[v1] Re-add fp32 support to v1 engine through FlexAttention

.github/workflows/lint-and-deploy.yaml

@@ -68,7 +68,7 @@ jobs:
           export AWS_ACCESS_KEY_ID=minioadmin
           export AWS_SECRET_ACCESS_KEY=minioadmin
           sleep 30 && kubectl -n ns-vllm logs -f "$(kubectl -n ns-vllm get pods | awk '/deployment/ {print $1;exit}')" &
-          helm install --wait --wait-for-jobs --timeout 5m0s --debug --create-namespace --namespace=ns-vllm test-vllm examples/online_serving/chart-helm -f examples/online_serving/chart-helm/values.yaml --set secrets.s3endpoint=http://minio:9000 --set secrets.s3bucketname=testbucket --set secrets.s3accesskeyid=$AWS_ACCESS_KEY_ID --set secrets.s3accesskey=$AWS_SECRET_ACCESS_KEY --set resources.requests.cpu=1 --set resources.requests.memory=4Gi --set resources.limits.cpu=2 --set resources.limits.memory=5Gi --set image.env[0].name=VLLM_CPU_KVCACHE_SPACE --set image.env[1].name=VLLM_LOGGING_LEVEL --set-string image.env[0].value="1" --set-string image.env[1].value="DEBUG" --set-string extraInit.s3modelpath="opt-125m/" --set-string 'resources.limits.nvidia\.com/gpu=0' --set-string 'resources.requests.nvidia\.com/gpu=0' --set-string image.repository="vllm-cpu-env"
+          helm install --wait --wait-for-jobs --timeout 5m0s --debug --create-namespace --namespace=ns-vllm test-vllm examples/online_serving/chart-helm -f examples/online_serving/chart-helm/values.yaml --set secrets.s3endpoint=http://minio:9000 --set secrets.s3bucketname=testbucket --set secrets.s3accesskeyid=$AWS_ACCESS_KEY_ID --set secrets.s3accesskey=$AWS_SECRET_ACCESS_KEY --set resources.requests.cpu=1 --set resources.requests.memory=4Gi --set resources.limits.cpu=2 --set resources.limits.memory=5Gi --set image.env[0].name=VLLM_CPU_KVCACHE_SPACE --set image.env[1].name=VLLM_LOGGING_LEVEL --set image.env[2].name=VLLM_CPU_CI_ENV --set-string image.env[0].value="1" --set-string image.env[1].value="DEBUG" --set-string image.env[2].value="1" --set-string extraInit.s3modelpath="opt-125m/" --set-string 'resources.limits.nvidia\.com/gpu=0' --set-string 'resources.requests.nvidia\.com/gpu=0' --set-string image.repository="vllm-cpu-env"
 
       - name: curl test
         run: |

tests/kernels/attention/test_attention_selector.py

@@ -181,6 +181,34 @@ 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

@@ -450,6 +450,7 @@ 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"
@@ -478,6 +479,7 @@ 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"
@@ -506,6 +508,7 @@ 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"
@@ -534,6 +537,7 @@ 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()
@@ -601,6 +605,7 @@ 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

@@ -1393,13 +1393,6 @@ 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 Mamba or Encoder-Decoder so far.
         if not model_config.is_v1_compatible:
             _raise_or_fallback(feature_name=model_config.architectures,

vllm/model_executor/model_loader/tensorizer_loader.py

@@ -104,8 +104,12 @@ def load_model(self, vllm_config: VllmConfig,
 
         if is_vllm_tensorized(self.tensorizer_config):
             tensorizer_config = self._patch_tensorizer_config(model_config)
-            model = init_tensorizer_model(tensorizer_config=tensorizer_config,
-                                          vllm_config=vllm_config)
+            device_config = vllm_config.device_config
+            with set_default_torch_dtype(model_config.dtype):
+                with torch.device(device_config.device):
+                    model = init_tensorizer_model(
+                        tensorizer_config=tensorizer_config,
+                        vllm_config=vllm_config)
             self.load_weights(model, model_config)
             return model
         return self._load_model_serialized_cpu(vllm_config=vllm_config)

vllm/platforms/cuda.py

@@ -251,6 +251,10 @@ def get_attn_backend_cls(cls, selected_backend, head_size, dtype,
 
             # Default backends for V1 engine
             # Prefer FlashInfer for Blackwell GPUs if installed
+            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):
                 try:
                     import flashinfer  # noqa: F401

vllm/v1/attention/backends/flex_attention.py

@@ -463,6 +463,13 @@ def forward(
         query = query[:, :, :num_actual_tokens, :]
         # Doesn't work for now -> constraint violation
         # torch._dynamo.try_mark_dynamic(query, 2)
+
+        # default M=64, N=64 may run out of shared memory on
+        # some GPUs with fp32, so we use smaller M and N.
+        extra_kernel_options = {
+            "BLOCK_M": 32,
+            "BLOCK_N": 32
+        } if query.dtype == torch.float32 else {}
         out = flex_attention_compiled(
             query,
             key_cache,
@@ -471,7 +478,10 @@ def forward(
             attn_metadata.block_mask,
             self.scale,
             enable_gqa=enable_gqa,
-            kernel_options={"FORCE_USE_FLEX_ATTENTION": True},
+            kernel_options={
+                "FORCE_USE_FLEX_ATTENTION": True,
+                **extra_kernel_options
+            },
         )
 
         # Flex doesn't have an out variant today, rely on epilogue fusion

vllm/v1/sample/ops/topk_topp_sampler.py

@@ -101,7 +101,10 @@ def forward_cuda(
                            "per-request generators. Falling back to "
                            "PyTorch-native implementation.")
             return self.forward_native(logits, generators, k, p)
-        return flashinfer_sample(logits, k, p, generators)
+        # flashinfer sampling functions expect contiguous logits.
+        # In flex_attn/triton_attn fp32 inference, logits can be non-contiguous
+        # because of slicing operation in logits_processor.
+        return flashinfer_sample(logits.contiguous(), k, p, generators)
 
     def forward_tpu(
         self,