[BugFix][Attention] Fix sliding window attention in V1 giving incorrect results

vllm/v1/attention/backends/flash_attn.py

@@ -10,9 +10,11 @@
 from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
                                               AttentionMetadata, AttentionType,
                                               is_quantized_kv_cache)
+from vllm.attention.layer import Attention
 from vllm.attention.ops.merge_attn_states import merge_attn_states
 from vllm.attention.utils.fa_utils import (flash_attn_supports_fp8,
                                            get_flash_attn_version)
+from vllm.config import VllmConfig, get_layers_from_vllm_config
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
 from vllm.utils import cdiv
@@ -273,20 +275,35 @@ def make_local_attention_virtual_batches(
         block_table_local
 
 
+def _get_sliding_window_configs(
+        vllm_config: VllmConfig) -> set[Optional[tuple[int, int]]]:
+    """Get the set of all sliding window configs used in the model."""
+    sliding_window_configs: set[Optional[tuple[int, int]]] = set()
+    layers = get_layers_from_vllm_config(vllm_config, Attention)
+    for layer in layers.values():
+        assert isinstance(layer.impl, FlashAttentionImpl)
+        sliding_window_configs.add(layer.impl.sliding_window)
+    return sliding_window_configs
+
+
 class FlashAttentionMetadataBuilder:
 
     def __init__(self, runner: "GPUModelRunner"):
         model_config = runner.model_config
 
         self.runner = runner
-        self.aot_schedule = (get_flash_attn_version() == 3)
         self.num_heads_q = model_config.get_num_attention_heads(
             runner.parallel_config)
         self.num_heads_kv = model_config.get_num_kv_heads(
             runner.parallel_config)
         self.headdim = model_config.get_head_size()
         self.page_size = self.runner.block_size
 
+        self.aot_schedule = (get_flash_attn_version() == 3)
+        # Sliding window size to be used with the AOT scheduler will be
+        # populated on first build() call.
+        self.aot_sliding_window: Optional[tuple[int, int]] = None
+
     def reorder_batch(self, input_batch: "InputBatch",
                       scheduler_output: "SchedulerOutput") -> bool:
         return False
@@ -304,6 +321,22 @@ def build(self, num_reqs: int, num_actual_tokens: int, max_query_len: int,
         slot_mapping = self.runner.slot_mapping_cpu[:num_actual_tokens].to(
             self.runner.device, non_blocking=True).long()
 
+        if self.aot_sliding_window is None:
+            self.aot_sliding_window = (-1, -1)
+            # For the AOT scheduler we need the sliding window value to be
+            # constant for all layers to. We have to populate this on the first
+            # build() call so the layers are constructed (cannot populate)
+            # in __init__.
+            if self.aot_schedule:
+                sliding_window_configs = _get_sliding_window_configs(
+                    self.runner.vllm_config)
+                if len(sliding_window_configs) == 1:
+                    sliding_window_config = sliding_window_configs.pop()
+                    if sliding_window_config is not None:
+                        self.aot_sliding_window = sliding_window_config
+                elif len(sliding_window_configs) > 1:
+                    self.aot_schedule = False
+
         def schedule(batch_size, cu_query_lens, max_query_len, seqlens,
                      max_seq_len, causal):
             if self.aot_schedule:
@@ -318,6 +351,7 @@ def schedule(batch_size, cu_query_lens, max_query_len, seqlens,
                     page_size=self.page_size,
                     cu_seqlens_q=cu_query_lens,
                     causal=causal,
+                    window_size=self.aot_sliding_window,
                 )
             return None