Allow skipping the automatic sliding mask

gemma/gm/nn/gemma4/_modules.py

@@ -246,6 +246,7 @@ def __call__(
       cache: LayerCache | None,
       attn_mask: jax.Array,
       kv_shared_cache: LayerCache | None = None,
+      skip_sliding_mask: bool = False,
   ) -> tuple[LayerCache | None, jax.Array]:
     """Applies multi-head attention to the inputs.
 
@@ -255,6 +256,7 @@ def __call__(
       cache: KV cache or None.
       attn_mask: Attention mask of shape [batch_size, seq_len, cache_size].
       kv_shared_cache: Cache for shared KV layers.
+      skip_sliding_mask: If True, skip the sliding mask.
 
     Returns:
       cache: Updated attention KV cache.
@@ -335,7 +337,7 @@ def __call__(
       logits = jnp.tanh(logits / self.attn_logits_soft_cap)
       logits = logits * self.attn_logits_soft_cap
 
-    if self.attn_type == AttentionType.LOCAL_SLIDING:
+    if self.attn_type == AttentionType.LOCAL_SLIDING and not skip_sliding_mask:
       if self.sliding_window_size is None:
         raise ValueError(
             'Sliding_window_size must be set if Local Sliding attention type'
@@ -596,6 +598,7 @@ def __call__(
       attn_mask: jax.Array,
       per_layer_input: jax.Array | None = None,
       kv_shared_cache: LayerCache | None = None,
+      skip_sliding_mask: bool = False,
   ) -> tuple[LayerCache | None, jax.Array]:
     """Applies the block to the inputs.
 
@@ -607,6 +610,7 @@ def __call__(
       per_layer_input: Per-layer input of shape [batch_size, seq_len,
         per_layer_input_dim].
       kv_shared_cache: Cache for shared KV layers.
+      skip_sliding_mask: If True, skip the sliding mask.
 
     Returns:
       cache: Updated attention KV cache.
@@ -621,6 +625,7 @@ def __call__(
         cache,
         attn_mask,
         kv_shared_cache,
+        skip_sliding_mask=skip_sliding_mask,
     )
 
     if self.post_attention_norm is not None:

gemma/gm/nn/gemma4/_transformer.py

@@ -366,11 +366,13 @@ def _apply_attention(
         kv_shared_cache = None
       # Select the appropriate attention mask for this layer type.
       attn_mask = inputs.attention_mask
+      skip_sliding_mask = False
       if (
           inputs.sliding_attention_mask is not None
           and block.attn_type == _modules.AttentionType.LOCAL_SLIDING
       ):
         attn_mask = inputs.sliding_attention_mask
+        skip_sliding_mask = True
       layer_cache, x = block(
           x,
           inputs.positions,
@@ -380,6 +382,7 @@ def _apply_attention(
           if self.config.per_layer_input_dim
           else None,
           kv_shared_cache=kv_shared_cache,
+          skip_sliding_mask=skip_sliding_mask,
       )
       new_cache[layer_name] = layer_cache  # pytype: disable=container-type-mismatch