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https://github.com/ml-explore/mlx-examples.git
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27c0a8c002
* add llms subdir + update README * nits * use same pre-commit as mlx * update readmes a bit * format
271 lines
8.0 KiB
Python
271 lines
8.0 KiB
Python
import argparse
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import json
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from dataclasses import dataclass
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import mlx.core as mx
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import mlx.nn as nn
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from mlx.utils import tree_unflatten
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from transformers import AutoTokenizer
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@dataclass
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class ModelArgs:
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hidden_size: int = 2048
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num_attention_heads: int = 16
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num_hidden_layers: int = 24
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kv_channels: int = 128
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max_position_embeddings: int = 8192
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layer_norm_epsilon: float = 1e-6
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intermediate_size: int = 11008
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no_bias: bool = True
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vocab_size: int = 151936
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class RMSNorm(nn.Module):
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def __init__(self, dims: int, eps: float = 1e-5):
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super().__init__()
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self.weight = mx.ones((dims,))
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self.eps = eps
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def _norm(self, x):
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return x * mx.rsqrt(x.square().mean(-1, keepdims=True) + self.eps)
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def __call__(self, x):
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output = self._norm(x.astype(mx.float32)).astype(x.dtype)
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return self.weight * output
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class Attention(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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hidden_size = args.hidden_size
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self.num_attention_heads = args.num_attention_heads
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hidden_size_per_attention_head = hidden_size // self.num_attention_heads
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self.rotary_emb = nn.RoPE(hidden_size_per_attention_head, traditional=False)
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proj_size = args.kv_channels * self.num_attention_heads
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self.c_attn = nn.Linear(hidden_size, proj_size * 3, bias=True)
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self.c_proj = nn.Linear(hidden_size, proj_size, bias=not args.no_bias)
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self.scale = hidden_size_per_attention_head**-0.5
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def __call__(self, x, mask=None, cache=None):
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qkv = self.c_attn(x)
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q, k, v = mx.split(qkv, 3, axis=-1)
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B, L, _ = q.shape
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q = q.reshape(B, L, self.num_attention_heads, -1).transpose(0, 2, 1, 3)
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k = k.reshape(B, L, self.num_attention_heads, -1).transpose(0, 2, 1, 3)
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v = v.reshape(B, L, self.num_attention_heads, -1).transpose(0, 2, 1, 3)
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if cache is not None:
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k_cache, v_cache = cache
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q = self.rotary_emb(q, offset=k_cache.shape[2])
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k = self.rotary_emb(k, offset=k_cache.shape[2])
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k = mx.concatenate([k_cache, k], axis=2)
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v = mx.concatenate([v_cache, v], axis=2)
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else:
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q = self.rotary_emb(q)
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k = self.rotary_emb(k)
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scores = (q * self.scale) @ k.transpose(0, 1, 3, 2)
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if mask is not None:
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scores = scores + mask
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scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(scores.dtype)
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v_hat = (scores @ v).transpose(0, 2, 1, 3).reshape(B, L, -1)
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return self.c_proj(v_hat), (k, v)
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class MLP(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.w1 = nn.Linear(
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args.hidden_size, args.intermediate_size // 2, bias=not args.no_bias
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)
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self.w2 = nn.Linear(
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args.intermediate_size // 2, args.hidden_size, bias=not args.no_bias
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)
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self.c_proj = nn.Linear(
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args.intermediate_size // 2, args.hidden_size, bias=not args.no_bias
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)
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def __call__(self, x):
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a1 = self.w1(x)
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a2 = self.w2(x)
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return self.c_proj(a1 * nn.silu(a2))
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class TransformerBlock(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.ln_1 = RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
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self.attn = Attention(args)
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self.ln_2 = RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
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self.mlp = MLP(args)
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def __call__(self, x, mask=None, cache=None):
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residual = x
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x = self.ln_1(x)
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x, cache = self.attn(x, mask=mask, cache=cache)
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residual = x + residual
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x = self.ln_2(residual)
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x = self.mlp(x)
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x = x + residual
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return x, cache
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class Qwen(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.embed_dim = args.hidden_size
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self.wte = nn.Embedding(args.vocab_size, args.hidden_size)
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self.h = [TransformerBlock(args) for _ in range(args.num_hidden_layers)]
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self.ln_f = RMSNorm(self.embed_dim, eps=args.layer_norm_epsilon)
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self.lm_head = nn.Linear(self.embed_dim, args.vocab_size, bias=False)
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def __call__(self, inputs, mask=None, cache=None):
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x = self.wte(inputs)
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mask = None
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T = x.shape[1]
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if T > 1:
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mask = nn.MultiHeadAttention.create_additive_causal_mask(T)
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mask = mask.astype(x.dtype)
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if cache is None:
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cache = [None] * len(self.h)
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for e, layer in enumerate(self.h):
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x, cache[e] = layer(x, mask, cache[e])
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x = self.ln_f(x[:, T - 1 : T, :])
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return self.lm_head(x), cache
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def generate(prompt: mx.array, model: Qwen, temp: 0.0):
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def sample(logits):
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if temp == 0:
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return mx.argmax(logits, axis=-1)
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else:
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return mx.random.categorical(logits * (1 / temp))
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logits, cache = model(prompt)
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y = sample(logits[:, -1, :])
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yield y
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while True:
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logits, cache = model(y[:, None], cache=cache)
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y = sample(logits.squeeze(1))
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yield y
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def load_model(
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tokenizer_path: str = "Qwen/Qwen-1_8B", config_path: str = "config.json"
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):
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model_args = ModelArgs()
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with open(config_path, "r") as f:
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config = json.load(f)
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model_args.vocab_size = config["vocab_size"]
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model_args.hidden_size = config["hidden_size"]
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model_args.num_attention_heads = config["num_attention_heads"]
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model_args.num_hidden_layers = config["num_hidden_layers"]
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model_args.kv_channels = config["kv_channels"]
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model_args.max_position_embeddings = config["max_position_embeddings"]
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model_args.layer_norm_epsilon = config["layer_norm_epsilon"]
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model_args.intermediate_size = config["intermediate_size"]
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model_args.no_bias = config["no_bias"]
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model = Qwen(model_args)
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weights = mx.load("weights.npz")
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model.update(tree_unflatten(list(weights.items())))
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tokenizer = AutoTokenizer.from_pretrained(
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tokenizer_path, trust_remote_code=True, eos_token="<|endoftext|>"
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)
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return model, tokenizer
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Qwen inference script")
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parser.add_argument(
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"--tokenizer",
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help="The tokenizer to be used, defaults to Qwen/Qwen-1_8B",
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default="Qwen/Qwen-1_8B",
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)
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parser.add_argument(
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"--prompt",
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help="The message to be processed by the model",
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# The example from the official huggingface repo of Qwen
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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",
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)
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parser.add_argument(
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"--max_tokens",
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"-m",
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type=int,
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default=100,
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help="Maximum number of tokens to generate",
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)
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parser.add_argument(
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"--temp",
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help="The sampling temperature.",
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type=float,
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default=0.0,
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)
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parser.add_argument("--seed", type=int, default=0, help="The PRNG seed")
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args = parser.parse_args()
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mx.random.seed(args.seed)
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model, tokenizer = load_model(args.tokenizer)
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prompt = tokenizer(
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args.prompt,
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return_tensors="np",
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return_attention_mask=False,
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)["input_ids"]
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prompt = mx.array(prompt)
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print(args.prompt, end="", flush=True)
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tokens = []
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for token, _ in zip(generate(prompt, model, args.temp), range(args.max_tokens)):
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tokens.append(token)
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if (len(tokens) % 10) == 0:
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mx.eval(tokens)
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eos_index = next(
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(i for i, t in enumerate(tokens) if t.item() == tokenizer.eos_token_id),
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None,
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)
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if eos_index is not None:
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tokens = tokens[:eos_index]
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s = tokenizer.decode([t.item() for t in tokens])
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print(s, end="", flush=True)
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tokens = []
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if eos_index is not None:
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break
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mx.eval(tokens)
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s = tokenizer.decode([t.item() for t in tokens])
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print(s, flush=True)
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