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tinyblas_cpu.inc
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tinyblas_cpu.inc
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// -*- mode:c++;indent-tabs-mode:nil;c-basic-offset:4;coding:utf-8 -*-
// vi: set et ft=c++ ts=4 sts=4 sw=4 fenc=utf-8 :vi
//
// Copyright 2024 Mozilla Foundation
//
// 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.
//
// _ _ ___ _ _ ___
// | |_(_)_ _ _ _| _ ) | /_\ / __|
// | _| | ' \ || | _ \ |__ / _ \\__ \.
// \__|_|_||_\_, |___/____/_/ \_\___/
// |__/
//
// BASIC LINEAR ALGEBRA SUBPROGRAMS
//
//
// This file implements multithreaded CPU matrix multiplication for the
// common contiguous use case C = Aᵀ * B. These kernels are designed to
// have excellent performance[1] for matrices that fit in the CPU cache
// without imposing any overhead such as cache filling or malloc calls.
//
// This implementation does not guarantee any upper bound with rounding
// errors, which grow along with k. Our goal's to maximally exploit the
// hardware for performance, and then use whatever resources remain for
// improving numerical accuracy.
//
// [1] J. Tunney, ‘LLaMA Now Goes Faster on CPUs’, Mar. 2024. [Online].
// Available: https://justine.lol/matmul/. [Accessed: 29-Mar-2024].
#include "llama.cpp/ggml-impl.h"
#include "llama.cpp/ggml-quants.h"
#include "llamafile.h"
#include <algorithm>
#include <cosmo.h>
#pragma GCC diagnostic ignored "-Wpedantic"
#pragma GCC diagnostic ignored "-Wignored-attributes"
#define ROW_ALIGN 64
#define MATRIX_ALIGN 4096
#define MAX_ALIGN 4096
#ifdef _MSC_VER
#define NOINLINE __declspec(noinline)
#else
#define NOINLINE __attribute__((__noinline__))
#endif
#if defined(__ARM_NEON) || defined(__AVX512F__)
#define VECTOR_REGISTERS 32
#else
#define VECTOR_REGISTERS 16
#endif
namespace {
inline float unhalf(ggml_fp16_t d) {
return GGML_FP16_TO_FP32(d);
}
////////////////////////////////////////////////////////////////////////////////////////////////////
// MATRIX MEMORY INDEXING
#define NCA 1
#define NCB 2
#define NCC 4
#define INDEX(A, lda, j, i) (CONFIG & NC##A ? ((T##A **)A)[j] + i : A + lda * (j) + i)
////////////////////////////////////////////////////////////////////////////////////////////////////
// GGML TYPE TRAITS
template <typename T>
struct ggml_type_trait;
template <>
struct ggml_type_trait<float> {
static constexpr ggml_type id = GGML_TYPE_F32;
};
template <>
struct ggml_type_trait<ggml_bf16_t> {
static constexpr ggml_type id = GGML_TYPE_BF16;
};
template <>
struct ggml_type_trait<ggml_fp16_t> {
static constexpr ggml_type id = GGML_TYPE_F16;
};
template <>
struct ggml_type_trait<block_q8_0> {
static constexpr ggml_type id = GGML_TYPE_Q8_0;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
// VECTORIZED ARITHMETIC OPERATIONS
#if defined(__SSE__) || defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
inline __m128 add(__m128 x, __m128 y) {
return _mm_add_ps(x, y);
}
inline __m128 sub(__m128 x, __m128 y) {
return _mm_sub_ps(x, y);
}
inline __m128 mul(__m128 x, __m128 y) {
return _mm_mul_ps(x, y);
}
#endif // __SSE__
#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
inline __m256 add(__m256 x, __m256 y) {
return _mm256_add_ps(x, y);
}
inline __m256 sub(__m256 x, __m256 y) {
return _mm256_sub_ps(x, y);
}
inline __m256 mul(__m256 x, __m256 y) {
return _mm256_mul_ps(x, y);
}
#endif // __AVX__
#if defined(__AVX512F__)
inline __m512 add(__m512 x, __m512 y) {
return _mm512_add_ps(x, y);
}
inline __m512 sub(__m512 x, __m512 y) {
return _mm512_sub_ps(x, y);
}
inline __m512 mul(__m512 x, __m512 y) {
return _mm512_mul_ps(x, y);
}
#endif // __AVX512F__
#if defined(__ARM_NEON)
inline float32x4_t add(float32x4_t x, float32x4_t y) {
return vaddq_f32(x, y);
}
inline float32x4_t sub(float32x4_t x, float32x4_t y) {
return vsubq_f32(x, y);
}
inline float32x4_t mul(float32x4_t x, float32x4_t y) {
return vmulq_f32(x, y);
}
#endif // __ARM_NEON
#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
inline float16x8_t add(float16x8_t x, float16x8_t y) {
return vaddq_f16(x, y);
}
inline float16x8_t sub(float16x8_t x, float16x8_t y) {
return vsubq_f16(x, y);
}
inline float16x8_t mul(float16x8_t x, float16x8_t y) {
return vmulq_f16(x, y);
}
#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
////////////////////////////////////////////////////////////////////////////////////////////////////
// VECTORIZED FUSED MULTIPLY ADD
/**
* Computes a * b + c.
*/
template <typename T, typename U>
inline U madd(T a, T b, U c) {
return add(mul(a, b), c);
}
/**
* Computes a * b + c with error correction.
*
* @see W. Kahan, "Further remarks on reducing truncation errors,"
* Communications of the ACM, vol. 8, no. 1, p. 40, Jan. 1965,
* doi: 10.1145/363707.363723.
*/
template <typename T, typename U>
inline U madder(T a, T b, U c, U *e) {
U y = sub(mul(a, b), *e);
U t = add(c, y);
*e = sub(sub(t, c), y);
return t;
}
#if defined(__FMA__)
#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
template <>
inline __m256 madd(__m256 a, __m256 b, __m256 c) {
return _mm256_fmadd_ps(a, b, c);
}
#endif
#if defined(__AVX512F__)
template <>
inline __m512 madd(__m512 a, __m512 b, __m512 c) {
return _mm512_fmadd_ps(a, b, c);
}
#endif
#endif
#if defined(__ARM_FEATURE_FMA)
template <>
inline float32x4_t madd(float32x4_t a, float32x4_t b, float32x4_t c) {
return vfmaq_f32(c, a, b);
}
#if 0 // todo: this specialization chops gcc 12.3 performance in half
#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(_MSC_VER) && 0
template <>
inline float16x8_t madd(float16x8_t a, float16x8_t b, float16x8_t c) {
return vfmaq_f16(c, b, a);
}
#endif
#endif
#endif
#if defined(__AVX512BF16__)
template <>
inline __m512 madd(__m512bh x, __m512bh y, __m512 z) {
return _mm512_dpbf16_ps(z, x, y);
}
template <>
inline __m512 madder(__m512bh x, __m512bh y, __m512 z, __m512 *_) {
return _mm512_dpbf16_ps(z, x, y);
}
#endif
////////////////////////////////////////////////////////////////////////////////////////////////////
// VECTORIZED HORIZONTAL SUM
#if defined(__ARM_NEON)
inline float hsum(float32x4_t x) {
return vaddvq_f32(x);
}
#endif // __ARM_NEON
#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(_MSC_VER)
inline float hsum(float16x8_t x) {
// todo: this works great on clang but it produces terrible code on gcc 12.3
return vaddvq_f32(vaddq_f32(vcvt_f32_f16(vget_low_f16(x)), vcvt_f32_f16(vget_high_f16(x))));
}
#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
#if defined(__SSE__) || defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
inline float hsum(__m128 x) {
#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
x = _mm_add_ps(x, _mm_movehl_ps(x, x));
x = _mm_add_ss(x, _mm_movehdup_ps(x));
#else
__m128 t;
t = _mm_shuffle_ps(x, x, _MM_SHUFFLE(2, 3, 0, 1));
x = _mm_add_ps(x, t);
t = _mm_movehl_ps(t, x);
x = _mm_add_ss(x, t);
#endif
return _mm_cvtss_f32(x);
}
#endif
#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
inline float hsum(__m256 x) {
return hsum(_mm_add_ps(_mm256_extractf128_ps(x, 1), _mm256_castps256_ps128(x)));
}
#endif // __AVX__
#if defined(__AVX512F__)
inline float hsum(__m512 x) {
return _mm512_reduce_add_ps(x);
}
#endif // __AVX512F__
////////////////////////////////////////////////////////////////////////////////////////////////////
// VECTORIZED MEMORY LOADING
template <typename T, typename U>
T load(const U *);
#if defined(__ARM_NEON)
template <>
inline float32x4_t load(const float *p) {
return vld1q_f32(p);
}
template <>
inline float32x4_t load(const ggml_bf16_t *p) {
return vreinterpretq_f32_u32(vshll_n_u16(vld1_u16((const unsigned short *)p), 16));
}
#if !defined(_MSC_VER)
template <>
inline float16x8_t load(const ggml_fp16_t *p) {
return vld1q_f16((const float16_t *)p);
}
template <>
inline float32x4_t load(const ggml_fp16_t *p) {
return vcvt_f32_f16(vld1_f16((const float16_t *)p));
}
#endif // _MSC_VER
#endif // __ARM_NEON
#if defined(__SSE__) || defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
template <>
inline __m128 load(const float *p) {
return _mm_loadu_ps(p);
}
#endif // __SSE__
#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
template <>
inline __m256 load(const float *p) {
return _mm256_loadu_ps(p);
}
#endif // __AVX__
#if defined(__AVX2__) || defined(__AVX512F__)
template <>
inline __m256 load(const ggml_bf16_t *p) {
return _mm256_castsi256_ps(
_mm256_slli_epi32(_mm256_cvtepu16_epi32(_mm_loadu_si128((const __m128i *)p)), 16));
}
#endif // __AVX2__
#if defined(__F16C__)
template <>
inline __m256 load(const ggml_fp16_t *p) {
return _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)p));
}
#endif // __F16C__
#if defined(__AVX512F__)
template <>
inline __m512 load(const float *p) {
return _mm512_loadu_ps(p);
}
template <>
inline __m512 load(const ggml_fp16_t *p) {
return _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)p));
}
template <>
inline __m512 load(const ggml_bf16_t *p) {
return _mm512_castsi512_ps(
_mm512_slli_epi32(_mm512_cvtepu16_epi32(_mm256_loadu_si256((const __m256i *)p)), 16));
}
#endif // __AVX512F__
#if defined(__AVX512BF16__)
template <>
inline __m512bh load(const ggml_bf16_t *p) {
return (__m512bh)_mm512_loadu_ps((const float *)p);
}
#endif // __AVX512BF16__
////////////////////////////////////////////////////////////////////////////////////////////////////
// FLOATING POINT MATRIX MULTIPLICATION
template <int CONFIG, int KN, typename D, typename V, typename TA, typename TB, typename TC>
class tinyBLAS {
public:
tinyBLAS(int k, const TA *A, int lda, const TB *B, int ldb, TC *C, int ldc, int ith, int nth)
: A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) {
}
void matmul(int m, int n, int task) {
if (task == GGML_TASK_TYPE_COMPUTE)
mnpack(0, m, 0, n);
}
private:
NOINLINE void mnpack(int m0, int m, int n0, int n) {
int mc, nc, mp, np;
switch ((std::min(m - m0, 5) << 4) | std::min(n - n0, 5)) {
#if VECTOR_REGISTERS == 32
case 0x55:
case 0x54:
mc = 5;
nc = 4;
gemm<5, 4, false>(m0, m, n0, n);
break;
case 0x45:
mc = 4;
nc = 5;
gemm<4, 5, false>(m0, m, n0, n);
break;
case 0x44:
mc = 4;
nc = 4;
gemm<4, 4, false>(m0, m, n0, n);
break;
case 0x53:
mc = 5;
nc = 3;
gemm<5, 3, false>(m0, m, n0, n);
break;
case 0x35:
mc = 3;
nc = 5;
gemm<3, 5, false>(m0, m, n0, n);
break;
case 0x43:
mc = 4;
nc = 3;
gemm<4, 3, false>(m0, m, n0, n);
break;
#else
case 0x55:
case 0x54:
case 0x53:
case 0x45:
case 0x44:
case 0x43:
mc = 4;
nc = 3;
gemm<4, 3, false>(m0, m, n0, n);
break;
case 0x35:
#endif
case 0x34:
mc = 3;
nc = 4;
gemm<3, 4, false>(m0, m, n0, n);
break;
case 0x52:
mc = 5;
nc = 2;
gemm<5, 2, false>(m0, m, n0, n);
break;
case 0x33:
mc = 3;
nc = 3;
gemm<3, 3, false>(m0, m, n0, n);
break;
case 0x25:
mc = 2;
nc = 5;
gemm<2, 5, false>(m0, m, n0, n);
break;
case 0x42:
mc = 4;
nc = 2;
gemm<4, 2, false>(m0, m, n0, n);
break;
case 0x24:
mc = 2;
nc = 4;
gemm<2, 4, false>(m0, m, n0, n);
break;
case 0x32:
mc = 3;
nc = 2;
gemm<3, 2, true>(m0, m, n0, n);
break;
case 0x23:
mc = 2;
nc = 3;
gemm<2, 3, true>(m0, m, n0, n);
break;
case 0x51:
mc = 5;
nc = 1;
gemm<5, 1, true>(m0, m, n0, n);
break;
case 0x41:
mc = 4;
nc = 1;
gemm<4, 1, true>(m0, m, n0, n);
break;
case 0x22:
mc = 2;
nc = 2;
gemm<2, 2, true>(m0, m, n0, n);
break;
case 0x15:
mc = 1;
nc = 5;
gemm<1, 5, true>(m0, m, n0, n);
break;
case 0x14:
mc = 1;
nc = 4;
gemm<1, 4, true>(m0, m, n0, n);
break;
case 0x31:
mc = 3;
nc = 1;
gemm<3, 1, true>(m0, m, n0, n);
break;
case 0x13:
mc = 1;
nc = 3;
gemm<1, 3, true>(m0, m, n0, n);
break;
case 0x21:
mc = 2;
nc = 1;
gemm<2, 1, true>(m0, m, n0, n);
break;
case 0x12:
mc = 1;
nc = 2;
gemm<1, 2, true>(m0, m, n0, n);
break;
case 0x11:
mc = 1;
nc = 1;
gemm<1, 1, true>(m0, m, n0, n);
break;
default:
return;
}
mp = m0 + (m - m0) / mc * mc;
np = n0 + (n - n0) / nc * nc;
mnpack(mp, m, n0, np);
mnpack(m0, m, np, n);
}
template <int RM, int RN, int KAHAN>
NOINLINE void gemm(int m0, int m, int n0, int n) {
int ytiles = RM > 1 ? (m - m0) / RM : 1;
int xtiles = RN > 1 ? (n - n0) / RN : 1;
int tiles = xtiles * ytiles;
int duty = (tiles + nth - 1) / nth;
int start = duty * ith;
int end = start + duty;
if (end > tiles)
end = tiles;
for (int job = start; job < end; ++job) {
int ii = m0 + job / xtiles * RM;
int jj = n0 + job % xtiles * RN;
D Cv[RN][RM] = {};
D Ce[RN][RM] = {};
for (int l = 0; l < k; l += KN)
for (int j = 0; j < RN; ++j)
for (int i = 0; i < RM; ++i)
if (KAHAN)
Cv[j][i] = madder(load<V>(INDEX(A, lda, ii + i, l)), //
load<V>(INDEX(B, ldb, jj + j, l)), //
Cv[j][i], &Ce[j][i]);
else
Cv[j][i] = madd(load<V>(INDEX(A, lda, ii + i, l)), //
load<V>(INDEX(B, ldb, jj + j, l)), //
Cv[j][i]);
for (int j = 0; j < RN; ++j)
for (int i = 0; i < RM; ++i)
*INDEX(C, ldc, jj + j, ii + i) = hsum(Cv[j][i]);
}
}
const TA *const A;
const TB *const B;
TC *const C;
const int k;
const int lda;
const int ldb;
const int ldc;
const int ith;
const int nth;
};
//////////////////////////////////////////////////////////////////////////////////////////
// QUANT ZERO MATRIX MULTIPLICATION
#if defined(__ARM_FEATURE_DOTPROD)
template <int CONFIG, typename TA, typename TB, typename TC>
class tinyBLAS_Q0_ARM {
public:
tinyBLAS_Q0_ARM(int k, const TA *A, int lda, const TB *B, int ldb, TC *C, int ldc, int ith,
int nth)
: A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) {
}
void matmul(int m, int n, int task) {
if (task == GGML_TASK_TYPE_COMPUTE)
mnpack(0, m, 0, n);
}
private:
NOINLINE void mnpack(int m0, int m, int n0, int n) {
int mc, nc, mp, np;
switch ((std::min(m - m0, 3) << 4) | std::min(n - n0, 3)) {
case 0x33:
mc = 3;
nc = 3;
gemm<3, 3>(m0, m, n0, n);
break;
case 0x32:
mc = 3;
nc = 2;
gemm<3, 2>(m0, m, n0, n);
break;
case 0x23:
mc = 2;
nc = 3;
gemm<2, 3>(m0, m, n0, n);
break;
case 0x22:
mc = 2;
nc = 2;
gemm<2, 2>(m0, m, n0, n);
break;
case 0x31:
mc = 3;
nc = 1;
gemm<3, 1>(m0, m, n0, n);
break;
case 0x13:
mc = 1;
nc = 3;
gemm<1, 3>(m0, m, n0, n);
break;
case 0x21:
mc = 2;
nc = 1;
gemm<2, 1>(m0, m, n0, n);
break;
case 0x12:
mc = 1;
nc = 2;
gemm<1, 2>(m0, m, n0, n);
break;
case 0x11:
mc = 1;
nc = 1;
gemm<1, 1>(m0, m, n0, n);
break;
default:
return;
}
mp = m0 + (m - m0) / mc * mc;
np = n0 + (n - n0) / nc * nc;
mnpack(mp, m, n0, np);
mnpack(m0, m, np, n);
}
template <int RM, int RN>
NOINLINE void gemm(int m0, int m, int n0, int n) {
int ytiles = RM > 1 ? (m - m0) / RM : 1;
int xtiles = RN > 1 ? (n - n0) / RN : 1;
int tiles = xtiles * ytiles;
int duty = (tiles + nth - 1) / nth;
int start = duty * ith;
int end = start + duty;
if (end > tiles)
end = tiles;
for (int job = start; job < end; ++job) {
int ii = m0 + job / xtiles * RM;
int jj = n0 + job % xtiles * RN;
float32x4_t Cv[RN][RM] = {};
for (int l = 0; l < k; ++l)
for (int j = 0; j < RN; ++j)
for (int i = 0; i < RM; ++i)
Cv[j][i] = vmlaq_n_f32(
Cv[j][i],
vcvtq_f32_s32(vdotq_s32(vdotq_s32(vdupq_n_s32(0),
load_lo(INDEX(A, lda, ii + i, l)),
load_lo(INDEX(B, ldb, jj + j, l))),
load_hi(INDEX(A, lda, ii + i, l)),
load_hi(INDEX(B, ldb, jj + j, l)))),
(unhalf(INDEX(A, lda, ii + i, l)->d) *
unhalf(INDEX(B, ldb, jj + j, l)->d)));
for (int j = 0; j < RN; ++j)
for (int i = 0; i < RM; ++i)
*INDEX(C, ldc, jj + j, ii + i) = hsum(Cv[j][i]);
}
}
inline int8x16_t load_lo(const block_q8_0 *b) {
return vld1q_s8(b->qs);
}
inline int8x16_t load_hi(const block_q8_0 *b) {
return vld1q_s8(b->qs + 16);
}
inline int8x16_t load_lo(const block_q4_0 *b) {
return vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vld1q_u8(b->qs), vdupq_n_u8(0x0f))),
vdupq_n_s8(0x8));
}
inline int8x16_t load_hi(const block_q4_0 *b) {
return vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(vld1q_u8(b->qs), 4)), vdupq_n_s8(0x8));
}
const TA *const A;
const TB *const B;
TC *const C;
const int k;
const int lda;
const int ldb;
const int ldc;
const int ith;
const int nth;
};
#endif // __ARM_FEATURE_DOTPROD
#if defined(__AVX2__) || defined(__AVX512F__)
template <int CONFIG, typename TA, typename TB, typename TC>
class tinyBLAS_Q0_AVX2 {
public:
tinyBLAS_Q0_AVX2(int k, const TA *A, int lda, const TB *B, int ldb, TC *C, int ldc, int ith,
int nth)
: A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) {
}
void matmul(int m, int n, int task) {
if (task == GGML_TASK_TYPE_COMPUTE)
mnpack(0, m, 0, n);
}
private:
void mnpack(int m0, int m, int n0, int n) {
int mc, nc, mp, np;
switch ((std::min(m - m0, 4) << 4) | std::min(n - n0, 4)) {
#if VECTOR_REGISTERS == 32
case 0x44:
mc = 4;
nc = 4;
gemm<4, 4>(m0, m, n0, n);
break;
case 0x43:
mc = 4;
nc = 3;
gemm<4, 3>(m0, m, n0, n);
break;
case 0x34:
mc = 3;
nc = 4;
gemm<3, 4>(m0, m, n0, n);
break;
case 0x33:
mc = 3;
nc = 3;
gemm<3, 3>(m0, m, n0, n);
break;
case 0x42:
mc = 4;
nc = 2;
gemm<4, 2>(m0, m, n0, n);
break;
case 0x24:
mc = 2;
nc = 4;
gemm<2, 4>(m0, m, n0, n);
break;
#else
case 0x44:
case 0x43:
case 0x42:
mc = 4;
nc = 2;
gemm<4, 2>(m0, m, n0, n);
break;
case 0x34:
case 0x24:
mc = 2;
nc = 4;
gemm<2, 4>(m0, m, n0, n);
break;
case 0x33:
#endif
case 0x32:
mc = 3;
nc = 2;
gemm<3, 2>(m0, m, n0, n);
break;
case 0x23:
mc = 2;
nc = 3;
gemm<2, 3>(m0, m, n0, n);
break;
case 0x41:
mc = 4;
nc = 1;
gemm<4, 1>(m0, m, n0, n);
break;
case 0x22:
mc = 2;
nc = 2;
gemm<2, 2>(m0, m, n0, n);
break;
case 0x14:
mc = 1;
nc = 4;
gemm<1, 4>(m0, m, n0, n);
break;
case 0x31:
mc = 3;
nc = 1;
gemm<3, 1>(m0, m, n0, n);
break;
case 0x13:
mc = 1;
nc = 3;
gemm<1, 3>(m0, m, n0, n);
break;
case 0x21:
mc = 2;
nc = 1;
gemm<2, 1>(m0, m, n0, n);
break;
case 0x12:
mc = 1;
nc = 2;
gemm<1, 2>(m0, m, n0, n);
break;
case 0x11:
mc = 1;
nc = 1;
gemm<1, 1>(m0, m, n0, n);
break;
default:
return;
}
mp = m0 + (m - m0) / mc * mc;
np = n0 + (n - n0) / nc * nc;
mnpack(mp, m, n0, np);
mnpack(m0, m, np, n);
}
template <int RM, int RN>
NOINLINE void gemm(int m0, int m, int n0, int n) {
int ytiles = RM > 1 ? (m - m0) / RM : 1;
int xtiles = RN > 1 ? (n - n0) / RN : 1;
int tiles = xtiles * ytiles;
int duty = (tiles + nth - 1) / nth;
int start = duty * ith;
int end = start + duty;
if (end > tiles)
end = tiles;
for (int job = start; job < end; ++job) {
int ii = m0 + job / xtiles * RM;
int jj = n0 + job % xtiles * RN;
__m256 Cv[RN][RM] = {};
for (int l = 0; l < k; ++l)
for (int j = 0; j < RN; ++j)
for (int i = 0; i < RM; ++i)
Cv[j][i] = madd(_mm256_set1_ps(unhalf(INDEX(A, lda, ii + i, l)->d) *
unhalf(INDEX(B, ldb, jj + j, l)->d)),
updot(_mm256_sign_epi8(load(INDEX(A, lda, ii + i, l)),
load(INDEX(A, lda, ii + i, l))),
_mm256_sign_epi8(load(INDEX(B, ldb, jj + j, l)),
load(INDEX(A, lda, ii + i, l)))),
Cv[j][i]);
for (int j = 0; j < RN; ++j)
for (int i = 0; i < RM; ++i)
*INDEX(C, ldc, jj + j, ii + i) = hsum(Cv[j][i]);
}
}
inline __m256i load(const block_q8_0 *b) {
return _mm256_loadu_si256((const __m256i *)b->qs);
}
inline __m256i load(const block_q4_0 *b) {
return _mm256_sub_epi8(denibble(b->qs), _mm256_set1_epi8(8));
}
inline __m256 updot(__m256i u, __m256i s) {
__m256i res;
#if defined(__AVXVNNI__) || (defined(__AVX512VNNI__) && defined(__AVX512VL__))
res = _mm256_dpbusd_epi32(_mm256_setzero_si256(), u, s);
#else
res = _mm256_madd_epi16(_mm256_set1_epi16(1), _mm256_maddubs_epi16(u, s));
#endif
return _mm256_cvtepi32_ps(res);
}
static inline __m256i denibble(const uint8_t *p) {
__m128i x = _mm_loadu_si128((const __m128i *)p);
return _mm256_and_si256(
_mm256_set1_epi8(15),
_mm256_insertf128_si256(_mm256_castsi128_si256(x), _mm_srli_epi16(x, 4), 1));
}
const TA *const A;
const TB *const B;
TC *const C;
const int k;
const int lda;
const int ldb;
const int ldc;
const int ith;
const int nth;
};
#endif // __AVX2__
} // namespace
/**
* Performs optimized matrix multiplication on CPU.
*
* This subroutine may compute C = Aᵀ * B with column major ordering.
* Despite its name, this isn't a generalized implementation. Work is
* only performed when a handwritten kernel is written and available.
* Otherwise the caller should fall back to a general matmul routine.
*
* For example, for single-threaded single-precision GEMM you can say
*
* llamafile_sgemm(m, n, k, A, lda, B, ldb, C, ldc,
* 0, 1, GGML_TASK_TYPE_COMPUTE,
* GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32);
*
* @param m is rows in `A` and `C`
* @param n is cols in `B` and `C`
* @param k is cols in `A` and rows in `B`
* @param A is first input matrix (always transposed)
* @param lda is row stride of `A`
* @param B is second input matrix (never transposed)
* @param ldb is row stride of `B`
* @param C is input/output array of output matrices
* @param ldc is row stride of `C`
* @param ith is thread id (must be less than `nth`)
* @param nth is number of threads (must be greater than zero)
* @param task is GGML task type
* @param Atype is GGML data type of `A`
* @param Btype is GGML data type of `B`
* @param Ctype is GGML data type of `C`
* @return true if this function was able to service the matmul request
*/
bool llamafile_sgemm(int m, int n, int k, const void *A, int lda, const void *B, int ldb, void *C,
int ldc, int ith, int nth, int task, int Atype, int Btype, int Ctype) {
assert(m >= 0);
assert(n >= 0);
assert(k >= 0);
assert(lda >= k);
assert(ldb >= k);
assert(ldc >= m);
assert(nth > 0);
assert(ith < nth);
assert(1ll * lda * m <= 0x7fffffff);
assert(1ll * ldb * n <= 0x7fffffff);
assert(1ll * ldc * n <= 0x7fffffff);
if (Ctype != GGML_TYPE_F32)
return false;
switch (Atype) {
case GGML_TYPE_F32: {
if (Btype != GGML_TYPE_F32)
return false;
#if defined(__AVX512F__)
if (k % 16)
return false;
tinyBLAS<0, 16, __m512, __m512, float, float, float> tb{
k, (const float *)A, lda, (const float *)B, ldb, (float *)C, ldc, ith, nth};
tb.matmul(m, n, task);
return true;
#elif defined(__AVX__) || defined(__AVX2__)
if (k % 8)
return false;
tinyBLAS<0, 8, __m256, __m256, float, float, float> tb{
k, (const float *)A, lda, (const float *)B, ldb, (float *)C, ldc, ith, nth};
tb.matmul(m, n, task);
return true;
#elif defined(__ARM_NEON)
if (n < 4)
return false;
if (k % 4)
return false;
tinyBLAS<0, 4, float32x4_t, float32x4_t, float, float, float> tb{
k, (const float *)A, lda, (const float *)B, ldb, (float *)C, ldc, ith, nth};
tb.matmul(m, n, task);
return true;
#else
return false;
#endif
}
case GGML_TYPE_BF16: {
#if defined(__AVX512BF16__)
if (k % 32)
return false;
if (Btype != GGML_TYPE_BF16)
return false;
tinyBLAS<0, 32, __m512, __m512bh, ggml_bf16_t, ggml_bf16_t, float> tb{
k, (const ggml_bf16_t *)A, lda, (const ggml_bf16_t *)B, ldb, (float *)C, ldc, ith, nth};
tb.matmul(m, n, task);
return true;
#elif defined(__AVX512F__)
if (k % 16)
return false;
if (Btype != GGML_TYPE_F32)
return false;
tinyBLAS<0, 16, __m512, __m512, ggml_bf16_t, float, float> tb{
k, (const ggml_bf16_t *)A, lda, (const float *)B, ldb, (float *)C, ldc, ith, nth};
tb.matmul(m, n, task);
return true;
#elif defined(__AVX2__)
if (k % 8)
return false;
if (Btype != GGML_TYPE_F32)
return false;
tinyBLAS<0, 8, __m256, __m256, ggml_bf16_t, float, float> tb{
k, (const ggml_bf16_t *)A, lda, (const float *)B, ldb, (float *)C, ldc, ith, nth};
tb.matmul(m, n, task);
return true;