import argparse from dataclasses import dataclass import json import mlx.core as mx import mlx.nn as nn from mlx.utils import tree_unflatten from transformers import AutoTokenizer @dataclass class ModelArgs: hidden_size: int = 2048 num_attention_heads: int = 16 num_hidden_layers: int = 24 kv_channels: int = 128 max_position_embeddings: int = 8192 layer_norm_epsilon: float = 1e-6 intermediate_size: int = 11008 no_bias: bool = True vocab_size: int = 151936 class RMSNorm(nn.Module): def __init__(self, dims: int, eps: float = 1e-5): super().__init__() self.weight = mx.ones((dims,)) self.eps = eps def _norm(self, x): return x * mx.rsqrt(x.square().mean(-1, keepdims=True) + self.eps) def __call__(self, x): output = self._norm(x.astype(mx.float32)).astype(x.dtype) return self.weight * output class Attention(nn.Module): def __init__(self, args: ModelArgs): super().__init__() hidden_size = args.hidden_size self.num_attention_heads = args.num_attention_heads hidden_size_per_attention_head = hidden_size // self.num_attention_heads self.rotary_emb = nn.RoPE(hidden_size_per_attention_head, traditional=False) proj_size = args.kv_channels * self.num_attention_heads self.c_attn = nn.Linear(hidden_size, proj_size * 3, bias=True) self.c_proj = nn.Linear(hidden_size, proj_size, bias=not args.no_bias) self.scale = hidden_size_per_attention_head**-0.5 def __call__(self, x, mask=None, cache=None): qkv = self.c_attn(x) q, k, v = mx.split(qkv, 3, axis=-1) B, L, _ = q.shape q = q.reshape(B, L, self.num_attention_heads, -1).transpose(0, 2, 1, 3) k = k.reshape(B, L, self.num_attention_heads, -1).transpose(0, 2, 1, 3) v = v.reshape(B, L, self.num_attention_heads, -1).transpose(0, 2, 1, 3) if cache is not None: k_cache, v_cache = cache q = self.rotary_emb(q, offset=k_cache.shape[2]) k = self.rotary_emb(k, offset=k_cache.shape[2]) k = mx.concatenate([k_cache, k], axis=2) v = mx.concatenate([v_cache, v], axis=2) else: q = self.rotary_emb(q) k = self.rotary_emb(k) scores = (q * self.scale) @ k.transpose(0, 1, 3, 2) if mask is not None: scores = scores + mask scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(scores.dtype) v_hat = (scores @ v).transpose(0, 2, 1, 3).reshape(B, L, -1) return self.c_proj(v_hat), (k, v) class MLP(nn.Module): def __init__(self, args: ModelArgs): super().__init__() self.w1 = nn.Linear( args.hidden_size, args.intermediate_size // 2, bias=not args.no_bias ) self.w2 = nn.Linear( args.intermediate_size // 2, args.hidden_size, bias=not args.no_bias ) self.c_proj = nn.Linear( args.intermediate_size // 2, args.hidden_size, bias=not args.no_bias ) def __call__(self, x): a1 = self.w1(x) a2 = self.w2(x) return self.c_proj(a1 * nn.silu(a2)) class TransformerBlock(nn.Module): def __init__(self, args: ModelArgs): super().__init__() self.ln_1 = RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon) self.attn = Attention(args) self.ln_2 = RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon) self.mlp = MLP(args) def __call__(self, x, mask=None, cache=None): residual = x x = self.ln_1(x) x, cache = self.attn(x, mask=mask, cache=cache) residual = x + residual x = self.ln_2(residual) x = self.mlp(x) x = x + residual return x, cache class Qwen(nn.Module): def __init__(self, args: ModelArgs): super().__init__() self.embed_dim = args.hidden_size self.wte = nn.Embedding(args.vocab_size, args.hidden_size) self.h = [TransformerBlock(args) for _ in range(args.num_hidden_layers)] self.ln_f = RMSNorm(self.embed_dim, eps=args.layer_norm_epsilon) self.lm_head = nn.Linear(self.embed_dim, args.vocab_size, bias=False) def __call__(self, inputs, mask=None, cache=None): x = self.wte(inputs) mask = None T = x.shape[1] if T > 1: mask = nn.MultiHeadAttention.create_additive_causal_mask(T) mask = mask.astype(x.dtype) if cache is None: cache = [None] * len(self.h) for e, layer in enumerate(self.h): x, cache[e] = layer(x, mask, cache[e]) x = self.ln_f(x[:, T - 1 : T, :]) return self.lm_head(x), cache def generate(prompt: mx.array, model: Qwen, temp: 0.0): def sample(logits): if temp == 0: return mx.argmax(logits, axis=-1) else: return mx.random.categorical(logits * (1 / temp)) logits, cache = model(prompt) y = sample(logits[:, -1, :]) yield y while True: logits, cache = model(y[:, None], cache=cache) y = sample(logits.squeeze(1)) yield y def load_model( tokenizer_path: str = "Qwen/Qwen-1_8B", config_path: str = "config.json" ): model_args = ModelArgs() with open(config_path, "r") as f: config = json.load(f) model_args.vocab_size = config["vocab_size"] model_args.hidden_size = config["hidden_size"] model_args.num_attention_heads = config["num_attention_heads"] model_args.num_hidden_layers = config["num_hidden_layers"] model_args.kv_channels = config["kv_channels"] model_args.max_position_embeddings = config["max_position_embeddings"] model_args.layer_norm_epsilon = config["layer_norm_epsilon"] model_args.intermediate_size = config["intermediate_size"] model_args.no_bias = config["no_bias"] model = Qwen(model_args) weights = mx.load("weights.npz") model.update(tree_unflatten(list(weights.items()))) tokenizer = AutoTokenizer.from_pretrained( tokenizer_path, trust_remote_code=True, eos_token="<|endoftext|>" ) return model, tokenizer if __name__ == "__main__": parser = argparse.ArgumentParser(description="Qwen inference script") parser.add_argument( "--tokenizer", help="The tokenizer to be used, defaults to Qwen/Qwen-1_8B", default="Qwen/Qwen-1_8B", ) parser.add_argument( "--prompt", help="The message to be processed by the model", # The example from the official huggingface repo of Qwen default="蒙古国的首都是乌兰巴托（Ulaanbaatar）\n冰岛的首都是雷克雅未克（Reykjavik）\n埃塞俄比亚的首都是", ) parser.add_argument( "--max_tokens", "-m", type=int, default=100, help="Maximum number of tokens to generate", ) parser.add_argument( "--temp", help="The sampling temperature.", type=float, default=0.0, ) parser.add_argument("--seed", type=int, default=0, help="The PRNG seed") args = parser.parse_args() mx.random.seed(args.seed) model, tokenizer = load_model(args.tokenizer) prompt = tokenizer( args.prompt, return_tensors="np", return_attention_mask=False, )["input_ids"] prompt = mx.array(prompt) print(args.prompt, end="", flush=True) tokens = [] for token, _ in zip(generate(prompt, model, args.temp), range(args.max_tokens)): tokens.append(token) if (len(tokens) % 10) == 0: mx.eval(tokens) eos_index = next( (i for i, t in enumerate(tokens) if t.item() == tokenizer.eos_token_id), None, ) if eos_index is not None: tokens = tokens[:eos_index] s = tokenizer.decode([t.item() for t in tokens]) print(s, end="", flush=True) tokens = [] if eos_index is not None: break mx.eval(tokens) s = tokenizer.decode([t.item() for t in tokens]) print(s, flush=True)