Fix plamo2 model to use rms_norm and enable sliding window attention

llms/mlx_lm/models/plamo2.py

@@ -53,6 +53,16 @@ class RMSNorm(nn.Module):
         )
 
 
+def _rms_norm(hidden_states: mx.array, eps: float, offset: float = 1.0) -> mx.array:
+    input_dtype = hidden_states.dtype
+    hidden_states = hidden_states.astype(mx.float32)
+    variance = mx.power(hidden_states, 2).mean(-1, keepdims=True)
+    hidden_states = hidden_states * mx.rsqrt(variance + eps)
+    hidden_states = hidden_states.astype(input_dtype)
+
+    return hidden_states
+
+
 def get_initial_dt_bias(num_heads: int) -> mx.array:
     dt_min = 0.001
     dt_max = 0.1
@@ -344,6 +354,15 @@ class Mamba(nn.Module):
         return y
 
 
+def swa_mask(q_len: int, kv_len: int, window_size: int) -> mx.array:
+    max_len = max(q_len, kv_len)
+    mask = mx.tril(
+        mx.triu(mx.ones((max_len, max_len), dtype=mx.bool_), k=-window_size),  # type: ignore
+        k=window_size,
+    )
+    return mask[-q_len:, -kv_len:]
+
+
 class Attention(nn.Module):
     def __init__(self, config: ModelArgs) -> None:
         super().__init__()
@@ -392,8 +411,8 @@ class Attention(nn.Module):
         k = k.reshape(B, T, self.k_num_heads, self.qk_dim).transpose(0, 2, 1, 3)
         v = v.reshape(B, T, self.v_num_heads, self.v_dim).transpose(0, 2, 1, 3)
 
-        q = mx.fast.layer_norm(q, None, None, 1e-6) * self.q_weight[:, None]
+        q = _rms_norm(q, 1e-6) * self.q_weight[:, None]
-        k = mx.fast.layer_norm(k, None, None, 1e-6) * self.k_weight[:, None]
+        k = _rms_norm(k, 1e-6) * self.k_weight[:, None]
 
         if cache is not None:
             q = self.rope(q, offset=cache.offset)
@@ -403,6 +422,23 @@ class Attention(nn.Module):
             q = self.rope(q)
             k = self.rope(k)
 
+        if mask is not None:
+            if mask.dtype == bool:
+                mask = mx.where(mask, mx.array(0.0, dtype=mx.float16), float("-inf"))
+            if len(mask.shape) == 2:
+                mask = mask[None, None]
+            assert len(mask.shape) == 4
+
+            m_swa = swa_mask(
+                q.shape[2],
+                k.shape[2],
+                self.config.attention_window_size,
+            )
+            # `generate` function creates attention mask that does not consider sliding window
+            m_swa = m_swa[None, None]
+            mask = mask[:, :, -q.shape[2] :, -k.shape[2] :]
+            mask = mx.where(m_swa, mask, float("-inf"))
+
         output = mx.fast.scaled_dot_product_attention(
             q,
             k,
@@ -556,7 +592,6 @@ class PlamoModel(nn.Module):
 
 
 class Model(nn.Module):
-
     def __init__(self, config: ModelArgs) -> None:
         super().__init__()
         self.config = config