Files
kangkang/康康/AI/MNN/MNNLLMBridge.mm
link2026 ddfd474bb3 feat(AI): MNN 4B 多模态一肩挑文本+视觉,合并为单模型(MLX 仍兜底)
利用 Qwen3.5-4B-MNN 本身是多模态(含 visual.mnn),让同一个 MNN 模型
同时做文本生成与拍照识别 → MNN 路径只需下 1 个模型(7.4GB→2.64GB)。
MLX(.llm/.vl)保留作兜底,尤其开发机 iPhone 15 Pro(A17 无 SME2)。

- MNN.xcframework 重建为 OMNI(MNN_BUILD_LLM_OMNI=ON,加 OpenCV 图像解码);
  构建脚本同步加 OMNI flag
- MNNLLMBridge.analyzeImages:把图片路径拼成 <img>路径</img> 标签 + response,
  Omni 内部 CV::imread 加载(无需桥接 include OpenCV);与 generateText 共用 runResponse
- MNNBackend.analyze:detached 线程跑 blocking VL 调用,聚合为字符串
- AIRuntime:engine=.mnn 且就绪时,prepareVL→prepareMNN、analyzeReport→mnn.analyze;
  否则回退 MLX VL

device + 模拟器 BUILD SUCCEEDED,0 error,OMNI 框架链接干净。
VL 实际识别质量需真机用化验单 A/B(demo 核心)。

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-08 20:52:58 +08:00

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//
// MNNLLMBridge.mm
// 康康
//
// ObjC++ 实现。device 真机用 <MNN/llm/llm.hpp>;模拟器编为桩(返回不可用,上层回退 MLX)。
//
#import "MNNLLMBridge.h"
#include <sys/sysctl.h>
// MARK: - 性能统计(私有 readwrite 重声明)
@interface MNNGenerateStats ()
@property (nonatomic, readwrite) int promptTokens;
@property (nonatomic, readwrite) int genTokens;
@property (nonatomic, readwrite) double prefillMs;
@property (nonatomic, readwrite) double decodeMs;
@end
@implementation MNNGenerateStats
- (double)decodeTokensPerSecond {
return self.decodeMs > 0 ? (self.genTokens / (self.decodeMs / 1000.0)) : 0;
}
@end
// MARK: - SME2 / 可用性探测(device + simulator 都可编)
static BOOL kk_sysctlFlag(const char *name) {
int64_t v = 0; size_t sz = sizeof(v);
if (sysctlbyname(name, &v, &sz, NULL, 0) != 0) return NO;
return v != 0;
}
#if TARGET_OS_SIMULATOR
// ============ 模拟器桩:无真实 MNN ============
@implementation MNNLLMBridge
+ (BOOL)isAvailable { return NO; }
+ (BOOL)cpuSupportsSME2 { return NO; }
- (nullable instancetype)initWithConfigPath:(NSString *)configPath { return nil; }
- (BOOL)isLoaded { return NO; }
- (MNNGenerateStats *)generateText:(NSString *)prompt maxTokens:(int)maxTokens
onToken:(void (^)(NSString *))onToken { return [MNNGenerateStats new]; }
- (nullable MNNGenerateStats *)analyzeImages:(NSArray<NSString *> *)imagePaths prompt:(NSString *)prompt
maxTokens:(int)maxTokens onToken:(void (^)(NSString *))onToken
error:(NSError **)error {
if (error) *error = [NSError errorWithDomain:@"MNN" code:-1
userInfo:@{NSLocalizedDescriptionKey: @"MNN 在模拟器不可用"}];
return nil;
}
- (void)cancel {}
@end
#else
// ============ 真机:真实 MNN-LLM ============
#include <MNN/llm/llm.hpp>
#include <string>
#include <ostream>
#include <streambuf>
#include <atomic>
using MNN::Transformer::Llm;
namespace {
/// 把 MNN 写入 ostream 的解码文本转成 NSString 回调;按 UTF-8 完整边界聚合,避免截断多字节。
class TokenStreamBuf : public std::streambuf {
public:
TokenStreamBuf(void (^onToken)(NSString *), std::atomic<bool> *cancel)
: _onToken(onToken), _cancel(cancel) {}
void flush() {
if (_pending.empty()) return;
emitPending(); // 末尾尽力 emit(即便非完整 UTF-8 也交出去)
_pending.clear();
}
protected:
std::streamsize xsputn(const char *s, std::streamsize n) override {
append(s, (size_t)n);
return n;
}
int overflow(int c) override {
if (c != EOF) { char ch = (char)c; append(&ch, 1); }
return c;
}
private:
void append(const char *s, size_t n) {
if (_cancel && _cancel->load()) return; // 已取消,吞掉不回调
_pending.append(s, n);
// 仅当整个 pending 是合法 UTF-8 才 emit(token 通常是完整字/词,边界自然对齐)
NSString *str = [[NSString alloc] initWithBytes:_pending.data()
length:_pending.size()
encoding:NSUTF8StringEncoding];
if (str) { if (_onToken) _onToken(str); _pending.clear(); }
}
void emitPending() {
NSString *str = [[NSString alloc] initWithBytes:_pending.data()
length:_pending.size()
encoding:NSUTF8StringEncoding];
if (str && _onToken) _onToken(str);
}
void (^_onToken)(NSString *);
std::atomic<bool> *_cancel;
std::string _pending;
};
} // namespace
@implementation MNNLLMBridge {
Llm *_llm;
std::atomic<bool> _cancel;
BOOL _loaded;
}
+ (BOOL)isAvailable { return YES; }
+ (BOOL)cpuSupportsSME2 {
// Apple 通过 sysctl 暴露 ARM 特性位:FEAT_SME2(A19/iPhone17+)。
return kk_sysctlFlag("hw.optional.arm.FEAT_SME2");
}
- (nullable instancetype)initWithConfigPath:(NSString *)configPath {
self = [super init];
if (!self) return nil;
_cancel = false;
_llm = Llm::createLLM(std::string(configPath.UTF8String));
if (_llm == nullptr) return nil;
_loaded = _llm->load();
if (!_loaded) { Llm::destroy(_llm); _llm = nullptr; return nil; }
return self;
}
- (void)dealloc {
if (_llm) { Llm::destroy(_llm); _llm = nullptr; }
}
- (BOOL)isLoaded { return _loaded; }
- (void)cancel { _cancel = true; }
// 统一生成:full 已是最终 prompt(文本,或含 <img>路径</img> 标签)。
// 多模态模型 createLLM 返回 Omni,response 解析 <img> 标签并对路径 CV::imread(OMNI 框架内)。
- (MNNGenerateStats *)runResponse:(NSString *)full
maxTokens:(int)maxTokens
onToken:(void (^)(NSString *))onToken {
_cancel = false;
TokenStreamBuf buf(onToken, &_cancel);
std::ostream os(&buf);
if (_llm) {
_llm->response(std::string(full.UTF8String), &os, nullptr, maxTokens);
}
buf.flush();
return [self statsFromContext];
}
- (MNNGenerateStats *)generateText:(NSString *)prompt
maxTokens:(int)maxTokens
onToken:(void (^)(NSString *))onToken {
return [self runResponse:prompt maxTokens:maxTokens onToken:onToken];
}
- (nullable MNNGenerateStats *)analyzeImages:(NSArray<NSString *> *)imagePaths
prompt:(NSString *)prompt
maxTokens:(int)maxTokens
onToken:(void (^)(NSString *))onToken
error:(NSError **)error {
// 在 prompt 前拼 <img>本地路径</img>;Omni 解析标签并对路径 imread(需 OMNI 框架)。
NSMutableString *full = [NSMutableString string];
for (NSString *p in imagePaths) {
[full appendFormat:@"<img>%@</img>", p];
}
[full appendString:prompt];
return [self runResponse:full maxTokens:maxTokens onToken:onToken];
}
- (MNNGenerateStats *)statsFromContext {
MNNGenerateStats *s = [MNNGenerateStats new];
if (_llm) {
const MNN::Transformer::LlmContext *ctx = _llm->getContext();
if (ctx) {
s.promptTokens = ctx->prompt_len;
s.genTokens = ctx->gen_seq_len;
s.prefillMs = ctx->prefill_us / 1000.0;
s.decodeMs = ctx->decode_us / 1000.0;
}
}
return s;
}
@end
#endif