mirror of
https://github.com/ollama/ollama.git
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247 lines
8.9 KiB
C++
Vendored
247 lines
8.9 KiB
C++
Vendored
/**
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* llama.cpp - commit ba1cb19cdd0d92e012e0f6e009e0620f854b6afd - do not edit this file
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*
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* MIT License
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*
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* Copyright (c) 2023-2024 The ggml authors
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "amx.h"
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#include "common.h"
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#include "mmq.h"
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#include "ggml-backend-impl.h"
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#include "ggml-backend.h"
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#include "ggml-impl.h"
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#include "ggml-cpu.h"
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#include "ggml-cpu-traits.h"
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#if defined(__gnu_linux__)
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#include <sys/syscall.h>
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#include <unistd.h>
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#endif
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#include <cstdlib>
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#include <cstring>
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#include <memory>
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#if defined(__AMX_INT8__) && defined(__AVX512VNNI__)
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// AMX type_trais
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namespace ggml::cpu::amx {
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class tensor_traits : public ggml::cpu::tensor_traits {
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bool work_size(int /* n_threads */, const struct ggml_tensor * op, size_t & size) override {
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size = ggml_backend_amx_desired_wsize(op);
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return true;
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}
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bool compute_forward(struct ggml_compute_params * params, struct ggml_tensor * op) override {
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if (op->op == GGML_OP_MUL_MAT) {
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ggml_backend_amx_mul_mat(params, op);
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return true;
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}
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return false;
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}
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};
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static ggml::cpu::tensor_traits * get_tensor_traits(ggml_backend_buffer_t, struct ggml_tensor *) {
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static tensor_traits traits;
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return &traits;
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}
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} // namespace ggml::cpu::amx
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// AMX buffer interface
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static void ggml_backend_amx_buffer_free_buffer(ggml_backend_buffer_t buffer) {
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free(buffer->context);
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}
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static void * ggml_backend_amx_buffer_get_base(ggml_backend_buffer_t buffer) {
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return (void *) (buffer->context);
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}
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static void ggml_backend_amx_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
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tensor->extra = (void *) ggml::cpu::amx::get_tensor_traits(buffer, tensor);
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GGML_UNUSED(buffer);
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}
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static void ggml_backend_amx_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor,
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uint8_t value, size_t offset, size_t size) {
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memset((char *) tensor->data + offset, value, size);
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GGML_UNUSED(buffer);
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}
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static void ggml_backend_amx_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor,
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const void * data, size_t offset, size_t size) {
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if (qtype_has_amx_kernels(tensor->type)) {
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GGML_LOG_DEBUG("%s: amx repack tensor %s of type %s\n", __func__, tensor->name, ggml_type_name(tensor->type));
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ggml_backend_amx_convert_weight(tensor, data, offset, size);
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} else {
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memcpy((char *) tensor->data + offset, data, size);
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}
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GGML_UNUSED(buffer);
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}
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/*
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// need to figure what we need to do with buffer->extra.
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static void ggml_backend_amx_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
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GGML_ASSERT(!qtype_has_amx_kernels(tensor->type));
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memcpy(data, (const char *)tensor->data + offset, size);
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GGML_UNUSED(buffer);
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}
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static bool ggml_backend_amx_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) {
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if (ggml_backend_buffer_is_host(src->buffer)) {
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if (qtype_has_amx_kernels(src->type)) {
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ggml_backend_amx_convert_weight(dst, src->data, 0, ggml_nbytes(dst));
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} else {
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memcpy(dst->data, src->data, ggml_nbytes(src));
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}
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return true;
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}
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return false;
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GGML_UNUSED(buffer);
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}
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*/
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static void ggml_backend_amx_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
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memset(buffer->context, value, buffer->size);
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}
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static ggml_backend_buffer_i ggml_backend_amx_buffer_interface = {
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/* .free_buffer = */ ggml_backend_amx_buffer_free_buffer,
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/* .get_base = */ ggml_backend_amx_buffer_get_base,
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/* .init_tensor = */ ggml_backend_amx_buffer_init_tensor,
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/* .memset_tensor = */ ggml_backend_amx_buffer_memset_tensor,
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/* .set_tensor = */ ggml_backend_amx_buffer_set_tensor,
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/* .get_tensor = */ nullptr,
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/* .cpy_tensor = */ nullptr,
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/* .clear = */ ggml_backend_amx_buffer_clear,
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/* .reset = */ nullptr,
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};
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static const char * ggml_backend_amx_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
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return "AMX";
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GGML_UNUSED(buft);
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}
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static ggml_backend_buffer_t ggml_backend_amx_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
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void * data = ggml_aligned_malloc(size);
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if (data == NULL) {
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fprintf(stderr, "%s: failed to allocate buffer of size %zu\n", __func__, size);
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return NULL;
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}
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return ggml_backend_buffer_init(buft, ggml_backend_amx_buffer_interface, data, size);
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}
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static size_t ggml_backend_amx_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
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return TENSOR_ALIGNMENT;
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GGML_UNUSED(buft);
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}
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namespace ggml::cpu::amx {
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class extra_buffer_type : ggml::cpu::extra_buffer_type {
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bool supports_op(ggml_backend_dev_t, const struct ggml_tensor * op) override {
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// handle only 2d gemm for now
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auto is_contiguous_2d = [](const struct ggml_tensor * t) {
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return ggml_is_contiguous(t) && t->ne[3] == 1 && t->ne[2] == 1;
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};
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if (op->op == GGML_OP_MUL_MAT && is_contiguous_2d(op->src[0]) && // src0 must be contiguous
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is_contiguous_2d(op->src[1]) && // src1 must be contiguous
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op->src[0]->buffer && op->src[0]->buffer->buft == ggml_backend_amx_buffer_type() &&
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op->ne[0] % (TILE_N * 2) == 0 && // out_features is 32x
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(qtype_has_amx_kernels(op->src[0]->type) || (op->src[0]->type == GGML_TYPE_F16))) {
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// src1 must be host buffer
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if (op->src[1]->buffer && !ggml_backend_buft_is_host(op->src[1]->buffer->buft)) {
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return false;
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}
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// src1 must be float32
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if (op->src[1]->type == GGML_TYPE_F32) {
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return true;
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}
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}
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return false;
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}
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ggml::cpu::tensor_traits * get_tensor_traits(const struct ggml_tensor * op) override {
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if (op->op == GGML_OP_MUL_MAT && op->src[0]->buffer &&
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op->src[0]->buffer->buft == ggml_backend_amx_buffer_type()) {
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return (ggml::cpu::tensor_traits *) op->src[0]->extra;
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}
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return nullptr;
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}
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};
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} // namespace ggml::cpu::amx
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static size_t ggml_backend_amx_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
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return ggml_backend_amx_get_alloc_size(tensor);
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GGML_UNUSED(buft);
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}
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#define ARCH_GET_XCOMP_PERM 0x1022
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#define ARCH_REQ_XCOMP_PERM 0x1023
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#define XFEATURE_XTILECFG 17
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#define XFEATURE_XTILEDATA 18
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static bool ggml_amx_init() {
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#if defined(__gnu_linux__)
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if (syscall(SYS_arch_prctl, ARCH_REQ_XCOMP_PERM, XFEATURE_XTILEDATA)) {
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fprintf(stderr, "AMX is not ready to be used!\n");
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return false;
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}
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return true;
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#elif defined(_WIN32)
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return true;
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#endif
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}
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ggml_backend_buffer_type_t ggml_backend_amx_buffer_type() {
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static struct ggml_backend_buffer_type ggml_backend_buffer_type_amx = {
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/* .iface = */ {
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/* .get_name = */ ggml_backend_amx_buffer_type_get_name,
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/* .alloc_buffer = */ ggml_backend_amx_buffer_type_alloc_buffer,
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/* .get_alignment = */ ggml_backend_amx_buffer_type_get_alignment,
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/* .get_max_size = */ nullptr, // defaults to SIZE_MAX
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/* .get_alloc_size = */ ggml_backend_amx_buffer_type_get_alloc_size,
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/* .is_host = */ nullptr,
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},
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/* .device = */ ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0),
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/* .context = */ new ggml::cpu::amx::extra_buffer_type(),
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};
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if (!ggml_amx_init()) {
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return nullptr;
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}
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return &ggml_backend_buffer_type_amx;
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}
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#endif // defined(__AMX_INT8__) && defined(__AVX512VNNI__)
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