浏览器端LLM推理WebLLM WASM

# WebLLM推理后端选择与可行性评估# WebGPU优先, WASM兜底import mathBACKEND = {    'webgpu': {'compute': 'compute_shader+storage_buffer', 'tok_s_factor': 1.0},    'wasm':   {'compute': 'cpu_wasm+simd',                 'tok_s_factor': 0.18},}def estimate_throughput(backend, base_tps=30.0):    return round(base_tps * BACKEND[backend]['tok_s_factor'], 1)# INT4 7B模型内存可行性检查def feasible(params_b, quant_bytes, browser_mem_gb=8):    mem_gb = params_b * quant_bytes  # 7B*0.5=3.5GB(INT4)    return mem_gb < browser_mem_gb, round(mem_gb, 2)ok, mem = feasible(7, 0.5)   # INT4=0.5字节/参数print(f'INT4 7B memory={mem}GB feasible={ok}, webgpu tps={estimate_throughput("webgpu")}')# INT4 7B memory=3.5GB feasible=True, webgpu tps=30.0

# GGUF量化类型映射与体积压缩比计算# Q4_K_M -> INT4, Q8_0 -> INT8, F16 -> FP16GGUF_QUANT = {    'Q4_0': 0.5,  'Q4_K_M': 0.55,  # 4位量化, ~0.5字节/参数    'Q8_0': 1.0,                   # 8位量化, 1字节/参数    'F16':  2.0,                    # 半精度浮点, 2字节/参数}def model_size_gb(params_b, gguf_type):    bytes_per_param = GGUF_QUANT[gguf_type]    return round(params_b * bytes_per_param, 2)for q in ['F16', 'Q8_0', 'Q4_K_M']:    print(f'Llama-2-7B {q}: {model_size_gb(7, q)}GB')# F16: 14.0GB, Q8_0: 7.0GB, Q4_K_M: 3.85GB (INT4压缩到约1/4)

# KV Cache内存占用与滑动窗口驱逐模拟# 每token KV = 2层K+V张量def kv_bytes_per_token(n_layers, n_heads, head_dim, bytes_elem=2):    return 2 * n_layers * n_heads * head_dim * bytes_elemper_tok = kv_bytes_per_token(32, 32, 128)   # Llama-2-7B参数print(f'per token KV: {per_tok/1024/1024:.2f}MB')  # 0.50MB# 滑动窗口: 超过max_len驱逐最旧tokenclass SlidingKV:    def __init__(self, max_len=2048, window=1024):        self.max_len, self.window = max_len, window        self.kv = []    def append(self, tok):        self.kv.append(tok)        if len(self.kv) > self.max_len:            self.kv = self.kv[-self.window:]   # 驱逐旧token        return len(self.kv)cache = SlidingKV()for i in range(2100):    cache.append(i)print(f'after 2100 tokens: {len(cache.kv)} entries (window={cache.window})')# after 2100 tokens: 1024 entries (window=1024)

# INT4 vs INT8 vs FP16 内存与质量权衡QUANT_PROFILE = {    'fp16': {'bytes_per_param': 2.0,  'quality': 100, 'use_case': '高精度推理基准'},    'int8': {'bytes_per_param': 1.0,  'quality': 96,  'use_case': '推理任务平衡选择'},    'int4': {'bytes_per_param': 0.5,  'quality': 88,  'use_case': '对话闲聊内存优先'},}for q, p in QUANT_PROFILE.items():    mem = 7 * p['bytes_per_param']   # 7B模型    print(f"{q}: {mem:.1f}GB, quality={p['quality']}, {p['use_case']}")# fp16: 14.0GB quality=100, int8: 7.0GB quality=96, int4: 3.5GB quality=88# 浏览器内存上限检查(8GB设备)for q, p in QUANT_PROFILE.items():    mem = 7 * p['bytes_per_param']    print(f"{q} 7B on 8GB browser: {'OK' if mem < 6 else 'OOM risk'}")

# WebLLM渐进式加载与性能监控模拟# 分片下载 + 流式输出 + TTFT/吞吐统计SHARDS = [f'shard_{i}.bin' for i in range(8)]class WebLLMLoader:    def __init__(self):        self.loaded = []        self.ttft_ms = None        self.tokens = 0    def progressive_load(self, needed_layers=3):        for s in SHARDS[:needed_layers]:   # 首屏只加载即时所需层            self.loaded.append(s)    def stream_generate(self, n, tps=30):        for i in range(n):            self.tokens += 1            if self.ttft_ms is None:                self.ttft_ms = round(1000/tps, 1)   # 首token时间        return {'ttft_ms': self.ttft_ms, 'total_tokens': self.tokens,                'tps': tps}loader = WebLLMLoader()loader.progressive_load(3)   # 渐进加载3层即可开始stat = loader.stream_generate(64)print(f"TTFT={stat['ttft_ms']}ms, tokens={stat['total_tokens']}, tps={stat['tps']}")# TTFT=33.3ms, tokens=64, tps=30 (渐进加载降低首token等待)

from dataclasses import dataclassfrom typing import List, Dict, Optional # WebLLM模型量化类型与内存占用系数 (相对FP16=1.0)QUANT_MEMORY_FACTOR = {    'fp16': 1.0,    # 16位浮点, 基准    'int8': 0.5,    # 8位整数, 内存减半    'int4': 0.25,   # 4位整数, 内存1/4} # WebLLM推理后端基准吞吐系数 (tokens/s, 相对系数)BACKEND_THROUGHPUT = {    'webgpu': 1.0,   # WebGPU Compute Shader, 最快    'wasm':   0.18,  # WASM CPU, 较慢} # GGUF量化类型到WebLLM可加载格式的映射GGUF_QUANT_MAP = {    'Q4_0': 'int4', 'Q4_1': 'int4', 'Q4_K_M': 'int4',    'Q8_0': 'int8', 'Q5_0': 'int8', 'Q5_1': 'int8',    'F16': 'fp16', 'F32': 'fp16',}  @dataclassclass GGUFModel:    name: str    params_b: float          # 参数量(十亿)    quant: str               # 量化类型 fp16/int8/int4    gguf_type: str           # GGUF原始量化标识  # 模拟WebLLM GGUF模型加载: 计算内存占用def load_gguf_model(name: str, params_b: float, quant: str) -> GGUFModel:    gguf_type = next((k for k, v in GGUF_QUANT_MAP.items() if v == quant), 'Q4_K_M')    return GGUFModel(name=name, params_b=params_b, quant=quant, gguf_type=gguf_type)  # 计算模型在浏览器中的内存占用(MB)def estimate_memory_mb(model: GGUFModel) -> float:    # FP16基准: 每十亿参数约2GB=2048MB    base_mb = model.params_b * 2048.0    factor = QUANT_MEMORY_FACTOR.get(model.quant, 1.0)    return base_mb * factor  # WebGPU vs WASM后端性能对比def compare_backends(model: GGUFModel, base_tokens_s: float = 30.0) -> Dict:    metrics = {}    for backend, factor in BACKEND_THROUGHPUT.items():        tps = base_tokens_s * factor        metrics[backend] = {'tokens_per_s': round(tps, 2)}    return metrics  # KV Cache条目: 存储每层的Key/Value张量@dataclassclass KVCacheEntry:    layer: int    token_id: int    k_bytes: float    v_bytes: float  # KV Cache管理器: 支持滑动窗口驱逐策略class KVCacheManager:    def __init__(self, max_entries: int = 512, sliding_window: int = 256):        self.max_entries = max_entries        self.sliding_window = sliding_window        self.cache: List[KVCacheEntry] = []     # 追加token的KV, 超限时按滑动窗口驱逐旧条目    def append(self, entry: KVCacheEntry) -> Optional[int]:        self.cache.append(entry)        evicted = None        if len(self.cache) > self.max_entries:            evicted = self.cache[0].token_id            self.cache = self.cache[-self.sliding_window:]        return evicted     # 当前KV Cache内存占用(MB)    def memory_mb(self) -> float:        total = sum(e.k_bytes + e.v_bytes for e in self.cache)        return total / (1024 * 1024)  # Token生成模拟: 带内存跟踪def generate_tokens(model: GGUFModel, backend: str, n_tokens: int, kv: KVCacheManager) -> Dict:    tps = BACKEND_THROUGHPUT[backend] * 30.0    elapsed_ms = (n_tokens / tps) * 1000    evicted_tokens = []    for i in range(n_tokens):        entry = KVCacheEntry(layer=0, token_id=i, k_bytes=4096, v_bytes=4096)        ev = kv.append(entry)        if ev is not None:            evicted_tokens.append(ev)    return {        'tokens': n_tokens,        'backend': backend,        'elapsed_ms': round(elapsed_ms, 1),        'kv_memory_mb': round(kv.memory_mb(), 3),        'evicted': len(evicted_tokens),    }  # 量化影响对比: INT4 vs INT8 vs FP16def quantization_impact(params_b: float) -> List[Dict]:    results = []    for quant in ['fp16', 'int8', 'int4']:        m = GGUFModel(name='llama', params_b=params_b, quant=quant, gguf_type='Q4_K_M')        results.append({            'quant': quant,            'memory_mb': round(estimate_memory_mb(m), 1),            'quality_score': {'fp16': 100, 'int8': 96, 'int4': 88}[quant],        })    return results  # 主流程: 模拟WebLLM端到端推理管线def run_webllm_pipeline():    model = load_gguf_model('Llama-2-7B', 7.0, 'int4')    mem = estimate_memory_mb(model)    backends = compare_backends(model)    kv = KVCacheManager(max_entries=512, sliding_window=256)    gen = generate_tokens(model, 'webgpu', 128, kv)    impact = quantization_impact(7.0)    print(f"Model: {model.name} {model.quant}, memory: {mem:.1f}MB")    print(f"WebGPU throughput: {backends['webgpu']['tokens_per_s']} tok/s")    print(f"Generated 128 tokens, KV memory: {gen['kv_memory_mb']}MB, evicted: {gen['evicted']}")    return {'model': model, 'memory_mb': mem, 'backends': backends, 'gen': gen, 'impact': impact}  result = run_webllm_pipeline()