Add support for Kanana-2 Tiny (kanana2_tiny)

mlx_lm/models/kanana2_tiny.py

@@ -0,0 +1,240 @@
+# Copyright © 2023-2024 Apple Inc.
+
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Union
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from .activations import swiglu
+from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention
+from .cache import KVCache, RotatingKVCache
+from .rope_utils import initialize_rope
+
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    model_type: str
+    hidden_size: int
+    num_hidden_layers: int
+    intermediate_size: int
+    num_attention_heads: int
+    rms_norm_eps: float
+    vocab_size: int
+    num_key_value_heads: int
+    max_position_embeddings: int
+    head_dim: int
+    layer_types: List[str]
+    sliding_window: int
+    # Absent from the checkpoint config; the weights carry no lm_head, so the
+    # embedding is tied. Matches the upstream default.
+    tie_word_embeddings: bool = True
+    # Rotary parameters are given per attention type, e.g.
+    #   {"full_attention":    {"rope_type": "yarn", "rope_theta": 10000, ...},
+    #    "sliding_attention": {"rope_type": "default", "rope_theta": 10000.0}}
+    rope_parameters: Dict[str, Dict[str, Union[float, str]]] = None
+
+
+class Attention(nn.Module):
+    """Qwen3 attention, except the rotary embedding follows the layer's type.
+
+    Full-attention layers use a yarn-scaled rope for long context; sliding
+    layers keep the unscaled rope and only attend within `sliding_window`.
+    """
+
+    def __init__(self, args: ModelArgs, layer_type: str):
+        super().__init__()
+
+        dim = args.hidden_size
+        self.n_heads = n_heads = args.num_attention_heads
+        assert args.num_key_value_heads is not None
+        self.n_kv_heads = n_kv_heads = args.num_key_value_heads
+
+        head_dim = args.head_dim
+        self.scale = head_dim**-0.5
+
+        self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=False)
+        self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
+        self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
+        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False)
+
+        self.q_norm = nn.RMSNorm(head_dim, eps=args.rms_norm_eps)
+        self.k_norm = nn.RMSNorm(head_dim, eps=args.rms_norm_eps)
+
+        params = dict(args.rope_parameters[layer_type])
+        base = params.pop("rope_theta")
+        rope_type = params.get("rope_type", "default")
+        if rope_type in ("default", "linear"):
+            # An unscaled rope: passing a scaling config would be a no-op at
+            # best and misread at worst.
+            self.rope = initialize_rope(head_dim, base=base, traditional=False)
+        else:
+            self.rope = initialize_rope(
+                head_dim,
+                base=base,
+                traditional=False,
+                scaling_config=params,
+                max_position_embeddings=args.max_position_embeddings,
+            )
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = None,
+    ) -> mx.array:
+        B, L, D = x.shape
+
+        queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
+
+        queries = self.q_norm(queries.reshape(B, L, self.n_heads, -1)).transpose(
+            0, 2, 1, 3
+        )
+        keys = self.k_norm(keys.reshape(B, L, self.n_kv_heads, -1)).transpose(
+            0, 2, 1, 3
+        )
+        values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
+
+        if cache is not None:
+            queries = self.rope(queries, offset=cache.offset)
+            keys = self.rope(keys, offset=cache.offset)
+            keys, values = cache.update_and_fetch(keys, values)
+        else:
+            queries = self.rope(queries)
+            keys = self.rope(keys)
+
+        output = scaled_dot_product_attention(
+            queries, keys, values, cache=cache, scale=self.scale, mask=mask
+        )
+        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
+        return self.o_proj(output)
+
+
+class MLP(nn.Module):
+    def __init__(self, dim, hidden_dim):
+        super().__init__()
+        self.gate_proj = nn.Linear(dim, hidden_dim, bias=False)
+        self.down_proj = nn.Linear(hidden_dim, dim, bias=False)
+        self.up_proj = nn.Linear(dim, hidden_dim, bias=False)
+
+    def __call__(self, x) -> mx.array:
+        return self.down_proj(swiglu(self.gate_proj(x), self.up_proj(x)))
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, args: ModelArgs, layer_type: str):
+        super().__init__()
+        self.num_attention_heads = args.num_attention_heads
+        self.hidden_size = args.hidden_size
+        self.self_attn = Attention(args, layer_type)
+        self.mlp = MLP(args.hidden_size, args.intermediate_size)
+        self.input_layernorm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
+        self.post_attention_layernorm = nn.RMSNorm(
+            args.hidden_size, eps=args.rms_norm_eps
+        )
+        self.args = args
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = None,
+    ) -> mx.array:
+        r = self.self_attn(self.input_layernorm(x), mask, cache)
+        h = x + r
+        r = self.mlp(self.post_attention_layernorm(h))
+        out = h + r
+        return out
+
+
+class Kanana2TinyModel(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.vocab_size = args.vocab_size
+        self.num_hidden_layers = args.num_hidden_layers
+        assert self.vocab_size > 0
+        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
+        self.layers = [
+            TransformerBlock(args, layer_type) for layer_type in args.layer_types
+        ]
+        self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
+        # Index of a layer of each kind, so the two masks are built against a
+        # cache entry of the matching kind.
+        self.full_idx = args.layer_types.index("full_attention")
+        self.sliding_idx = (
+            args.layer_types.index("sliding_attention")
+            if "sliding_attention" in args.layer_types
+            else None
+        )
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None,
+        input_embeddings: Optional[mx.array] = None,
+    ):
+        if input_embeddings is not None:
+            h = input_embeddings
+        else:
+            h = self.embed_tokens(inputs)
+
+        if cache is None:
+            cache = [None] * len(self.layers)
+
+        full_mask = create_attention_mask(h, cache[self.full_idx])
+        sliding_mask = (
+            create_attention_mask(
+                h, cache[self.sliding_idx], window_size=self.args.sliding_window
+            )
+            if self.sliding_idx is not None
+            else None
+        )
+
+        for layer_type, layer, c in zip(self.args.layer_types, self.layers, cache):
+            mask = full_mask if layer_type == "full_attention" else sliding_mask
+            h = layer(h, mask, c)
+
+        return self.norm(h)
+
+
+class Model(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.model_type = args.model_type
+        self.model = Kanana2TinyModel(args)
+        if not args.tie_word_embeddings:
+            self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None,
+        input_embeddings: Optional[mx.array] = None,
+    ):
+        out = self.model(inputs, cache, input_embeddings)
+        if self.args.tie_word_embeddings:
+            out = self.model.embed_tokens.as_linear(out)
+        else:
+            out = self.lm_head(out)
+        return out
+
+    def sanitize(self, weights):
+        if self.args.tie_word_embeddings:
+            weights.pop("lm_head.weight", None)
+        return weights
+
+    @property
+    def layers(self):
+        return self.model.layers
+
+    def make_cache(self):
+        return [
+            (
+                KVCache()
+                if layer_type == "full_attention"
+                else RotatingKVCache(max_size=self.args.sliding_window)
+            )
+            for layer_type in self.args.layer_types
+        ]

tests/test_models.py

@@ -627,6 +627,50 @@ def test_qwen3(self):
             model, args.model_type, args.vocab_size, args.num_hidden_layers
         )
 
+    def test_kanana2_tiny(self):
+        from mlx_lm.models import kanana2_tiny
+
+        # Alternating pattern as shipped by the checkpoint: three sliding
+        # layers then one full-attention layer, each type with its own rope.
+        layer_types = ["sliding_attention"] * 3 + ["full_attention"]
+        args = kanana2_tiny.ModelArgs(
+            model_type="kanana2_tiny",
+            hidden_size=128,
+            num_hidden_layers=len(layer_types),
+            intermediate_size=256,
+            num_attention_heads=4,
+            num_key_value_heads=2,
+            rms_norm_eps=1e-5,
+            vocab_size=10_000,
+            head_dim=32,
+            max_position_embeddings=4096,
+            layer_types=layer_types,
+            sliding_window=8,
+            rope_parameters={
+                "full_attention": {
+                    "rope_type": "yarn",
+                    "rope_theta": 10000,
+                    "factor": 40.0,
+                    "original_max_position_embeddings": 512,
+                },
+                "sliding_attention": {
+                    "rope_type": "default",
+                    "rope_theta": 10000.0,
+                },
+            },
+        )
+        model = kanana2_tiny.Model(args)
+        self.model_test_runner(
+            model, args.model_type, args.vocab_size, args.num_hidden_layers
+        )
+
+        # The two attention types must not share a rope: the full-attention
+        # layers are yarn-scaled for long context, the sliding ones are not.
+        sliding_rope = model.layers[0].self_attn.rope
+        full_rope = model.layers[3].self_attn.rope
+        self.assertIsNot(sliding_rope, full_rope)
+        self.assertNotEqual(type(sliding_rope), type(full_rope))
+
     def test_qwen3_5_family_convert_then_load_norm_not_shift_twice(self):
         text_config = {
             "hidden_size": 8,