Add FlexAttention to V1

tests/kernels/test_flex_attention.py

@@ -0,0 +1,93 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Integration tests for FlexAttention backend vs default backend"""
+
+import random
+
+import numpy as np
+import pytest
+import torch
+from packaging import version
+
+from vllm import LLM, SamplingParams
+
+TORCH_VERSION = version.parse(torch.__version__)
+MINIMUM_TORCH_VERSION = version.parse("2.7.0")
+
+
+def set_seed(seed):
+    """Set seeds for reproducibility"""
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)
+
+
+@pytest.mark.skipif(
+    not torch.cuda.is_available() or TORCH_VERSION < MINIMUM_TORCH_VERSION,
+    reason="CUDA not available or PyTorch version < 2.7",
+)
+def test_flex_attention_vs_default_backend(monkeypatch):
+    """Test that FlexAttention produces the same outputs as the default backend.
+
+    This test compares the outputs from the FlexAttention backend with
+    the default backend, ensuring they are identical when using the same seed.
+    """
+    model_name = "Qwen/Qwen2.5-1.5B-Instruct"
+    seed = 42
+    max_tokens = 32
+    prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+    ]
+
+    sampling_params = SamplingParams(temperature=0.0,
+                                     top_p=1.0,
+                                     seed=seed,
+                                     max_tokens=max_tokens)
+
+    # Run with flex attention
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_USE_V1", "1")
+        m.setenv("VLLM_ATTENTION_BACKEND", "FLEX_ATTENTION")
+        m.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0")
+
+        set_seed(seed)
+
+        llm_flex = LLM(
+            model_name,
+            tensor_parallel_size=1,
+            num_gpu_blocks_override=128,
+            enforce_eager=True,
+        )
+        output_flex = llm_flex.generate(prompts, sampling_params)
+
+    # Run with default backend
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_USE_V1", "1")
+        m.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0")
+        set_seed(seed)
+        llm_default = LLM(
+            model_name,
+            tensor_parallel_size=1,
+            num_gpu_blocks_override=128,
+            enforce_eager=True,
+        )
+        output_default = llm_default.generate(prompts, sampling_params)
+
+    # Compare outputs from both backends
+    for i, (flex_result,
+            default_result) in enumerate(zip(output_flex, output_default)):
+        prompt = prompts[i]
+        flex_text = flex_result.outputs[0].text
+        default_text = default_result.outputs[0].text
+
+        assert flex_text == default_text, (
+            f"FlexAttention output doesn't match default for: {prompt!r}\n"
+            f"FlexAttention: {flex_text!r}\n"
+            f"Default: {default_text!r}")
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])

vllm/engine/arg_utils.py

@@ -1409,6 +1409,7 @@ def _is_v1_supported_oracle(self, model_config: ModelConfig) -> bool:
             "FLASHINFER_VLLM_V1",
             "ROCM_AITER_MLA",
             "TORCH_SDPA_VLLM_V1",
+            "FLEX_ATTENTION",
         ]
         if (envs.is_set("VLLM_ATTENTION_BACKEND")
                 and envs.VLLM_ATTENTION_BACKEND not in V1_BACKENDS):

vllm/platforms/cuda.py

@@ -226,6 +226,9 @@ def get_attn_backend_cls(cls, selected_backend, head_size, dtype,
             if selected_backend == _Backend.FLASHINFER:
                 logger.info_once("Using FlashInfer backend on V1 engine.")
                 return "vllm.v1.attention.backends.flashinfer.FlashInferBackend"
+            if selected_backend == _Backend.FLEX_ATTENTION:
+                logger.info("Using FlexAttenion backend on V1 engine.")
+                return "vllm.v1.attention.backends.flex_attention.FlexAttentionBackend"  # noqa: E501
             if selected_backend == _Backend.TRITON_ATTN_VLLM_V1:
                 logger.info_once("Using Triton backend on V1 engine.")
                 return ("vllm.v1.attention.backends."

vllm/platforms/interface.py

@@ -60,6 +60,7 @@ class _Backend(enum.Enum):
     BLOCK_SPARSE_FLASH_ATTN = enum.auto()
     DUAL_CHUNK_FLASH_ATTN = enum.auto()
     NO_ATTENTION = enum.auto()
+    FLEX_ATTENTION = enum.auto()
 
 
 class PlatformEnum(enum.Enum):