[Core] Deprecate xformers (#29262)

Signed-off-by: Roger Wang <hey@rogerw.io>

docker/Dockerfile.nightly_torch

@@ -76,34 +76,6 @@ RUN --mount=type=cache,target=/root/.cache/uv \
 RUN --mount=type=cache,target=/root/.cache/uv \
 uv pip install --system -r requirements/common.txt
 
-# must put before installing xformers, so it can install the correct version of xfomrers.
-ARG torch_cuda_arch_list='8.0;8.6;8.9;9.0'
-ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
-
-# Build xformers with cuda and torch nightly
-# following official xformers guidance: https://github.com/facebookresearch/xformers#build
-# todo(elainewy): cache xformers build result for faster build
-ARG max_jobs=16
-ENV MAX_JOBS=${max_jobs}
-ARG XFORMERS_COMMIT=f2de641ef670510cadab099ce6954031f52f191c
-
-ENV CCACHE_DIR=/root/.cache/ccache
-RUN --mount=type=cache,target=/root/.cache/ccache \
-     --mount=type=cache,target=/root/.cache/uv \
-    echo 'git clone xformers...' \
-    && git clone https://github.com/facebookresearch/xformers.git --recursive \
-    && cd xformers \
-    && git checkout ${XFORMERS_COMMIT} \
-    && git submodule update --init --recursive \
-    && echo 'finish git clone xformers...' \
-    && rm -rf build \
-    && python3 setup.py bdist_wheel --dist-dir=../xformers-dist --verbose \
-    && cd .. \
-    && rm -rf xformers
-
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system xformers-dist/*.whl --verbose
-
 # build can take a long time, and the torch nightly version fetched from url can be different in next docker stage.
 # track the nightly torch version used in the build, when we set up runtime environment we can make sure the version is the same
 RUN uv pip freeze | grep -i '^torch\|^torchvision\|^torchaudio' > torch_build_versions.txt
@@ -233,11 +205,6 @@ RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/vllm
     --mount=type=cache,target=/root/.cache/uv \
     uv pip install --system vllm-dist/*.whl --verbose
 
-# install xformers again for the new environment
-RUN --mount=type=bind,from=base,src=/workspace/xformers-dist,target=/vllm-workspace/xformers-dist \
-    --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system /vllm-workspace/xformers-dist/*.whl --verbose
-
 ARG torch_cuda_arch_list='8.0;8.6;8.9;9.0'
 
 # install package for build flashinfer
@@ -307,7 +274,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install --system -r requirements/nightly_torch_test.txt
 
 # Logging to confirm the torch versions
-RUN pip freeze | grep -E 'torch|xformers|vllm|flashinfer'
+RUN pip freeze | grep -E 'torch|vllm|flashinfer'
 
 # Logging to confirm all the packages are installed
 RUN pip freeze

docs/contributing/ci/update_pytorch_version.md

@@ -98,21 +98,6 @@ to warm it up so that future builds are faster.
     <img width="60%" alt="Buildkite new build popup" src="https://github.com/user-attachments/assets/a8ff0fcd-76e0-4e91-b72f-014e3fdb6b94">
 </p>
 
-## Update dependencies
-
-Several vLLM dependencies like xFormers depend on PyTorch and need
-to be updated accordingly. Rather than waiting for all of them to publish new
-releases (which would take too much time), they can be built from
-source to unblock the update process.
-
-### xFormers
-
-```bash
-export TORCH_CUDA_ARCH_LIST='7.5 8.0+PTX 9.0a'
-MAX_JOBS=16 uv pip install --system \
-    --no-build-isolation "git+https://github.com/facebookresearch/xformers@v0.0.32.post2"
-```
-
 ## Update all the different vLLM platforms
 
 Rather than attempting to update all vLLM platforms in a single pull request, it's more manageable

docs/getting_started/quickstart.md

@@ -283,7 +283,7 @@ Currently, vLLM supports multiple backends for efficient Attention computation a
 
 If desired, you can also manually set the backend of your choice by configuring the environment variable `VLLM_ATTENTION_BACKEND` to one of the following options:
 
-- On NVIDIA CUDA: `FLASH_ATTN`, `FLASHINFER` or `XFORMERS`.
+- On NVIDIA CUDA: `FLASH_ATTN` or `FLASHINFER`.
 - On AMD ROCm: `TRITON_ATTN`, `ROCM_ATTN`, `ROCM_AITER_FA` or `ROCM_AITER_UNIFIED_ATTN`.
 
 For AMD ROCm, you can further control the specific Attention implementation using the following variables:

examples/online_serving/openai_embedding_long_text/service.sh

@@ -22,7 +22,6 @@ API_KEY=${API_KEY:-"your-api-key"}
 POOLING_TYPE=${POOLING_TYPE:-"auto"}  # auto, MEAN, CLS, LAST
 export VLLM_ENABLE_CHUNKED_PROCESSING=true
 export CUDA_VISIBLE_DEVICES=2,3,4,5
-# export VLLM_ATTENTION_BACKEND=XFORMERS
 
 echo "🚀 Starting vLLM Embedding Server with Enhanced Chunked Processing"
 echo "=================================================================="

requirements/cuda.txt

@@ -9,6 +9,5 @@ torch==2.9.0
 torchaudio==2.9.0
 # These must be updated alongside torch
 torchvision==0.24.0 # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version
-xformers==0.0.33.post1; platform_system == 'Linux' and platform_machine == 'x86_64'  # Requires PyTorch >= 2.9
 # FlashInfer should be updated together with the Dockerfile
 flashinfer-python==0.5.2

tests/basic_correctness/test_basic_correctness.py

@@ -74,9 +74,6 @@ def test_models(
     model_executor: str,
     enable_prompt_embeds: bool,
 ) -> None:
-    if backend == "XFORMERS" and model == "google/gemma-2-2b-it":
-        pytest.skip(f"{backend} does not support gemma2 with full context length.")
-
     with monkeypatch.context() as m:
         m.setenv("VLLM_ATTENTION_BACKEND", backend)
 

tests/kernels/attention/test_attention.py

@@ -13,12 +13,6 @@ from vllm.attention.layer import Attention, MultiHeadAttention
 from vllm.platforms import current_platform
 from vllm.utils.mem_utils import get_max_shared_memory_bytes
 
-if not current_platform.is_rocm():
-    from xformers import ops as xops
-    from xformers.ops.fmha.attn_bias import BlockDiagonalCausalMask
-
-    from tests.kernels.utils import make_alibi_bias
-
 FLOAT32_BYTES = torch.finfo(torch.float).bits // 8
 # This will change depending on the compute capability.
 # - 512 as a buffer
@@ -448,129 +442,6 @@ def ref_multi_query_kv_attention(
     return torch.cat(ref_outputs, dim=0)
 
 
-@pytest.mark.parametrize("num_seqs", NUM_PREFILL_SEQS)
-@pytest.mark.parametrize("num_heads", NUM_HEADS)
-@pytest.mark.parametrize("head_size", HEAD_SIZES)
-@pytest.mark.parametrize("dtype", DTYPES)
-@pytest.mark.parametrize("seed", SEEDS)
-@pytest.mark.parametrize("device", CUDA_DEVICES)
-@pytest.mark.skipif(
-    current_platform.is_rocm(), reason="Xformers backend is not supported on ROCm."
-)
-@torch.inference_mode()
-def test_multi_query_kv_attention(
-    num_seqs: int,
-    num_heads: tuple[int, int],
-    head_size: int,
-    dtype: torch.dtype,
-    seed: int,
-    device: str,
-    use_alibi: bool = False,
-) -> None:
-    current_platform.seed_everything(seed)
-    torch.set_default_device(device)
-    # MAX_SEQ_LEN sometimes causes OOM in the reference implementation.
-    # As the xformers library is already tested with its own tests, we can use
-    # a smaller MAX_SEQ_LEN here.
-    max_len = min(MAX_SEQ_LEN, 4096)
-    seq_lens = random.sample(range(1, max_len), num_seqs)
-    num_tokens = sum(seq_lens)
-
-    scale = float(1.0 / (head_size**0.5))
-    num_query_heads, num_kv_heads = num_heads
-    qkv = torch.empty(
-        num_tokens, num_query_heads + 2 * num_kv_heads, head_size, dtype=dtype
-    )
-    qkv.uniform_(-scale, scale)
-    query, key, value = qkv.split([num_query_heads, num_kv_heads, num_kv_heads], dim=1)
-
-    num_queries_per_kv = num_query_heads // num_kv_heads
-    if num_queries_per_kv > 1:
-        # Handle MQA and GQA
-        key = torch.repeat_interleave(key, num_queries_per_kv, dim=1)
-        value = torch.repeat_interleave(value, num_queries_per_kv, dim=1)
-    alibi_bias = None
-    if use_alibi:
-        alibi_slopes = torch.randn(num_query_heads, dtype=torch.float)
-        attn_bias = make_alibi_bias(alibi_slopes, num_kv_heads, dtype, seq_lens)
-        output = torch.empty_like(query)
-        start = 0
-        # Dynamic sequence length not supported with custom attn_bias.
-        for i, seq_len in enumerate(seq_lens):
-            end = start + seq_len
-            out = xops.memory_efficient_attention_forward(
-                query[None, start:end],
-                key[None, start:end],
-                value[None, start:end],
-                attn_bias=attn_bias[i],
-                p=0.0,
-                scale=scale,
-            )
-            output[start:end].copy_(out.view_as(query[start:end]))
-            start += seq_len
-        # xformers.AttentionBias to Tensor for use in reference impl.
-        alibi_bias = [
-            b.materialize((1, num_query_heads, i, i), device=device).squeeze()
-            for b, i in zip(attn_bias, seq_lens)
-        ]
-    else:
-        attn_bias = BlockDiagonalCausalMask.from_seqlens(seq_lens)
-        output = xops.memory_efficient_attention_forward(
-            query.unsqueeze(0),
-            key.unsqueeze(0),
-            value.unsqueeze(0),
-            attn_bias=attn_bias,
-            p=0.0,
-            scale=scale,
-        )
-        output = output.squeeze(0)
-
-    cu_seq_lens = [0]
-    for seq_len in seq_lens:
-        cu_seq_lens.append(cu_seq_lens[-1] + seq_len)
-    ref_output = ref_multi_query_kv_attention(
-        cu_seq_lens,
-        query,
-        key,
-        value,
-        scale,
-        alibi_bias,
-        dtype,
-    )
-    atol = get_default_atol(output) if current_platform.is_rocm() else 1e-3
-    rtol = get_default_rtol(output) if current_platform.is_rocm() else 1e-5
-    torch.testing.assert_close(output, ref_output, atol=atol, rtol=rtol)
-
-
-@pytest.mark.parametrize("num_seqs", NUM_PREFILL_SEQS)
-@pytest.mark.parametrize("num_heads", NUM_HEADS)
-@pytest.mark.parametrize("head_size", [64])
-@pytest.mark.parametrize("dtype", DTYPES)
-@pytest.mark.parametrize("seed", SEEDS)
-@pytest.mark.parametrize("device", CUDA_DEVICES)
-@pytest.mark.skipif(
-    current_platform.is_rocm(), reason="Xformers backend is not supported on ROCm."
-)
-@torch.inference_mode()
-def test_multi_query_kv_attention_with_alibi(
-    num_seqs: int,
-    num_heads: tuple[int, int],
-    head_size: int,
-    dtype: torch.dtype,
-    seed: int,
-    device: str,
-) -> None:
-    return test_multi_query_kv_attention(
-        num_seqs,
-        num_heads,
-        head_size,
-        dtype,
-        seed,
-        device,
-        use_alibi=True,
-    )
-
-
 @pytest.mark.parametrize("attention_cls", [Attention, MultiHeadAttention])
 def test_num_heads_not_divisble_by_num_kv_heads(attention_cls: type) -> None:
     head_size = 64

tests/kernels/attention/test_attention_selector.py

@@ -34,7 +34,7 @@ DEVICE_MLA_BACKENDS = {
 }
 
 DEVICE_REGULAR_ATTN_BACKENDS = {
-    "cuda": ["XFORMERS", "FLASHINFER", "FLASH_ATTN"],
+    "cuda": ["FLASHINFER", "FLASH_ATTN"],
     "hip": ["ROCM_ATTN"],
     "cpu": ["CPU_ATTN"],
 }
@@ -207,12 +207,6 @@ def test_env(
                     )
                     expected = "FLASHINFER"
                     assert backend.get_name() == expected
-                elif name == "XFORMERS":
-                    backend = get_attn_backend(
-                        32, torch.float16, None, block_size, use_mla=use_mla
-                    )
-                    expected = "XFORMERS"
-                    assert backend.get_name() == expected
                 elif name == "FLASH_ATTN":
                     backend = get_attn_backend(
                         32, torch.float16, None, block_size, use_mla=use_mla

tests/kernels/attention/test_mha_attn.py

@@ -24,10 +24,6 @@ from vllm.platforms.rocm import RocmPlatform
 def clear_cache():
     """Clear lru cache to ensure each test case runs without caching."""
     _cached_get_attn_backend.cache_clear()
-    # Clear xformers availability cache
-    import vllm.attention.layer as layer_module
-
-    layer_module.USE_XFORMERS_OPS = None
 
 
 @pytest.mark.parametrize("device", ["cpu", "hip", "cuda"])

tests/kernels/utils.py

@@ -509,43 +509,6 @@ def pack_qkv(qkv: QKVInputs, device: torch.device | str) -> PackedQKVInputs:
     )
 
 
-def make_alibi_bias(
-    alibi_slopes: torch.Tensor,
-    num_kv_heads: int,
-    dtype: torch.dtype,
-    seq_lens: list[int],
-) -> list[Any]:
-    """Create ALiBi biases compatible with xFormers attention tests."""
-    from xformers.ops.fmha.attn_bias import LowerTriangularMaskWithTensorBias
-
-    if alibi_slopes is None:
-        return [None for _ in seq_lens]
-
-    attn_biases: list[Any] = []
-    num_heads = alibi_slopes.shape[0]
-    assert num_heads >= num_kv_heads, (
-        "ALiBi slopes expect at least as many heads as KV heads"
-    )
-
-    for seq_len in seq_lens:
-        bias = torch.arange(seq_len, dtype=dtype, device=alibi_slopes.device)
-        bias = bias[None, :] - bias[:, None]
-
-        padded_len = (seq_len + 7) // 8 * 8
-        bias_tensor = torch.empty(
-            1,
-            num_heads,
-            seq_len,
-            padded_len,
-            device=alibi_slopes.device,
-            dtype=dtype,
-        )[:, :, :, :seq_len].copy_(bias)
-        bias_tensor.mul_(alibi_slopes[:, None, None])
-        attn_biases.append(LowerTriangularMaskWithTensorBias(bias_tensor))
-
-    return attn_biases
-
-
 def _make_metadata_tensors(
     seq_lens: list[int] | None,
     context_lens: list[int] | None,
@@ -649,23 +612,12 @@ def make_kv_cache(
 
     Returns:
 
-    * kv_cache: 2 x num_blocks x (block_size * num_heads * head_size)
-    *     for backend 'XFORMERS'
     * kv_cache: 2 x num_blocks x block_size x num_heads x head_size
     *     for backend 'FLASH_ATTN'
     """
-    if backend == "XFORMERS":
+    if backend != "FLASH_ATTN":
-        kv_cache = torch.rand((2, num_blocks, block_size * num_heads * head_size)).to(
+        raise ValueError(f"Unknown backend value: '{backend}'. Expected 'FLASH_ATTN'.")
-            device
+    kv_cache = torch.rand((2, num_blocks, block_size, num_heads, head_size)).to(device)
-        )
-    elif backend == "FLASH_ATTN":
-        kv_cache = torch.rand((2, num_blocks, block_size, num_heads, head_size)).to(
-            device
-        )
-    else:
-        raise ValueError(
-            f"Unknown backend value: '{backend}'. Expected 'XFORMERS' or 'FLASH_ATTN'."
-        )
     if default_val is not None:
         kv_cache[:, :, :] = default_val
     return kv_cache
@@ -843,22 +795,14 @@ def assert_actual_matches_ideal(
     * output_under_test: actually observed output value
     """
     ideal_output = test_params.packed_qkvo.ideal_output
-    if backend == "XFORMERS":
+    if backend != "FLASH_ATTN":
-        torch.testing.assert_close(
+        raise ValueError(f"Unknown backend value: '{backend}'. Expected 'FLASH_ATTN'.")
-            ideal_output, output_under_test.view_as(ideal_output)
+    # For FlashAttention override the accuracy thresholds to non default
-        )
+    # values since we notice a higher difference between the ideal and
-
+    # actual output.
-    elif backend == "FLASH_ATTN":
+    torch.testing.assert_close(
-        # For FlashAttention override the accuracy thresholds to non default
+        ideal_output, output_under_test.view_as(ideal_output), atol=0.01, rtol=0.016
-        # values since we notice a higher difference between the ideal and
+    )
-        # actual output.
-        torch.testing.assert_close(
-            ideal_output, output_under_test.view_as(ideal_output), atol=0.01, rtol=0.016
-        )
-    else:
-        raise ValueError(
-            f"Unknown backend value: '{backend}'. Expected 'XFORMERS' or 'FLASH_ATTN'."
-        )
 
 
 # Copied/modified from torch._refs.__init__.py

tests/lora/test_minicpmv_tp.py

@@ -57,10 +57,6 @@ def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> list[str]:
     return generated_texts
 
 
-@pytest.mark.xfail(
-    current_platform.is_rocm(),
-    reason="MiniCPM-V dependency xformers incompatible with ROCm",
-)
 def test_minicpmv_lora(minicpmv_lora_files):
     llm = vllm.LLM(
         MODEL_PATH,
@@ -84,10 +80,6 @@ def test_minicpmv_lora(minicpmv_lora_files):
 @pytest.mark.skipif(
     current_platform.is_cuda_alike(), reason="Skipping to avoid redundant model tests"
 )
-@pytest.mark.xfail(
-    current_platform.is_rocm(),
-    reason="MiniCPM-V dependency xformers incompatible with ROCm",
-)
 @multi_gpu_test(num_gpus=4)
 def test_minicpmv_tp4_wo_fully_sharded_loras(minicpmv_lora_files):
     llm = vllm.LLM(
@@ -108,10 +100,6 @@ def test_minicpmv_tp4_wo_fully_sharded_loras(minicpmv_lora_files):
 @pytest.mark.skipif(
     current_platform.is_cuda_alike(), reason="Skipping to avoid redundant model tests"
 )
-@pytest.mark.xfail(
-    current_platform.is_rocm(),
-    reason="MiniCPM-V dependency xformers incompatible with ROCm",
-)
 @multi_gpu_test(num_gpus=4)
 def test_minicpmv_tp4_fully_sharded_loras(minicpmv_lora_files):
     llm = vllm.LLM(

tests/lora/test_qwen2vl.py

@@ -2,12 +2,9 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from dataclasses import dataclass
 
-import pytest
-
 import vllm
 from vllm.assets.image import ImageAsset
 from vllm.lora.request import LoRARequest
-from vllm.platforms import current_platform
 from vllm.sampling_params import BeamSearchParams
 
 
@@ -142,10 +139,6 @@ QWEN2VL_MODEL_PATH = "Qwen/Qwen2-VL-2B-Instruct"
 QWEN25VL_MODEL_PATH = "Qwen/Qwen2.5-VL-3B-Instruct"
 
 
-@pytest.mark.xfail(
-    current_platform.is_rocm(),
-    reason="Qwen2-VL dependency xformers incompatible with ROCm",
-)
 def test_qwen2vl_lora(qwen2vl_lora_files):
     """Test Qwen 2.0 VL model with LoRA"""
     config = TestConfig(model_path=QWEN2VL_MODEL_PATH, lora_path=qwen2vl_lora_files)
@@ -156,10 +149,6 @@ def test_qwen2vl_lora(qwen2vl_lora_files):
         tester.run_test(TEST_IMAGES, expected_outputs=EXPECTED_OUTPUTS, lora_id=lora_id)
 
 
-@pytest.mark.xfail(
-    current_platform.is_rocm(),
-    reason="Qwen2-VL dependency xformers incompatible with ROCm",
-)
 def test_qwen2vl_lora_beam_search(qwen2vl_lora_files):
     """Test Qwen 2.0 VL model with LoRA through beam search."""
     config = TestConfig(model_path=QWEN2VL_MODEL_PATH, lora_path=qwen2vl_lora_files)
@@ -178,10 +167,6 @@ def test_qwen2vl_lora_beam_search(qwen2vl_lora_files):
         )
 
 
-@pytest.mark.xfail(
-    current_platform.is_rocm(),
-    reason="Qwen2.5-VL dependency xformers incompatible with ROCm",
-)
 def test_qwen25vl_lora(qwen25vl_lora_files):
     """Test Qwen 2.5 VL model with LoRA"""
     config = TestConfig(model_path=QWEN25VL_MODEL_PATH, lora_path=qwen25vl_lora_files)

vllm/attention/backends/registry.py

@@ -43,7 +43,6 @@ class AttentionBackendEnum(Enum, metaclass=_AttentionBackendEnumMeta):
 
     FLASH_ATTN = "vllm.v1.attention.backends.flash_attn.FlashAttentionBackend"
     TRITON_ATTN = "vllm.v1.attention.backends.triton_attn.TritonAttentionBackend"
-    XFORMERS = "vllm.v1.attention.backends.xformers.XFormersAttentionBackend"
     ROCM_ATTN = "vllm.v1.attention.backends.rocm_attn.RocmAttentionBackend"
     ROCM_AITER_MLA = "vllm.v1.attention.backends.mla.rocm_aiter_mla.AiterMLABackend"
     ROCM_AITER_TRITON_MLA = (

vllm/attention/layer.py

@@ -51,31 +51,6 @@ else:
 
 FP8_DTYPE = current_platform.fp8_dtype()
 logger = init_logger(__name__)
-USE_XFORMERS_OPS = None
-
-
-def check_xformers_availability():
-    global USE_XFORMERS_OPS
-    if USE_XFORMERS_OPS is not None:
-        return USE_XFORMERS_OPS
-
-    if current_platform.is_cuda() and current_platform.has_device_capability(100):
-        # Xformers FA is not compatible with B200
-        USE_XFORMERS_OPS = False
-    else:
-        try:
-            from importlib.util import find_spec
-
-            find_spec("xformers.ops")
-            USE_XFORMERS_OPS = True
-        except ImportError:
-            USE_XFORMERS_OPS = False
-
-    # the warning only needs to be shown once
-    if not USE_XFORMERS_OPS:
-        logger.warning("Xformers is not available, falling back.")
-
-    return USE_XFORMERS_OPS
 
 
 def check_upstream_fa_availability(dtype: torch.dtype):
@@ -533,7 +508,6 @@ class MultiHeadAttention(nn.Module):
             if backend
             in {
                 AttentionBackendEnum.TORCH_SDPA,
-                AttentionBackendEnum.XFORMERS,
                 AttentionBackendEnum.PALLAS,
                 AttentionBackendEnum.ROCM_AITER_FA,
                 AttentionBackendEnum.FLASH_ATTN,
@@ -549,12 +523,6 @@ class MultiHeadAttention(nn.Module):
             )
         )
 
-        if (
-            self.attn_backend == AttentionBackendEnum.XFORMERS
-            and not check_xformers_availability()
-        ):
-            self.attn_backend = AttentionBackendEnum.TORCH_SDPA
-
         self.is_flash_attn_backend = self.attn_backend in {
             AttentionBackendEnum.FLASH_ATTN,
             AttentionBackendEnum.ROCM_AITER_FA,
@@ -614,12 +582,6 @@ class MultiHeadAttention(nn.Module):
                 max_seqlen_k=kv_len,
                 softmax_scale=self.scale,
             )
-        elif self.attn_backend == AttentionBackendEnum.XFORMERS:
-            from xformers import ops as xops
-
-            out = xops.memory_efficient_attention_forward(
-                query, key, value, scale=self.scale
-            )
         elif self.attn_backend == AttentionBackendEnum.TORCH_SDPA:
             query, key, value = (x.transpose(1, 2) for x in (query, key, value))
             out = F.scaled_dot_product_attention(query, key, value, scale=self.scale)

vllm/attention/ops/vit_attn_wrappers.py

@@ -3,7 +3,7 @@
 """
 This file contains ops for ViT attention to be compatible with torch.compile
 as there are operations here not supported by torch.compile (for instance,
-`to_list` in xformers attn, or `.item()` in flash attention)
+`.item()` in flash attention)
 
 Using these ops and wrapping vision blocks with `torch.compile` can speed up
 throughput in vision models by ~5% relative on H100, and improve token
@@ -19,42 +19,6 @@ import torch.nn.functional as F
 from vllm.utils.torch_utils import direct_register_custom_op
 
 
-def xformers_attn_seqlens_wrapper(
-    q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, seqlens: torch.Tensor
-) -> torch.Tensor:
-    from xformers import ops as xops
-    from xformers.ops.fmha.attn_bias import BlockDiagonalMask
-
-    attn_bias = BlockDiagonalMask.from_seqlens(
-        q_seqlen=seqlens.tolist(), kv_seqlen=None, device=q.device
-    )
-    context_layer = xops.memory_efficient_attention_forward(
-        q, k, v, attn_bias=attn_bias, p=0, scale=None
-    )
-    context_layer = einops.rearrange(context_layer, "b s h d -> s b (h d)").contiguous()
-    return context_layer
-
-
-def xformers_attn_seqlens_wrapper_fake(
-    q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, seqlens: torch.Tensor
-) -> torch.Tensor:
-    b, s, h, d = q.shape
-    return torch.empty((s, b, h * d), dtype=q.dtype, device=q.device)
-
-
-direct_register_custom_op(
-    op_name="xformers_attn_seqlens_wrapper",
-    op_func=xformers_attn_seqlens_wrapper,
-    fake_impl=xformers_attn_seqlens_wrapper_fake,
-)
-
-
-def vit_xformers_attn_wrapper(
-    q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, seqlens: torch.Tensor
-) -> torch.Tensor:
-    return torch.ops.vllm.xformers_attn_seqlens_wrapper(q, k, v, seqlens)
-
-
 def flash_attn_maxseqlen_wrapper(
     q: torch.Tensor,
     k: torch.Tensor,

vllm/attention/selector.py

@@ -36,7 +36,14 @@ def get_env_variable_attn_backend() -> AttentionBackendEnum | None:
     * None otherwise
     """
     backend_name = os.environ.get(STR_BACKEND_ENV_VAR)
-    return None if backend_name is None else AttentionBackendEnum[backend_name]
+    if backend_name is None:
+        return None
+    if backend_name == "XFORMERS":
+        raise ValueError(
+            "Attention backend 'XFORMERS' has been removed (See PR #29262 for "
+            "details). Please select a supported attention backend."
+        )
+    return AttentionBackendEnum[backend_name]
 
 
 # Global state allows a particular choice of backend

vllm/config/multimodal.py

@@ -173,6 +173,12 @@ class MultiModalConfig:
         # We need to import the real type here (deferred to avoid circular import).
         from vllm.attention.backends.registry import AttentionBackendEnum
 
+        if isinstance(value, str) and value.upper() == "XFORMERS":
+            raise ValueError(
+                "Attention backend 'XFORMERS' has been removed (See PR #29262 for "
+                "details). Please select a supported attention backend."
+            )
+
         if value is None or isinstance(value, AttentionBackendEnum):
             return value
 

vllm/envs.py

@@ -640,7 +640,6 @@ environment_variables: dict[str, Callable[[], Any]] = {
     # Example options:
     # - "TORCH_SDPA": use torch.nn.MultiheadAttention
     # - "FLASH_ATTN": use FlashAttention
-    # - "XFORMERS": use XFormers
     # - "FLASHINFER": use flashinfer
     # - "FLASHMLA": use FlashMLA
     # - "FLASH_ATTN_MLA": use FlashAttention for MLA

vllm/model_executor/models/dots_ocr.py

@@ -306,7 +306,6 @@ class DotsVisionAttention(nn.Module):
         if self.attn_backend not in {
             AttentionBackendEnum.FLASH_ATTN,
             AttentionBackendEnum.TORCH_SDPA,
-            AttentionBackendEnum.XFORMERS,
             AttentionBackendEnum.ROCM_AITER_FA,
         }:
             raise RuntimeError(
@@ -324,7 +323,6 @@ class DotsVisionAttention(nn.Module):
         rotary_pos_emb: torch.Tensor | None = None,
         *,
         max_seqlen: int | None = None,
-        seqlens: list[int] | None = None,
     ) -> torch.Tensor:
         # [S, C] -> [S, B=1, C]
         x = hidden_states.unsqueeze(1)
@@ -374,16 +372,6 @@ class DotsVisionAttention(nn.Module):
                 out_i = out_i.permute(0, 2, 1, 3)
                 outputs.append(out_i)
             context_layer = torch.cat(outputs, dim=1) if outputs else q[:, :0]
-        elif self.attn_backend == AttentionBackendEnum.XFORMERS:
-            from xformers import ops as xops
-            from xformers.ops.fmha.attn_bias import BlockDiagonalMask
-
-            attn_bias = BlockDiagonalMask.from_seqlens(
-                q_seqlen=seqlens, kv_seqlen=None, device=q.device
-            )
-            context_layer = xops.memory_efficient_attention_forward(
-                q, k, v, attn_bias=attn_bias, p=0, scale=None
-            )
         else:
             raise RuntimeError("Unsupported attention backend")
 
@@ -545,14 +533,12 @@ class DotsVisionBlock(nn.Module):
         cu_seqlens: torch.Tensor,
         rotary_pos_emb: torch.Tensor,
         max_seqlen: int | None = None,
-        seqlens: list[int] | None = None,
     ) -> torch.Tensor:
         hidden_states = hidden_states + self.attn(
             self.norm1(hidden_states),
             cu_seqlens=cu_seqlens,
             rotary_pos_emb=rotary_pos_emb,
             max_seqlen=max_seqlen,
-            seqlens=seqlens,
         )
         hidden_states = hidden_states + self.mlp(self.norm2(hidden_states))
         return hidden_states
@@ -663,18 +649,14 @@ class DotsVisionTransformer(nn.Module):
         rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
         return rotary_pos_emb
 
-    def compute_attn_mask_seqlen(
+    def compute_attn_mask_seqlen(self, cu_seqlens: torch.Tensor) -> int | None:
-        self, cu_seqlens: torch.Tensor
+        max_seqlen = None
-    ) -> tuple[int | None, list[int] | None]:
-        max_seqlen, seqlens = None, None
         if (
             self.attn_backend == AttentionBackendEnum.FLASH_ATTN
             or self.attn_backend == AttentionBackendEnum.ROCM_AITER_FA
         ):
             max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
-        elif self.attn_backend == AttentionBackendEnum.XFORMERS:
+        return max_seqlen
-            seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
-        return max_seqlen, seqlens
 
     def forward(
         self, hidden_states: torch.Tensor, grid_thw: list[list[int]]
@@ -694,14 +676,13 @@ class DotsVisionTransformer(nn.Module):
         )
         cu_seqlens = torch.cat([cu_seqlens.new_zeros(1), cu_seqlens])
 
-        max_seqlen, seqlens = self.compute_attn_mask_seqlen(cu_seqlens)
+        max_seqlen = self.compute_attn_mask_seqlen(cu_seqlens)
         for blk in self.blocks:
             hidden_states = blk(
                 hidden_states,
                 cu_seqlens=cu_seqlens,
                 rotary_pos_emb=rotary_pos_emb,
                 max_seqlen=max_seqlen,
-                seqlens=seqlens,
             )
 
         if self.post_trunk_norm is not None:

vllm/model_executor/models/ernie45_vl.py

@@ -214,7 +214,6 @@ class Ernie4_5_VisionAttention(nn.Module):
         if self.attn_backend not in {
             AttentionBackendEnum.FLASH_ATTN,
             AttentionBackendEnum.TORCH_SDPA,
-            AttentionBackendEnum.XFORMERS,
             AttentionBackendEnum.ROCM_AITER_FA,
         }:
             raise RuntimeError(
@@ -259,7 +258,6 @@ class Ernie4_5_VisionAttention(nn.Module):
         cu_seqlens: torch.Tensor,
         rotary_pos_emb: torch.Tensor,
         max_seqlen: int | None = None,  # Only used for Flash Attention
-        seqlens: list[int] | None = None,  # Only used for xFormers
     ) -> torch.Tensor:
         # [s, b, c] --> [s, b, head * 3 * head_dim]
         x, _ = self.qkv(x)
@@ -311,20 +309,6 @@ class Ernie4_5_VisionAttention(nn.Module):
             context_layer = rearrange(
                 context_layer, "b s h d -> s b (h d)"
             ).contiguous()
-        elif self.attn_backend == AttentionBackendEnum.XFORMERS:
-            from xformers import ops as xops
-            from xformers.ops.fmha.attn_bias import BlockDiagonalMask
-
-            attn_bias = BlockDiagonalMask.from_seqlens(
-                q_seqlen=seqlens, kv_seqlen=None, device=q.device
-            )
-
-            context_layer = xops.memory_efficient_attention_forward(
-                q, k, v, attn_bias=attn_bias, p=0, scale=None
-            )
-            context_layer = rearrange(
-                context_layer, "b s h d -> s b (h d)"
-            ).contiguous()
 
         output, _ = self.proj(context_layer)
         return output
@@ -404,14 +388,12 @@ class Ernie4_5_VisionBlock(nn.Module):
         cu_seqlens: torch.Tensor,
         rotary_pos_emb: torch.Tensor,
         max_seqlen: int | None = None,  # Only used for Flash Attention
-        seqlens: list[int] | None = None,  # Only used for xFormers
     ) -> torch.Tensor:
         hidden_states = hidden_states + self.attn(
             self.norm1(hidden_states),
             cu_seqlens=cu_seqlens,
             rotary_pos_emb=rotary_pos_emb,
             max_seqlen=max_seqlen,
-            seqlens=seqlens,
         )
         hidden_states = hidden_states + self.mlp(self.norm2(hidden_states))
         return hidden_states
@@ -562,18 +544,14 @@ class Ernie4_5_VisionTransformer(nn.Module):
         rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
         return rotary_pos_emb
 
-    def compute_attn_mask_seqlen(
+    def compute_attn_mask_seqlen(self, cu_seqlens: torch.Tensor) -> int | None:
-        self, cu_seqlens: torch.Tensor
+        max_seqlen = None
-    ) -> tuple[int | None, list[int] | None]:
-        max_seqlen, seqlens = None, None
         if (
             self.attn_backend == AttentionBackendEnum.FLASH_ATTN
             or self.attn_backend == AttentionBackendEnum.ROCM_AITER_FA
         ):
             max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
-        elif self.attn_backend == AttentionBackendEnum.XFORMERS:
+        return max_seqlen
-            seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
-        return max_seqlen, seqlens
 
     def forward(
         self, hidden_states: torch.Tensor, grid_thw: torch.Tensor, num_pad=0
@@ -598,8 +576,8 @@ class Ernie4_5_VisionTransformer(nn.Module):
         if hidden_states.ndim == 2:
             hidden_states = hidden_states.unsqueeze(dim=1)
 
-        # pre-compute seqlens for attn mask to reduce cuMemcpy operations
+        # pre-compute max_seqlen for attn mask to reduce cuMemcpy operations
-        max_seqlen, seqlens = self.compute_attn_mask_seqlen(cu_seqlens)
+        max_seqlen = self.compute_attn_mask_seqlen(cu_seqlens)
 
         for i, blk in enumerate(self.blocks):
             hidden_states = blk(
@@ -607,7 +585,6 @@ class Ernie4_5_VisionTransformer(nn.Module):
                 cu_seqlens=cu_seqlens,
                 rotary_pos_emb=rotary_pos_emb,
                 max_seqlen=max_seqlen,
-                seqlens=seqlens,
             )
 
         final_output = self.ln(hidden_states)

vllm/model_executor/models/glm4_1v.py

@@ -309,7 +309,6 @@ class Glm4vVisionAttention(nn.Module):
         if self.attn_backend not in {
             AttentionBackendEnum.FLASH_ATTN,
             AttentionBackendEnum.TORCH_SDPA,
-            AttentionBackendEnum.XFORMERS,
             AttentionBackendEnum.ROCM_AITER_FA,
         }:
             raise RuntimeError(
@@ -345,7 +344,6 @@ class Glm4vVisionAttention(nn.Module):
         rotary_pos_emb_cos: torch.Tensor,
         rotary_pos_emb_sin: torch.Tensor,
         max_seqlen: int | None = None,  # Only used for Flash Attention
-        seqlens: list[int] | None = None,  # Only used for xFormers
     ) -> torch.Tensor:
         # [s, b, c] --> [s, b, head * 3 * head_dim]
         x, _ = self.qkv(x)
@@ -400,20 +398,6 @@ class Glm4vVisionAttention(nn.Module):
             context_layer = rearrange(
                 context_layer, "b s h d -> s b (h d)"
             ).contiguous()
-        elif self.attn_backend == AttentionBackendEnum.XFORMERS:
-            from xformers import ops as xops
-            from xformers.ops.fmha.attn_bias import BlockDiagonalMask
-
-            attn_bias = BlockDiagonalMask.from_seqlens(
-                q_seqlen=seqlens, kv_seqlen=None, device=q.device
-            )
-
-            context_layer = xops.memory_efficient_attention_forward(
-                q, k, v, attn_bias=attn_bias, p=0, scale=None
-            )
-            context_layer = rearrange(
-                context_layer, "b s h d -> s b (h d)"
-            ).contiguous()
 
         output, _ = self.proj(context_layer)
         return output
@@ -461,7 +445,6 @@ class Glm4vVisionBlock(nn.Module):
         rotary_pos_emb_cos: torch.Tensor,
         rotary_pos_emb_sin: torch.Tensor,
         max_seqlen: int | None = None,  # Only used for Flash Attention
-        seqlens: list[int] | None = None,  # Only used for xFormers
     ) -> torch.Tensor:
         x_attn = self.attn(
             self.norm1(x),
@@ -469,7 +452,6 @@ class Glm4vVisionBlock(nn.Module):
             rotary_pos_emb_cos=rotary_pos_emb_cos,
             rotary_pos_emb_sin=rotary_pos_emb_sin,
             max_seqlen=max_seqlen,
-            seqlens=seqlens,
         )
         x_fused_norm, residual = self.norm2(x, residual=x_attn)
         x = residual + self.mlp(x_fused_norm)
@@ -803,15 +785,14 @@ class Glm4vVisionTransformer(nn.Module):
     def compute_attn_mask_seqlen(
         self,
         cu_seqlens: torch.Tensor,
-    ) -> tuple[int | None, list[int] | None]:
+    ) -> int | None:
-        max_seqlen, seqlens = None, None
+        max_seqlen = None
-        seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
         if (
             self.attn_backend == AttentionBackendEnum.FLASH_ATTN
             or self.attn_backend == AttentionBackendEnum.ROCM_AITER_FA
         ):
             max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
-        return max_seqlen, seqlens
+        return max_seqlen
 
     def forward(
         self,
@@ -836,8 +817,9 @@ class Glm4vVisionTransformer(nn.Module):
         ).cumsum(dim=0, dtype=torch.int32)
         cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0)
 
-        # pre-compute seqlens for attn mask to reduce cuMemcpy operations
+        # pre-compute max_seqlen for attn mask to reduce cuMemcpy operations
-        max_seqlen, seqlens = self.compute_attn_mask_seqlen(cu_seqlens)
+        max_seqlen = self.compute_attn_mask_seqlen(cu_seqlens)
+        seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
         x = self.embeddings(
             x, seqlens, grid_thw, image_type_ids[:, 0], image_type_ids[:, 1]
         )
@@ -851,7 +833,6 @@ class Glm4vVisionTransformer(nn.Module):
                 rotary_pos_emb_cos=rotary_pos_emb_cos,
                 rotary_pos_emb_sin=rotary_pos_emb_sin,
                 max_seqlen=max_seqlen,
-                seqlens=seqlens,
             )
 
         # adapter

vllm/model_executor/models/keye.py

@@ -9,6 +9,7 @@ from typing import Annotated, Any, Literal, TypeAlias, TypeVar
 import numpy as np
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 from einops import rearrange
 from transformers import PretrainedConfig
 from transformers.activations import GELUActivation
@@ -424,7 +425,7 @@ class KeyeSiglipAttention(nn.Module):
 
         if self.attn_backend not in {
             AttentionBackendEnum.FLASH_ATTN,
-            AttentionBackendEnum.XFORMERS,
+            AttentionBackendEnum.TORCH_SDPA,
             AttentionBackendEnum.ROCM_AITER_FA,
         }:
             raise RuntimeError(
@@ -451,7 +452,6 @@ class KeyeSiglipAttention(nn.Module):
         )
 
         max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
-        seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
         batch_size = q.shape[0]
 
         if rope_emb is None:
@@ -498,17 +498,21 @@ class KeyeSiglipAttention(nn.Module):
                 softmax_scale=self.scale,
             )
             context_layer = rearrange(output, "(b s) ... -> b s ...", b=batch_size)
-        elif self.attn_backend == AttentionBackendEnum.XFORMERS:
+        elif self.attn_backend == AttentionBackendEnum.TORCH_SDPA:
-            from xformers import ops as xops
+            outputs = []
-            from xformers.ops.fmha.attn_bias import BlockDiagonalMask
+            for i in range(1, len(cu_seqlens)):
-
+                start_idx = cu_seqlens[i - 1]
-            attn_bias = BlockDiagonalMask.from_seqlens(
+                end_idx = cu_seqlens[i]
-                q_seqlen=seqlens, kv_seqlen=None, device=q.device
+                q_i = q[:, start_idx:end_idx]
-            )
+                k_i = k[:, start_idx:end_idx]
-
+                v_i = v[:, start_idx:end_idx]
-            context_layer = xops.memory_efficient_attention_forward(
+                q_i, k_i, v_i = (
-                q, k, v, attn_bias=attn_bias, p=0, scale=None
+                    rearrange(x, "b s h d -> b h s d") for x in (q_i, k_i, v_i)
-            )
+                )
+                output_i = F.scaled_dot_product_attention(q_i, k_i, v_i, dropout_p=0.0)
+                output_i = rearrange(output_i, "b h s d -> b s h d ")
+                outputs.append(output_i)
+            context_layer = torch.cat(outputs, dim=1) if outputs else q[:, :0]
 
         context_layer = rearrange(context_layer, "b s h d -> b s (h d)").contiguous()
 

vllm/model_executor/models/paddleocr_vl.py

@@ -38,7 +38,6 @@ from vllm.attention.layer import (
 )
 from vllm.attention.ops.vit_attn_wrappers import (
     vit_flash_attn_wrapper,
-    vit_xformers_attn_wrapper,
 )
 from vllm.config import VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
@@ -657,7 +656,6 @@ class SiglipAttention(nn.Module):
         cu_seqlens: torch.Tensor,
         rotary_pos_emb: torch.Tensor | None,
         max_seqlen: torch.Tensor | None,
-        seqlens: torch.Tensor | None,
     ) -> torch.Tensor:
         batch_size, _, _ = hidden_states.shape
 
@@ -703,10 +701,6 @@ class SiglipAttention(nn.Module):
             context_layer = rearrange(
                 context_layer, "b s h d -> s b (h d)"
             ).contiguous()
-        elif self.attn_backend == AttentionBackendEnum.XFORMERS:
-            if seqlens is None:
-                raise ValueError("xFormers attention backend requires seqlens tensor.")
-            context_layer = vit_xformers_attn_wrapper(q, k, v, seqlens)
         else:
             raise RuntimeError(
                 f"PaddleOCR-VL does not support {self.attn_backend} backend now."
@@ -818,7 +812,6 @@ class SiglipEncoderLayer(nn.Module):
         cu_seqlens: torch.Tensor,
         rotary_pos_emb: torch.Tensor | None,
         max_seqlen: torch.Tensor | None,
-        seqlens: torch.Tensor | None,
     ) -> torch.Tensor:
         residual = hidden_states
 
@@ -828,7 +821,6 @@ class SiglipEncoderLayer(nn.Module):
             cu_seqlens=cu_seqlens,
             rotary_pos_emb=rotary_pos_emb,
             max_seqlen=max_seqlen,
-            seqlens=seqlens,
         )
 
         hidden_states = residual + hidden_states
@@ -870,7 +862,6 @@ class SiglipEncoder(nn.Module):
         if self.attn_backend not in {
             AttentionBackendEnum.FLASH_ATTN,
             AttentionBackendEnum.TORCH_SDPA,
-            AttentionBackendEnum.XFORMERS,
             AttentionBackendEnum.ROCM_AITER_FA,
         }:
             raise RuntimeError(
@@ -943,14 +934,11 @@ class SiglipEncoder(nn.Module):
             cu_seqlens = cu_seqlens.to(device=device)
 
         max_seqlen = None
-        seqlens = None
         if self.attn_backend in {
             AttentionBackendEnum.FLASH_ATTN,
             AttentionBackendEnum.ROCM_AITER_FA,
         }:
             max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
-        elif self.attn_backend == AttentionBackendEnum.XFORMERS:
-            seqlens = cu_seqlens[1:] - cu_seqlens[:-1]
 
         hidden_states = inputs_embeds
         for encoder_layer in self.layers:
@@ -959,7 +947,6 @@ class SiglipEncoder(nn.Module):
                 cu_seqlens=cu_seqlens,
                 rotary_pos_emb=rotary_pos_emb,
                 max_seqlen=max_seqlen,
-                seqlens=seqlens,
             )
         return hidden_states
 

vllm/model_executor/models/pixtral.py

@@ -74,6 +74,7 @@ from .vision import (
 )
 
 try:
+    # Note: vLLM does not install xformers by default.
     from xformers import ops as xops
 
     if current_platform.is_cuda() and current_platform.has_device_capability(100):

vllm/model_executor/models/qwen2_5_vl.py

@@ -46,7 +46,6 @@ from vllm.attention.layer import maybe_get_vit_flash_attn_backend
 from vllm.attention.ops.vit_attn_wrappers import (
     vit_flash_attn_wrapper,
     vit_torch_sdpa_wrapper,
-    vit_xformers_attn_wrapper,
 )
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import VllmConfig
@@ -375,7 +374,6 @@ class Qwen2_5_VisionAttention(nn.Module):
         rotary_pos_emb_cos: torch.Tensor,
         rotary_pos_emb_sin: torch.Tensor,
         max_seqlen: torch.Tensor,  # Only used for Flash Attention
-        seqlens: torch.Tensor,  # Only used for xFormers
     ) -> torch.Tensor:
         # [s, b, c] --> [s, b, head * 3 * head_dim]
         x, _ = self.qkv(x)
@@ -435,8 +433,6 @@ class Qwen2_5_VisionAttention(nn.Module):
                 v,
                 cu_seqlens,
             )
-        elif self.attn_backend == AttentionBackendEnum.XFORMERS:
-            context_layer = vit_xformers_attn_wrapper(q, k, v, seqlens)
 
         output, _ = self.proj(context_layer)
         return output
@@ -448,9 +444,7 @@ class Qwen2_5_VisionAttention(nn.Module):
         "cu_seqlens": 0,
         "rotary_pos_emb_cos": 0,
         "rotary_pos_emb_sin": 0,
-        "seqlens": 0,
     },
-    mark_unbacked_dims={"seqlens": 0},
     enable_if=should_torch_compile_mm_vit,
 )
 class Qwen2_5_VisionBlock(nn.Module):
@@ -501,7 +495,6 @@ class Qwen2_5_VisionBlock(nn.Module):
         rotary_pos_emb_cos: torch.Tensor,
         rotary_pos_emb_sin: torch.Tensor,
         max_seqlen: torch.Tensor,  # Only used for Flash Attention
-        seqlens: torch.Tensor,  # Only used for xFormers
     ) -> torch.Tensor:
         x_attn = self.attn(
             self.norm1(x),
@@ -509,7 +502,6 @@ class Qwen2_5_VisionBlock(nn.Module):
             rotary_pos_emb_cos=rotary_pos_emb_cos,
             rotary_pos_emb_sin=rotary_pos_emb_sin,
             max_seqlen=max_seqlen,
-            seqlens=seqlens,
         )
         x_fused_norm, residual = self.norm2(x, residual=x_attn)
         x = residual + self.mlp(x_fused_norm)
@@ -670,7 +662,6 @@ class Qwen2_5_VisionTransformer(nn.Module):
         if self.attn_backend not in {
             AttentionBackendEnum.FLASH_ATTN,
             AttentionBackendEnum.TORCH_SDPA,
-            AttentionBackendEnum.XFORMERS,
             AttentionBackendEnum.ROCM_AITER_FA,
         }:
             raise RuntimeError(
@@ -822,17 +813,14 @@ class Qwen2_5_VisionTransformer(nn.Module):
     def compute_attn_mask_seqlen(
         self,
         cu_seqlens: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
+    ) -> torch.Tensor:
         max_seqlen = torch.zeros([], device=cu_seqlens.device)
-        seqlens = torch.zeros(1, device=cu_seqlens.device)
         if self.attn_backend in {
             AttentionBackendEnum.FLASH_ATTN,
             AttentionBackendEnum.ROCM_AITER_FA,
         }:
             max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
-        elif self.attn_backend == AttentionBackendEnum.XFORMERS:
+        return max_seqlen
-            seqlens = cu_seqlens[1:] - cu_seqlens[:-1]
-        return max_seqlen, seqlens
 
     @staticmethod
     def invert_permutation(perm: torch.Tensor) -> torch.Tensor:
@@ -897,10 +885,8 @@ class Qwen2_5_VisionTransformer(nn.Module):
 
         # transformers
         # pre-compute seqlens for window/full attn to reduce cuMemcpy operations
-        max_seqlen_full, seqlens_full = self.compute_attn_mask_seqlen(cu_seqlens)
+        max_seqlen_full = self.compute_attn_mask_seqlen(cu_seqlens)
-        max_seqlen_window, seqlens_window = self.compute_attn_mask_seqlen(
+        max_seqlen_window = self.compute_attn_mask_seqlen(cu_window_seqlens)
-            cu_window_seqlens
-        )
 
         cu_seqlens = cu_seqlens.to(device=self.device, non_blocking=True)
         cu_window_seqlens = cu_window_seqlens.to(device=self.device, non_blocking=True)
@@ -927,11 +913,9 @@ class Qwen2_5_VisionTransformer(nn.Module):
             if layer_num in self.fullatt_block_indexes:
                 cu_seqlens_now = cu_seqlens
                 max_seqlen_now = max_seqlen_full
-                seqlens_now = seqlens_full
             else:
                 cu_seqlens_now = cu_window_seqlens
                 max_seqlen_now = max_seqlen_window
-                seqlens_now = seqlens_window
 
             hidden_states = blk(
                 hidden_states,
@@ -939,7 +923,6 @@ class Qwen2_5_VisionTransformer(nn.Module):
                 rotary_pos_emb_cos=rotary_pos_emb_cos,
                 rotary_pos_emb_sin=rotary_pos_emb_sin,
                 max_seqlen=max_seqlen_now,
-                seqlens=seqlens_now,
             )
 
         # For Qwen2.5-VL-3B, float16 will overflow at last block

vllm/model_executor/models/qwen2_vl.py

@@ -348,7 +348,6 @@ class Qwen2VisionAttention(nn.Module):
         if self.attn_backend not in {
             AttentionBackendEnum.FLASH_ATTN,
             AttentionBackendEnum.TORCH_SDPA,
-            AttentionBackendEnum.XFORMERS,
             AttentionBackendEnum.ROCM_AITER_FA,
         }:
             raise RuntimeError(
@@ -384,7 +383,6 @@ class Qwen2VisionAttention(nn.Module):
         rotary_pos_emb_cos: torch.Tensor,
         rotary_pos_emb_sin: torch.Tensor,
         max_seqlen: int | None = None,  # Only used for Flash Attention
-        seqlens: list[int] | None = None,  # Only used for xFormers
     ) -> torch.Tensor:
         # [s, b, c] --> [s, b, 3 * head * head_dim]
         x, _ = self.qkv(x)
@@ -445,20 +443,6 @@ class Qwen2VisionAttention(nn.Module):
             context_layer = rearrange(
                 context_layer, "b s h d -> s b (h d)"
             ).contiguous()
-        elif self.attn_backend == AttentionBackendEnum.XFORMERS:
-            from xformers import ops as xops
-            from xformers.ops.fmha.attn_bias import BlockDiagonalMask
-
-            attn_bias = BlockDiagonalMask.from_seqlens(
-                q_seqlen=seqlens, kv_seqlen=None, device=q.device
-            )
-
-            context_layer = xops.memory_efficient_attention_forward(
-                q, k, v, attn_bias=attn_bias, p=0, scale=None
-            )
-            context_layer = rearrange(
-                context_layer, "b s h d -> s b (h d)"
-            ).contiguous()
 
         output, _ = self.proj(context_layer)
         return output
@@ -509,7 +493,6 @@ class Qwen2VisionBlock(nn.Module):
         rotary_pos_emb_cos: torch.Tensor,
         rotary_pos_emb_sin: torch.Tensor,
         max_seqlen: int | None = None,  # Only used for Flash Attention
-        seqlens: list[int] | None = None,  # Only used for xFormers
     ) -> torch.Tensor:
         x = x + self.attn(
             self.norm1(x),
@@ -517,7 +500,6 @@ class Qwen2VisionBlock(nn.Module):
             rotary_pos_emb_cos=rotary_pos_emb_cos,
             rotary_pos_emb_sin=rotary_pos_emb_sin,
             max_seqlen=max_seqlen,
-            seqlens=seqlens,
         )
 
         x = x + self.mlp(self.norm2(x))
@@ -728,18 +710,14 @@ class Qwen2VisionTransformer(nn.Module):
         sin_combined = sin[pos_ids].flatten(1)
         return cos_combined, sin_combined
 
-    def compute_attn_mask_seqlen(
+    def compute_attn_mask_seqlen(self, cu_seqlens: torch.Tensor) -> int | None:
-        self, cu_seqlens: torch.Tensor
+        max_seqlen = None
-    ) -> tuple[int | None, list[int] | None]:
-        max_seqlen, seqlens = None, None
         if self.attn_backend in {
             AttentionBackendEnum.FLASH_ATTN,
             AttentionBackendEnum.ROCM_AITER_FA,
         }:
             max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
-        elif self.attn_backend == AttentionBackendEnum.XFORMERS:
+        return max_seqlen
-            seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
-        return max_seqlen, seqlens
 
     def forward(
         self,
@@ -771,7 +749,7 @@ class Qwen2VisionTransformer(nn.Module):
         x = x.unsqueeze(1)
 
         # pre-compute seqlens for attn mask to reduce cuMemcpy operations
-        max_seqlen, seqlens = self.compute_attn_mask_seqlen(cu_seqlens)
+        max_seqlen = self.compute_attn_mask_seqlen(cu_seqlens)
         cu_seqlens = cu_seqlens.to(self.device, non_blocking=True)
         for blk in self.blocks:
             x = blk(
@@ -780,7 +758,6 @@ class Qwen2VisionTransformer(nn.Module):
                 rotary_pos_emb_cos=rotary_pos_emb_cos,
                 rotary_pos_emb_sin=rotary_pos_emb_sin,
                 max_seqlen=max_seqlen,
-                seqlens=seqlens,
             )
 
         # adapter

vllm/model_executor/models/qwen3_omni_moe_thinker.py

@@ -224,7 +224,6 @@ class Qwen3_VisionBlock(nn.Module):
         rotary_pos_emb_cos: torch.Tensor,
         rotary_pos_emb_sin: torch.Tensor,
         max_seqlen: torch.Tensor,  # Only used for Flash Attention
-        seqlens: torch.Tensor,  # Only used for xFormers
     ) -> torch.Tensor:
         x = x + self.attn(
             self.norm1(x),
@@ -232,7 +231,6 @@ class Qwen3_VisionBlock(nn.Module):
             rotary_pos_emb_cos=rotary_pos_emb_cos,
             rotary_pos_emb_sin=rotary_pos_emb_sin,
             max_seqlen=max_seqlen,
-            seqlens=seqlens,
         )
 
         x = x + self.mlp(self.norm2(x))
@@ -500,14 +498,11 @@ class Qwen3Omni_VisionTransformer(nn.Module):
     def compute_attn_mask_seqlen(
         self,
         cu_seqlens: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
+    ) -> torch.Tensor:
         max_seqlen = torch.zeros([], device=cu_seqlens.device)
-        seqlens = torch.zeros(1, device=cu_seqlens.device)
         if self.attn_backend == AttentionBackendEnum.FLASH_ATTN:
             max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
-        elif self.attn_backend == AttentionBackendEnum.XFORMERS:
+        return max_seqlen
-            seqlens = cu_seqlens[1:] - cu_seqlens[:-1]
-        return max_seqlen, seqlens
 
     def forward(
         self,
@@ -533,7 +528,7 @@ class Qwen3Omni_VisionTransformer(nn.Module):
         hidden_states = hidden_states.unsqueeze(1)
         rotary_pos_emb_cos = rotary_pos_emb_cos.to(hidden_states.device)
         rotary_pos_emb_sin = rotary_pos_emb_sin.to(hidden_states.device)
-        max_seqlen, seqlens = self.compute_attn_mask_seqlen(cu_seqlens)
+        max_seqlen = self.compute_attn_mask_seqlen(cu_seqlens)
 
         hidden_states_list = []
         deepstack_visual_indexes = self.deepstack_visual_indexes
@@ -545,7 +540,6 @@ class Qwen3Omni_VisionTransformer(nn.Module):
                 rotary_pos_emb_cos=rotary_pos_emb_cos,
                 rotary_pos_emb_sin=rotary_pos_emb_sin,
                 max_seqlen=max_seqlen,
-                seqlens=seqlens,
             )
             if (
                 deepstack_visual_indexes is not None

vllm/model_executor/models/qwen3_vl.py

@@ -235,7 +235,6 @@ class Qwen3_VisionBlock(nn.Module):
         rotary_pos_emb_cos: torch.Tensor,
         rotary_pos_emb_sin: torch.Tensor,
         max_seqlen: torch.Tensor,  # Only used for Flash Attention
-        seqlens: torch.Tensor,  # Only used for xFormers
     ) -> torch.Tensor:
         x = x + self.attn(
             self.norm1(x),
@@ -243,7 +242,6 @@ class Qwen3_VisionBlock(nn.Module):
             rotary_pos_emb_cos=rotary_pos_emb_cos,
             rotary_pos_emb_sin=rotary_pos_emb_sin,
             max_seqlen=max_seqlen,
-            seqlens=seqlens,
         )
 
         x = x + self.mlp(self.norm2(x))
@@ -391,7 +389,6 @@ class Qwen3_VisionTransformer(nn.Module):
         if self.attn_backend not in {
             AttentionBackendEnum.FLASH_ATTN,
             AttentionBackendEnum.TORCH_SDPA,
-            AttentionBackendEnum.XFORMERS,
             AttentionBackendEnum.ROCM_AITER_FA,
         }:
             raise RuntimeError(
@@ -531,17 +528,14 @@ class Qwen3_VisionTransformer(nn.Module):
     def compute_attn_mask_seqlen(
         self,
         cu_seqlens: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
+    ) -> torch.Tensor:
         max_seqlen = torch.zeros([], device=cu_seqlens.device)
-        seqlens = torch.zeros(1, device=cu_seqlens.device)
         if (
             self.attn_backend == AttentionBackendEnum.FLASH_ATTN
             or self.attn_backend == AttentionBackendEnum.ROCM_AITER_FA
         ):
             max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
-        elif self.attn_backend == AttentionBackendEnum.XFORMERS:
+        return max_seqlen
-            seqlens = cu_seqlens[1:] - cu_seqlens[:-1]
-        return max_seqlen, seqlens
 
     def forward(
         self,
@@ -569,7 +563,7 @@ class Qwen3_VisionTransformer(nn.Module):
         cu_seqlens = torch.from_numpy(cu_seqlens)
 
         hidden_states = hidden_states.unsqueeze(1)
-        max_seqlen, seqlens = self.compute_attn_mask_seqlen(cu_seqlens)
+        max_seqlen = self.compute_attn_mask_seqlen(cu_seqlens)
         cu_seqlens = cu_seqlens.to(self.device, non_blocking=True)
 
         deepstack_feature_lists = []
@@ -580,7 +574,6 @@ class Qwen3_VisionTransformer(nn.Module):
                 rotary_pos_emb_cos=rotary_pos_emb_cos,
                 rotary_pos_emb_sin=rotary_pos_emb_sin,
                 max_seqlen=max_seqlen,
-                seqlens=seqlens,
             )
             if layer_num in self.deepstack_visual_indexes:
                 deepstack_merger_idx = self.deepstack_visual_indexes.index(layer_num)

vllm/platforms/cuda.py

@@ -277,12 +277,7 @@ class CudaPlatformBase(Platform):
         except ImportError:
             pass
 
-        if cls.has_device_capability(100):
+        return AttentionBackendEnum.TORCH_SDPA
-            # xFormers doesn't support Blackwell, fall back to SDPA
-            # See https://github.com/facebookresearch/xformers/issues/1317#issuecomment-3199392579 # noqa: E501
-            return AttentionBackendEnum.TORCH_SDPA
-        else:
-            return AttentionBackendEnum.XFORMERS
 
     @classmethod
     def get_valid_backends(

vllm/utils/__init__.py

@@ -49,7 +49,6 @@ STR_BACKEND_ENV_VAR: str = "VLLM_ATTENTION_BACKEND"
 # Possible string values of STR_BACKEND_ENV_VAR
 # register, corresponding to possible backends
 STR_FLASHINFER_ATTN_VAL: str = "FLASHINFER"
-STR_XFORMERS_ATTN_VAL: str = "XFORMERS"
 STR_FLASH_ATTN_VAL: str = "FLASH_ATTN"
 STR_INVALID_VAL: str = "INVALID"
 

vllm/v1/attention/backends/xformers.py

@@ -1,420 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Attention layer with XFormersAttention."""
-
-from dataclasses import dataclass
-from typing import ClassVar, Optional
-
-import torch
-
-from vllm.attention.backends.abstract import (
-    AttentionBackend,
-    AttentionImpl,
-    AttentionType,
-    MultipleOf,
-)
-from vllm.attention.ops.triton_unified_attention import unified_attention
-from vllm.config import VllmConfig
-from vllm.logger import init_logger
-from vllm.v1.attention.backends.utils import (
-    AttentionMetadataBuilder,
-    CommonAttentionMetadata,
-    split_decodes_and_prefills,
-)
-from vllm.v1.kv_cache_interface import AttentionSpec
-
-try:
-    from xformers import ops as xops
-    from xformers.ops.fmha.attn_bias import (
-        AttentionBias,
-        PagedBlockDiagonalCausalWithOffsetPaddedKeysMask,
-    )
-
-    XFORMERS_AVAILABLE = True
-except ImportError:
-    XFORMERS_AVAILABLE = False
-
-from vllm import _custom_ops as ops
-
-logger = init_logger(__name__)
-
-
-class XFormersAttentionBackend(AttentionBackend):
-    accept_output_buffer: bool = True
-    supported_dtypes: ClassVar[list[torch.dtype]] = [torch.float16, torch.bfloat16]
-
-    @staticmethod
-    def get_supported_kernel_block_sizes() -> list[int | MultipleOf]:
-        return [MultipleOf(16)]
-
-    @classmethod
-    def get_supported_head_sizes(cls) -> list[int]:
-        return [
-            32,
-            40,
-            48,
-            56,
-            64,
-            72,
-            80,
-            88,
-            96,
-            104,
-            112,
-            120,
-            128,
-            136,
-            144,
-            152,
-            160,
-            168,
-            176,
-            184,
-            192,
-            200,
-            208,
-            216,
-            224,
-            232,
-            240,
-            248,
-            256,
-        ]
-
-    @staticmethod
-    def get_name() -> str:
-        return "XFORMERS"
-
-    @staticmethod
-    def get_impl_cls() -> type["XFormersAttentionImpl"]:
-        return XFormersAttentionImpl
-
-    @staticmethod
-    def get_kv_cache_shape(
-        num_blocks: int,
-        block_size: int,
-        num_kv_heads: int,
-        head_size: int,
-        cache_dtype_str: str = "auto",
-    ) -> tuple[int, ...]:
-        if block_size % 16 != 0:
-            raise ValueError("Block size must be a multiple of 16.")
-        return (2, num_blocks, block_size, num_kv_heads, head_size)
-
-    @staticmethod
-    def get_builder_cls() -> type["XFormersAttentionMetadataBuilder"]:
-        return XFormersAttentionMetadataBuilder
-
-    @staticmethod
-    def use_cascade_attention(*args, **kwargs) -> bool:
-        return False
-
-
-@dataclass
-class XFormersAttentionMetadata:
-    num_actual_tokens: int  # Number of tokens excluding padding.
-    max_query_len: int
-    query_start_loc: torch.Tensor
-    max_seq_len: int
-    seq_lens: torch.Tensor
-    block_table: torch.Tensor
-    slot_mapping: torch.Tensor
-
-    num_prefill_tokens: int = 0
-    num_decode_tokens: int = 0
-    num_prefills: int = 0
-    num_decodes: int = 0
-
-    # Biases for different attention types.
-    attn_bias: Optional["AttentionBias"] = None
-
-    # Self-attention prefill/decode metadata cache
-    _cached_prefill_metadata: Optional["XFormersAttentionMetadata"] = None
-    _cached_decode_metadata: Optional["XFormersAttentionMetadata"] = None
-
-    @property
-    def prefill_metadata(self) -> Optional["XFormersAttentionMetadata"]:
-        if self.num_prefills == 0:
-            return None
-
-        if self._cached_prefill_metadata is not None:
-            # Recover cached prefill-phase attention
-            # metadata structure
-            return self._cached_prefill_metadata
-
-        q_start_loc = self.query_start_loc[self.num_decodes :]
-        q_seqlens = torch.diff(q_start_loc)
-        kv_seqlens = self.seq_lens[self.num_decodes :]
-        # Construct & cache prefill-phase attention metadata structure
-        self._cached_prefill_metadata = XFormersAttentionMetadata(
-            num_actual_tokens=self.num_prefill_tokens,
-            max_query_len=int(q_seqlens.max().item()),
-            query_start_loc=q_start_loc - q_start_loc[0],
-            max_seq_len=int(kv_seqlens.max().item()),
-            seq_lens=kv_seqlens,
-            block_table=self.block_table[self.num_decodes :],
-            slot_mapping=self.slot_mapping[self.num_decode_tokens :],
-        )
-        return self._cached_prefill_metadata
-
-    @property
-    def decode_metadata(self) -> Optional["XFormersAttentionMetadata"]:
-        if self.num_decode_tokens == 0:
-            return None
-
-        if self._cached_decode_metadata is not None:
-            # Recover cached decode-phase attention
-            # metadata structure
-            return self._cached_decode_metadata
-
-        q_start_loc = self.query_start_loc
-        q_seqlens = torch.diff(q_start_loc)
-        decode_kv_seqlens = self.seq_lens[: self.num_decodes]
-        # Construct & cache decode-phase attention metadata structure
-        self._cached_decode_metadata = XFormersAttentionMetadata(
-            num_actual_tokens=self.num_decode_tokens,
-            max_query_len=int(q_seqlens[: self.num_decodes].max().item()),
-            query_start_loc=q_start_loc[: self.num_decodes + 1],
-            max_seq_len=int(decode_kv_seqlens.max().item()),
-            seq_lens=decode_kv_seqlens,
-            block_table=self.block_table[: self.num_decodes],
-            slot_mapping=self.slot_mapping[: self.num_decode_tokens],
-            attn_bias=self.attn_bias,
-        )
-        return self._cached_decode_metadata
-
-
-class XFormersAttentionMetadataBuilder(
-    AttentionMetadataBuilder[XFormersAttentionMetadata]
-):
-    reorder_batch_threshold: int = 1
-
-    def __init__(
-        self,
-        kv_cache_spec: AttentionSpec,
-        layer_names: list[str],
-        vllm_config: VllmConfig,
-        device: torch.device,
-    ):
-        super().__init__(kv_cache_spec, layer_names, vllm_config, device)
-
-        assert XFORMERS_AVAILABLE
-        self.block_size = kv_cache_spec.block_size
-        self._num_decodes = 0
-        self._num_decode_tokens = 0
-
-    def build(
-        self,
-        common_prefix_len: int,
-        common_attn_metadata: CommonAttentionMetadata,
-        fast_build: bool = False,
-    ) -> XFormersAttentionMetadata:
-        num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
-            split_decodes_and_prefills(
-                common_attn_metadata, decode_threshold=self.reorder_batch_threshold
-            )
-        )
-
-        num_actual_tokens = common_attn_metadata.num_actual_tokens
-        q_start_loc = common_attn_metadata.query_start_loc
-        q_seqlens = torch.diff(q_start_loc)
-        max_query_len = common_attn_metadata.max_query_len
-        kv_seqlens = common_attn_metadata.seq_lens
-        max_seq_len = common_attn_metadata.max_seq_len
-        block_table = common_attn_metadata.block_table_tensor
-        slot_mapping = common_attn_metadata.slot_mapping
-
-        bias = None
-        if num_decodes > 0:
-            # Construct the decoder bias.
-            decode_q_seqlens = q_seqlens[:num_decodes]
-            decode_kv_seqlens = kv_seqlens[:num_decodes]
-            bias = PagedBlockDiagonalCausalWithOffsetPaddedKeysMask.from_seqlens(
-                q_seqlen=decode_q_seqlens.tolist(),
-                kv_seqlen=decode_kv_seqlens.tolist(),
-                page_size=self.block_size,
-                block_tables=block_table[:num_decodes],
-                device=block_table.device,
-            )
-
-        return XFormersAttentionMetadata(
-            num_actual_tokens=num_actual_tokens,
-            num_prefill_tokens=num_prefill_tokens,
-            num_decode_tokens=num_decode_tokens,
-            num_prefills=num_prefills,
-            num_decodes=num_decodes,
-            max_query_len=max_query_len,
-            query_start_loc=q_start_loc,
-            max_seq_len=max_seq_len,
-            seq_lens=kv_seqlens,
-            block_table=block_table,
-            slot_mapping=slot_mapping,
-            attn_bias=bias,
-        )
-
-
-class XFormersAttentionImpl(AttentionImpl):
-    def __init__(
-        self,
-        num_heads: int,
-        head_size: int,
-        scale: float,
-        num_kv_heads: int,
-        alibi_slopes: list[float] | None,
-        sliding_window: int | None,
-        kv_cache_dtype: str,
-        logits_soft_cap: float | None = None,
-        attn_type: AttentionType = AttentionType.DECODER,
-        kv_sharing_target_layer_name: str | None = None,
-    ) -> None:
-        if kv_sharing_target_layer_name is not None:
-            raise NotImplementedError("KV sharing is not supported in V0.")
-        if alibi_slopes is not None:
-            raise NotImplementedError("XFormers does not support alibi slopes yet.")
-        self.num_heads = num_heads
-        self.head_size = head_size
-        self.scale = float(scale)
-        self.num_kv_heads = num_kv_heads
-        self.num_queries_per_kv = self.num_heads // self.num_kv_heads
-        self.kv_cache_dtype = kv_cache_dtype
-        self.kv_sharing_target_layer_name = kv_sharing_target_layer_name
-        if alibi_slopes is not None:
-            alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32)
-        self.alibi_slopes = alibi_slopes
-        if sliding_window is None:
-            self.sliding_window = (-1, -1)
-        else:
-            self.sliding_window = (sliding_window - 1, 0)
-        if logits_soft_cap is None:
-            # Setting logits_soft_cap to 0 means no soft cap.
-            logits_soft_cap = 0
-        self.logits_soft_cap = logits_soft_cap
-
-        if attn_type != AttentionType.DECODER:
-            raise NotImplementedError(
-                "Encoder self-attention and "
-                "encoder/decoder cross-attention "
-                "are not implemented for "
-                "XFormersAttentionImpl."
-            )
-
-    def forward(
-        self,
-        layer: torch.nn.Module,
-        query: torch.Tensor,
-        key: torch.Tensor,
-        value: torch.Tensor,
-        kv_cache: torch.Tensor,
-        attn_metadata: XFormersAttentionMetadata,
-        output: torch.Tensor | None = None,
-        output_scale: torch.Tensor | None = None,
-        output_block_scale: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        """Forward pass with XFormers.
-
-        Args:
-            query: shape = [num_tokens, num_heads, head_size]
-            key: shape = [num_tokens, num_kv_heads, head_size]
-            value: shape = [num_tokens, num_kv_heads, head_size]
-            kv_cache: shape =
-                [2, num_blocks, block_size, num_kv_heads, head_size]
-            attn_metadata: Metadata for attention.
-        Returns:
-            shape = [num_tokens, num_heads * head_size]
-        """
-        assert output is not None, "Output tensor must be provided."
-
-        if output_scale is not None or output_block_scale is not None:
-            raise NotImplementedError(
-                "fused output quantization is not yet supported"
-                " for XFormersAttentionImpl"
-            )
-
-        if attn_metadata is None:
-            # Profiling run.
-            return output.fill_(0)
-
-        # Cache the input KVs.
-        key_cache, value_cache = kv_cache.unbind(0)
-        if self.kv_sharing_target_layer_name is None:
-            # Reshape the input keys and values and store them in the cache.
-            # Skip this if sharing KV cache with an earlier attention layer.
-            # NOTE(woosuk): Here, key and value are padded while slot_mapping is
-            # not padded. However, we don't need to do key[:num_actual_tokens]
-            # and value[:num_actual_tokens] because the reshape_and_cache_flash
-            # op uses the slot_mapping's shape to determine the number of
-            # actual tokens.
-            ops.reshape_and_cache_flash(
-                key,
-                value,
-                key_cache,
-                value_cache,
-                attn_metadata.slot_mapping,
-                self.kv_cache_dtype,
-                layer._k_scale,
-                layer._v_scale,
-            )
-
-        num_actual_tokens = attn_metadata.num_actual_tokens
-        num_decode_tokens = attn_metadata.num_decode_tokens
-        if prefill_meta := attn_metadata.prefill_metadata:
-            descale_shape = (prefill_meta.query_start_loc.shape[0] - 1, key.shape[1])
-            unified_attention(
-                q=query[num_decode_tokens:num_actual_tokens],
-                k=key_cache,
-                v=value_cache,
-                out=output[num_decode_tokens:num_actual_tokens],
-                cu_seqlens_q=prefill_meta.query_start_loc,
-                max_seqlen_q=prefill_meta.max_query_len,
-                seqused_k=prefill_meta.seq_lens,
-                max_seqlen_k=prefill_meta.max_seq_len,
-                softmax_scale=self.scale,
-                causal=True,
-                alibi_slopes=self.alibi_slopes,
-                window_size=self.sliding_window,
-                block_table=prefill_meta.block_table,
-                softcap=self.logits_soft_cap,
-                q_descale=None,  # Not supported
-                k_descale=layer._k_scale.expand(descale_shape),
-                v_descale=layer._v_scale.expand(descale_shape),
-            )
-
-        if decode_meta := attn_metadata.decode_metadata:
-            # Query for decode. KV is not needed because it is already cached.
-            decode_query = query[:num_decode_tokens]
-            # Reshape query to [1, B_T, G, H, D].
-            q = decode_query.view(
-                1, -1, self.num_kv_heads, self.num_queries_per_kv, self.head_size
-            )
-            # Reshape the k and v caches to [1, Bkv_T, G, H, D]
-            cache_k = key_cache.view(
-                1, -1, self.num_kv_heads, 1, self.head_size
-            ).expand(
-                1,
-                -1,
-                self.num_kv_heads,
-                self.num_queries_per_kv,
-                self.head_size,
-            )
-            cache_v = value_cache.view(
-                1, -1, self.num_kv_heads, 1, self.head_size
-            ).expand(
-                1,
-                -1,
-                self.num_kv_heads,
-                self.num_queries_per_kv,
-                self.head_size,
-            )
-
-            attn_bias = decode_meta.attn_bias
-            output[:num_decode_tokens] = xops.memory_efficient_attention_forward(
-                q,
-                cache_k,
-                cache_v,
-                attn_bias=attn_bias,
-                p=0.0,
-                scale=self.scale,
-            ).view(decode_query.shape)
-
-        # Reshape the output tensor.
-        return output