Add qwen model draft

qwen/convert.py

@@ -0,0 +1,23 @@
+from transformers import AutoModelForCausalLM
+import numpy as np
+
+
+def replace_key(key: str) -> str:
+    if key.startswith("transformer."):
+        # remove transformer prefix
+        key = key.replace("transformer.", "")
+
+    return key
+
+
+def convert():
+    model = AutoModelForCausalLM.from_pretrained(
+        "Qwen/Qwen-1_8B", trust_remote_code=True
+    )
+    state_dict = model.state_dict()
+    weights = {replace_key(k): v.numpy() for k, v in state_dict.items()}
+    np.savez("weights.npz", **weights)
+
+
+if __name__ == "__main__":
+    convert()

qwen/qwen.py

@@ -0,0 +1,249 @@
+# The architecture of Qwen is similar to Llama.
+
+import argparse
+
+from typing import Any
+import mlx.core as mx
+import mlx.nn as nn
+from mlx.utils import tree_unflatten
+
+from dataclasses import dataclass
+
+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 QWenAttntion(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+
+        self.hidden_size = args.hidden_size
+        self.num_attention_heads = args.num_attention_heads
+
+        self.hidden_size_per_attention_head = (
+            self.hidden_size // self.num_attention_heads
+        )
+
+        self.rotary_emb = nn.RoPE(
+            self.hidden_size_per_attention_head, traditional=False
+        )
+
+        self.proj_size = args.kv_channels * self.num_attention_heads
+
+        self.c_attn = nn.Linear(self.hidden_size, self.proj_size * 3, bias=True)
+        self.c_proj = nn.Linear(self.hidden_size, self.proj_size, bias=not args.no_bias)
+
+        self.scale = self.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, D = 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)
+
+        q = q.astype(mx.float32)
+        k = k.astype(mx.float32)
+
+        scores = (q * self.scale) @ k.transpose(0, 1, 3, 2)
+
+        if mask is not None:
+            scores = scores + mask
+
+        scores = mx.softmax(scores, axis=-1).astype(v.dtype)
+        v_hat = (scores @ v).transpose(0, 2, 1, 3).reshape(B, L, -1)
+
+        return self.c_proj(v_hat), (k, v)
+
+
+class QWenMlp(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) -> Any:
+        a1 = self.w1(x)
+        a2 = self.w2(x)
+        intermediate_parallel = a1 * nn.silu(a2)
+        out = self.c_proj(intermediate_parallel)
+        return out
+
+
+class QWenBlock(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+
+        self.ln_1 = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
+        self.attn = QWenAttntion(args)
+        self.ln_2 = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
+        self.mlp = QWenMlp(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 = [QWenBlock(args) for _ in range(args.num_hidden_layers)]
+        self.ln_f = nn.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
+        if x.shape[1] > 1:
+            mask = nn.MultiHeadAttention.create_additive_causal_mask(x.shape[1])
+            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)
+
+        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():
+    model = QWen(ModelArgs())
+    weights = mx.load("weights.npz")
+    model.update(tree_unflatten(list(weights.items())))
+    # print([x for x, _ in tree_flatten(model.parameters())])
+    tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-1_8B", trust_remote_code=True)
+    return model, tokenizer
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Phi-2 inference script")
+    parser.add_argument(
+        "--prompt",
+        help="The message to be processed by the model",
+        default="Write a detailed analogy between mathematics and a lighthouse.",
+    )
+    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()
+
+    prompt = tokenizer(
+        args.prompt,
+        return_tensors="np",
+        return_attention_mask=False,
+    )["input_ids"]
+
+    prompt = mx.array(prompt)
+
+    print("[INFO] Generating with QWen...", flush=True)
+    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)