Add support for Gemma3 (#1336)

* add support for gemma3 * fix model loading * revert rmsnorm * revert is sliding pattern * revert * add tests * formatting * Update llms/mlx_lm/models/gemma3_text.py Co-authored-by: Awni Hannun <awni.hannun@gmail.com> * Update llms/mlx_lm/models/gemma3_text.py Co-authored-by: Awni Hannun <awni.hannun@gmail.com> * Update llms/mlx_lm/models/gemma3_text.py Co-authored-by: Awni Hannun <awni.hannun@gmail.com> * Update llms/mlx_lm/models/gemma3_text.py Co-authored-by: Awni Hannun <awni.hannun@gmail.com> * Update llms/mlx_lm/models/gemma3_text.py Co-authored-by: Awni Hannun <awni.hannun@gmail.com> * Update llms/mlx_lm/models/gemma3_text.py Co-authored-by: Awni Hannun <awni.hannun@gmail.com> * Update llms/mlx_lm/models/gemma3_text.py Co-authored-by: Awni Hannun <awni.hannun@gmail.com> * fix sliding window mask --------- Co-authored-by: Awni Hannun <awni.hannun@gmail.com> Co-authored-by: Awni Hannun <awni@apple.com>
2025-06-24 01:17:28 +08:00 · 2025-03-13 16:14:25 +01:00 · 2025-03-13 16:14:25 +01:00 · 2fce02acd8
commit 2fce02acd8
parent 3e5baf583b
2 changed files with 258 additions and 0 deletions
--- a/llms/mlx_lm/models/gemma3_text.py
+++ b/llms/mlx_lm/models/gemma3_text.py
@ -0,0 +1,238 @@
+# Copyright © 2025 Apple Inc.
+
+from dataclasses import dataclass
+from typing import Any, Optional
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from .base import BaseModelArgs, create_attention_mask
+from .cache import KVCache, RotatingKVCache
+
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    model_type: str
+    hidden_size: int = 1152
+    num_hidden_layers: int = 26
+    intermediate_size: int = 6912
+    num_attention_heads: int = 4
+    head_dim: int = 256
+    rms_norm_eps: float = 1.0e-6
+    vocab_size: int = 262144
+    num_key_value_heads: int = 1
+    rope_global_base_freq: float = 1_000_000.0
+    rope_local_base_freq: float = 10_000.0
+    rope_traditional: bool = False
+    query_pre_attn_scalar: float = 256
+    sliding_window: int = 512
+    sliding_window_pattern: int = 6
+
+
+class Attention(nn.Module):
+    def __init__(self, args: ModelArgs, layer_idx: int):
+        super().__init__()
+
+        dim = args.hidden_size
+        self.n_heads = n_heads = args.num_attention_heads
+        self.n_kv_heads = n_kv_heads = args.num_key_value_heads
+        self.repeats = n_heads // n_kv_heads
+        self.head_dim = head_dim = args.head_dim
+        self.layer_idx = layer_idx
+
+        self.scale = args.query_pre_attn_scalar**-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 = RMSNorm(dims=head_dim, eps=args.rms_norm_eps)
+        self.k_norm = RMSNorm(dims=head_dim, eps=args.rms_norm_eps)
+        self.is_sliding = (layer_idx + 1) % args.sliding_window_pattern != 0
+
+        self.rope = nn.RoPE(
+            head_dim,
+            traditional=args.rope_traditional,
+            base=(
+                args.rope_local_base_freq
+                if self.is_sliding
+                else args.rope_global_base_freq
+            ),
+        )
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = None,
+    ) -> mx.array:
+        B, L, _ = x.shape
+        queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
+        queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
+
+        keys = 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)
+
+        queries = self.q_norm(queries)
+        keys = self.k_norm(keys)
+
+        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)
+
+        # Sliding window
+        if mask is not None and mask.shape[-1] != keys.shape[-2]:
+            mask = mask[..., -keys.shape[-2] :]
+
+        output = mx.fast.scaled_dot_product_attention(
+            queries, keys, values, scale=self.scale, mask=mask
+        )
+        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
+        return self.o_proj(output)
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dims: int, eps: float = 1e-5):
+        super().__init__()
+        self.weight = mx.ones((dims,))
+        self.eps = eps
+
+    def __call__(self, x):
+        return mx.fast.rms_norm(x, 1.0 + self.weight, self.eps)
+
+
+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(nn.gelu_approx(self.gate_proj(x)) * self.up_proj(x))
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, args: ModelArgs, layer_idx: int):
+        super().__init__()
+        self.num_attention_heads = args.num_attention_heads
+        self.hidden_size = args.hidden_size
+        self.self_attn = Attention(args, layer_idx)
+        self.mlp = MLP(args.hidden_size, args.intermediate_size)
+        self.input_layernorm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
+        self.pre_feedforward_layernorm = RMSNorm(
+            args.hidden_size, eps=args.rms_norm_eps
+        )
+        self.post_feedforward_layernorm = RMSNorm(
+            args.hidden_size, eps=args.rms_norm_eps
+        )
+
+    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 + self.post_attention_layernorm(r)
+        r = self.mlp(self.pre_feedforward_layernorm(h))
+        out = h + self.post_feedforward_layernorm(r)
+        return out
+
+
+class Gemma3Model(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=args, layer_idx=layer_idx)
+            for layer_idx in range(args.num_hidden_layers)
+        ]
+        self.norm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        mask: mx.array = None,
+        cache=None,
+    ):
+
+        h = self.embed_tokens(inputs)
+        h *= mx.array(self.args.hidden_size**0.5, mx.bfloat16).astype(h.dtype)
+
+        if cache is None:
+            cache = [None] * len(self.layers)
+
+        if mask is None:
+            j = self.args.sliding_window_pattern
+            full_mask = create_attention_mask(h, cache[j - 1 : j])
+            sliding_window_mask = create_attention_mask(h, cache)
+
+        for i, (layer, c) in enumerate(zip(self.layers, cache)):
+            is_sliding = (
+                i % self.args.sliding_window_pattern
+                == self.args.sliding_window_pattern - 1
+            )
+
+            if mask is None and is_sliding:
+                mask = sliding_window_mask
+            elif mask is None:
+                mask = full_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 = Gemma3Model(args)
+        self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None,
+        mask: Optional[mx.array] = None,
+    ):
+        out = self.model(inputs, mask, cache)
+        out = self.lm_head(out)
+        return out
+
+    def sanitize(self, weights):
+        if "lm_head.weight" not in weights:
+            weights["lm_head.weight"] = weights["model.embed_tokens.weight"]
+        return {
+            k: v for k, v in weights.items() if "self_attn.rotary_emb.inv_freq" not in k
+        }
+
+    @property
+    def layers(self):
+        return self.model.layers
+
+    def make_cache(self):
+        caches = []
+        for i in range(self.args.num_hidden_layers):
+            if (
+                i % self.args.sliding_window_pattern
+                == self.args.sliding_window_pattern - 1
+            ):
+                caches.append(KVCache())
+            else:
+                caches.append(
+                    RotatingKVCache(max_size=self.args.sliding_window, keep=0)
+                )
+        return caches
--- a/llms/tests/test_models.py
+++ b/llms/tests/test_models.py
@ -755,6 +755,26 @@ class TestModels(unittest.TestCase):
            model, args.model_type, args.vocab_size, args.num_hidden_layers
        )

+    def test_gemma3_text(self):
+        from mlx_lm.models import gemma3_text
+
+        args = gemma3_text.ModelArgs(
+            model_type="gemma3_text",
+            hidden_size=128,
+            num_hidden_layers=12,
+            intermediate_size=256,
+            num_attention_heads=4,
+            head_dim=32,
+            rms_norm_eps=1e-4,
+            num_key_value_heads=1,
+            sliding_window=1024,
+            sliding_window_pattern=6,
+        )
+        model = gemma3_text.Model(args)
+        self.model_test_runner(
+            model, args.model_type, args.vocab_size, args.num_hidden_layers
+        )
+
    def test_gpt_bigcode(self):
        from mlx_lm.models import gpt_bigcode