--- /dev/null
+// -*- 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
+//
+// Permission is hereby granted, free of charge, to any person obtaining
+// a copy of this software and associated documentation files (the
+// "Software"), to deal in the Software without restriction, including
+// without limitation the rights to use, copy, modify, merge, publish,
+// distribute, sublicense, and/or sell copies of the Software, and to
+// permit persons to whom the Software is furnished to do so, subject to
+// the following conditions:
+//
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+//
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
+// BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
+// ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+// CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+// SOFTWARE.
+
+//
+// _ _ ___ _ _ ___
+// | |_(_)_ _ _ _| _ ) | /_\ / __|
+// | _| | ' \ || | _ \ |__ / _ \\__ \.
+// \__|_|_||_\_, |___/____/_/ \_\___/
+// |__/
+//
+// 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].
+
+#pragma GCC diagnostic ignored "-Wpedantic"
+#pragma GCC diagnostic ignored "-Wignored-attributes"
+
+#include "sgemm.h"
+#include "ggml-impl.h"
+#include "ggml-quants.h"
+
+#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
+
+// there will be blocks
+#define BEGIN_KERNEL(RM, RN) \
+ int ytiles = (m - m0) / RM; \
+ int xtiles = (n - n0) / RN; \
+ int tiles = ytiles * xtiles; \
+ 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 i = m0 + job / xtiles * RM; \
+ int j = n0 + job % xtiles * RN;
+
+#define END_KERNEL() }
+
+#define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1)
+
+namespace {
+
+inline float unhalf(ggml_fp16_t d) {
+ return GGML_FP16_TO_FP32(d);
+}
+
+////////////////////////////////////////////////////////////////////////////////////////////////////
+// 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 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) {
+ 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);
+}
+#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(__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));
+}
+#endif // __AVX512F__
+
+////////////////////////////////////////////////////////////////////////////////////////////////////
+// ABSTRACTIONS
+
+/**
+ * Computes a * b + c.
+ *
+ * This operation will become fused into a single arithmetic instruction
+ * if the hardware has support for this feature, e.g. Intel Haswell+ (c.
+ * 2013), AMD Bulldozer+ (c. 2011), etc.
+ */
+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;
+}
+
+////////////////////////////////////////////////////////////////////////////////////////////////////
+// FLOATING POINT MATRIX MULTIPLICATION
+
+template <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;
+ if (m - m0 <= 0 || n - n0 <= 0)
+ return;
+ if (VECTOR_REGISTERS >= 32 && n - n0 >= 5 && m - m0 >= 5) {
+ mc = 5;
+ nc = 5;
+ gemm5x5(m0, m, n0, n);
+ } else if (n - n0 >= 4 && m - m0 >= 3) {
+ mc = 3;
+ nc = 4;
+ gemm3x4(m0, m, n0, n);
+ } else if (n - n0 >= 4) {
+ mc = 1;
+ nc = 4;
+ gemm1x4(m0, m, n0, n);
+ } else if (m - m0 >= 4) {
+ mc = 4;
+ nc = 1;
+ gemm4x1(m0, m, n0, n);
+ } else {
+ mc = 1;
+ nc = 1;
+ gemm1x1(m0, m, n0, n);
+ }
+ mp = m0 + (m - m0) / mc * mc;
+ np = n0 + (n - n0) / nc * nc;
+ mnpack(mp, m, n0, np);
+ mnpack(m0, mp, np, n);
+ mnpack(mp, m, np, n);
+ }
+
+ NOINLINE void gemm5x5(int m0, int m, int n0, int n) {
+ BEGIN_KERNEL(5, 5)
+ D c00 = {0};
+ D c01 = {0};
+ D c02 = {0};
+ D c03 = {0};
+ D c04 = {0};
+ D c10 = {0};
+ D c11 = {0};
+ D c12 = {0};
+ D c13 = {0};
+ D c14 = {0};
+ D c20 = {0};
+ D c21 = {0};
+ D c22 = {0};
+ D c23 = {0};
+ D c24 = {0};
+ D c30 = {0};
+ D c31 = {0};
+ D c32 = {0};
+ D c33 = {0};
+ D c34 = {0};
+ D c40 = {0};
+ D c41 = {0};
+ D c42 = {0};
+ D c43 = {0};
+ D c44 = {0};
+ for (int l = 0; l < k; l += KN) {
+ V k0 = load<V>(B + ldb * (j + 0) + l);
+ V k1 = load<V>(B + ldb * (j + 1) + l);
+ V k2 = load<V>(B + ldb * (j + 2) + l);
+ V k3 = load<V>(B + ldb * (j + 3) + l);
+ V k4 = load<V>(B + ldb * (j + 4) + l);
+ V a0 = load<V>(A + lda * (i + 0) + l);
+ c00 = madd(a0, k0, c00);
+ c01 = madd(a0, k1, c01);
+ c02 = madd(a0, k2, c02);
+ c03 = madd(a0, k3, c03);
+ c04 = madd(a0, k4, c04);
+ V a1 = load<V>(A + lda * (i + 1) + l);
+ c10 = madd(a1, k0, c10);
+ c11 = madd(a1, k1, c11);
+ c12 = madd(a1, k2, c12);
+ c13 = madd(a1, k3, c13);
+ c14 = madd(a1, k4, c14);
+ V a2 = load<V>(A + lda * (i + 2) + l);
+ c20 = madd(a2, k0, c20);
+ c21 = madd(a2, k1, c21);
+ c22 = madd(a2, k2, c22);
+ c23 = madd(a2, k3, c23);
+ c24 = madd(a2, k4, c24);
+ V a3 = load<V>(A + lda * (i + 3) + l);
+ c30 = madd(a3, k0, c30);
+ c31 = madd(a3, k1, c31);
+ c32 = madd(a3, k2, c32);
+ c33 = madd(a3, k3, c33);
+ c34 = madd(a3, k4, c34);
+ V a4 = load<V>(A + lda * (i + 4) + l);
+ c40 = madd(a4, k0, c40);
+ c41 = madd(a4, k1, c41);
+ c42 = madd(a4, k2, c42);
+ c43 = madd(a4, k3, c43);
+ c44 = madd(a4, k4, c44);
+ }
+ C[ldc * (j + 0) + (i + 0)] = hsum(c00);
+ C[ldc * (j + 0) + (i + 1)] = hsum(c10);
+ C[ldc * (j + 0) + (i + 2)] = hsum(c20);
+ C[ldc * (j + 0) + (i + 3)] = hsum(c30);
+ C[ldc * (j + 0) + (i + 4)] = hsum(c40);
+ C[ldc * (j + 1) + (i + 0)] = hsum(c01);
+ C[ldc * (j + 1) + (i + 1)] = hsum(c11);
+ C[ldc * (j + 1) + (i + 2)] = hsum(c21);
+ C[ldc * (j + 1) + (i + 3)] = hsum(c31);
+ C[ldc * (j + 1) + (i + 4)] = hsum(c41);
+ C[ldc * (j + 2) + (i + 0)] = hsum(c02);
+ C[ldc * (j + 2) + (i + 1)] = hsum(c12);
+ C[ldc * (j + 2) + (i + 2)] = hsum(c22);
+ C[ldc * (j + 2) + (i + 3)] = hsum(c32);
+ C[ldc * (j + 2) + (i + 4)] = hsum(c42);
+ C[ldc * (j + 3) + (i + 0)] = hsum(c03);
+ C[ldc * (j + 3) + (i + 1)] = hsum(c13);
+ C[ldc * (j + 3) + (i + 2)] = hsum(c23);
+ C[ldc * (j + 3) + (i + 3)] = hsum(c33);
+ C[ldc * (j + 3) + (i + 4)] = hsum(c43);
+ C[ldc * (j + 4) + (i + 0)] = hsum(c04);
+ C[ldc * (j + 4) + (i + 1)] = hsum(c14);
+ C[ldc * (j + 4) + (i + 2)] = hsum(c24);
+ C[ldc * (j + 4) + (i + 3)] = hsum(c34);
+ C[ldc * (j + 4) + (i + 4)] = hsum(c44);
+ END_KERNEL()
+ }
+
+ NOINLINE void gemm3x4(int m0, int m, int n0, int n) {
+ BEGIN_KERNEL(3, 4)
+ D c00 = {0};
+ D c01 = {0};
+ D c02 = {0};
+ D c03 = {0};
+ D c10 = {0};
+ D c11 = {0};
+ D c12 = {0};
+ D c13 = {0};
+ D c20 = {0};
+ D c21 = {0};
+ D c22 = {0};
+ D c23 = {0};
+ for (int l = 0; l < k; l += KN) {
+ V k0 = load<V>(B + ldb * (j + 0) + l);
+ V k1 = load<V>(B + ldb * (j + 1) + l);
+ V k2 = load<V>(B + ldb * (j + 2) + l);
+ V k3 = load<V>(B + ldb * (j + 3) + l);
+ V a0 = load<V>(A + lda * (i + 0) + l);
+ c00 = madd(a0, k0, c00);
+ c01 = madd(a0, k1, c01);
+ c02 = madd(a0, k2, c02);
+ c03 = madd(a0, k3, c03);
+ V a1 = load<V>(A + lda * (i + 1) + l);
+ c10 = madd(a1, k0, c10);
+ c11 = madd(a1, k1, c11);
+ c12 = madd(a1, k2, c12);
+ c13 = madd(a1, k3, c13);
+ V a2 = load<V>(A + lda * (i + 2) + l);
+ c20 = madd(a2, k0, c20);
+ c21 = madd(a2, k1, c21);
+ c22 = madd(a2, k2, c22);
+ c23 = madd(a2, k3, c23);
+ }
+ C[ldc * (j + 0) + (i + 0)] = hsum(c00);
+ C[ldc * (j + 0) + (i + 1)] = hsum(c10);
+ C[ldc * (j + 0) + (i + 2)] = hsum(c20);
+ C[ldc * (j + 1) + (i + 0)] = hsum(c01);
+ C[ldc * (j + 1) + (i + 1)] = hsum(c11);
+ C[ldc * (j + 1) + (i + 2)] = hsum(c21);
+ C[ldc * (j + 2) + (i + 0)] = hsum(c02);
+ C[ldc * (j + 2) + (i + 1)] = hsum(c12);
+ C[ldc * (j + 2) + (i + 2)] = hsum(c22);
+ C[ldc * (j + 3) + (i + 0)] = hsum(c03);
+ C[ldc * (j + 3) + (i + 1)] = hsum(c13);
+ C[ldc * (j + 3) + (i + 2)] = hsum(c23);
+ END_KERNEL()
+ }
+
+ NOINLINE void gemm1x4(int m0, int m, int n0, int n) {
+ BEGIN_KERNEL(1, 4)
+ D c00 = {0}, e00 = {0};
+ D c01 = {0}, e01 = {0};
+ D c02 = {0}, e02 = {0};
+ D c03 = {0}, e03 = {0};
+ for (int l = 0; l < k; l += KN) {
+ V a = load<V>(A + lda * (i + 0) + l);
+ c00 = madder(a, load<V>(B + ldb * (j + 0) + l), c00, &e00);
+ c01 = madder(a, load<V>(B + ldb * (j + 1) + l), c01, &e01);
+ c02 = madder(a, load<V>(B + ldb * (j + 2) + l), c02, &e02);
+ c03 = madder(a, load<V>(B + ldb * (j + 3) + l), c03, &e03);
+ }
+ C[ldc * (j + 0) + (i + 0)] = hsum(c00);
+ C[ldc * (j + 1) + (i + 0)] = hsum(c01);
+ C[ldc * (j + 2) + (i + 0)] = hsum(c02);
+ C[ldc * (j + 3) + (i + 0)] = hsum(c03);
+ END_KERNEL()
+ }
+
+ NOINLINE void gemm4x1(int m0, int m, int n0, int n) {
+ BEGIN_KERNEL(4, 1)
+ D c00 = {0}, e00 = {0};
+ D c10 = {0}, e10 = {0};
+ D c20 = {0}, e20 = {0};
+ D c30 = {0}, e30 = {0};
+ for (int l = 0; l < k; l += KN) {
+ V b = load<V>(B + ldb * (j + 0) + l);
+ c00 = madder(load<V>(A + lda * (i + 0) + l), b, c00, &e00);
+ c10 = madder(load<V>(A + lda * (i + 1) + l), b, c10, &e10);
+ c20 = madder(load<V>(A + lda * (i + 2) + l), b, c20, &e20);
+ c30 = madder(load<V>(A + lda * (i + 3) + l), b, c30, &e30);
+ }
+ C[ldc * (j + 0) + (i + 0)] = hsum(c00);
+ C[ldc * (j + 0) + (i + 1)] = hsum(c10);
+ C[ldc * (j + 0) + (i + 2)] = hsum(c20);
+ C[ldc * (j + 0) + (i + 3)] = hsum(c30);
+ END_KERNEL()
+ }
+
+ NOINLINE void gemm1x1(int m0, int m, int n0, int n) {
+ BEGIN_KERNEL(1, 1)
+ D c = {0}, e = {0};
+ for (int l = 0; l < k; l += KN)
+ c = madder(load<V>(A + lda * i + l),
+ load<V>(B + ldb * j + l), c, &e);
+ C[ldc * j + i] = hsum(c);
+ END_KERNEL()
+ }
+
+ 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 <typename TA>
+class tinyBLAS_Q0_ARM {
+ public:
+ tinyBLAS_Q0_ARM(int k,
+ const TA *A, int lda,
+ const block_q8_0 *B, int ldb,
+ float *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;
+ if (m - m0 <= 0 || n - n0 <= 0)
+ return;
+ if (m - m0 >= 3 && n - n0 >= 3) {
+ mc = 3;
+ nc = 3;
+ gemm3x3(m0, m, n0, n);
+ } else {
+ mc = 1;
+ nc = 1;
+ gemm1x1(m0, m, n0, n);
+ }
+ mp = m0 + (m - m0) / mc * mc;
+ np = n0 + (n - n0) / nc * nc;
+ mnpack(mp, m, n0, np);
+ mnpack(m0, mp, np, n);
+ mnpack(mp, m, np, n);
+ }
+
+ NOINLINE void gemm3x3(int m0, int m, int n0, int n) {
+ BEGIN_KERNEL(3, 3)
+ int32x4_t zero = vdupq_n_s32(0);
+ float32x4_t c00 = vdupq_n_f32(0.f);
+ float32x4_t c01 = vdupq_n_f32(0.f);
+ float32x4_t c02 = vdupq_n_f32(0.f);
+ float32x4_t c10 = vdupq_n_f32(0.f);
+ float32x4_t c11 = vdupq_n_f32(0.f);
+ float32x4_t c12 = vdupq_n_f32(0.f);
+ float32x4_t c20 = vdupq_n_f32(0.f);
+ float32x4_t c21 = vdupq_n_f32(0.f);
+ float32x4_t c22 = vdupq_n_f32(0.f);
+ const TA *Ap0 = A + lda * (i + 0);
+ const TA *Ap1 = A + lda * (i + 1);
+ const TA *Ap2 = A + lda * (i + 2);
+ const block_q8_0 *Bp0 = B + ldb * (j + 0);
+ const block_q8_0 *Bp1 = B + ldb * (j + 1);
+ const block_q8_0 *Bp2 = B + ldb * (j + 2);
+ for (int l = 0; l < k; ++l) {
+ c00 = vmlaq_n_f32(
+ c00,
+ vcvtq_f32_s32(vdotq_s32(vdotq_s32(zero, load_lo(Ap0 + l), load_lo(Bp0 + l)),
+ load_hi(Ap0 + l), load_hi(Bp0 + l))),
+ unhalf(Ap0[l].d) * unhalf(Bp0[l].d));
+ c01 = vmlaq_n_f32(
+ c01,
+ vcvtq_f32_s32(vdotq_s32(vdotq_s32(zero, load_lo(Ap0 + l), load_lo(Bp1 + l)),
+ load_hi(Ap0 + l), load_hi(Bp1 + l))),
+ unhalf(Ap0[l].d) * unhalf(Bp1[l].d));
+ c02 = vmlaq_n_f32(
+ c02,
+ vcvtq_f32_s32(vdotq_s32(vdotq_s32(zero, load_lo(Ap0 + l), load_lo(Bp2 + l)),
+ load_hi(Ap0 + l), load_hi(Bp2 + l))),
+ unhalf(Ap0[l].d) * unhalf(Bp2[l].d));
+ c10 = vmlaq_n_f32(
+ c10,
+ vcvtq_f32_s32(vdotq_s32(vdotq_s32(zero, load_lo(Ap1 + l), load_lo(Bp0 + l)),
+ load_hi(Ap1 + l), load_hi(Bp0 + l))),
+ unhalf(Ap1[l].d) * unhalf(Bp0[l].d));
+ c11 = vmlaq_n_f32(
+ c11,
+ vcvtq_f32_s32(vdotq_s32(vdotq_s32(zero, load_lo(Ap1 + l), load_lo(Bp1 + l)),
+ load_hi(Ap1 + l), load_hi(Bp1 + l))),
+ unhalf(Ap1[l].d) * unhalf(Bp1[l].d));
+ c12 = vmlaq_n_f32(
+ c12,
+ vcvtq_f32_s32(vdotq_s32(vdotq_s32(zero, load_lo(Ap1 + l), load_lo(Bp2 + l)),
+ load_hi(Ap1 + l), load_hi(Bp2 + l))),
+ unhalf(Ap1[l].d) * unhalf(Bp2[l].d));
+ c20 = vmlaq_n_f32(
+ c20,
+ vcvtq_f32_s32(vdotq_s32(vdotq_s32(zero, load_lo(Ap2 + l), load_lo(Bp0 + l)),
+ load_hi(Ap2 + l), load_hi(Bp0 + l))),
+ unhalf(Ap2[l].d) * unhalf(Bp0[l].d));
+ c21 = vmlaq_n_f32(
+ c21,
+ vcvtq_f32_s32(vdotq_s32(vdotq_s32(zero, load_lo(Ap2 + l), load_lo(Bp1 + l)),
+ load_hi(Ap2 + l), load_hi(Bp1 + l))),
+ unhalf(Ap2[l].d) * unhalf(Bp1[l].d));
+ c22 = vmlaq_n_f32(
+ c22,
+ vcvtq_f32_s32(vdotq_s32(vdotq_s32(zero, load_lo(Ap2 + l), load_lo(Bp2 + l)),
+ load_hi(Ap2 + l), load_hi(Bp2 + l))),
+ unhalf(Ap2[l].d) * unhalf(Bp2[l].d));
+ }
+ C[ldc * (j + 0) + (i + 0)] = hsum(c00);
+ C[ldc * (j + 0) + (i + 1)] = hsum(c10);
+ C[ldc * (j + 0) + (i + 2)] = hsum(c20);
+ C[ldc * (j + 1) + (i + 0)] = hsum(c01);
+ C[ldc * (j + 1) + (i + 1)] = hsum(c11);
+ C[ldc * (j + 1) + (i + 2)] = hsum(c21);
+ C[ldc * (j + 2) + (i + 0)] = hsum(c02);
+ C[ldc * (j + 2) + (i + 1)] = hsum(c12);
+ C[ldc * (j + 2) + (i + 2)] = hsum(c22);
+ END_KERNEL()
+ }
+
+ NOINLINE void gemm1x1(int m0, int m, int n0, int n) {
+ BEGIN_KERNEL(1, 1)
+ float32x4_t acc = vdupq_n_f32(0.f);
+ const TA *Ap = A + lda * i;
+ const block_q8_0 *Bp = B + ldb * j;
+ for (int l = 0; l < k; ++l) {
+ acc = vmlaq_n_f32(acc,
+ vcvtq_f32_s32(vdotq_s32(
+ vdotq_s32(vdupq_n_s32(0), load_lo(Ap + l), load_lo(Bp + l)),
+ load_hi(Ap + l), load_hi(Bp + l))),
+ unhalf(Ap[l].d) * unhalf(Bp[l].d));
+ }
+ C[ldc * j + i] = hsum(acc);
+ END_KERNEL()
+ }
+
+ 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 block_q8_0 *const B;
+ float *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 <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:
+ NOINLINE void mnpack(int m0, int m, int n0, int n) {
+ int mc, nc, mp, np;
+ if (m - m0 <= 0 || n - n0 <= 0)
+ return;
+ if (m - m0 >= 4 && n - n0 >= 3) {
+ mc = 4;
+ nc = 3;
+ gemm4x3(m0, m, n0, n);
+ } else if (m - m0 >= 4 && n - n0 >= 1) {
+ mc = 4;
+ nc = 1;
+ gemm4x1(m0, m, n0, n);
+ } else if (m - m0 >= 1 && n - n0 >= 4) {
+ mc = 1;
+ nc = 4;
+ gemm1x4(m0, m, n0, n);
+ } else {
+ mc = 1;
+ nc = 1;
+ gemm1x1(m0, m, n0, n);
+ }
+ mp = m0 + (m - m0) / mc * mc;
+ np = n0 + (n - n0) / nc * nc;
+ mnpack(mp, m, n0, np);
+ mnpack(m0, mp, np, n);
+ mnpack(mp, m, np, n);
+ }
+
+ NOINLINE void gemm4x3(int m0, int m, int n0, int n) {
+ BEGIN_KERNEL(4, 3)
+ __m256 c00 = _mm256_setzero_ps();
+ __m256 c10 = _mm256_setzero_ps();
+ __m256 c20 = _mm256_setzero_ps();
+ __m256 c30 = _mm256_setzero_ps();
+ __m256 c01 = _mm256_setzero_ps();
+ __m256 c11 = _mm256_setzero_ps();
+ __m256 c21 = _mm256_setzero_ps();
+ __m256 c31 = _mm256_setzero_ps();
+ __m256 c02 = _mm256_setzero_ps();
+ __m256 c12 = _mm256_setzero_ps();
+ __m256 c22 = _mm256_setzero_ps();
+ __m256 c32 = _mm256_setzero_ps();
+ const TA *Ap0 = A + lda * (i + 0);
+ const TA *Ap1 = A + lda * (i + 1);
+ const TA *Ap2 = A + lda * (i + 2);
+ const TA *Ap3 = A + lda * (i + 3);
+ const TB *Bp0 = B + ldb * (j + 0);
+ const TB *Bp1 = B + ldb * (j + 1);
+ const TB *Bp2 = B + ldb * (j + 2);
+ for (int l = 0; l < k; ++l) {
+ float da0 = unhalf(Ap0[l].d);
+ float da1 = unhalf(Ap1[l].d);
+ float da2 = unhalf(Ap2[l].d);
+ float da3 = unhalf(Ap3[l].d);
+ __m256i e0 = load(Ap0 + l);
+ __m256i e1 = load(Ap1 + l);
+ __m256i e2 = load(Ap2 + l);
+ __m256i e3 = load(Ap3 + l);
+ float db0 = unhalf(Bp0[l].d);
+ __m256 d00 = _mm256_set1_ps(da0 * db0);
+ __m256 d10 = _mm256_set1_ps(da1 * db0);
+ __m256 d20 = _mm256_set1_ps(da2 * db0);
+ __m256 d30 = _mm256_set1_ps(da3 * db0);
+ __m256i f0 = load(Bp0 + l);
+ __m256i u0 = _mm256_sign_epi8(f0, f0);
+ __m256i s00 = _mm256_sign_epi8(e0, f0);
+ __m256i s10 = _mm256_sign_epi8(e1, f0);
+ __m256i s20 = _mm256_sign_epi8(e2, f0);
+ __m256i s30 = _mm256_sign_epi8(e3, f0);
+ c00 = madd(d00, updot(u0, s00), c00);
+ c10 = madd(d10, updot(u0, s10), c10);
+ c20 = madd(d20, updot(u0, s20), c20);
+ c30 = madd(d30, updot(u0, s30), c30);
+ float db1 = unhalf(Bp1[l].d);
+ __m256 d01 = _mm256_set1_ps(da0 * db1);
+ __m256 d11 = _mm256_set1_ps(da1 * db1);
+ __m256 d21 = _mm256_set1_ps(da2 * db1);
+ __m256 d31 = _mm256_set1_ps(da3 * db1);
+ __m256i f1 = load(Bp1 + l);
+ __m256i u1 = _mm256_sign_epi8(f1, f1);
+ __m256i s01 = _mm256_sign_epi8(e0, f1);
+ __m256i s11 = _mm256_sign_epi8(e1, f1);
+ __m256i s21 = _mm256_sign_epi8(e2, f1);
+ __m256i s31 = _mm256_sign_epi8(e3, f1);
+ c01 = madd(d01, updot(u1, s01), c01);
+ c11 = madd(d11, updot(u1, s11), c11);
+ c21 = madd(d21, updot(u1, s21), c21);
+ c31 = madd(d31, updot(u1, s31), c31);
+ float db2 = unhalf(Bp2[l].d);
+ __m256 d02 = _mm256_set1_ps(da0 * db2);
+ __m256 d12 = _mm256_set1_ps(da1 * db2);
+ __m256 d22 = _mm256_set1_ps(da2 * db2);
+ __m256 d32 = _mm256_set1_ps(da3 * db2);
+ __m256i f2 = load(Bp2 + l);
+ __m256i u2 = _mm256_sign_epi8(f2, f2);
+ __m256i s02 = _mm256_sign_epi8(e0, f2);
+ __m256i s12 = _mm256_sign_epi8(e1, f2);
+ __m256i s22 = _mm256_sign_epi8(e2, f2);
+ __m256i s32 = _mm256_sign_epi8(e3, f2);
+ c02 = madd(d02, updot(u2, s02), c02);
+ c12 = madd(d12, updot(u2, s12), c12);
+ c22 = madd(d22, updot(u2, s22), c22);
+ c32 = madd(d32, updot(u2, s32), c32);
+ }
+ C[ldc * (j + 0) + (i + 0)] = hsum(c00);
+ C[ldc * (j + 0) + (i + 1)] = hsum(c10);
+ C[ldc * (j + 0) + (i + 2)] = hsum(c20);
+ C[ldc * (j + 0) + (i + 3)] = hsum(c30);
+ C[ldc * (j + 1) + (i + 0)] = hsum(c01);
+ C[ldc * (j + 1) + (i + 1)] = hsum(c11);
+ C[ldc * (j + 1) + (i + 2)] = hsum(c21);
+ C[ldc * (j + 1) + (i + 3)] = hsum(c31);
+ C[ldc * (j + 2) + (i + 0)] = hsum(c02);
+ C[ldc * (j + 2) + (i + 1)] = hsum(c12);
+ C[ldc * (j + 2) + (i + 2)] = hsum(c22);
+ C[ldc * (j + 2) + (i + 3)] = hsum(c32);
+ END_KERNEL()
+ }
+
+ NOINLINE void gemm4x1(int m0, int m, int n0, int n) {
+ BEGIN_KERNEL(4, 1)
+ __m256 c0 = _mm256_setzero_ps();
+ __m256 c1 = _mm256_setzero_ps();
+ __m256 c2 = _mm256_setzero_ps();
+ __m256 c3 = _mm256_setzero_ps();
+ const TA *Ap0 = A + lda * (i + 0);
+ const TA *Ap1 = A + lda * (i + 1);
+ const TA *Ap2 = A + lda * (i + 2);
+ const TA *Ap3 = A + lda * (i + 3);
+ const TB *Bp = B + ldb * j;
+ for (int l = 0; l < k; ++l) {
+ float db0 = unhalf(Bp[l].d);
+ __m256i f = load(Bp + l);
+ __m256i u = _mm256_sign_epi8(f, f);
+ __m256 d0 = _mm256_set1_ps(unhalf(Ap0[l].d) * db0);
+ __m256 d1 = _mm256_set1_ps(unhalf(Ap1[l].d) * db0);
+ __m256 d2 = _mm256_set1_ps(unhalf(Ap2[l].d) * db0);
+ __m256 d3 = _mm256_set1_ps(unhalf(Ap3[l].d) * db0);
+ __m256i e0 = load(Ap0 + l);
+ __m256i e1 = load(Ap1 + l);
+ __m256i e2 = load(Ap2 + l);
+ __m256i e3 = load(Ap3 + l);
+ __m256i s0 = _mm256_sign_epi8(e0, f);
+ __m256i s1 = _mm256_sign_epi8(e1, f);
+ __m256i s2 = _mm256_sign_epi8(e2, f);
+ __m256i s3 = _mm256_sign_epi8(e3, f);
+ __m256 g0 = updot(u, s0);
+ __m256 g1 = updot(u, s1);
+ __m256 g2 = updot(u, s2);
+ __m256 g3 = updot(u, s3);
+ c0 = madd(d0, g0, c0);
+ c1 = madd(d1, g1, c1);
+ c2 = madd(d2, g2, c2);
+ c3 = madd(d3, g3, c3);
+ }
+ C[ldc * j + (i + 0)] = hsum(c0);
+ C[ldc * j + (i + 1)] = hsum(c1);
+ C[ldc * j + (i + 2)] = hsum(c2);
+ C[ldc * j + (i + 3)] = hsum(c3);
+ END_KERNEL()
+ }
+
+ NOINLINE void gemm1x4(int m0, int m, int n0, int n) {
+ BEGIN_KERNEL(1, 4)
+ __m256 c0 = _mm256_setzero_ps();
+ __m256 c1 = _mm256_setzero_ps();
+ __m256 c2 = _mm256_setzero_ps();
+ __m256 c3 = _mm256_setzero_ps();
+ const TB *Bp0 = B + ldb * (j + 0);
+ const TB *Bp1 = B + ldb * (j + 1);
+ const TB *Bp2 = B + ldb * (j + 2);
+ const TB *Bp3 = B + ldb * (j + 3);
+ const TA *Ap = A + lda * i;
+ for (int l = 0; l < k; ++l) {
+ float da0 = unhalf(Ap[l].d);
+ __m256i f = load(Ap + l);
+ __m256i u = _mm256_sign_epi8(f, f);
+ __m256 d0 = _mm256_set1_ps(unhalf(Bp0[l].d) * da0);
+ __m256 d1 = _mm256_set1_ps(unhalf(Bp1[l].d) * da0);
+ __m256 d2 = _mm256_set1_ps(unhalf(Bp2[l].d) * da0);
+ __m256 d3 = _mm256_set1_ps(unhalf(Bp3[l].d) * da0);
+ __m256 g0 = updot(u, _mm256_sign_epi8(load(Bp0 + l), f));
+ __m256 g1 = updot(u, _mm256_sign_epi8(load(Bp1 + l), f));
+ __m256 g2 = updot(u, _mm256_sign_epi8(load(Bp2 + l), f));
+ __m256 g3 = updot(u, _mm256_sign_epi8(load(Bp3 + l), f));
+ c0 = madd(d0, g0, c0);
+ c1 = madd(d1, g1, c1);
+ c2 = madd(d2, g2, c2);
+ c3 = madd(d3, g3, c3);
+ }
+ C[ldc * (j + 0) + i] = hsum(c0);
+ C[ldc * (j + 1) + i] = hsum(c1);
+ C[ldc * (j + 2) + i] = hsum(c2);
+ C[ldc * (j + 3) + i] = hsum(c3);
+ END_KERNEL()
+ }
+
+ NOINLINE void gemm1x1(int m0, int m, int n0, int n) {
+ BEGIN_KERNEL(1, 1)
+ __m256 c = _mm256_setzero_ps();
+ const TA *Ap = A + lda * i;
+ const TB *Bp = B + ldb * j;
+ for (int l = 0; l < k; ++l) {
+ __m256 d = _mm256_set1_ps(unhalf(Ap[l].d) * unhalf(Bp[l].d));
+ __m256i e = load(Ap + l);
+ __m256i f = load(Bp + l);
+ __m256 g = updot(_mm256_sign_epi8(e, e), _mm256_sign_epi8(f, e));
+ c = madd(d, g, c);
+ }
+ C[ldc * j + i] = hsum(c);
+ END_KERNEL()
+ }
+
+ 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) {
+ const __m128i tmp = _mm_loadu_si128((const __m128i *)p);
+ const __m256i bytes = MM256_SET_M128I(_mm_srli_epi16(tmp, 4), tmp);
+ const __m256i lowMask = _mm256_set1_epi8(15);
+ return _mm256_and_si256(lowMask, bytes);
+ }
+
+ 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<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<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<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_F16: {
+#if defined(__AVX512F__)
+ if (k % 16)
+ return false;
+ if (Btype != GGML_TYPE_F32)
+ return false;
+ tinyBLAS<16, __m512, __m512, ggml_fp16_t, float, float> tb{
+ k, (const ggml_fp16_t *)A, lda,
+ (const float *)B, ldb,
+ (float *)C, ldc,
+ ith, nth};
+ tb.matmul(m, n, task);
+ return true;
+#elif (defined(__AVX__) || defined(__AVX2__)) && defined(__F16C__)
+ if (k % 8)
+ return false;
+ if (Btype != GGML_TYPE_F32)
+ return false;
+ tinyBLAS<8, __m256, __m256, ggml_fp16_t, float, float> tb{
+ k, (const ggml_fp16_t *)A, lda,
+ (const float *)B, ldb,
+ (float *)C, ldc,
+ ith, nth};
+ tb.matmul(m, n, task);
+ return true;
+#elif defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(_MSC_VER)
+ if (n < 8)
+ return false;
+ if (k % 8)
+ return false;
+ if (Btype != GGML_TYPE_F16)
+ return false;
+ tinyBLAS<8, float16x8_t, float16x8_t, ggml_fp16_t, ggml_fp16_t, float> tb{
+ k, (const ggml_fp16_t *)A, lda,
+ (const ggml_fp16_t *)B, ldb,
+ (float *)C, ldc,
+ ith, nth};
+ tb.matmul(m, n, task);
+ return true;
+#elif defined(__ARM_NEON) && !defined(_MSC_VER)
+ if (k % 4)
+ return false;
+ if (Btype != GGML_TYPE_F32)
+ return false;
+ tinyBLAS<4, float32x4_t, float32x4_t, ggml_fp16_t, float, float> tb{
+ k, (const ggml_fp16_t *)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_Q8_0: {
+ if (Btype != GGML_TYPE_Q8_0)
+ return false;
+#if defined(__AVX2__) || defined(__AVX512F__)
+ tinyBLAS_Q0_AVX2<block_q8_0, block_q8_0, float> tb{
+ k, (const block_q8_0 *)A, lda,
+ (const block_q8_0 *)B, ldb,
+ (float *)C, ldc,
+ ith, nth};
+ tb.matmul(m, n, task);
+ return true;
+#elif defined(__ARM_FEATURE_DOTPROD)
+ tinyBLAS_Q0_ARM<block_q8_0> tb{
+ k, (const block_q8_0 *)A, lda,
+ (const block_q8_0 *)B, ldb,
+ (float *)C, ldc,
+ ith, nth};
+ tb.matmul(m, n, task);
+ return true;
+#else
+ return false;
+#endif
+ }
+
+ case GGML_TYPE_Q4_0: {
+ if (Btype != GGML_TYPE_Q8_0)
+ return false;
+#if defined(__AVX2__) || defined(__AVX512F__)
+ tinyBLAS_Q0_AVX2<block_q4_0, block_q8_0, float> tb{
+ k, (const block_q4_0 *)A, lda,
+ (const block_q8_0 *)B, ldb,
+ (float *)C, ldc,
+ ith, nth};
+ tb.matmul(m, n, task);
+ return true;
+#elif defined(__ARM_FEATURE_DOTPROD)
+ tinyBLAS_Q0_ARM<block_q4_0> tb{
+ k, (const block_q4_0 *)A, lda,
+ (const block_q8_0 *)B, ldb,
+ (float *)C, ldc,
+ ith, nth};
+ tb.matmul(m, n, task);
+ return true;
+#else
+ return false;
+#endif
+ }
+
+ default:
+ return false;
+ }
+
+ (void)m;
+ (void)n;
+ (void)k;
+ (void)A;
+ (void)lda;
+ (void)B;
+ (void)ldb;
+ (void)C;
+ (void)ldc;
+ (void)ith;
+ (void)nth;
+ (void)task;
+ (void)Atype;
+ (void)Btype;
+ (void)Ctype;
+}
overview / pkg / ggml / sources / llama.cpp / commitdiff