[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/attention/backends/flashinfer.py

-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import dataclasses
-from collections import defaultdict
-from contextlib import contextmanager
-from dataclasses import dataclass
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Set, Tuple, Type
-
-from vllm.multimodal import MultiModalPlaceholderMap
-
-try:
-    from flashinfer import BatchDecodeWithPagedKVCacheWrapper
-    from flashinfer.decode import (CUDAGraphBatchDecodeWithPagedKVCacheWrapper,
-                                   trtllm_batch_decode_with_kv_cache)
-    from flashinfer.prefill import BatchPrefillWithPagedKVCacheWrapper
-
-    from vllm.vllm_flash_attn import flash_attn_varlen_func
-    FLASHINFER_WORKSPACE_BUFFER_SIZE = 256 * 1024 * 1024
-except ImportError:
-    # Avoid turning these types into variables during type checking
-    if not TYPE_CHECKING:
-        BatchDecodeWithPagedKVCacheWrapper = None
-        CUDAGraphBatchDecodeWithPagedKVCacheWrapper = None
-        BatchPrefillWithPagedKVCacheWrapper = None
-        trtllm_batch_decode_with_kv_cache = None
-    FLASHINFER_WORKSPACE_BUFFER_SIZE = 0
-    raise ImportError("FlashInfer is not installed. Please install it from "
-                      "https://github.com/flashinfer-ai/flashinfer") from None
-
-import torch
-
-import vllm.envs as envs
-from vllm import _custom_ops as ops
-from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
-                                              AttentionLayer,
-                                              AttentionMetadata,
-                                              AttentionMetadataBuilder,
-                                              AttentionState, AttentionType)
-from vllm.attention.backends.utils import (PAD_SLOT_ID, compute_slot_mapping,
-                                           compute_slot_mapping_start_idx,
-                                           is_block_tables_empty)
-from vllm.attention.layer import Attention
-from vllm.attention.ops.paged_attn import PagedAttention
-from vllm.config import VllmConfig, get_layers_from_vllm_config
-from vllm.logger import init_logger
-from vllm.utils import (async_tensor_h2d, get_kv_cache_torch_dtype,
-                        make_tensor_with_pad)
-from vllm.utils.flashinfer import use_trtllm_attention
-
-logger = init_logger(__name__)
-
-if TYPE_CHECKING:
-    from vllm.worker.model_runner import ModelInputForGPUBuilder
-
-
-class FlashInferBackend(AttentionBackend):
-
-    @staticmethod
-    def get_name() -> str:
-        return "FLASHINFER"
-
-    @staticmethod
-    def get_impl_cls() -> Type["FlashInferImpl"]:
-        return FlashInferImpl
-
-    @staticmethod
-    def get_metadata_cls() -> Type["AttentionMetadata"]:
-        return FlashInferMetadata
-
-    @staticmethod
-    def get_builder_cls() -> Type["FlashInferMetadataBuilder"]:
-        return FlashInferMetadataBuilder
-
-    @staticmethod
-    def get_state_cls() -> Type["FlashInferState"]:
-        return FlashInferState
-
-    @staticmethod
-    def get_kv_cache_shape(
-        num_blocks: int,
-        block_size: int,
-        num_kv_heads: int,
-        head_size: int,
-    ) -> Tuple[int, ...]:
-        return (num_blocks, 2, block_size, num_kv_heads, head_size)
-
-    @staticmethod
-    def get_kv_cache_stride_order() -> Tuple[int, ...]:
-        cache_layout = FlashInferState.get_kv_cache_layout()
-        assert (cache_layout in ("NHD", "HND"))
-        stride_order = (0, 1, 2, 3, 4) if cache_layout == "NHD" else (0, 1, 3,
-                                                                      2, 4)
-        return stride_order
-
-    @staticmethod
-    def swap_blocks(
-        src_kv_cache: torch.Tensor,
-        dst_kv_cache: torch.Tensor,
-        src_to_dst: torch.Tensor,
-    ) -> None:
-        PagedAttention.swap_blocks(src_kv_cache, dst_kv_cache, src_to_dst)
-
-    @staticmethod
-    def copy_blocks(
-        kv_caches: List[torch.Tensor],
-        src_to_dists: torch.Tensor,
-    ) -> None:
-        PagedAttention.copy_blocks(kv_caches, src_to_dists)
-
-    @staticmethod
-    def get_supported_head_sizes() -> List[int]:
-        return [64, 128, 256]
-
-    @staticmethod
-    def get_fp8_dtype_for_flashinfer(kv_cache_dtype: str) -> torch.dtype:
-        if kv_cache_dtype in ("fp8", "fp8_e4m3"):
-            return torch.float8_e4m3fn
-        elif kv_cache_dtype == "fp8_e5m2":
-            return torch.float8_e5m2
-        else:
-            raise ValueError(f"Unrecognized FP8 dtype: {kv_cache_dtype}")
-
-
-@dataclass
-class PerLayerParameters:
-    """
-    Currently, FlashInfer backend only support models in which all layers share
-    the same values for the following hyperparameters.
-    """
-
-    window_left: int
-    logits_soft_cap: Optional[float]
-    sm_scale: float
-
-
-def get_per_layer_parameters(
-        vllm_config: VllmConfig) -> Dict[str, PerLayerParameters]:
-    """
-    Scan all attention layers and determine some hyperparameters
-    to use during `plan`.
-    """
-
-    layers = get_layers_from_vllm_config(vllm_config, Attention)
-    per_layer_params: Dict[str, PerLayerParameters] = {}
-
-    for key, layer in layers.items():
-        impl = layer.impl
-        assert isinstance(impl, FlashInferImpl)
-
-        # Infer hyperparameters from the attention layer
-        window_size = impl.sliding_window
-        window_left = window_size[0] if window_size is not None else -1
-        logits_soft_cap = impl.logits_soft_cap
-        sm_scale = impl.scale
-
-        per_layer_params[key] = PerLayerParameters(window_left,
-                                                   logits_soft_cap, sm_scale)
-
-    return per_layer_params
-
-
-def infer_global_hyperparameters(
-        per_layer_params: Dict[str, PerLayerParameters]) -> PerLayerParameters:
-    """
-    Currently, FlashInfer backend only support models in which all layers share
-    the same values for the following hyperparameters:
-    - `window_left`
-    - `logits_soft_cap`
-    - `sm_scale`
-
-    So this function asserts that all layers share the same values for these
-    hyperparameters and returns the global values.
-    """
-
-    assert len(per_layer_params) > 0, "No attention layers found in the model."
-
-    param_sets = list(per_layer_params.values())
-    global_params = param_sets[0]
-    for params in param_sets:
-        assert params == global_params, (
-            "FlashInfer backend currently only supports models in which all "
-            "layers share the same values for the following hyperparameters: "
-            "`window_left`, `logits_soft_cap`, `sm_scale`.")
-
-    return global_params
-
-
-class FlashInferState(AttentionState):
-
-    def __init__(self, runner):
-        self.runner = runner
-        self._is_graph_capturing = False
-        self._workspace_buffer = None
-        self._decode_wrapper = None
-        self._prefill_wrapper = None
-
-        # Global hyperparameters shared by all attention layers
-        self.global_hyperparameters: Optional[PerLayerParameters] = None
-
-        self.vllm_config = self.runner.vllm_config
-        self._kv_cache_layout = None
-
-    def _get_workspace_buffer(self):
-        if self._workspace_buffer is None:
-            self._workspace_buffer = torch.zeros(
-                FLASHINFER_WORKSPACE_BUFFER_SIZE,
-                dtype=torch.uint8,
-                device=self.runner.device)
-        return self._workspace_buffer
-
-    @staticmethod
-    def get_kv_cache_layout():
-        from vllm.v1.attention.backends.utils import _KV_CACHE_LAYOUT_OVERRIDE
-        if _KV_CACHE_LAYOUT_OVERRIDE is not None:
-            logger.info_once("Using KV cache layout %s",
-                             _KV_CACHE_LAYOUT_OVERRIDE)
-            return _KV_CACHE_LAYOUT_OVERRIDE
-        cache_layout = envs.VLLM_KV_CACHE_LAYOUT
-        if cache_layout is None:
-            logger.info_once("Using default KV cache layout NHD")
-            return "NHD"
-        logger.info_once("Using KV cache layout %s", cache_layout)
-        return cache_layout
-
-    def _get_prefill_wrapper(self):
-        if self._prefill_wrapper is None:
-            self._prefill_wrapper = BatchPrefillWithPagedKVCacheWrapper(
-                self._get_workspace_buffer(), self.get_kv_cache_layout())
-        return self._prefill_wrapper
-
-    def _get_decode_wrapper(self):
-        if self._decode_wrapper is None:
-            num_qo_heads = (self.runner.model_config.get_num_attention_heads(
-                self.runner.parallel_config))
-            num_kv_heads = self.runner.model_config.get_num_kv_heads(
-                self.runner.parallel_config)
-            use_tensor_cores = envs.VLLM_FLASHINFER_FORCE_TENSOR_CORES or (
-                num_qo_heads // num_kv_heads > 4)
-            self._decode_wrapper = BatchDecodeWithPagedKVCacheWrapper(
-                self._get_workspace_buffer(),
-                self.get_kv_cache_layout(),
-                use_tensor_cores=use_tensor_cores)
-        return self._decode_wrapper
-
-    @contextmanager
-    def graph_capture(self, max_batch_size: int):
-        self._is_graph_capturing = True
-        self._graph_decode_wrapper = None
-        self._graph_slot_mapping = torch.full((max_batch_size, ),
-                                              PAD_SLOT_ID,
-                                              dtype=torch.long,
-                                              device=self.runner.device)
-        self._graph_seq_lens = torch.ones(max_batch_size,
-                                          dtype=torch.int32,
-                                          device=self.runner.device)
-        self._graph_block_tables = torch.from_numpy(
-            self.runner.graph_block_tables).to(device=self.runner.device)
-        self._graph_decode_workspace_buffer = self._get_workspace_buffer()
-        self._graph_indices_buffer = torch.empty(
-            max_batch_size * self.runner.cache_config.num_gpu_blocks,
-            dtype=torch.int32,
-            device=self.runner.device)
-        self._graph_indptr_buffer = torch.empty(max_batch_size + 1,
-                                                dtype=torch.int32,
-                                                device=self.runner.device)
-        self._graph_last_page_len_buffer = torch.empty(
-            max_batch_size, dtype=torch.int32, device=self.runner.device)
-        yield
-        self._is_graph_capturing = False
-        del self._graph_slot_mapping
-        del self._graph_seq_lens
-        del self._graph_block_tables
-        del self._graph_decode_workspace_buffer
-        del self._graph_indices_buffer
-        del self._graph_indptr_buffer
-        del self._graph_last_page_len_buffer
-        del self._graph_decode_wrapper
-
-    def graph_clone(self, batch_size: int):
-        assert self._is_graph_capturing
-        state = self.__class__(self.runner)
-        state._workspace_buffer = self._graph_decode_workspace_buffer
-        state._decode_wrapper = self._graph_decode_wrapper
-        state._prefill_wrapper = self._get_prefill_wrapper()
-        return state
-
-    def graph_capture_get_metadata_for_batch(
-            self, batch_size: int, is_encoder_decoder_model: bool = False):
-        assert self._is_graph_capturing
-        _indptr_buffer = self._graph_indptr_buffer[:batch_size + 1]
-        _last_page_len_buffer = self._graph_last_page_len_buffer[:batch_size]
-
-        num_qo_heads = (self.runner.model_config.get_num_attention_heads(
-            self.runner.parallel_config))
-        num_kv_heads = self.runner.model_config.get_num_kv_heads(
-            self.runner.parallel_config)
-        use_tensor_cores = envs.VLLM_FLASHINFER_FORCE_TENSOR_CORES or (
-            num_qo_heads // num_kv_heads > 4)
-        self._graph_decode_wrapper = \
-            CUDAGraphBatchDecodeWithPagedKVCacheWrapper(
-            self._graph_decode_workspace_buffer, _indptr_buffer,
-            self._graph_indices_buffer, _last_page_len_buffer,
-            self.get_kv_cache_layout(),
-            use_tensor_cores)
-        if self.runner.kv_cache_dtype.startswith("fp8"):
-            kv_cache_dtype = FlashInferBackend.get_fp8_dtype_for_flashinfer(
-                self.runner.kv_cache_dtype)
-        else:
-            kv_cache_dtype = get_kv_cache_torch_dtype(
-                self.runner.kv_cache_dtype, self.runner.model_config.dtype)
-
-        paged_kv_indptr_tensor_host = torch.arange(0,
-                                                   batch_size + 1,
-                                                   dtype=torch.int32)
-        paged_kv_indices_tensor_host = torch.arange(0,
-                                                    batch_size,
-                                                    dtype=torch.int32)
-        paged_kv_last_page_len_tensor_host = torch.full((batch_size, ),
-                                                        self.runner.block_size,
-                                                        dtype=torch.int32)
-        query_start_loc_host = torch.arange(0,
-                                            batch_size + 1,
-                                            dtype=torch.int32)
-
-        global_params = infer_global_hyperparameters(
-            get_per_layer_parameters(self.vllm_config))
-
-        attn_metadata = self.runner.attn_backend.make_metadata(
-            num_prefills=0,
-            slot_mapping=self._graph_slot_mapping[:batch_size],
-            multi_modal_placeholder_index_maps=None,
-            enable_kv_scales_calculation=False,
-            num_prefill_tokens=0,
-            num_decode_tokens=batch_size,
-            max_prefill_seq_len=0,
-            max_decode_seq_len=0,
-            seq_lens_tensor=self._graph_seq_lens,
-            block_tables=self._graph_block_tables,
-            paged_kv_indptr=paged_kv_indptr_tensor_host,
-            paged_kv_indices=paged_kv_indices_tensor_host,
-            paged_kv_last_page_len=paged_kv_last_page_len_tensor_host,
-            num_qo_heads=num_qo_heads,
-            num_kv_heads=num_kv_heads,
-            head_dim=self.runner.model_config.get_head_size(),
-            page_size=self.runner.block_size,
-            seq_start_loc=None,
-            query_start_loc=query_start_loc_host,
-            device=self.runner.device,
-            data_type=kv_cache_dtype,
-            q_data_type=self.runner.model_config.dtype,
-            use_cuda_graph=True,
-            decode_wrapper=self._graph_decode_wrapper,
-            prefill_wrapper=None,
-            **dataclasses.asdict(global_params),
-        )
-        attn_metadata.begin_forward()
-        return attn_metadata
-
-    def get_graph_input_buffers(self,
-                                attn_metadata,
-                                is_encoder_decoder_model: bool = False):
-        return {
-            "block_tables": attn_metadata.block_tables,
-            "seq_lens_tensor": attn_metadata.seq_lens_tensor,
-            "slot_mapping": attn_metadata.slot_mapping,
-        }
-
-    def prepare_graph_input_buffers(self,
-                                    input_buffers,
-                                    attn_metadata,
-                                    is_encoder_decoder_model: bool = False):
-        # FlashInfer-specific logic: copy additional tensors
-        num_total_blocks = attn_metadata.decode_metadata.seq_lens_tensor.shape[
-            0]
-        input_buffers["seq_lens_tensor"][:num_total_blocks].copy_(
-            attn_metadata.seq_lens_tensor, non_blocking=True)
-        input_buffers["block_tables"][:num_total_blocks].copy_(
-            attn_metadata.block_tables, non_blocking=True)
-
-    def begin_forward(self, model_input):
-        assert not self._is_graph_capturing
-        state = self
-        use_cuda_graph = model_input.attn_metadata.use_cuda_graph
-        is_decode = model_input.attn_metadata.num_prefills == 0
-        # In case of multistep chunked-prefill, there might be prefill requests
-        # scheduled while CUDA graph mode is enabled. We don't run graph in that
-        # case.
-        if use_cuda_graph and is_decode:
-            if model_input.inputs_embeds is None:
-                batch_size = model_input.input_tokens.shape[0]
-                state = (
-                    self.runner.graph_runners[model_input.virtual_engine][(
-                        batch_size, False)].attn_state)
-            else:
-                batch_size = model_input.inputs_embeds.shape[0]
-                state = (
-                    self.runner.graph_runners[model_input.virtual_engine][(
-                        batch_size, True)].attn_state)
-
-        model_input.attn_metadata.prefill_wrapper = state._get_prefill_wrapper(
-        )
-        model_input.attn_metadata.decode_wrapper = state._get_decode_wrapper()
-        model_input.attn_metadata.begin_forward()
-
-
-@dataclass
-class FlashInferMetadata(AttentionMetadata):
-    # Maximum sequence length among prefill batch. 0 if there are decoding
-    # requests only.
-    max_prefill_seq_len: int
-    max_decode_seq_len: int
-
-    # Number of query tokens for each request in the batch.
-    # Currently, we require that all requests have the same number of query
-    # tokens during the decoding phase. When speculavie decoding is enabled,
-    # decode_query_len might be greater than 1. In all other cases, it is 1.
-    decode_query_len: Optional[int] = 1
-
-    use_cuda_graph: bool = True
-
-    prefill_wrapper: Optional[BatchPrefillWithPagedKVCacheWrapper] = None
-    decode_wrapper: Optional[BatchDecodeWithPagedKVCacheWrapper] = None
-
-    # Metadata for the prefill stage
-    seq_start_loc: Optional[torch.Tensor] = None
-    query_start_loc: Optional[torch.Tensor] = None
-    block_tables: Optional[torch.Tensor] = None
-
-    # used for GPU operations
-    seq_lens_tensor: Optional[torch.Tensor] = None
-    block_table_bound: Optional[torch.Tensor] = None
-
-    # An example for paged_kv_indices, paged_kv_indptr:
-    # request 1, page indices [0, 5, 8]
-    # request 2, page indices [1, 6, 7]
-    # request 3, page indices [3, 4]
-    # paged_kv_indices is a concatenation of page indices of all requests:
-    # [0, 5, 8, 1, 6, 7, 3, 4]
-    # paged_kv_indptr is used to index into paged_kv_indices:
-    # [0, 3, 6, 8]
-    # The indptr of the paged kv cache, shape: [batch_size + 1]
-    paged_kv_indptr: Optional[torch.Tensor] = None
-    # The page indices of the paged kv cache
-    paged_kv_indices: Optional[torch.Tensor] = None
-    # The number of entries in the last page of each request in
-    # the paged kv cache, shape: [batch_size]
-    paged_kv_last_page_len: Optional[torch.Tensor] = None
-    # The number of query/output heads
-    num_qo_heads: Optional[int] = None
-    # The number of key/value heads
-    num_kv_heads: Optional[int] = None
-    # The dimension of the attention heads
-    head_dim: Optional[int] = None
-    # Block size of vllm
-    page_size: Optional[int] = None
-    # The data type of the paged kv cache
-    data_type: torch.dtype = None
-    # The data type of the query
-    q_data_type: torch.dtype = None
-    # FlashInfer 0.2 encourages passing host tensors
-    device: torch.device = torch.device("cpu")
-    is_profile_run: bool = False
-
-    # The FlashInfer backend currently supports only models in which all layers
-    # share the same following hyperparameters:
-
-    # The left (inclusive) window size for the attention window, when
-    # set to `-1`, the window size will be set to the full length of
-    # the sequence. Defaults to `-1`.
-    window_left: int = -1
-    # The attention logits soft capping value (used in Gemini, Grok and
-    # Gemma-2, etc.), if not provided, will be set to `0`. If greater
-    # than 0, the logits will be capped according to formula:
-    # $$\texttt{logits\_soft\_cap} \times
-    # \mathrm{tanh}(x / \texttt{logits\_soft\_cap})$$,
-    # where $x$ is the input logits.
-    logits_soft_cap: Optional[float] = None
-    # The scale used in softmax, if not provided, will be set to
-    # `1.0 / sqrt(head_dim)`.
-    sm_scale: Optional[float] = None
-
-    def __post_init__(self):
-        # Refer to
-        # https://github.com/flashinfer-ai/flashinfer/blob/3d55c71a62052c590c130897d3a3db49b14fcc34/include/flashinfer/utils.cuh#L157
-        supported_head_sizes = FlashInferBackend.get_supported_head_sizes()
-        if self.head_dim is not None and self.head_dim \
-                not in supported_head_sizes:
-            raise ValueError(
-                f"Only {supported_head_sizes} are supported for head_dim,",
-                f" received {self.head_dim}.")
-
-    def begin_forward(self):
-        if self.num_prefill_tokens > 0:
-            if self.paged_kv_indices is None:
-                return
-
-            assert self.prefill_wrapper is not None
-            assert self.query_start_loc is not None
-            assert self.paged_kv_indices is not None
-            assert self.paged_kv_indptr is not None
-            assert self.paged_kv_last_page_len is not None
-            assert self.block_table_bound is not None
-            assert self.seq_lens_tensor is not None
-            self.query_start_loc = self.query_start_loc[:self.num_prefills + 1]
-            batch_size = self.query_start_loc.shape[0] - 1
-            assert batch_size >= 0
-            # We will use flash attention for profiling to
-            # determine the number of blocks. Therefore,
-            # we don't need to prepare the input for flashinfer for profile run.
-            if not self.is_profile_run:
-                self.paged_kv_indptr = self.paged_kv_indptr.to(self.device)
-                self.paged_kv_last_page_len = self.paged_kv_last_page_len.to(
-                    self.device)
-                self.block_table_bound = self.block_table_bound.to(self.device)
-                self.seq_lens_tensor = self.seq_lens_tensor.to(self.device)
-                self.paged_kv_indices = self.paged_kv_indices.to(self.device)
-                self.prefill_wrapper.plan(
-                    self.query_start_loc,
-                    self.paged_kv_indptr[:self.num_prefills + 1],
-                    self.paged_kv_indices,
-                    self.paged_kv_last_page_len[:self.num_prefills],
-                    self.num_qo_heads,
-                    self.num_kv_heads,
-                    self.head_dim,
-                    self.page_size,
-                    causal=True,
-                    sm_scale=self.sm_scale,
-                    window_left=self.window_left,
-                    logits_soft_cap=self.logits_soft_cap,
-                    q_data_type=self.q_data_type,
-                    kv_data_type=self.data_type)
-        if self.num_decode_tokens > 0:
-            assert self.paged_kv_indices is not None
-            assert self.paged_kv_indptr is not None
-            assert self.paged_kv_last_page_len is not None
-            self.paged_kv_indices = self.paged_kv_indices.to(self.device)
-            self.paged_kv_indptr = self.paged_kv_indptr.to(self.device)
-            self.paged_kv_last_page_len = self.paged_kv_last_page_len.to(
-                self.device)
-            # handle model warmup path
-            if self.block_table_bound is not None:
-                self.block_table_bound = self.block_table_bound.to(self.device)
-            if self.seq_lens_tensor is not None:
-                self.seq_lens_tensor = self.seq_lens_tensor.to(self.device)
-
-            assert self.decode_wrapper is not None
-            self.decode_wrapper.plan(
-                self.paged_kv_indptr[self.num_prefills:],
-                self.paged_kv_indices,
-                self.paged_kv_last_page_len[self.num_prefills:],
-                self.num_qo_heads,
-                self.num_kv_heads,
-                self.head_dim,
-                self.page_size,
-                # Disable flashinfer's pos encoding and use vllm's rope.
-                pos_encoding_mode="NONE",
-                window_left=self.window_left,
-                logits_soft_cap=self.logits_soft_cap,
-                sm_scale=self.sm_scale,
-                # kv-cache data type.
-                kv_data_type=self.data_type,
-                # query data type.
-                q_data_type=self.q_data_type)
-
-    def asdict_zerocopy(self,
-                        skip_fields: Optional[Set[str]] = None
-                        ) -> Dict[str, Any]:
-        if skip_fields is None:
-            skip_fields = set()
-        # We need to skip the prefill/decode_wrapper field since it cannot be
-        # broadcasted with nccl when TP is enabled.
-        skip_fields.add('prefill_wrapper')
-        skip_fields.add('decode_wrapper')
-        return super().asdict_zerocopy(skip_fields)
-
-    @property
-    def prefill_metadata(self) -> Optional["FlashInferMetadata"]:
-        if self.num_prefills == 0:
-            return None
-        return self
-
-    @property
-    def decode_metadata(self) -> Optional["FlashInferMetadata"]:
-        if self.num_decode_tokens == 0:
-            return None
-        return self
-
-
-class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
-
-    def __init__(self, input_builder: "ModelInputForGPUBuilder"):
-
-        self.input_builder = input_builder
-        self.runner = input_builder.runner
-
-        self.sliding_window = input_builder.sliding_window
-        self.block_size = input_builder.block_size
-
-        # Global hyperparameters shared by all attention layers
-        self.global_hyperparameters: Optional[PerLayerParameters] = None
-
-        self.vllm_config = self.runner.vllm_config
-
-    def prepare(self):
-        self.slot_mapping: List[int] = []
-        self.prefill_seq_lens: List[int] = []
-        self.context_lens: List[int] = []
-        self.block_tables: List[List[int]] = []
-        self.curr_seq_lens: List[int] = []
-        self.multimodal_placeholder_maps: Dict[
-            str,
-            MultiModalPlaceholderMap] = defaultdict(MultiModalPlaceholderMap)
-        self.num_prefills = 0
-        self.num_prefill_tokens = 0
-        self.num_decode_tokens = 0
-
-        # Please follow https://docs.flashinfer.ai/tutorials/kv_layout.html#page-layout
-        # for the precise definition of the following fields.
-        # An example:
-        # request 1, page indices [0, 5, 8]
-        # request 2, page indices [1, 6, 7]
-        # request 3, page indices [3, 4]
-        # paged_kv_indices is a concatenation of page indices of all requests:
-        # [0, 5, 8, 1, 6, 7, 3, 4]
-        # paged_kv_indptr is used to index into paged_kv_indices:
-        # [0, 3, 6, 8]
-        self.paged_kv_indices: List[int] = []
-        # 0 at the beginning of paged_kv_indptr indicates the start of the
-        # first request’s page indices in the paged_kv_indices list.
-        self.paged_kv_indptr: List[int] = [0]
-        # paged_kv_last_page_len is the length of the last page of each request
-        self.paged_kv_last_page_len: List[int] = []
-        self.total_blocks = 0
-        self.is_profile_run: bool = False
-
-        if self.global_hyperparameters is None:
-            # Infer global hyperparameters, since currently we only support
-            # models in which all layers share the same values for the
-            # following hyperparameters:
-            # - `window_left`
-            # - `logits_soft_cap`
-            # - `sm_scale`
-            inferred_params = infer_global_hyperparameters(
-                get_per_layer_parameters(self.vllm_config))
-            self.global_hyperparameters = inferred_params
-            self.window_left = inferred_params.window_left
-            self.logits_soft_cap = inferred_params.logits_soft_cap
-            self.sm_scale = inferred_params.sm_scale
-
-    def _add_seq_group(
-            self, inter_data: "ModelInputForGPUBuilder.InterDataForSeqGroup",
-            chunked_prefill_enabled: bool):
-        """Add a sequence group to the metadata. Specifically update/append
-        1. context length.
-        2. block table.
-        3. slot mapping.
-        """
-        is_prompt = inter_data.is_prompt
-        block_tables = inter_data.block_tables
-        computed_block_nums = inter_data.computed_block_nums
-
-        for (seq_id, token_len, seq_len, curr_seq_len, query_len, context_len,
-             curr_sliding_window_block) in zip(
-                 inter_data.seq_ids, [len(t) for t in inter_data.input_tokens],
-                 inter_data.orig_seq_lens, inter_data.seq_lens,
-                 inter_data.query_lens, inter_data.context_lens,
-                 inter_data.curr_sliding_window_blocks):
-            self.context_lens.append(context_len)
-            if is_prompt:
-                mm_maps = inter_data.multi_modal_placeholder_maps
-                if mm_maps:
-                    for modality, placeholders in mm_maps.items():
-                        self.multimodal_placeholder_maps[modality].extend(
-                            placeholders)
-                self.num_prefills += 1
-                self.num_prefill_tokens += token_len
-                self.prefill_seq_lens.append(seq_len)
-            else:
-                assert query_len == 1, (
-                    "seq_len: {}, context_len: {}, query_len: {}".format(
-                        seq_len, context_len, query_len))
-                self.num_decode_tokens += query_len
-                self.curr_seq_lens.append(curr_seq_len)
-
-            # Compute block table.
-            # TODO(sang): Combine chunked prefill and prefix caching by
-            # only allowing multiple of block_size chunk size.
-            # NOTE: This only works for oooooooxxx style attention.
-            block_table = []
-            if inter_data.prefix_cache_hit:
-                block_table = computed_block_nums
-            elif ((chunked_prefill_enabled or not is_prompt)
-                  and block_tables is not None):
-                block_table = block_tables[seq_id][-curr_sliding_window_block:]
-            self.block_tables.append(block_table)
-
-            is_profile_run = is_block_tables_empty(block_tables)
-
-            # Compute slot mapping.
-            start_idx = compute_slot_mapping_start_idx(is_prompt, query_len,
-                                                       context_len,
-                                                       self.sliding_window)
-            compute_slot_mapping(is_profile_run, self.slot_mapping, seq_id,
-                                 seq_len, context_len, start_idx,
-                                 self.block_size, inter_data.block_tables)
-
-            # It is not necessary to add paged_kv_indices, paged_kv_indptr,
-            # and paged_kv_last_page_len for profile run because we will
-            # create dummy inputs.
-            if is_profile_run:
-                self.is_profile_run = is_profile_run
-                return
-
-            block_table = block_tables[seq_id]
-            self._update_paged_kv_tensors(block_table, seq_len)
-
-    def _update_paged_kv_tensors(self, block_table: List[int], seq_len: int):
-        # Get the number of valid blocks based on sequence length.
-        # If seq_len = 16, block_size = 16,
-        # block_table_bound is 1 with 1 valid block.
-        # If seq_len = 15, block_size = 16,
-        # block_table_bound is 0 + 1 with 1 valid block.
-        self.total_blocks += len(block_table)
-        block_table_bound = seq_len // self.block_size + 1 \
-                            if seq_len % self.block_size != 0 \
-                            else seq_len // self.block_size
-        self.paged_kv_indices.extend(block_table[:block_table_bound])
-        self.paged_kv_indptr.append(self.paged_kv_indptr[-1] +
-                                    block_table_bound)
-
-        last_page_len = seq_len % self.block_size
-        if last_page_len == 0:
-            last_page_len = self.block_size
-        self.paged_kv_last_page_len.append(last_page_len)
-
-    def build(self, seq_lens: List[int], query_lens: List[int],
-              cuda_graph_pad_size: int, batch_size: int):
-        """Build attention metadata with on-device tensors.
-
-        Args:
-            seq_lens: The maybe padded sequence lengths of the input sequences.
-            query_lens: The query lengths of the input sequences.
-            cuda_graph_pad_size: The padding size for cuda graph.
-                                 -1 if cuda graph is not used.
-            batch_size: The maybe padded batch size.
-        """
-        for inter_data in self.input_builder.inter_data_list:
-            self._add_seq_group(inter_data,
-                                self.input_builder.chunked_prefill_enabled)
-
-        device = self.runner.device
-        use_captured_graph = cuda_graph_pad_size != -1
-
-        max_prefill_seq_len = max(self.prefill_seq_lens, default=0)
-        max_decode_seq_len = max(self.curr_seq_lens, default=0)
-        num_decode_tokens = self.num_decode_tokens
-        decode_query_len = max(query_lens[self.num_prefills:], default=1)
-
-        if use_captured_graph:
-            self.slot_mapping.extend([PAD_SLOT_ID] * cuda_graph_pad_size)
-            self.block_tables.extend([] * cuda_graph_pad_size)
-            num_decode_tokens = batch_size - self.num_prefill_tokens
-
-            # The shape of graph_block_tables is
-            # [max batch size, max context len // block size].
-            input_block_tables = self.runner.graph_block_tables[:batch_size]
-            max_blocks = input_block_tables.shape[1]
-            for i, block_table in enumerate(self.block_tables):
-                if block_table:
-                    num_blocks = len(block_table)
-                    if num_blocks <= max_blocks:
-                        input_block_tables[i, :num_blocks] = block_table
-                    else:
-                        # It may be possible to have more blocks allocated due
-                        # to lookahead slots of multi-step, however, they are
-                        # not used anyway, so can be safely ignored.
-                        input_block_tables[
-                            i, :max_blocks] = block_table[:max_blocks]
-
-            block_tables = torch.from_numpy(input_block_tables).to(
-                device, non_blocking=True)
-
-            last_paged_kv_indptr = self.paged_kv_indptr[-1]
-            self.paged_kv_indptr.extend([last_paged_kv_indptr] *
-                                        cuda_graph_pad_size)
-            self.paged_kv_last_page_len.extend([0] * cuda_graph_pad_size)
-        else:
-            block_tables = make_tensor_with_pad(
-                self.block_tables,
-                pad=0,
-                dtype=torch.int,
-                device=device,
-            )
-
-        assert device is not None
-        seq_lens_tensor = async_tensor_h2d(seq_lens, torch.int, device,
-                                           self.runner.pin_memory)
-        query_lens_tensor = async_tensor_h2d(query_lens, torch.long, device,
-                                             self.runner.pin_memory)
-        slot_mapping_tensor = async_tensor_h2d(self.slot_mapping, torch.long,
-                                               device, self.runner.pin_memory)
-        query_start_loc = torch.zeros(query_lens_tensor.shape[0] + 1,
-                                      dtype=torch.int32,
-                                      device=device)
-        seq_start_loc = torch.zeros(seq_lens_tensor.shape[0] + 1,
-                                    dtype=torch.int32,
-                                    device=device)
-        placeholder_index_maps = {
-            modality: placeholder_map.index_map()
-            for modality, placeholder_map in
-            self.multimodal_placeholder_maps.items()
-        }
-        torch.cumsum(seq_lens_tensor,
-                     dim=0,
-                     dtype=seq_start_loc.dtype,
-                     out=seq_start_loc[1:])
-        torch.cumsum(query_lens_tensor,
-                     dim=0,
-                     dtype=query_start_loc.dtype,
-                     out=query_start_loc[1:])
-
-        if len(self.paged_kv_indptr) > 0:
-            # extend to the maximum number of blocks as returned by the
-            # scheduler
-            self.paged_kv_indices.extend(
-                [0] * (self.total_blocks - len(self.paged_kv_indices)))
-            paged_kv_indices_tensor = torch.tensor(self.paged_kv_indices,
-                                                   device="cpu",
-                                                   dtype=torch.int)
-            paged_kv_indptr_tensor = torch.tensor(self.paged_kv_indptr,
-                                                  device="cpu",
-                                                  dtype=torch.int)
-            paged_kv_last_page_len_tensor = torch.tensor(
-                self.paged_kv_last_page_len, device="cpu", dtype=torch.int)
-            block_table_bound_tensor = torch.zeros(len(self.paged_kv_indptr) -
-                                                   1,
-                                                   device="cpu",
-                                                   dtype=torch.int)
-        else:
-            paged_kv_indices_tensor = None
-            paged_kv_indptr_tensor = None
-            paged_kv_last_page_len_tensor = None
-            block_table_bound_tensor = None
-
-        if self.runner.kv_cache_dtype.startswith("fp8"):
-            kv_cache_dtype = FlashInferBackend.get_fp8_dtype_for_flashinfer(
-                self.runner.kv_cache_dtype)
-        else:
-            kv_cache_dtype = get_kv_cache_torch_dtype(
-                self.runner.kv_cache_dtype, self.runner.model_config.dtype)
-
-        return FlashInferMetadata(
-            decode_query_len=decode_query_len,
-            num_prefills=self.num_prefills,
-            slot_mapping=slot_mapping_tensor,
-            multi_modal_placeholder_index_maps=placeholder_index_maps,
-            enable_kv_scales_calculation=False,
-            num_prefill_tokens=self.num_prefill_tokens,
-            num_decode_tokens=num_decode_tokens,
-            max_prefill_seq_len=max_prefill_seq_len,
-            max_decode_seq_len=max_decode_seq_len,
-            block_tables=block_tables,
-            paged_kv_indptr=paged_kv_indptr_tensor,
-            paged_kv_indices=paged_kv_indices_tensor,
-            paged_kv_last_page_len=paged_kv_last_page_len_tensor,
-            block_table_bound=block_table_bound_tensor,
-            seq_lens_tensor=seq_lens_tensor,
-            num_qo_heads=self.runner.model_config.get_num_attention_heads(
-                self.runner.parallel_config),
-            num_kv_heads=self.runner.model_config.get_num_kv_heads(
-                self.runner.parallel_config),
-            head_dim=self.runner.model_config.get_head_size(),
-            page_size=self.block_size,
-            seq_start_loc=seq_start_loc,
-            query_start_loc=query_start_loc,
-            device=device,
-            data_type=kv_cache_dtype,
-            q_data_type=self.runner.model_config.dtype,
-            use_cuda_graph=use_captured_graph,
-            is_profile_run=self.is_profile_run,
-            window_left=self.window_left,
-            logits_soft_cap=self.logits_soft_cap,
-            sm_scale=self.sm_scale,
-        )
-
-
-class FlashInferImpl(AttentionImpl):
-
-    def __init__(
-        self,
-        num_heads: int,
-        head_size: int,
-        scale: float,
-        num_kv_heads: int,
-        alibi_slopes: Optional[List[float]],
-        sliding_window: Optional[int],
-        kv_cache_dtype: str,
-        logits_soft_cap: Optional[float] = None,
-        attn_type: str = AttentionType.DECODER,
-        kv_sharing_target_layer_name: Optional[str] = None,
-        use_irope: bool = False,
-    ) -> None:
-        if kv_sharing_target_layer_name is not None:
-            raise NotImplementedError("KV sharing is not supported in V0 "
-                                      "FLASHINFER backend.")
-        if use_irope:
-            logger.warning_once(
-                "Using irope in FlashInfer is not supported yet, it will fall"
-                " back to global attention for long context.")
-        self.num_heads = num_heads
-        self.head_size = head_size
-        self.scale = float(scale)
-        self.num_kv_heads = num_kv_heads
-        if alibi_slopes is not None:
-            alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32)
-        self.alibi_slopes = alibi_slopes
-        self.sliding_window = ((sliding_window - 1,
-                                0) if sliding_window is not None else (-1, -1))
-        self.kv_cache_dtype = kv_cache_dtype
-        self.logits_soft_cap = logits_soft_cap
-
-        self.num_queries_per_kv = self.num_heads // self.num_kv_heads
-
-        if attn_type != AttentionType.DECODER:
-            raise NotImplementedError("Encoder self-attention and "
-                                      "encoder/decoder cross-attention "
-                                      "are not implemented for "
-                                      "FlashInferImpl")
-
-    def forward(
-        self,
-        layer: AttentionLayer,
-        query: torch.Tensor,
-        key: torch.Tensor,
-        value: torch.Tensor,
-        kv_cache: torch.Tensor,
-        attn_metadata: FlashInferMetadata,
-        output: Optional[torch.Tensor] = None,
-        output_scale: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-
-        if output_scale is not None:
-            raise NotImplementedError(
-                "fused output quantization is not yet supported"
-                " for FlashInferImpl")
-
-        # TODO: directly write to output tensor
-        num_heads: int = self.num_heads
-        head_size: int = self.head_size
-        num_kv_heads: int = self.num_kv_heads
-        kv_cache_dtype: str = self.kv_cache_dtype
-        softmax_scale: float = self.scale
-        window_size = self.sliding_window
-        alibi_slopes = self.alibi_slopes
-        logits_soft_cap = self.logits_soft_cap
-
-        num_tokens, hidden_size = query.shape
-        query = query.view(-1, num_heads, head_size)
-        key = key.view(-1, num_kv_heads, head_size)
-        value = value.view(-1, num_kv_heads, head_size)
-
-        if kv_cache.numel() > 0:
-            # Use the same reshape and cache kernel as flash attention.
-            ops.reshape_and_cache_flash(
-                key,
-                value,
-                kv_cache[:, 0],
-                kv_cache[:, 1],
-                attn_metadata.slot_mapping.flatten(),
-                kv_cache_dtype,
-                layer._k_scale,
-                layer._v_scale,
-            )
-            # The FlashInfer api requires data to be in fp8_e4m3 or fp8_e5m2
-            # to process the cache when the kv_cache_dtype is fp8
-            if kv_cache_dtype.startswith("fp8"):
-                torch_dtype = FlashInferBackend.get_fp8_dtype_for_flashinfer(
-                    kv_cache_dtype)
-                kv_cache = kv_cache.view(torch_dtype)
-
-        num_prefill_tokens = attn_metadata.num_prefill_tokens
-        num_decode_tokens = attn_metadata.num_decode_tokens
-        assert key.shape[0] == num_prefill_tokens + num_decode_tokens, \
-                    f"key : {key.shape} : #prefill tokens {num_prefill_tokens} : #decode tokens {num_decode_tokens}" # noqa
-        assert value.shape[0] == num_prefill_tokens + num_decode_tokens, \
-                    f"value : {value.shape} : #prefill toks {num_prefill_tokens} : #decode toks {num_decode_tokens}" # noqa
-        query = query.contiguous(
-        )  # Flashinfer requires query to be contiguous
-        # Query for decode. KV is not needed because it is already cached.
-        # QKV for prefill.
-        decode_query = query[num_prefill_tokens:]
-        query = query[:num_prefill_tokens]
-
-        key = key[:num_prefill_tokens]
-        value = value[:num_prefill_tokens]
-
-        assert query.shape[0] == num_prefill_tokens
-        assert decode_query.shape[0] == num_decode_tokens
-
-        window_left = window_size[0] if window_size is not None else -1
-
-        prefill_output: Optional[torch.Tensor] = None
-        if num_decode_tokens > 0:
-            decode_output = torch.empty(decode_query.shape,
-                                        dtype=decode_query.dtype,
-                                        device=decode_query.device)
-        else:
-            decode_output = None
-        stride_order = FlashInferBackend.get_kv_cache_stride_order()
-        if prefill_meta := attn_metadata.prefill_metadata:
-            # We will use flash attention for prefill
-            # when kv_cache is not provided.
-            # This happens when vllm runs the profiling to
-            # determine the number of blocks.
-            if kv_cache.numel() == 0:
-                prefill_output = flash_attn_varlen_func(
-                    q=query,
-                    k=key,
-                    v=value,
-                    cu_seqlens_q=prefill_meta.seq_start_loc,
-                    cu_seqlens_k=prefill_meta.seq_start_loc,
-                    max_seqlen_q=prefill_meta.max_prefill_seq_len,
-                    max_seqlen_k=prefill_meta.max_prefill_seq_len,
-                    softmax_scale=softmax_scale,
-                    causal=True,
-                    window_size=window_size,
-                    alibi_slopes=alibi_slopes,
-                )
-            else:
-                assert prefill_meta is not None
-                assert prefill_meta.prefill_wrapper is not None
-
-                assert prefill_meta.prefill_wrapper._causal
-                assert prefill_meta.prefill_wrapper._window_left == window_left
-                assert prefill_meta.prefill_wrapper._logits_soft_cap == (
-                    logits_soft_cap or 0.0)
-                assert prefill_meta.prefill_wrapper._sm_scale == softmax_scale
-
-                prefill_output = prefill_meta.prefill_wrapper.run(
-                    query,
-                    kv_cache.permute(*stride_order),
-                    k_scale=layer._k_scale_float,
-                    v_scale=layer._v_scale_float,
-                )
-        if decode_meta := attn_metadata.decode_metadata:
-            assert decode_meta is not None
-            assert decode_meta.decode_wrapper is not None
-
-            assert decode_meta.decode_wrapper._window_left == window_left
-            assert decode_meta.decode_wrapper._logits_soft_cap == (
-                logits_soft_cap or 0.0)
-            assert decode_meta.decode_wrapper._sm_scale == softmax_scale
-            # TODO: @pavanimajety Remove this once the switch happens
-            # inside flashinfer.
-            if not use_trtllm_attention(
-                    num_decode_tokens, attn_metadata.max_decode_seq_len,
-                    kv_cache_dtype, attn_metadata.num_qo_heads,
-                    attn_metadata.num_kv_heads, attn_metadata.head_dim):
-                decode_meta.decode_wrapper.run(
-                    decode_query,
-                    kv_cache.permute(*stride_order),
-                    k_scale=layer._k_scale_float,
-                    v_scale=layer._v_scale_float,
-                    out=decode_output,
-                )
-            else:
-                workspace_buffer = (
-                    decode_meta.decode_wrapper._float_workspace_buffer)
-                assert FlashInferState.get_kv_cache_layout() == "HND"
-                trtllm_batch_decode_with_kv_cache(
-                    query=decode_query,
-                    kv_cache=kv_cache.permute(*stride_order),
-                    workspace_buffer=workspace_buffer,
-                    block_tables=attn_metadata.block_tables,
-                    seq_lens=decode_meta.seq_lens_tensor,
-                    max_seq_len=attn_metadata.max_decode_seq_len,
-                    bmm1_scale=layer._k_scale_float * softmax_scale,
-                    bmm2_scale=layer._v_scale_float,
-                    out=decode_output,
-                )
-
-        if prefill_output is None and decode_output is not None:
-            # Decode only batch.
-            output, num_tokens = decode_output, num_decode_tokens
-        elif decode_output is None and prefill_output is not None:
-            # Prefill only batch.
-            output, num_tokens = prefill_output, num_prefill_tokens
-        else:
-            # Chunked prefill batch does not work with speculative decoding in
-            # FlashInfer backend, so the query length for decode should be 1.
-            assert prefill_output is not None
-            assert decode_output is not None
-            assert decode_meta is not None
-            assert decode_meta.decode_query_len == 1
-            decode_output = decode_output.squeeze(1)
-            output = torch.cat([prefill_output, decode_output], dim=0)
-        return output.view(num_tokens, hidden_size)

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(