Revert "[v1] Add fp32 support to v1 engine through flex attn"

tests/kernels/attention/test_attention_selector.py

@@ -183,34 +183,6 @@ 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

@@ -4,7 +4,6 @@
 import random
 
 import pytest
-import torch
 
 from vllm.attention import Attention
 from vllm.config import (CacheConfig, ModelConfig, ParallelConfig,
@@ -400,7 +399,6 @@ 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"
@@ -429,7 +427,6 @@ 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"
@@ -458,7 +455,6 @@ 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"
@@ -487,7 +483,6 @@ 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()
@@ -555,7 +550,6 @@ 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

@@ -1337,6 +1337,13 @@ 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

@@ -233,10 +233,6 @@ 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: