refactor(qwen): moving qwen into mlx-lm (#312)

* refactor(qwen): moving qwen into mlx-lm

* chore: update doc

* chore: fix type hint

* add qwen model support in convert

* chore: fix doc

* chore: only load model in quantize_model

* chore: make the convert script only copy tokenizer files instead of load it and save

* chore: update docstring

* chore: remove unnecessary try catch

* chore: clean up for tokenizer and update  transformers 4.37

* nits in README

---------

Co-authored-by: Awni Hannun <awni@apple.com>

llms/mlx_lm/models/qwen.py

@@ -0,0 +1,175 @@
+from dataclasses import dataclass
+from typing import Tuple
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from .base import BaseModelArgs
+
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    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
+    num_key_value_heads = None
+
+    def __post_init__(self):
+        if self.num_key_value_heads is None:
+            self.num_key_value_heads = self.num_attention_heads
+
+
+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.hidden_size, args.intermediate_size // 2, 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 QwenModel(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        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(args.hidden_size, eps=args.layer_norm_epsilon)
+
+    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 x, cache
+
+
+class Model(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        self.transformer = QwenModel(config)
+        self.lm_head = nn.Linear(
+            config.hidden_size, config.vocab_size, bias=not config.no_bias
+        )
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: mx.array = None,
+        cache: mx.array = None,
+    ) -> Tuple[mx.array, mx.array]:
+        y, cache = self.transformer(x, mask, cache)
+        return self.lm_head(y), cache