[Core] Add Sliding Window Support with Flashinfer (vllm-project#10462)

Signed-off-by: Pavani Majety <pmajety@nvidia.com>

Author: pavanimajety

tests/core/block/e2e/test_correctness_sliding_window.py

@@ -3,6 +3,7 @@
 
 import pytest
 
+from tests.kernels.utils import override_backend_env_variable
 from vllm import LLM, SamplingParams
 
 from .conftest import get_text_from_llm_generator
@@ -28,8 +29,9 @@
 @pytest.mark.parametrize("test_llm_kwargs", [{}])
 @pytest.mark.parametrize("batch_size", [5])
 @pytest.mark.parametrize("seed", [1])
+@pytest.mark.parametrize("backend", ["FLASH_ATTN", "FLASHINFER", "XFORMERS"])
 def test_sliding_window_retrival(baseline_llm_generator, test_llm_generator,
-                                 batch_size, seed):
+                                 batch_size, seed, backend, monkeypatch):
     """
     The test does a bunch of assignments "x1 = 10\nx2 = 33\n..." and then
     asks for value of one of them (which is outside the sliding window).
@@ -38,6 +40,8 @@ def test_sliding_window_retrival(baseline_llm_generator, test_llm_generator,
 
     Additionally, we compare the results of the v1 and v2 managers.
     """
+    override_backend_env_variable(monkeypatch, backend)
+
     sampling_params = SamplingParams(
         max_tokens=1024,
         ignore_eos=True,
@@ -84,7 +88,9 @@ def test_sliding_window_retrival(baseline_llm_generator, test_llm_generator,
 @pytest.mark.parametrize("test_llm_kwargs", [{"enable_chunked_prefill": True}])
 @pytest.mark.parametrize("batch_size", [5])
 @pytest.mark.parametrize("seed", [1])
-def test_sliding_window_chunked_prefill(test_llm_generator, batch_size, seed):
+@pytest.mark.parametrize("backend", ["FLASH_ATTN", "FLASHINFER", "XFORMERS"])
+def test_sliding_window_chunked_prefill(test_llm_generator, batch_size, seed,
+                                        backend, monkeypatch):
     """
     This is similar to test_sliding_window_retrival, however, it doesn't
     compare against the v1 block manager since v1 doesn't support
@@ -93,6 +99,8 @@ def test_sliding_window_chunked_prefill(test_llm_generator, batch_size, seed):
     The results with and without chunked prefill are not the same due to
     numerical instabilities.
     """
+    override_backend_env_variable(monkeypatch, backend)
+
     sampling_params = SamplingParams(
         max_tokens=10,
         ignore_eos=True,

vllm/attention/backends/flashinfer.py

@@ -757,9 +757,8 @@ def __init__(
         if alibi_slopes is not None:
             alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32)
         self.alibi_slopes = alibi_slopes
-        if sliding_window is not None:
-            raise ValueError("Sliding window is not supported in FlashInfer.")
-        self.sliding_window = (-1, -1)
+        self.sliding_window = ((sliding_window - 1,
+                                0) if sliding_window is not None else (-1, -1))
         self.kv_cache_dtype = kv_cache_dtype
         self.logits_soft_cap = logits_soft_cap
 
@@ -865,6 +864,8 @@ def unified_flash_infer(
     assert query.shape[0] == num_prefill_tokens
     assert decode_query.shape[0] == num_decode_tokens
 
+    window_left = window_size[0] if window_size is not None else -1
+
     prefill_output: Optional[torch.Tensor] = None
     decode_output: Optional[torch.Tensor] = None
     if prefill_meta := attn_metadata.prefill_metadata:
@@ -895,7 +896,8 @@ def unified_flash_infer(
                 logits_soft_cap=logits_soft_cap,
                 causal=True,
                 k_scale=k_scale,
-                v_scale=v_scale)
+                v_scale=v_scale,
+                window_left=window_left)
     if decode_meta := attn_metadata.decode_metadata:
         assert attn_metadata.decode_metadata is not None
         assert attn_metadata.decode_metadata.decode_wrapper is not None
@@ -905,7 +907,8 @@ def unified_flash_infer(
             sm_scale=softmax_scale,
             logits_soft_cap=logits_soft_cap,
             k_scale=k_scale,
-            v_scale=v_scale)
+            v_scale=v_scale,
+            window_left=window_left)
 
     if prefill_output is None and decode_output is not None:
         # Decode only batch.