Add Code World Model support (#505)

* Add sliding-window support to LLaMA

* nits

* version

---------

Co-authored-by: dnakov <[email protected]>
Co-authored-by: Awni Hannun <[email protected]>

Author: dnakov

mlx_lm/_version.py

@@ -1,3 +1,3 @@
 # Copyright © 2023-2025 Apple Inc.
 
-__version__ = "0.28.0"
+__version__ = "0.28.1"

mlx_lm/models/llama.py

@@ -1,12 +1,13 @@
 # Copyright © 2023-2024 Apple Inc.
 
 from dataclasses import dataclass
-from typing import Any, Dict, Optional, Union
+from typing import Any, Dict, List, Optional, Union
 
 import mlx.core as mx
 import mlx.nn as nn
 
 from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention
+from .cache import KVCache, RotatingKVCache
 from .rope_utils import initialize_rope
 
 
@@ -28,11 +29,16 @@ class ModelArgs(BaseModelArgs):
     rope_traditional: bool = False
     rope_scaling: Optional[Dict[str, Union[float, str]]] = None
     tie_word_embeddings: bool = True
+    layer_types: Optional[List[str]] = None
+    sliding_window: Optional[int] = None
 
     def __post_init__(self):
         if self.num_key_value_heads is None:
             self.num_key_value_heads = self.num_attention_heads
 
+        if self.layer_types is None:
+            self.layer_types = ["full_attention"] * self.num_hidden_layers
+
 
 class Attention(nn.Module):
     def __init__(self, args: ModelArgs):
@@ -114,10 +120,11 @@ def __call__(self, x) -> mx.array:
 
 
 class TransformerBlock(nn.Module):
-    def __init__(self, args: ModelArgs):
+    def __init__(self, args: ModelArgs, use_sliding: bool = False):
         super().__init__()
         self.num_attention_heads = args.num_attention_heads
         self.hidden_size = args.hidden_size
+        self.use_sliding = use_sliding
         self.self_attn = Attention(args)
         self.mlp = MLP(args)
         self.input_layernorm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
@@ -145,12 +152,21 @@ def __init__(self, args: ModelArgs):
         self.args = args
         self.vocab_size = args.vocab_size
         self.num_hidden_layers = args.num_hidden_layers
+        self.layer_types = args.layer_types
+        self.sliding_window = args.sliding_window
         assert self.vocab_size > 0
         self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
         self.layers = [
-            TransformerBlock(args=args) for _ in range(args.num_hidden_layers)
+            TransformerBlock(args=args, use_sliding=layer_type == "sliding_attention")
+            for layer_type in self.layer_types
         ]
         self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
+        self.fa_idx = self.layer_types.index("full_attention")
+        self.swa_idx = None
+        for e, l in enumerate(self.layers):
+            if l.use_sliding:
+                self.swa_idx = e
+                break
 
     def __call__(
         self,
@@ -166,10 +182,15 @@ def __call__(
         if cache is None:
             cache = [None] * len(self.layers)
 
-        mask = create_attention_mask(h, cache[0])
+        fa_mask = create_attention_mask(h, cache[self.fa_idx])
+        if self.swa_idx is not None:
+            swa_mask = create_attention_mask(
+                h, cache[self.swa_idx], window_size=self.sliding_window
+            )
 
-        for layer, c in zip(self.layers, cache):
-            h = layer(h, mask, cache=c)
+        for layer, cache in zip(self.layers, cache):
+            mask = swa_mask if layer.use_sliding else fa_mask
+            h = layer(h, mask, cache=cache)
 
         return self.norm(h)
 
@@ -208,3 +229,13 @@ def sanitize(self, weights):
     @property
     def layers(self):
         return self.model.layers
+
+    def make_cache(self):
+        return [
+            (
+                RotatingKVCache(max_size=self.model.sliding_window)
+                if layer.use_sliding
+                else KVCache()
+            )
+            for layer in self.layers
+        ]

tests/test_models.py

@@ -175,6 +175,49 @@ def test_mask_with_window(self):
         sums = mask.sum(axis=1)
         self.assertTrue(mx.array_equal(sums, expected_sums))
 
+    def test_llama_model_sliding_attention(self):
+        from mlx_lm.models import llama
+
+        args = llama.ModelArgs(
+            model_type="llama",
+            hidden_size=64,
+            num_hidden_layers=4,
+            intermediate_size=256,
+            num_attention_heads=8,
+            num_key_value_heads=4,
+            rms_norm_eps=1e-5,
+            vocab_size=128,
+            sliding_window=4,
+            layer_types=[
+                "full_attention",
+                "sliding_attention",
+                "sliding_attention",
+                "full_attention",
+            ],
+            tie_word_embeddings=False,
+            rope_theta=10000.0,
+        )
+        model = llama.Model(args)
+
+        tokens = mx.array([[1, 2, 3, 4, 5]], dtype=mx.int32)
+        out = model(tokens)
+        mx.eval(out)
+        self.assertEqual(out.shape, (1, 5, args.vocab_size))
+
+        caches = model.make_cache()
+        self.assertIsInstance(caches[0], KVCache)
+        self.assertIsInstance(caches[1], RotatingKVCache)
+        self.assertIsInstance(caches[2], RotatingKVCache)
+        self.assertIsInstance(caches[3], KVCache)
+
+        caches = model.make_cache()
+        step = model(tokens[:, :2], cache=caches)
+        mx.eval(step)
+        step = model(tokens[:, 2:3], cache=caches)
+        mx.eval(step)
+        self.assertEqual(caches[0].offset, 3)
+        self.assertEqual(caches[1].offset, 3)
+
     def test_rope(self):
         rope = rope_utils.initialize_rope(32, base=100, traditional=False)
         self.assertTrue(isinstance(rope, nn.RoPE))