return &per_key;
}
// Autotuning, first call: enumerate all feasible configs.
if (HWY_UNLIKELY(!tuner.HasCandidates())) {
// Ensure matrix dimensions match each other (off the hot path).
HWY_ASSERT(K == B.Cols());
HWY_ASSERT(M <= kMaxBatchSize);
HWY_ASSERT(K <= MMEntireA::kMaxK);
HWY_ASSERT(N % kNR == 0);
MMImpl::EnsureAligned(A, cache.VectorBytes());
tuner.SetCandidates(
MMCandidates(cache, M, K, N, num_B, sizeof(TC), env.print_config));
}
const MMConfig& cfg = tuner.NextConfig();
const MMArgs args(env, M, K, N, A.Scale(), add, options, tuner, cfg);
const uint64_t t0 = hwy::timer::Start();
MMLoops::Dispatch(A_view, B, B2, C_rows, args);
MMImpl::NotifyAutotuneResult(env, M, K, N, num_B, t0, tuner, cfg);
return &per_key;
}
// Performs A*B1 and A*B2 in parallel. This is useful for gated FFNs.
// Differences vs MatMul: The second result matrix is not materialized, it is
// only passed to the `options.func` callback. There is no `add` argument
// because it is not required for this use case. There is no default `options`
// argument because `options.func` must be set by the caller.
template <typename TB>
HWY_NOINLINE MMPerKey* TwoMatMul(const MatPtrT<BF16>& A, const MatPtrT<TB>& B1,
const MatPtrT<TB>& B2, MatMulEnv& env,
MatPtrT<BF16>& C, MMOptions options) {
static const auto zone = env.ctx.profiler.AddZone("MM.TwoMatMul");
const size_t cluster_idx = options.cluster_idx;
HWY_DASSERT(cluster_idx < env.row_ptrs.size());
PROFILER_ZONE3(env.ctx.profiler, env.ctx.Worker(cluster_idx), zone);
HWY_DASSERT(options.func != nullptr); // no other way to get access to C2.
RowPtrs<BF16> C_rows = GetOrSetTempRowPtrs(C, env.row_ptrs[cluster_idx]);
const size_t M = A.Rows();
const size_t K = A.Cols();
const size_t N = B1.Rows();
const size_t num_B = 2;
const CacheInfo& cache = env.ctx.cache_info;
MMPerKey& per_key = MMImpl::FindOrAddPerKey(
M, K, N, num_B, cache.VectorBytes(), env.per_cluster[cluster_idx]);
// (Also auto-tunes, hence outside the timed section to prevent interference.)
const StridedViewBF A_view(A, 0, 0, A.Cols());
MMAutoTune<MMConfig>& tuner = per_key.autotune;
if (HWY_LIKELY(tuner.Best())) {
// Only A scale - B1/B2 may differ, and are passed separately.
const MMArgs args(env, M, K, N, A.Scale(),
/*add=*/nullptr, options, tuner, *tuner.Best());
MMLoops::Dispatch(A_view, B1, &B2, C_rows, args);
return &per_key;
}
// Autotuning, first call: enumerate all feasible configs.
if (HWY_UNLIKELY(!tuner.HasCandidates())) {
// Ensure matrix dimensions match each other (off the hot path).
HWY_ASSERT(K == B1.Cols());
HWY_ASSERT(K == B2.Cols());
HWY_ASSERT(M <= kMaxBatchSize);
HWY_ASSERT(K <= MMEntireA::kMaxK);
HWY_ASSERT(N % kNR == 0);
MMImpl::EnsureAligned(A, cache.VectorBytes());
tuner.SetCandidates(
MMCandidates(cache, M, K, N, num_B, sizeof(BF16), env.print_config));
}
const MMConfig& cfg = tuner.NextConfig();
// Only A scale - B1/B2 may differ, and are passed separately.
const MMArgs args(env, M, K, N, A.Scale(), /*add=*/nullptr, options, tuner,
cfg);
const uint64_t t0 = hwy::timer::Start();
MMLoops::Dispatch(A_view, B1, &B2, C_rows, args);
MMImpl::NotifyAutotuneResult(env, M, K, N, num_B, t0, tuner, cfg);
return &per_key;
}
// NOLINTNEXTLINE(google-readability-namespace-comments)
} // namespace HWY_NAMESPACE
} // namespace gcpp
HWY_AFTER_NAMESPACE();
#endif // NOLINT
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