Fix SDPA KV-cache decode to respect sliding window (#456)

SDPA fallback now respects sliding window during single-token KV-cache
decode by slicing K/V to the last (window + 1) tokens.

Also simplifies the mask building for chunk inference to properly apply
sliding window in that path as well.

Fixes #452

Co-Authored-By: Kartik Vashishta <kartikv776@gmail.com>
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

nanochat/flash_attention.py

@@ -71,27 +71,26 @@ def _sdpa_attention(q, k, v, window_size, enable_gqa):
 
     # Single token generation
     if Tq == 1:
+        if window >= 0 and window < Tk:
+            # window is "left" tokens we need to include (window + 1) keys total
+            start = max(0, Tk - (window + 1))
+            k = k[:, :, start:, :]
+            v = v[:, :, start:, :]
         return F.scaled_dot_product_attention(q, k, v, is_causal=False, enable_gqa=enable_gqa)
 
-    # Need explicit mask
+    # Need explicit mask for sliding window/chunk inference
     device = q.device
-    if Tq == Tk:
-        # Causal + sliding window
-        mask = torch.tril(torch.ones(Tq, Tk, device=device, dtype=torch.bool))
-        if window > 0 and window < Tq:
-            row_idx = torch.arange(Tq, device=device).unsqueeze(1)
-            col_idx = torch.arange(Tk, device=device).unsqueeze(0)
-            mask = mask & ((row_idx - col_idx) <= window)
-    else:
-        # Chunk inference: attend to prefix + causal within chunk
-        prefix_len = Tk - Tq
-        mask = torch.zeros(Tq, Tk, device=device, dtype=torch.bool)
-        mask[:, :prefix_len] = True
-        mask[:, prefix_len:] = torch.tril(torch.ones(Tq, Tq, device=device, dtype=torch.bool))
+    # For chunk inference (Tq != Tk), is_causal is not aligned to cache position => build an explicit bool mask
+    row_idx = (Tk - Tq) + torch.arange(Tq, device=device).unsqueeze(1)
+    col_idx = torch.arange(Tk, device=device).unsqueeze(0)
+    mask = col_idx <= row_idx
 
+    # sliding window (left)
+    if window >= 0 and window < Tk:
+        mask = mask & ((row_idx - col_idx) <= window)
+    
     return F.scaled_dot_product_attention(q, k, v, attn_mask=mask, enable_gqa=enable_gqa)
 
-
 # =============================================================================
 # Public API: Same interface as FA3
 # =============================================================================

tests/test_attention_fallback.py

@@ -178,6 +178,39 @@ class TestFA3VsSDPA:
         max_diff, mean_diff = assert_close(y_fa3, y_sdpa, "single_token")
         print(f"single_token: max_diff={max_diff:.6f}, mean_diff={mean_diff:.6f}")
 
+    def test_kvcache_single_token_sliding_window(self):
+        """Test single token decode with sliding window smaller than cache size.
+
+        This catches the bug where SDPA ignores window_size during Tq=1 decode.
+        When window < Tk, FA3 only attends to the last (window+1) tokens,
+        but SDPA was attending to all cached tokens.
+        """
+        B, T_max, H, D = 2, 64, 4, 32
+        T_prefill = 32  # Enough tokens to exceed window
+        window = 8      # Window SMALLER than cache size
+
+        k_init = torch.randn(B, T_prefill, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        v_init = torch.randn(B, T_prefill, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        q_single = torch.randn(B, 1, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        k_single = torch.randn(B, 1, H, D, device=self.DEVICE, dtype=self.DTYPE)
+        v_single = torch.randn(B, 1, H, D, device=self.DEVICE, dtype=self.DTYPE)
+
+        def run():
+            k_cache = torch.zeros(B, T_max, H, D, device=self.DEVICE, dtype=self.DTYPE)
+            v_cache = torch.zeros(B, T_max, H, D, device=self.DEVICE, dtype=self.DTYPE)
+            k_cache[:, :T_prefill, :, :] = k_init
+            v_cache[:, :T_prefill, :, :] = v_init
+            cache_seqlens = torch.full((B,), T_prefill, dtype=torch.int32, device=self.DEVICE)
+            return flash_attn.flash_attn_with_kvcache(
+                q_single, k_cache, v_cache, k=k_single, v=v_single,
+                cache_seqlens=cache_seqlens,
+                causal=True, window_size=(window, 0)  # window=8 < Tk=33
+            )
+
+        y_fa3, y_sdpa = run_both_impls(run)
+        max_diff, mean_diff = assert_close(y_fa3, y_sdpa, "single_token_sliding_window")
+        print(f"single_token_sliding_window: max_diff={max_diff:.6f}, mean_diff={mean_diff:.6f}")
+
     def test_backward_gradients_match(self):
         """Verify gradients are similar between FA3 and SDPA."""
         B, T, H, D = 2, 32, 4, 16