[v1] Add fp32 support to v1 engine through flex attn (#19319)

Signed-off-by: Isotr0py <2037008807@qq.com>
Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>

Author: Isotr0py

tests/kernels/attention/test_attention_selector.py

@@ -183,6 +183,34 @@ def test_env(
                     assert backend.get_name() == expected
 
 
+@pytest.mark.parametrize("device", ["cpu", "cuda"])
+@pytest.mark.parametrize("use_v1", [True, False])
+def test_fp32_fallback(
+    device: str,
+    use_v1: bool,
+    monkeypatch: pytest.MonkeyPatch,
+):
+    """Test attention backend selection with fp32."""
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_USE_V1", "1" if use_v1 else "0")
+
+        if device == "cpu":
+            with patch("vllm.attention.selector.current_platform",
+                       CpuPlatform()):
+                backend = get_attn_backend(16, torch.float32, torch.float32,
+                                           16, False)
+            assert (backend.get_name() == "TORCH_SDPA_VLLM_V1"
+                    if use_v1 else "TORCH_SDPA")
+
+        elif device == "cuda":
+            with patch("vllm.attention.selector.current_platform",
+                       CudaPlatform()):
+                backend = get_attn_backend(16, torch.float32, torch.float32,
+                                           16, False)
+            assert (backend.get_name() == "FLEX_ATTENTION"
+                    if use_v1 else "XFORMERS")
+
+
 def test_flash_attn(monkeypatch: pytest.MonkeyPatch):
     """Test FlashAttn validation."""
     # TODO: When testing for v1, pipe in `use_v1` as an argument to

tests/v1/worker/test_gpu_model_runner.py

@@ -4,6 +4,7 @@
 import random
 
 import pytest
+import torch
 
 from vllm.attention import Attention
 from vllm.config import (CacheConfig, ModelConfig, ParallelConfig,
@@ -399,6 +400,7 @@ def test_load_model_weights_inplace(dist_init, model_runner, model_runner_2):
 
 
 def test_init_kv_cache_with_kv_sharing_invalid_target_layer_order():
+    torch.set_default_dtype(torch.float16)
     layer_0 = "model.layers.0.self_attn.attn"
     layer_1 = "model.layers.1.self_attn.attn"
     error_msg = f"{layer_1} must come before the current layer"
@@ -427,6 +429,7 @@ def test_init_kv_cache_with_kv_sharing_invalid_target_layer_order():
 
 
 def test_init_kv_cache_with_kv_sharing_target_layer_not_exist():
+    torch.set_default_dtype(torch.float16)
     layer_0 = "model.layers.0.self_attn.attn"
     layer_1 = "model.layers.1.self_attn.attn"
     invalid_layer = "model.layers.0.cross_attn.attn"
@@ -455,6 +458,7 @@ def test_init_kv_cache_with_kv_sharing_target_layer_not_exist():
 
 
 def test_init_kv_cache_with_kv_sharing_target_same_as_current():
+    torch.set_default_dtype(torch.float16)
     layer_0 = "model.layers.0.self_attn.attn"
     layer_1 = "model.layers.1.self_attn.attn"
     error_msg = f"{layer_1} cannot be the same as the current layer"
@@ -483,6 +487,7 @@ def test_init_kv_cache_with_kv_sharing_target_same_as_current():
 
 
 def test_init_kv_cache_without_kv_sharing():
+    torch.set_default_dtype(torch.float16)
     layer_0 = "model.layers.0.self_attn.attn"
     layer_1 = "model.layers.1.self_attn.attn"
     vllm_config = get_vllm_config()
@@ -550,6 +555,7 @@ def test_init_kv_cache_without_kv_sharing():
 
 
 def test_init_kv_cache_with_kv_sharing_valid():
+    torch.set_default_dtype(torch.float16)
     layer_0 = "model.layers.0.self_attn.attn"
     layer_1 = "model.layers.1.self_attn.attn"
     vllm_config = get_vllm_config()

vllm/engine/arg_utils.py

@@ -1337,13 +1337,6 @@ def _is_v1_supported_oracle(self, model_config: ModelConfig) -> bool:
                                recommend_to_remove=False)
             return False
 
-        # Only Fp16 and Bf16 dtypes since we only support FA.
-        V1_SUPPORTED_DTYPES = [torch.bfloat16, torch.float16]
-        if model_config.dtype not in V1_SUPPORTED_DTYPES:
-            _raise_or_fallback(feature_name=f"--dtype {model_config.dtype}",
-                               recommend_to_remove=False)
-            return False
-
         # No Embedding Models so far.
         if model_config.task not in ["generate"]:
             _raise_or_fallback(feature_name=f"--task {model_config.task}",

vllm/platforms/cuda.py

@@ -233,6 +233,10 @@ def get_attn_backend_cls(cls, selected_backend, head_size, dtype,
                 logger.info_once("Using Triton backend on V1 engine.")
                 return ("vllm.v1.attention.backends."
                         "triton_attn.TritonAttentionBackend")
+            if dtype not in (torch.float16, torch.bfloat16):
+                logger.info_once(
+                    f"Using FlexAttenion backend for {dtype} on V1 engine.")
+                return "vllm.v1.attention.backends.flex_attention.FlexAttentionBackend"  # noqa: E501
             if cls.is_device_capability(100):
                 # Prefer FlashInfer for V1 on Blackwell GPUs if installed
                 try: