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support npu
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parent
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34 changed files with 14004 additions and 5626 deletions
361
third_party/llamafile/tinyblas_cpu_sgemm_x86.inc
vendored
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361
third_party/llamafile/tinyblas_cpu_sgemm_x86.inc
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// Adapted from
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// https://github.com/Mozilla-Ocho/llamafile/blob/0.8.8/llamafile/tinyblas_cpu_sgemm.inc
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// Copyrigth 2024 Mozilla Foundation.
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// Copyright(c) 2024 by KVCache.AI, All Rights Reserved.
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// -*- mode:c++;indent-tabs-mode:nil;c-basic-offset:4;coding:utf-8 -*-
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// vi: set et ft=cpp ts=4 sts=4 sw=4 fenc=utf-8 :vi
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//
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// Copyright 2024 Mozilla Foundation
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "tinyblas_cpu.h"
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//
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//
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// ██████╗ ██╗ █████╗ ██████╗
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// ██████╗██╗██╗ ██╗██═██╗██╔══██╗██║ ██╔══██╗██╔═══╝
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// ╚═██╔═╝██║███▄██║██ ██║██████╔╝██║ ███████║██████╗
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// ██║ ██║██▀███║╚███╔╝██╔══██╗██║ ██╔══██║╔═══██║
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// ██║ ██║██║ ██║ ███║ ██████╔╝████╗██║ ██║██████║
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// ╚═╝ ╚═╝╚═╝ ╚═╝ ╚══╝ ╚═════╝ ╚═══╝╚═╝ ╚═╝╚═════╝
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//
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// BASIC LINEAR ALGEBRA SUBPROGRAMS
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//
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//
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// This file implements multithreaded CPU matrix multiplication for the
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// common contiguous use case C = Aᵀ * B. These kernels are designed to
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// have excellent performance[1] for matrices that fit in the CPU cache
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// without imposing any overhead such as cache filling or malloc calls.
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//
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// This implementation does not guarantee any upper bound with rounding
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// errors, which grow along with k. Our goal's to maximally exploit the
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// hardware for performance, and then use whatever resources remain for
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// improving numerical accuracy.
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//
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// [1] J. Tunney, ‘LLaMA Now Goes Faster on CPUs’, Mar. 2024. [Online].
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// Available: https://justine.lol/matmul/. [Accessed: 29-Mar-2024].
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namespace {
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template <typename TC>
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bool llamafile_sgemm_impl(long m, long n, long k, const void* A, long lda, const void* B, long ldb, TC* C, long ldc, int ith, int nth, int task, int Atype, int Btype, int Ctype, int precision) {
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switch (Atype) {
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case GGML_TYPE_F32: {
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if (Btype != GGML_TYPE_F32)
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return NOT_SUPPORTED;
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#if defined(__AVX512F__)
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if (k % 16)
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return NOT_SUPPORTED;
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tinyBLAS<0, 16, __m512, __m512, float, float, TC> tb{
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k, (const float*)A, lda, (const float*)B, ldb, C, ldc, ith, nth};
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tb.matmul(m, n, task);
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return true;
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#elif defined(__AVX__) || defined(__AVX2__)
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if (k % 8)
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return NOT_SUPPORTED;
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tinyBLAS<0, 8, __m256, __m256, float, float, TC> tb{
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k, (const float*)A, lda, (const float*)B, ldb, C, ldc, ith, nth};
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tb.matmul(m, n, task);
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return true;
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#elif defined(__ARM_NEON)
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if (k % 4)
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return NOT_SUPPORTED;
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tinyBLAS<0, 4, float32x4_t, float32x4_t, float, float, TC> tb{
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k, (const float*)A, lda, (const float*)B, ldb, C, ldc, ith, nth};
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tb.matmul(m, n, task);
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return true;
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#else
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return NOT_SUPPORTED;
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#endif
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}
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case GGML_TYPE_BF16: {
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#if defined(__AVX512BF16__)
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if (k % 32)
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return NOT_SUPPORTED;
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if (Btype == GGML_TYPE_F32 && n < 2) {
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tinyBLAS<0, 16, __m512, __m512, ggml_bf16_t, float, TC> tb{
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k, (const ggml_bf16_t*)A, lda, (const float*)B, ldb, C, ldc, ith, nth};
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tb.matmul(m, n, task);
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return true;
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}
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if (Btype == GGML_TYPE_F32)
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return WANT_QUANTIZATION;
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if (Btype != GGML_TYPE_BF16)
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return NOT_SUPPORTED;
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if (!FLAG_precise) {
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tinyBLAS<0, 32, __m512, __m512bh, ggml_bf16_t, ggml_bf16_t, TC> tb{
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k, (const ggml_bf16_t*)A, lda, (const ggml_bf16_t*)B, ldb, C, ldc, ith, nth};
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tb.matmul(m, n, task);
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return true;
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} else {
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tinyBLAS<0, 16, __m512, __m512, ggml_bf16_t, ggml_bf16_t, TC> tb{
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k, (const ggml_bf16_t*)A, lda, (const ggml_bf16_t*)B, ldb, C, ldc, ith, nth};
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tb.matmul(m, n, task);
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return true;
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}
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#elif defined(__AVX512F__)
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if (k % 16)
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return NOT_SUPPORTED;
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tinyBLAS<0, 16, __m512, __m512, ggml_bf16_t, float, TC> tb{
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k, (const ggml_bf16_t*)A, lda, (const float*)B, ldb, C, ldc, ith, nth};
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tb.matmul(m, n, task);
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return true;
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#elif defined(__AVX2__)
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if (k % 8)
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return NOT_SUPPORTED;
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if (Btype != GGML_TYPE_F32)
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return NOT_SUPPORTED;
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tinyBLAS<0, 8, __m256, __m256, ggml_bf16_t, float, TC> tb{
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k, (const ggml_bf16_t*)A, lda, (const float*)B, ldb, C, ldc, ith, nth};
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tb.matmul(m, n, task);
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return true;
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#elif defined(__ARM_NEON) && !defined(_MSC_VER)
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if (k % 4)
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return NOT_SUPPORTED;
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if (Btype != GGML_TYPE_F32)
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return NOT_SUPPORTED;
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tinyBLAS<0, 4, float32x4_t, float32x4_t, ggml_bf16_t, float, TC> tb{
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k, (const ggml_bf16_t*)A, lda, (const float*)B, ldb, C, ldc, ith, nth};
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tb.matmul(m, n, task);
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return true;
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#else
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return NOT_SUPPORTED;
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#endif
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}
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case GGML_TYPE_F16: {
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#if defined(__AVX512F__)
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if (k % 16)
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return NOT_SUPPORTED;
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if (Btype == GGML_TYPE_F32 && n < 2) {
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tinyBLAS<0, 16, __m512, __m512, ggml_fp16_t, float, TC> tb{
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k, (const ggml_fp16_t*)A, lda, (const float*)B, ldb, C, ldc, ith, nth};
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tb.matmul(m, n, task);
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return true;
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}
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if (Btype == GGML_TYPE_F32)
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return WANT_QUANTIZATION;
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if (Btype != GGML_TYPE_F16)
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return NOT_SUPPORTED;
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tinyBLAS<0, 16, __m512, __m512, ggml_fp16_t, ggml_fp16_t, TC> tb{
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k, (const ggml_fp16_t*)A, lda, (const ggml_fp16_t*)B, ldb, C, ldc, ith, nth};
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tb.matmul(m, n, task);
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return true;
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#elif (defined(__AVX__) || defined(__AVX2__)) && defined(__F16C__)
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// if (X86_CHECK(F16C)) {
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if (k % 8)
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return NOT_SUPPORTED;
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if (Btype == GGML_TYPE_F32 && n < 2) {
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tinyBLAS<0, 8, __m256, __m256, ggml_fp16_t, float, TC> tb{
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k, (const ggml_fp16_t*)A, lda, (const float*)B, ldb, C, ldc, ith, nth};
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tb.matmul(m, n, task);
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return true;
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}
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if (Btype == GGML_TYPE_F32)
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return WANT_QUANTIZATION;
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if (Btype != GGML_TYPE_F16)
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return NOT_SUPPORTED;
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tinyBLAS<0, 8, __m256, __m256, ggml_fp16_t, ggml_fp16_t, TC> tb{
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k, (const ggml_fp16_t*)A, lda, (const ggml_fp16_t*)B, ldb, C, ldc, ith, nth};
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tb.matmul(m, n, task);
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return true;
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// } else {
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// return NOT_SUPPORTED;
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// }
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#elif defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(_MSC_VER)
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if (n < 2 && !FLAG_precise)
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// TODO(jart): Why is ggml_vec_dot_f16_unroll() so fast at matvec?
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return NOT_SUPPORTED;
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if (precision == GGML_PREC_F32) {
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if (k % 4)
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return NOT_SUPPORTED;
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if (Btype != GGML_TYPE_F32)
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return NOT_SUPPORTED;
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tinyBLAS<0, 4, float32x4_t, float32x4_t, ggml_fp16_t, float, TC> tb{
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k, (const ggml_fp16_t*)A, lda, (const float*)B, ldb, C, ldc, ith, nth};
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tb.matmul(m, n, task);
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return true;
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} else {
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if (k % 8)
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return NOT_SUPPORTED;
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if (Btype == GGML_TYPE_F32)
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return WANT_QUANTIZATION;
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if (Btype != GGML_TYPE_F16)
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return NOT_SUPPORTED;
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tinyBLAS<0, 8, float16x8_t, float16x8_t, ggml_fp16_t, ggml_fp16_t, TC> tb{
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k, (const ggml_fp16_t*)A, lda, (const ggml_fp16_t*)B, ldb, C, ldc, ith, nth};
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tb.matmul(m, n, task);
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return true;
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}
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#elif defined(__ARM_NEON) && !defined(_MSC_VER)
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if (n < 2 && !FLAG_precise)
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// TODO(jart): Why is ggml_vec_dot_f16_unroll() so fast at matvec?
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return NOT_SUPPORTED;
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if (k % 4)
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return NOT_SUPPORTED;
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if (Btype != GGML_TYPE_F32)
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return NOT_SUPPORTED;
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tinyBLAS<0, 4, float32x4_t, float32x4_t, ggml_fp16_t, float, TC> tb{
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k, (const ggml_fp16_t*)A, lda, (const float*)B, ldb, C, ldc, ith, nth};
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tb.matmul(m, n, task);
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return true;
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#else
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return NOT_SUPPORTED;
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#endif
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}
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case GGML_TYPE_Q8_0: {
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if (Btype == GGML_TYPE_F32)
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return WANT_QUANTIZATION;
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if (Btype != GGML_TYPE_Q8_0)
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return NOT_SUPPORTED;
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#if defined(__AVX2__) || defined(__AVX512F__)
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tinyBLAS_Q0_AVX2<0, block_q8_0, block_q8_0, TC> tb{
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k, (const block_q8_0*)A, lda, (const block_q8_0*)B, ldb, C, ldc, ith, nth};
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tb.matmul(m, n, task);
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return true;
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#elif defined(__ARM_FEATURE_DOTPROD)
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tinyBLAS_Q0_ARM<0, block_q8_0, block_q8_0, TC> tb{
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k, (const block_q8_0*)A, lda, (const block_q8_0*)B, ldb, C, ldc, ith, nth};
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tb.matmul(m, n, task);
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return true;
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#else
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return NOT_SUPPORTED;
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#endif
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}
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case GGML_TYPE_Q4_0: {
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if (Btype == GGML_TYPE_F32)
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return WANT_QUANTIZATION;
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if (Btype != GGML_TYPE_Q8_0)
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return NOT_SUPPORTED;
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#if defined(__AVX2__) || defined(__AVX512F__)
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tinyBLAS_Q0_AVX2<0, block_q4_0, block_q8_0, TC> tb{
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k, (const block_q4_0*)A, lda, (const block_q8_0*)B, ldb, C, ldc, ith, nth};
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tb.matmul(m, n, task);
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return true;
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#elif defined(__ARM_FEATURE_DOTPROD)
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tinyBLAS_Q0_ARM<0, block_q4_0, block_q8_0, TC> tb{
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k, (const block_q4_0*)A, lda, (const block_q8_0*)B, ldb, C, ldc, ith, nth};
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tb.matmul(m, n, task);
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return true;
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#else
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return NOT_SUPPORTED;
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#endif
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}
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default:
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return NOT_SUPPORTED;
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}
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(void)m;
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(void)n;
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(void)k;
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(void)A;
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(void)lda;
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(void)B;
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(void)ldb;
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(void)C;
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(void)ldc;
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(void)ith;
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(void)nth;
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(void)Atype;
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(void)Btype;
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(void)precision;
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}
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} // namespace
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/**
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* Performs optimized matrix multiplication on CPU.
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*
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* This subroutine may compute C = Aᵀ * B with column major ordering.
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* Despite its name, this isn't a generalized implementation. Work is
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* only performed when a handwritten kernel is written and available.
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* Otherwise the caller should fall back to a general matmul routine.
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*
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* For example, for single-threaded single-precision GEMM you can say
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*
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* llamafile_sgemm(m, n, k, A, lda, B, ldb, C, ldc, 0, 1,
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* GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32,
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* GGML_PREC_DEFAULT);
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*
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* @param m is rows in `A` and `C`
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* @param n is cols in `B` and `C`
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* @param k is cols in `A` and rows in `B`
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* @param A is first input matrix (always transposed)
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* @param lda is row stride of `A`
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* @param B is second input matrix (never transposed)
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* @param ldb is row stride of `B`
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* @param C is input/output array of output matrices
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* @param ldc is row stride of `C`
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* @param ith is thread id (must be less than `nth`)
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* @param nth is number of threads (must be greater than zero)
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* @param Atype is GGML data type of `A`
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* @param Btype is GGML data type of `B`
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* @param Ctype is GGML data type of `C`
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* @param precision may be used to control the internal compute type
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* @return true if this function was able to service the matmul request
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*/
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bool llamafile_sgemm(long m, long n, long k, const void* A, long lda, const void* B, long ldb, void* C, long ldc, int ith, int nth, int task, int Atype, int Btype, int Ctype, int precision) {
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assert(m >= 0);
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assert(n >= 0);
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assert(k >= 0);
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assert(lda >= k);
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assert(ldb >= k);
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assert(ldc >= m);
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assert(nth > 0);
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assert(ith < nth);
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#if QK_K == 256
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#if defined(__x86_64__) || defined(_M_X64)
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#if defined(__AVX2__) && (defined(__FMA__) || (defined(_MSC_VER) && (defined(__AVX2__) || defined(__AVX512F__))))
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/*
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moonll
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more Btype accept
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}*/
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if (Ctype == GGML_TYPE_F32){
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if (iqk_mul_mat(m, n, k * ggml_blck_size(ggml_type(Atype)), Atype, A,lda,Btype, B,ldb, (float*)C, ldc, ith, nth)) {
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return true;
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}
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}
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#endif
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#elif defined __aarch64__ && defined __ARM_FEATURE_DOTPROD && !defined _MSC_VER
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if (Btype == GGML_TYPE_Q8_K && Ctype == GGML_TYPE_F32) {
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if (iqk_mul_mat(m, n, k * QK_K, Atype, A, B, (float*)C, ldc, ith, nth)) {
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return true;
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}
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}
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if ((Btype == GGML_TYPE_Q8_0 || Btype == GGML_TYPE_Q8_1) && Ctype == GGML_TYPE_F32) {
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// assert(QK8_0 == QK8_1 == QK4_0 == QK4_1 == QK5_0 == QK5_1 == 32);
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assert((QK8_0 == 32) && (QK8_1 == 32) && (QK4_0 == 32) && (QK4_1 == 32) && (QK5_0 == 32) && (QK5_1 == 32));
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if (iqk_mul_mat(m, n, k * QK8_0, Atype, A, B, (float*)C, ldc, ith, nth)) {
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return true;
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}
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}
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#endif
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#endif
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switch (Ctype) {
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case GGML_TYPE_F32:
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return llamafile_sgemm_impl(m, n, k, A, lda, B, ldb, (float*)C, ldc, ith, nth, task, Atype,
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Btype, Ctype, precision);
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default:
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return NOT_SUPPORTED;
|
||||
}
|
||||
}
|
||||
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