[V0 Deprecation] Remove V0 FlashInfer attention backend (#22776)

Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>

tests/basic_correctness/test_basic_correctness.py

@@ -12,7 +12,6 @@ import pytest
 import torch
 
 from vllm import LLM, envs
-from vllm.platforms import current_platform
 from vllm.v1.engine.llm_engine import LLMEngine as LLMEngineV1
 
 from ..conftest import HfRunner, VllmRunner
@@ -78,11 +77,7 @@ def test_models(
             "VLLM_USE_V1") and envs.VLLM_USE_V1:
         pytest.skip("enable_prompt_embeds is not supported in v1.")
 
-    if backend == "FLASHINFER" and current_platform.is_rocm():
-        pytest.skip("Flashinfer does not support ROCm/HIP.")
-
-    if backend in ("XFORMERS",
-                   "FLASHINFER") and model == "google/gemma-2-2b-it":
+    if backend == "XFORMERS" and model == "google/gemma-2-2b-it":
         pytest.skip(
             f"{backend} does not support gemma2 with full context length.")
 
@@ -141,8 +136,6 @@ def test_models(
         ("meta-llama/Llama-3.2-1B-Instruct", "mp", "", "L4", {}),
         ("distilbert/distilgpt2", "ray", "", "A100", {}),
         ("distilbert/distilgpt2", "mp", "", "A100", {}),
-        ("distilbert/distilgpt2", "mp", "FLASHINFER", "A100", {}),
-        ("meta-llama/Meta-Llama-3-8B", "ray", "FLASHINFER", "A100", {}),
     ])
 @pytest.mark.parametrize("enable_prompt_embeds", [True, False])
 def test_models_distributed(

tests/compile/test_basic_correctness.py

@@ -34,7 +34,7 @@ class TestSetting:
             model_args=["--max-model-len", "2048"],
             pp_size=2,
             tp_size=2,
-            attn_backend="FLASHINFER",
+            attn_backend="FLASH_ATTN",
             method="generate",
             fullgraph=True,
         ),

tests/core/block/e2e/test_correctness_sliding_window.py

@@ -32,7 +32,7 @@ BLOCK_SIZE = 16
 @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"])
+@pytest.mark.parametrize("backend", ["FLASH_ATTN", "XFORMERS"])
 def test_sliding_window_retrieval(baseline_llm_generator, test_llm_generator,
                                   batch_size, seed, backend, monkeypatch):
     """
@@ -43,8 +43,6 @@ def test_sliding_window_retrieval(baseline_llm_generator, test_llm_generator,
 
     Additionally, we compare the results of the v1 and v2 managers.
     """
-    if backend == "FLASHINFER" and current_platform.is_rocm():
-        pytest.skip("Flashinfer does not support ROCm/HIP.")
     if backend == "XFORMERS" and current_platform.is_rocm():
         pytest.skip("Xformers does not support ROCm/HIP.")
 
@@ -96,7 +94,7 @@ def test_sliding_window_retrieval(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])
-@pytest.mark.parametrize("backend", ["FLASH_ATTN", "FLASHINFER", "XFORMERS"])
+@pytest.mark.parametrize("backend", ["FLASH_ATTN", "XFORMERS"])
 def test_sliding_window_chunked_prefill(test_llm_generator, batch_size, seed,
                                         backend, monkeypatch):
     """
@@ -107,8 +105,6 @@ 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.
     """
-    if backend == "FLASHINFER" and current_platform.is_rocm():
-        pytest.skip("Flashinfer does not support ROCm/HIP.")
     if backend == "XFORMERS" and current_platform.is_rocm():
         pytest.skip("Xformers does not support ROCm/HIP.")
     override_backend_env_variable(monkeypatch, backend)

tests/distributed/test_pp_cudagraph.py

@@ -17,7 +17,6 @@ if TYPE_CHECKING:
 ])
 @pytest.mark.parametrize("ATTN_BACKEND", [
     "FLASH_ATTN",
-    "FLASHINFER",
 ])
 @create_new_process_for_each_test()
 def test_pp_cudagraph(

tests/kernels/attention/test_attention_selector.py

@@ -81,6 +81,9 @@ def test_env(
         m.setenv(STR_BACKEND_ENV_VAR, name)
         m.setenv("VLLM_MLA_DISABLE", "1" if use_mla else "0")
 
+        if name == "FLASHINFER" and not use_v1:
+            pytest.skip("FlashInfer backend is only available on V1 engine")
+
         if device == "cpu":
             if not use_v1:
                 pytest.skip("CPU backend only supports V1")

tests/models/quantization/test_fp8.py

@@ -32,7 +32,7 @@ from ..utils import check_logprobs_close
 # Due to low-precision numerical divergence, we only test logprob of 4 tokens
 @pytest.mark.parametrize("max_tokens", [4])
 @pytest.mark.parametrize("enforce_eager", [True])
-@pytest.mark.parametrize("backend", ["FLASH_ATTN", "XFORMERS", "FLASHINFER"])
+@pytest.mark.parametrize("backend", ["FLASH_ATTN", "XFORMERS"])
 # NOTE: Increasing this in this suite will fail CI because we currently cannot
 # reset distributed env properly. Use a value > 1 just when you test.
 @pytest.mark.parametrize("tensor_parallel_size", [1])
@@ -57,9 +57,6 @@ def test_models(
     numerical sensitive kernels.
     """
 
-    if backend == "FLASHINFER" and current_platform.is_rocm():
-        pytest.skip("Flashinfer does not support ROCm/HIP.")
-
     if kv_cache_dtype == "fp8_e5m2" and current_platform.is_rocm():
         pytest.skip(
             f"{kv_cache_dtype} is currently not supported on ROCm/HIP.")

vllm/platforms/cuda.py

@@ -350,17 +350,7 @@ class CudaPlatformBase(Platform):
             return FLEX_ATTENTION_V1
 
         # Backends for V0 engine
-        if selected_backend == _Backend.FLASHINFER:
-            logger.info("Using FlashInfer backend.")
-            if cls.has_device_capability(100):
-                from vllm.v1.attention.backends.utils import (
-                    set_kv_cache_layout)
-                logger.info_once(
-                    "Using HND KV cache layout on V1 engine by default for "
-                    "Blackwell (SM 10.0) GPUs.")
-                set_kv_cache_layout("HND")
-            return "vllm.attention.backends.flashinfer.FlashInferBackend"
-        elif selected_backend == _Backend.XFORMERS:
+        if selected_backend == _Backend.XFORMERS:
             logger.info("Using XFormers backend.")
             return "vllm.attention.backends.xformers.XFormersBackend"
         elif selected_backend == _Backend.DUAL_CHUNK_FLASH_ATTN:
@@ -416,10 +406,6 @@ class CudaPlatformBase(Platform):
                 if (fp8_kv_cache and not flash_attn_supports_fp8()):
                     logger.info(
                         "Cannot use FlashAttention backend for FP8 KV cache.")
-                    logger.warning(
-                        "Please use FlashInfer backend with FP8 KV Cache for "
-                        "better performance by setting environment variable "
-                        "VLLM_ATTENTION_BACKEND=FLASHINFER")
                     target_backend = _Backend.XFORMERS
             except ImportError:
                 logger.info(