[1/N][Attention] Restructure attention: move files (#31916)

Signed-off-by: Matthew Bonanni <mbonanni@redhat.com>

.buildkite/test-amd.yaml

@@ -1105,8 +1105,8 @@ steps:
   - vllm/v1/attention/backends/flashinfer.py
   - vllm/v1/attention/backends/mla/cutlass_mla.py
   - vllm/v1/attention/backends/mla/flashinfer_mla.py
+  - vllm/v1/attention/selector.py
   - vllm/platforms/cuda.py
-  - vllm/attention/selector.py
   commands:
     - nvidia-smi
     - python3 examples/offline_inference/basic/chat.py

.buildkite/test-pipeline.yaml

@@ -954,8 +954,8 @@ steps:
   - vllm/v1/attention/backends/flashinfer.py
   - vllm/v1/attention/backends/mla/cutlass_mla.py
   - vllm/v1/attention/backends/mla/flashinfer_mla.py
+  - vllm/v1/attention/selector.py
   - vllm/platforms/cuda.py
-  - vllm/attention/selector.py
   commands:
     - nvidia-smi
     - python3 examples/offline_inference/basic/chat.py

.buildkite/test_areas/kernels.yaml

@@ -90,8 +90,8 @@ steps:
   - vllm/v1/attention/backends/flashinfer.py
   - vllm/v1/attention/backends/mla/cutlass_mla.py
   - vllm/v1/attention/backends/mla/flashinfer_mla.py
+  - vllm/v1/attention/selector.py
   - vllm/platforms/cuda.py
-  - vllm/attention/selector.py
   commands:
     - nvidia-smi
     - python3 examples/offline_inference/basic/chat.py

.github/CODEOWNERS

@@ -3,7 +3,6 @@
 
 # This lists cover the "core" components of vLLM that require careful review
 /vllm/attention @LucasWilkinson
-/vllm/attention/backends/abstract.py @WoosukKwon @zhuohan123 @youkaichao @alexm-redhat @njhill
 /vllm/executor/executor_base.py @zhuohan123 @youkaichao @alexm-redhat @njhill @22quinn
 /vllm/model_executor/layers/fused_moe @mgoin @pavanimajety
 /vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256 @pavanimajety
@@ -27,6 +26,7 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 
 # vLLM V1
 /vllm/v1/attention @LucasWilkinson
+/vllm/v1/attention/backend.py @WoosukKwon @zhuohan123 @youkaichao @alexm-redhat @njhill
 /vllm/v1/attention/backends/mla @pavanimajety
 /vllm/v1/attention/backends/flashinfer.py @mgoin @pavanimajety
 /vllm/v1/attention/backends/triton_attn.py @tdoublep
@@ -117,15 +117,15 @@ mkdocs.yaml @hmellor
 /vllm/transformers_utils/tokenizers/mistral.py @patrickvonplaten
 
 # Kernels
-/vllm/attention/ops/chunked_prefill_paged_decode.py @tdoublep
-/vllm/attention/ops/triton_unified_attention.py @tdoublep
+/vllm/v1/attention/ops/chunked_prefill_paged_decode.py @tdoublep
+/vllm/v1/attention/ops/triton_unified_attention.py @tdoublep
 
 # ROCm related: specify owner with write access to notify AMD folks for careful code review
 /vllm/**/*rocm* @tjtanaa
 /docker/Dockerfile.rocm* @gshtras @tjtanaa
 /vllm/v1/attention/backends/rocm*.py @gshtras @tjtanaa
 /vllm/v1/attention/backends/mla/rocm*.py @gshtras @tjtanaa
-/vllm/attention/ops/rocm*.py @gshtras @tjtanaa
+/vllm/v1/attention/ops/rocm*.py @gshtras @tjtanaa
 /vllm/model_executor/layers/fused_moe/rocm*.py @gshtras @tjtanaa
 /csrc/rocm @gshtras @tjtanaa
 /requirements/*rocm* @tjtanaa

.github/mergify.yml

@@ -222,10 +222,10 @@ pull_request_rules:
       - files~=^csrc/rocm/
       - files~=^docker/Dockerfile.rocm
       - files~=^requirements/rocm.*\.txt
-      - files~=^vllm/attention/backends/rocm.*\.py
-      - files~=^vllm/attention/ops/rocm.*\.py
       - files~=^vllm/model_executor/layers/fused_moe/rocm.*\.py
+      - files~=^vllm/v1/attention/backends/rocm.*\.py
       - files~=^vllm/v1/attention/backends/mla/rocm.*\.py
+      - files~=^vllm/v1/attention/ops/rocm.*\.py
       - files~=^tests/kernels/.*_rocm.*\.py
       - files=vllm/platforms/rocm.py
       - title~=(?i)AMD

benchmarks/kernels/benchmark_reshape_and_cache_flash.py

@@ -7,9 +7,6 @@ import torch
 from tabulate import tabulate
 
 from vllm import _custom_ops as ops
-from vllm.attention.ops.triton_reshape_and_cache_flash import (
-    triton_reshape_and_cache_flash,
-)
 from vllm.logger import init_logger
 from vllm.utils.argparse_utils import FlexibleArgumentParser
 from vllm.utils.torch_utils import (
@@ -17,6 +14,9 @@ from vllm.utils.torch_utils import (
     create_kv_caches_with_random_flash,
     set_random_seed,
 )
+from vllm.v1.attention.ops.triton_reshape_and_cache_flash import (
+    triton_reshape_and_cache_flash,
+)
 
 logger = init_logger(__name__)
 

docs/contributing/model/basic.md

@@ -142,7 +142,7 @@ We use "mamba-like" to refer to layers that posses a state that is updated in-pl
 For implementing new custom mamba-like layers, one should inherit from `MambaBase` and implement the methods `get_state_dtype`, `get_state_shape` to calculate the data types and state shapes at runtime, as well as `mamba_type` and `get_attn_backend`.
 It is also necessary to implement the "attention meta-data" class which handles the meta-data that is common across all layers.
 Please see [`LinearAttentionMetadata`](../../../vllm/v1/attention/backends/linear_attn.py) or [`ShortConvAttentionMetadata`](../../../vllm/v1/attention/backends/short_conv_attn.py) for examples of this.
-It is also worth noting that we should update `MAMBA_TYPE_TO_BACKEND_MAP` and `MambaAttentionBackendEnum` in [`registry.py`](../../../vllm/attention/backends/registry.py) when adding a new mamba backend.
+It is also worth noting that we should update `MAMBA_TYPE_TO_BACKEND_MAP` and `MambaAttentionBackendEnum` in [`registry.py`](../../../vllm/v1/attention/backends/registry.py) when adding a new mamba backend.
 Finally, if one wants to support torch compile and CUDA graphs, it necessary to wrap the call to the mamba-like layer inside a custom op and register it.
 Please see the calls to `direct_register_custom_op` in [vllm/model_executor/models/minimax_text_01.py](../../../vllm/model_executor/models/minimax_text_01.py) or [vllm/model_executor/layers/mamba/short_conv.py](../../../vllm/model_executor/layers/mamba/short_conv.py) for examples of this.
 The new custom op should then be added to the list `_attention_ops` in [vllm/config/compilation.py](../../../vllm/config/compilation.py) to ensure that piecewise CUDA graphs works as intended.

docs/design/custom_op.md

@@ -60,7 +60,7 @@ For example:
 **1. Attention:**
 
 ```python
---8<-- "vllm/attention/layers/mm_encoder_attention.py:mm_encoder_attn"
+--8<-- "vllm/model_executor/layers/attention/mm_encoder_attention.py:mm_encoder_attn"
 
 --8<-- "vllm/model_executor/layers/mla.py:multi_head_latent_attention"
 ```

docs/design/plugin_system.md

@@ -124,7 +124,7 @@ Every plugin has three parts:
 
     Please look at the worker base class [WorkerBase][vllm.v1.worker.worker_base.WorkerBase] for more functions that can be implemented.
 
-5. Implement the attention backend class `MyDummyAttention` in `my_dummy_attention.py`. The attention backend class should inherit from [AttentionBackend][vllm.attention.backends.abstract.AttentionBackend]. It's used to calculate attentions with your device. Take `vllm.v1.attention.backends` as examples, it contains many attention backend implementations.
+5. Implement the attention backend class `MyDummyAttention` in `my_dummy_attention.py`. The attention backend class should inherit from [AttentionBackend][vllm.v1.attention.backend.AttentionBackend]. It's used to calculate attentions with your device. Take `vllm.v1.attention.backends` as examples, it contains many attention backend implementations.
 
 6. Implement custom ops for high performance. Most ops can be ran by pytorch native implementation, while the performance may not be good. In this case, you can implement specific custom ops for your plugins. Currently, there are kinds of custom ops vLLM supports:
 
@@ -153,5 +153,5 @@ The interface for the model/module may change during vLLM's development. If you
 
 !!! warning "Deprecations"
     - `use_v1` parameter in `Platform.get_attn_backend_cls` is deprecated. It has been removed in v0.13.0.
-    - `_Backend` in `vllm.attention` is deprecated. It has been removed in v0.13.0. Please use `vllm.attention.backends.registry.register_backend` to add new attention backend to `AttentionBackendEnum` instead.
+    - `_Backend` in `vllm.attention` is deprecated. It has been removed in v0.13.0. Please use `vllm.v1.attention.backends.registry.register_backend` to add new attention backend to `AttentionBackendEnum` instead.
     - `seed_everything` platform interface is deprecated. It will be removed in v0.15.0 or later. Please use `vllm.utils.torch_utils.set_random_seed` instead.

examples/offline_inference/basic/embed.py

@@ -4,10 +4,10 @@
 from argparse import Namespace
 
 from vllm import LLM, EngineArgs
-from vllm.attention.backends.registry import AttentionBackendEnum
 from vllm.config import AttentionConfig
 from vllm.platforms import current_platform
 from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
 
 def parse_args():

examples/offline_inference/basic/score.py

@@ -4,10 +4,10 @@
 from argparse import Namespace
 
 from vllm import LLM, EngineArgs
-from vllm.attention.backends.registry import AttentionBackendEnum
 from vllm.config import AttentionConfig
 from vllm.platforms import current_platform
 from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
 
 def parse_args():

tests/compile/fullgraph/test_full_cudagraph.py

@@ -9,10 +9,10 @@ import pytest
 from tests.utils import wait_for_gpu_memory_to_clear
 from tests.v1.attention.utils import full_cg_backend_configs as backend_configs
 from vllm import LLM, SamplingParams
-from vllm.attention.backends.registry import AttentionBackendEnum
 from vllm.config import CompilationConfig
 from vllm.platforms import current_platform
 from vllm.utils.torch_utils import is_torch_equal_or_newer
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
 
 @contextlib.contextmanager

tests/compile/fullgraph/test_full_graph.py

@@ -10,10 +10,10 @@ import torch
 
 from tests.quantization.utils import is_quant_method_supported
 from vllm import LLM, SamplingParams
-from vllm.attention.backends.registry import AttentionBackendEnum
 from vllm.config import CompilationConfig, CompilationMode, CUDAGraphMode, PassConfig
 from vllm.platforms import current_platform
 from vllm.utils.torch_utils import is_torch_equal_or_newer
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
 from ...utils import create_new_process_for_each_test
 

tests/compile/test_fusion_attn.py

@@ -9,8 +9,6 @@ from tests.compile.backend import LazyInitPass, TestBackend
 from tests.utils import flat_product
 from tests.v1.attention.utils import BatchSpec, create_common_attn_metadata
 from vllm._custom_ops import cutlass_scaled_fp4_mm, scaled_fp4_quant
-from vllm.attention.backends.abstract import AttentionMetadata
-from vllm.attention.backends.registry import AttentionBackendEnum
 from vllm.attention.layer import Attention
 from vllm.compilation.fusion_attn import ATTN_OP, AttnFusionPass
 from vllm.compilation.fx_utils import find_op_nodes
@@ -37,6 +35,8 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import Fp8LinearOp
 from vllm.platforms import current_platform
 from vllm.utils.flashinfer import has_flashinfer
+from vllm.v1.attention.backend import AttentionMetadata
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
 from vllm.v1.kv_cache_interface import AttentionSpec
 
 FP8_DTYPE = current_platform.fp8_dtype()

tests/compile/test_qk_norm_rope_fusion.py

@@ -5,7 +5,6 @@ import pytest
 import torch
 
 from tests.compile.backend import TestBackend
-from vllm.attention.backends.abstract import AttentionType
 from vllm.attention.layer import Attention
 from vllm.compilation.matcher_utils import FLASHINFER_ROTARY_OP, RMS_OP, ROTARY_OP
 from vllm.compilation.noop_elimination import NoOpEliminationPass
@@ -25,6 +24,7 @@ from vllm.config import (
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding
 from vllm.platforms import current_platform
+from vllm.v1.attention.backend import AttentionType
 
 RSQRT_OP = torch.ops.aten.rsqrt.default
 INDEX_SELECT_OP = torch.ops.aten.index.Tensor

tests/config/test_multimodal_config.py

@@ -3,8 +3,8 @@
 
 import pytest
 
-from vllm.attention.backends.registry import AttentionBackendEnum
 from vllm.config.multimodal import MultiModalConfig
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
 
 def test_mm_encoder_attn_backend_str_conversion():

tests/engine/test_arg_utils.py

@@ -299,7 +299,7 @@ def test_compilation_config():
 
 
 def test_attention_config():
-    from vllm.attention.backends.registry import AttentionBackendEnum
+    from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
     parser = EngineArgs.add_cli_args(FlexibleArgumentParser())
 

tests/kernels/attention/test_aiter_flash_attn.py

@@ -6,9 +6,9 @@ import pytest
 import torch
 
 import vllm.v1.attention.backends.rocm_aiter_fa  # noqa: F401
-from vllm.attention.utils.fa_utils import is_flash_attn_varlen_func_available
 from vllm.platforms import current_platform
 from vllm.utils.torch_utils import set_random_seed
+from vllm.v1.attention.backends.fa_utils import is_flash_attn_varlen_func_available
 
 NUM_HEADS = [(4, 4), (8, 2)]
 HEAD_SIZES = [128, 256]

tests/kernels/attention/test_attention.py

@@ -10,7 +10,7 @@ from tests.kernels.allclose_default import get_default_atol, get_default_rtol
 from tests.kernels.utils import opcheck
 from vllm import _custom_ops as ops
 from vllm.attention.layer import Attention
-from vllm.attention.layers.mm_encoder_attention import MMEncoderAttention
+from vllm.model_executor.layers.attention.mm_encoder_attention import MMEncoderAttention
 from vllm.platforms import current_platform
 from vllm.utils.mem_utils import get_max_shared_memory_bytes
 from vllm.utils.torch_utils import set_random_seed
@@ -30,7 +30,7 @@ NUM_PREFILL_SEQS = [3]  # Arbitrary values for testing
 NUM_HEADS = [(40, 40), (64, 8)]  # Arbitrary values for testing
 
 # This should be sync with get_supported_head_sizes() in
-# vllm.attention.ops.paged_attn.PagedAttention
+# vllm.v1.attention.ops.paged_attn.PagedAttention
 HEAD_SIZES = [32, 80, 128, 256]
 
 BLOCK_SIZES = [16, 32]

tests/kernels/attention/test_attention_selector.py

@@ -6,13 +6,13 @@ from unittest.mock import patch
 import pytest
 import torch
 
-from vllm.attention.backends.registry import AttentionBackendEnum
-from vllm.attention.selector import _cached_get_attn_backend, get_attn_backend
 from vllm.config import AttentionConfig, VllmConfig, set_current_vllm_config
 from vllm.platforms import current_platform
 from vllm.platforms.cpu import CpuPlatform
 from vllm.platforms.cuda import CudaPlatform
 from vllm.platforms.rocm import RocmPlatform
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
+from vllm.v1.attention.selector import _cached_get_attn_backend, get_attn_backend
 
 
 @pytest.fixture(autouse=True)
@@ -182,7 +182,7 @@ def test_backend_selection(
                         expected = name
                         assert backend.get_name() == expected
                     elif name == "FLASH_ATTN_MLA":
-                        from vllm.attention.utils.fa_utils import (
+                        from vllm.v1.attention.backends.fa_utils import (
                             flash_attn_supports_mla,
                         )
 

tests/kernels/attention/test_cache.py

@@ -270,7 +270,7 @@ def test_reshape_and_cache_flash(
             v_scale,
         )
     elif implementation == "triton":
-        from vllm.attention.ops.triton_reshape_and_cache_flash import (
+        from vllm.v1.attention.ops.triton_reshape_and_cache_flash import (
             triton_reshape_and_cache_flash,
         )
 

tests/kernels/attention/test_flashmla.py

@@ -7,12 +7,12 @@ import random
 import pytest
 import torch
 
-from vllm.attention.ops.flashmla import (
+from vllm.triton_utils import triton
+from vllm.v1.attention.ops.flashmla import (
     flash_mla_with_kvcache,
     get_mla_metadata,
     is_flashmla_dense_supported,
 )
-from vllm.triton_utils import triton
 
 
 def cal_diff(

tests/kernels/attention/test_flashmla_sparse.py

@@ -5,7 +5,7 @@ import torch
 
 
 def test_sparse_flashmla_metadata_smoke():
-    import vllm.attention.ops.flashmla as fm
+    import vllm.v1.attention.ops.flashmla as fm
 
     ok, reason = fm.is_flashmla_sparse_supported()
     if not ok:
@@ -34,7 +34,7 @@ def test_sparse_flashmla_metadata_smoke():
 
 
 def test_sparse_flashmla_decode_smoke():
-    import vllm.attention.ops.flashmla as fm
+    import vllm.v1.attention.ops.flashmla as fm
 
     ok, reason = fm.is_flashmla_sparse_supported()
     if not ok:
@@ -97,7 +97,7 @@ def test_sparse_flashmla_decode_smoke():
 
 
 def test_sparse_flashmla_prefill_smoke():
-    import vllm.attention.ops.flashmla as fm
+    import vllm.v1.attention.ops.flashmla as fm
 
     ok, reason = fm.is_flashmla_sparse_supported()
     if not ok:

tests/kernels/attention/test_merge_attn_states.py

@@ -5,10 +5,10 @@ import pytest
 import torch
 
 from vllm._custom_ops import merge_attn_states as merge_attn_states_cuda
-from vllm.attention.ops.triton_merge_attn_states import (
+from vllm.platforms import current_platform
+from vllm.v1.attention.ops.triton_merge_attn_states import (
     merge_attn_states as merge_attn_states_triton,
 )
-from vllm.platforms import current_platform
 
 
 # Naive PyTorch Implements section 2.2 of https://www.arxiv.org/pdf/2501.01005

tests/kernels/attention/test_mha_attn.py

@@ -12,14 +12,14 @@ from unittest.mock import patch
 import pytest
 import torch
 
-from vllm.attention.backends.registry import AttentionBackendEnum
-from vllm.attention.layers.mm_encoder_attention import MMEncoderAttention
-from vllm.attention.selector import _cached_get_attn_backend
+from vllm.model_executor.layers.attention.mm_encoder_attention import MMEncoderAttention
 from vllm.platforms import current_platform
 from vllm.platforms.cpu import CpuPlatform
 from vllm.platforms.cuda import CudaPlatform
 from vllm.platforms.rocm import RocmPlatform
 from vllm.utils.torch_utils import set_random_seed
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
+from vllm.v1.attention.selector import _cached_get_attn_backend
 
 
 @pytest.fixture(autouse=True)

tests/kernels/attention/test_pack_unpack_triton.py

@@ -4,7 +4,7 @@
 import torch
 from torch.testing import assert_close
 
-from vllm.attention.ops.common import pack_seq_triton, unpack_seq_triton
+from vllm.v1.attention.ops.common import pack_seq_triton, unpack_seq_triton
 
 
 def test_pack_seq_basic_fp8():

tests/kernels/attention/test_prefix_prefill.py

@@ -10,10 +10,12 @@ import pytest
 import torch
 import torch.nn.functional as F
 
-from vllm.attention.ops.chunked_prefill_paged_decode import chunked_prefill_paged_decode
-from vllm.attention.ops.prefix_prefill import context_attention_fwd
 from vllm.platforms import current_platform
 from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE, set_random_seed
+from vllm.v1.attention.ops.chunked_prefill_paged_decode import (
+    chunked_prefill_paged_decode,
+)
+from vllm.v1.attention.ops.prefix_prefill import context_attention_fwd
 
 NUM_HEADS = [64]
 NUM_QUERIES_PER_KV = [1, 64]

tests/kernels/attention/test_rocm_attention_selector.py

@@ -4,10 +4,10 @@
 import pytest
 import torch
 
-from vllm.attention.backends.registry import AttentionBackendEnum
-from vllm.attention.selector import _cached_get_attn_backend, get_attn_backend
 from vllm.config import AttentionConfig, VllmConfig, set_current_vllm_config
 from vllm.platforms.rocm import RocmPlatform
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
+from vllm.v1.attention.selector import _cached_get_attn_backend, get_attn_backend
 
 
 @pytest.fixture(autouse=True)
@@ -19,7 +19,7 @@ def clear_cache():
 @pytest.mark.skip(reason="Skipped for now. Should be revisited.")
 def test_selector(monkeypatch: pytest.MonkeyPatch):
     # Set the current platform to ROCm using monkeypatch
-    monkeypatch.setattr("vllm.attention.selector.current_platform", RocmPlatform())
+    monkeypatch.setattr("vllm.v1.attention.selector.current_platform", RocmPlatform())
 
     # Test standard ROCm attention
     attention_config = AttentionConfig(backend=AttentionBackendEnum.ROCM_ATTN)

tests/kernels/attention/test_triton_decode_attention.py

@@ -4,8 +4,8 @@
 import pytest
 import torch
 
-from vllm.attention.ops.triton_decode_attention import decode_attention_fwd
 from vllm.utils.math_utils import cdiv
+from vllm.v1.attention.ops.triton_decode_attention import decode_attention_fwd
 
 
 @pytest.mark.parametrize("B", [3, 5])

tests/kernels/attention/test_triton_prefill_attention.py

@@ -5,7 +5,7 @@ import pytest
 import torch
 import torch.nn.functional as F
 
-from vllm.attention.ops.triton_prefill_attention import context_attention_fwd
+from vllm.v1.attention.ops.triton_prefill_attention import context_attention_fwd
 
 
 def ref_masked_attention(

tests/kernels/attention/test_triton_unified_attention.py

@@ -5,10 +5,10 @@
 import pytest
 import torch
 
-from vllm.attention.ops.triton_unified_attention import unified_attention
 from vllm.platforms import current_platform
 from vllm.utils.math_utils import next_power_of_2
 from vllm.utils.torch_utils import set_random_seed
+from vllm.v1.attention.ops.triton_unified_attention import unified_attention
 
 NUM_HEADS = [(4, 4), (8, 2)]
 HEAD_SIZES = [128, 256]

tests/kernels/utils.py

@@ -13,11 +13,11 @@ import torch
 from torch._prims_common import TensorLikeType
 
 from tests.kernels.quant_utils import native_w8a8_block_matmul
-from vllm.attention.backends.abstract import AttentionType
 from vllm.model_executor.custom_op import CustomOp
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.fused_moe.utils import moe_kernel_quantize_input
 from vllm.utils.torch_utils import make_tensor_with_pad
+from vllm.v1.attention.backend import AttentionType
 
 # For now, disable "test_aot_dispatch_dynamic" since there are some
 # bugs related to this test in PyTorch 2.4.

tests/models/multimodal/generation/test_vit_backend_functionality.py

@@ -14,10 +14,10 @@ import pytest
 from transformers import AutoProcessor
 
 from vllm import LLM, EngineArgs, SamplingParams
-from vllm.attention.backends.registry import AttentionBackendEnum
 from vllm.multimodal.utils import encode_image_url
 from vllm.multimodal.video import sample_frames_from_video
 from vllm.platforms import current_platform
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
 from ....utils import create_new_process_for_each_test
 from ...utils import dummy_hf_overrides

tests/models/quantization/test_fp8.py

@@ -9,7 +9,7 @@ Note: these tests will only pass on L4 GPU.
 import pytest
 
 from tests.quantization.utils import is_quant_method_supported
-from vllm.attention.utils.fa_utils import flash_attn_supports_fp8
+from vllm.v1.attention.backends.fa_utils import flash_attn_supports_fp8
 from vllm.platforms import current_platform
 from ..utils import check_logprobs_close
 

tests/test_attention_backend_registry.py

@@ -1,10 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from vllm.attention.backends.abstract import (
+from vllm.v1.attention.backend import (
     AttentionBackend,
     AttentionImpl,
 )
-from vllm.attention.backends.registry import (
+from vllm.v1.attention.backends.registry import (
     AttentionBackendEnum,
     MambaAttentionBackendEnum,
     register_backend,

tests/v1/attention/test_attention_backends.py

@@ -15,8 +15,6 @@ from tests.v1.attention.utils import (
     create_vllm_config,
     try_get_attention_backend,
 )
-from vllm.attention.backends.abstract import AttentionType
-from vllm.attention.backends.registry import AttentionBackendEnum
 from vllm.config import ModelConfig
 from vllm.platforms import current_platform
 from vllm.utils.math_utils import cdiv
@@ -25,6 +23,8 @@ from vllm.utils.torch_utils import (
     is_torch_equal_or_newer,
     set_random_seed,
 )
+from vllm.v1.attention.backend import AttentionType
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
 from vllm.v1.attention.backends.utils import (
     CommonAttentionMetadata,
     set_kv_cache_layout,

tests/v1/attention/test_mla_backends.py

@@ -18,15 +18,15 @@ from tests.v1.attention.utils import (
     try_get_attention_backend,
 )
 from vllm import _custom_ops as ops
-from vllm.attention.backends.registry import AttentionBackendEnum
-from vllm.attention.ops.flashmla import is_flashmla_dense_supported
-from vllm.attention.utils.fa_utils import flash_attn_supports_mla
 from vllm.config.vllm import set_current_vllm_config
 from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
 from vllm.utils.math_utils import cdiv
 from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+from vllm.v1.attention.backends.fa_utils import flash_attn_supports_mla
 from vllm.v1.attention.backends.mla.common import QueryLenSupport
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
 from vllm.v1.attention.backends.utils import CommonAttentionMetadata
+from vllm.v1.attention.ops.flashmla import is_flashmla_dense_supported
 from vllm.v1.kv_cache_interface import FullAttentionSpec
 
 BACKENDS_TO_TEST = [

tests/v1/attention/test_rocm_attention_backends_selection.py

@@ -7,9 +7,9 @@ from unittest.mock import MagicMock, patch
 import pytest
 import torch
 
-from vllm.attention.backends.registry import AttentionBackendEnum
-from vllm.attention.selector import AttentionSelectorConfig
 from vllm.platforms import current_platform
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
+from vllm.v1.attention.selector import AttentionSelectorConfig
 
 # ROCm-specific attention backend selection tests
 pytestmark = pytest.mark.skipif(

tests/v1/attention/test_sparse_mla_backends.py

@@ -21,7 +21,6 @@ from tests.v1.attention.utils import (
     create_vllm_config,
 )
 from vllm import _custom_ops as ops
-from vllm.attention.ops import flashmla
 from vllm.config import set_current_vllm_config
 from vllm.model_executor.layers.linear import ColumnParallelLinear
 from vllm.platforms import current_platform
@@ -31,6 +30,7 @@ from vllm.v1.attention.backends.mla.flashmla_sparse import (
     triton_convert_req_index_to_global_index,
 )
 from vllm.v1.attention.backends.utils import split_prefill_chunks
+from vllm.v1.attention.ops import flashmla
 
 SPARSE_BACKEND_BATCH_SPECS = {
     name: BATCH_SPECS[name]

tests/v1/attention/utils.py

@@ -7,8 +7,6 @@ from dataclasses import dataclass
 import pytest
 import torch
 
-from vllm.attention.backends.abstract import AttentionImpl
-from vllm.attention.backends.registry import AttentionBackendEnum
 from vllm.config import (
     CacheConfig,
     CompilationConfig,
@@ -20,6 +18,8 @@ from vllm.config import (
     VllmConfig,
 )
 from vllm.config.model import ModelDType
+from vllm.v1.attention.backend import AttentionImpl
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
 from vllm.v1.attention.backends.utils import (
     AttentionMetadataBuilder,
     CommonAttentionMetadata,

tests/v1/determinism/utils.py

@@ -6,9 +6,9 @@ import random
 import pytest
 import torch
 
-from vllm.attention.utils.fa_utils import flash_attn_supports_mla
 from vllm.platforms import current_platform
 from vllm.utils.flashinfer import has_flashinfer
+from vllm.v1.attention.backends.fa_utils import flash_attn_supports_mla
 
 skip_unsupported = pytest.mark.skipif(
     not (current_platform.is_cuda() and current_platform.has_device_capability(80)),

tests/v1/kv_connector/unit/test_backwards_compatibility.py

@@ -14,12 +14,12 @@ from unittest.mock import patch
 
 import pytest
 
-from vllm.attention.backends.abstract import AttentionMetadata
 from vllm.distributed.kv_transfer.kv_connector.factory import KVConnectorFactory
 from vllm.distributed.kv_transfer.kv_connector.v1 import (
     KVConnectorBase_V1,
     KVConnectorRole,
 )
+from vllm.v1.attention.backend import AttentionMetadata
 from vllm.v1.core.sched.output import SchedulerOutput
 
 from .utils import create_scheduler, create_vllm_config

tests/v1/spec_decode/test_eagle.py

@@ -13,7 +13,6 @@ from tests.v1.attention.utils import (
     create_standard_kv_cache_spec,
     try_get_attention_backend,
 )
-from vllm.attention.backends.registry import AttentionBackendEnum
 from vllm.config import (
     AttentionConfig,
     CacheConfig,
@@ -27,6 +26,7 @@ from vllm.config import (
 from vllm.config.load import LoadConfig
 from vllm.model_executor.models.llama import LlamaForCausalLM
 from vllm.platforms import current_platform
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
 from vllm.v1.spec_decode.eagle import EagleProposer
 from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
 from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch

tests/v1/spec_decode/test_mtp.py

@@ -12,7 +12,6 @@ from tests.v1.attention.utils import (
     create_standard_kv_cache_spec,
     try_get_attention_backend,
 )
-from vllm.attention.backends.registry import AttentionBackendEnum
 from vllm.config import (
     CacheConfig,
     DeviceConfig,
@@ -25,6 +24,7 @@ from vllm.config import (
 from vllm.config.load import LoadConfig
 from vllm.model_executor.models.llama import LlamaForCausalLM
 from vllm.platforms import current_platform
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
 from vllm.v1.spec_decode.eagle import EagleProposer
 
 mimo_7b_dir = "XiaomiMiMo/MiMo-7B-Base"

tests/v1/spec_decode/test_tree_attention.py

@@ -11,9 +11,9 @@ from tests.v1.attention.utils import (
     create_vllm_config,
     try_get_attention_backend,
 )
-from vllm.attention.backends.registry import AttentionBackendEnum
-from vllm.attention.utils.fa_utils import is_flash_attn_varlen_func_available
 from vllm.config import ParallelConfig, SpeculativeConfig
+from vllm.v1.attention.backends.fa_utils import is_flash_attn_varlen_func_available
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
 from vllm.v1.attention.backends.utils import CommonAttentionMetadata
 
 if not is_flash_attn_varlen_func_available():

tests/v1/worker/test_gpu_model_runner.py

@@ -5,8 +5,6 @@ import numpy as np
 import pytest
 import torch
 
-from vllm.attention.backends.abstract import MultipleOf
-from vllm.attention.backends.registry import AttentionBackendEnum
 from vllm.attention.layer import Attention
 from vllm.config import (
     AttentionConfig,
@@ -27,6 +25,8 @@ from vllm.sampling_params import SamplingParams
 from vllm.utils.mem_constants import GiB_bytes
 from vllm.utils.system_utils import update_environment_variables
 from vllm.utils.torch_utils import set_random_seed
+from vllm.v1.attention.backend import MultipleOf
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
 from vllm.v1.core.kv_cache_utils import estimate_max_model_len, get_kv_cache_configs
 from vllm.v1.core.sched.output import CachedRequestData, NewRequestData, SchedulerOutput
 from vllm.v1.kv_cache_interface import (

tools/pre_commit/mypy.py

@@ -73,7 +73,9 @@ EXCLUDE = [
     "vllm/model_executor/models",
     "vllm/model_executor/layers/fla/ops",
     # Ignore triton kernels in ops.
-    "vllm/attention/ops",
+    "vllm/v1/attention/ops",
+    # TODO(matt): remove.
+    "vllm/v1/attention/backends/fa_utils.py",
 ]
 
 

vllm/attention/layer.py

@@ -8,13 +8,6 @@ import torch
 import torch.nn as nn
 
 import vllm.envs as envs
-from vllm.attention.backends.abstract import (
-    AttentionBackend,
-    AttentionType,
-    MLAAttentionImpl,
-)
-from vllm.attention.backends.registry import AttentionBackendEnum
-from vllm.attention.selector import get_attn_backend
 from vllm.attention.utils.kv_sharing_utils import validate_kv_sharing_target
 from vllm.attention.utils.kv_transfer_utils import maybe_transfer_kv_layer
 from vllm.config import CacheConfig, get_current_vllm_config
@@ -37,6 +30,13 @@ from vllm.utils.torch_utils import (
     direct_register_custom_op,
     kv_cache_dtype_str_to_dtype,
 )
+from vllm.v1.attention.backend import (
+    AttentionBackend,
+    AttentionType,
+    MLAAttentionImpl,
+)
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
+from vllm.v1.attention.selector import get_attn_backend
 from vllm.v1.kv_cache_interface import (
     FullAttentionSpec,
     KVCacheSpec,

vllm/config/attention.py

@@ -6,9 +6,9 @@ from typing import Any, Literal
 from pydantic import field_validator
 from pydantic.dataclasses import dataclass
 
-from vllm.attention.backends.registry import AttentionBackendEnum
 from vllm.config.utils import config
 from vllm.logger import init_logger
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
 logger = init_logger(__name__)
 

vllm/config/model.py

@@ -12,7 +12,6 @@ from pydantic import ConfigDict, Field, field_validator, model_validator
 from pydantic.dataclasses import dataclass
 
 import vllm.envs as envs
-from vllm.attention.backends.registry import AttentionBackendEnum
 from vllm.config.model_arch import (
     ModelArchitectureConfig,
 )
@@ -50,6 +49,7 @@ from vllm.transformers_utils.model_arch_config_convertor import (
 from vllm.transformers_utils.runai_utils import ObjectStorageModel, is_runai_obj_uri
 from vllm.transformers_utils.utils import maybe_model_redirect
 from vllm.utils.import_utils import LazyLoader
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
 if TYPE_CHECKING:
     from transformers import PretrainedConfig

vllm/config/multimodal.py

@@ -7,9 +7,9 @@ from typing import Any, Literal, TypeAlias
 from pydantic import ConfigDict, Field, field_validator, model_validator
 from pydantic.dataclasses import dataclass
 
-from vllm.attention.backends.registry import AttentionBackendEnum
 from vllm.config.utils import config
 from vllm.utils.hashing import safe_hash
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
 
 @dataclass
@@ -124,7 +124,7 @@ class MultiModalConfig:
     mm_encoder_attn_backend: AttentionBackendEnum | None = None
     """Optional override for the multi-modal encoder attention backend when
     using vision transformers. Accepts any value from
-    `vllm.attention.backends.registry.AttentionBackendEnum` (e.g. `FLASH_ATTN`)."""
+    `vllm.v1.attention.backends.registry.AttentionBackendEnum` (e.g. `FLASH_ATTN`)."""
     interleave_mm_strings: bool = False
     """Enable fully interleaved support for multimodal prompts, while using
     --chat-template-content-format=string."""

vllm/distributed/kv_transfer/kv_connector/utils.py

@@ -10,10 +10,10 @@ from typing import TYPE_CHECKING, Literal
 
 import torch
 
-from vllm.attention.backends.abstract import AttentionBackend
 from vllm.config import get_current_vllm_config
 from vllm.distributed.kv_transfer.kv_connector.factory import KVConnectorFactory
 from vllm.logger import init_logger
+from vllm.v1.attention.backend import AttentionBackend
 from vllm.v1.outputs import KVConnectorOutput, ModelRunnerOutput
 
 if TYPE_CHECKING:

vllm/distributed/kv_transfer/kv_connector/v1/base.py

@@ -42,8 +42,8 @@ from typing import TYPE_CHECKING, Any, Literal, Optional
 
 import torch
 
-from vllm.attention.backends.abstract import AttentionBackend, AttentionMetadata
 from vllm.logger import init_logger
+from vllm.v1.attention.backend import AttentionBackend, AttentionMetadata
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.outputs import KVConnectorOutput
 

vllm/distributed/kv_transfer/kv_connector/v1/decode_bench_connector.py

@@ -36,7 +36,6 @@ from typing import TYPE_CHECKING, Any, Optional
 
 import torch
 
-from vllm.attention.backends.abstract import AttentionMetadata
 from vllm.distributed.kv_transfer.kv_connector.v1 import (
     KVConnectorBase_V1,
     KVConnectorRole,
@@ -44,6 +43,7 @@ from vllm.distributed.kv_transfer.kv_connector.v1 import (
 from vllm.distributed.kv_transfer.kv_connector.v1.base import KVConnectorMetadata
 from vllm.logger import init_logger
 from vllm.utils.math_utils import cdiv
+from vllm.v1.attention.backend import AttentionMetadata
 
 if TYPE_CHECKING:
     from vllm.config import VllmConfig

vllm/distributed/kv_transfer/kv_connector/v1/example_connector.py

@@ -7,7 +7,6 @@ from typing import TYPE_CHECKING, Any, Optional
 import safetensors
 import torch
 
-from vllm.attention.backends.abstract import AttentionMetadata
 from vllm.config import VllmConfig
 from vllm.distributed.kv_transfer.kv_connector.v1.base import (
     KVConnectorBase_V1,
@@ -16,6 +15,7 @@ from vllm.distributed.kv_transfer.kv_connector.v1.base import (
 )
 from vllm.logger import init_logger
 from vllm.utils.hashing import safe_hash
+from vllm.v1.attention.backend import AttentionMetadata
 from vllm.v1.attention.backends.mla.common import MLACommonMetadata
 from vllm.v1.core.sched.output import SchedulerOutput
 

vllm/distributed/kv_transfer/kv_connector/v1/lmcache_connector.py

@@ -5,7 +5,6 @@ from typing import TYPE_CHECKING, Any
 
 import torch
 
-from vllm.attention.backends.abstract import AttentionMetadata
 from vllm.config import VllmConfig
 from vllm.distributed.kv_events import (
     BlockStored,
@@ -19,6 +18,7 @@ from vllm.distributed.kv_transfer.kv_connector.v1.base import (
     KVConnectorRole,
 )
 from vllm.logger import init_logger
+from vllm.v1.attention.backend import AttentionMetadata
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.outputs import KVConnectorOutput
 

vllm/distributed/kv_transfer/kv_connector/v1/lmcache_integration/vllm_v1_adapter.py

@@ -36,7 +36,6 @@ except ImportError:
         PluginLauncher as RuntimePluginLauncher,
     )
 
-from vllm.attention.backends.abstract import AttentionMetadata
 from vllm.config import VllmConfig
 from vllm.distributed.kv_transfer.kv_connector.v1.base import (
     KVConnectorBase_V1,
@@ -54,6 +53,7 @@ from vllm.distributed.parallel_state import get_tensor_model_parallel_rank, get_
 from vllm.sampling_params import SamplingParams
 from vllm.utils.math_utils import cdiv
 from vllm.utils.torch_utils import get_kv_cache_torch_dtype
+from vllm.v1.attention.backend import AttentionMetadata
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.version import __version__ as VLLM_VERSION
 

vllm/distributed/kv_transfer/kv_connector/v1/lmcache_mp_connector.py

@@ -10,13 +10,13 @@ import zmq
 from lmcache.integration.vllm.utils import mla_enabled
 from lmcache.utils import init_logger as lmcache_init_logger
 
-from vllm.attention.backends.abstract import AttentionMetadata
 from vllm.config import VllmConfig
 from vllm.distributed.kv_transfer.kv_connector.v1.base import (
     KVConnectorBase_V1,
     KVConnectorMetadata,
     KVConnectorRole,
 )
+from vllm.v1.attention.backend import AttentionMetadata
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.outputs import KVConnectorOutput
 from vllm.v1.request import RequestStatus

vllm/distributed/kv_transfer/kv_connector/v1/mooncake_connector.py

@@ -16,8 +16,6 @@ import zmq
 import zmq.asyncio
 
 from vllm import envs
-from vllm.attention.backends.abstract import AttentionMetadata
-from vllm.attention.selector import get_attn_backend
 from vllm.config import VllmConfig
 from vllm.distributed.kv_transfer.kv_connector.utils import TpKVTopology
 from vllm.distributed.kv_transfer.kv_connector.v1.base import (
@@ -33,7 +31,9 @@ from vllm.distributed.parallel_state import (
 from vllm.forward_context import ForwardContext
 from vllm.logger import init_logger
 from vllm.utils.network_utils import get_ip, make_zmq_path, make_zmq_socket
+from vllm.v1.attention.backend import AttentionMetadata
 from vllm.v1.attention.backends.utils import get_kv_cache_layout
+from vllm.v1.attention.selector import get_attn_backend
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.request import RequestStatus
 

vllm/distributed/kv_transfer/kv_connector/v1/moriio/moriio_connector.py

@@ -15,7 +15,6 @@ import numpy as np
 import torch
 import zmq
 
-from vllm.attention.selector import get_attn_backend
 from vllm.config import VllmConfig
 from vllm.distributed.kv_transfer.kv_connector.v1.base import (
     KVConnectorBase_V1,
@@ -56,11 +55,12 @@ from vllm.utils.network_utils import (
     make_zmq_path,
     make_zmq_socket,
 )
+from vllm.v1.attention.selector import get_attn_backend
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.request import RequestStatus
 
 if TYPE_CHECKING:
-    from vllm.attention.backends.abstract import AttentionMetadata
+    from vllm.v1.attention.backend import AttentionMetadata
     from vllm.v1.core.kv_cache_manager import KVCacheBlocks
     from vllm.v1.kv_cache_interface import KVCacheConfig
     from vllm.v1.request import Request

vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py

@@ -7,7 +7,6 @@ from typing import TYPE_CHECKING, Any
 
 import torch
 
-from vllm.attention.backends.abstract import AttentionBackend, AttentionMetadata
 from vllm.config import VllmConfig
 from vllm.config.kv_transfer import KVTransferConfig
 from vllm.distributed.kv_transfer.kv_connector.base import KVConnectorBaseType
@@ -24,6 +23,7 @@ from vllm.distributed.kv_transfer.kv_connector.v1.metrics import (
     PromMetricT,
 )
 from vllm.logger import init_logger
+from vllm.v1.attention.backend import AttentionBackend, AttentionMetadata
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.outputs import KVConnectorOutput
 

vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py

@@ -20,8 +20,6 @@ import torch
 import zmq
 
 from vllm import envs
-from vllm.attention.backends.abstract import AttentionMetadata
-from vllm.attention.selector import get_attn_backend
 from vllm.config import VllmConfig
 from vllm.distributed.kv_transfer.kv_connector.utils import (
     EngineId,
@@ -50,7 +48,9 @@ from vllm.forward_context import ForwardContext
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
 from vllm.utils.network_utils import make_zmq_path, make_zmq_socket
+from vllm.v1.attention.backend import AttentionMetadata
 from vllm.v1.attention.backends.utils import get_kv_cache_layout
+from vllm.v1.attention.selector import get_attn_backend
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.worker.block_table import BlockTable
 

vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py

@@ -8,7 +8,6 @@ from typing import Any
 
 import torch
 
-from vllm.attention.backends.abstract import AttentionBackend, AttentionMetadata
 from vllm.attention.layer import Attention
 from vllm.config import VllmConfig, get_layers_from_vllm_config
 from vllm.distributed.kv_events import BlockRemoved, BlockStored, KVCacheEvent
@@ -20,6 +19,7 @@ from vllm.distributed.kv_transfer.kv_connector.v1 import (
 from vllm.distributed.kv_transfer.kv_connector.v1.base import KVConnectorMetadata
 from vllm.forward_context import ForwardContext
 from vllm.logger import init_logger
+from vllm.v1.attention.backend import AttentionBackend, AttentionMetadata
 from vllm.v1.core.kv_cache_manager import KVCacheBlocks
 from vllm.v1.core.kv_cache_utils import BlockHash
 from vllm.v1.core.sched.output import SchedulerOutput

vllm/distributed/kv_transfer/kv_connector/v1/p2p/p2p_nccl_connector.py

@@ -7,7 +7,6 @@ from typing import TYPE_CHECKING, Any, Optional
 import regex as re
 import torch
 
-from vllm.attention.backends.abstract import AttentionMetadata
 from vllm.config import VllmConfig
 from vllm.distributed.kv_transfer.kv_connector.v1.base import (
     KVConnectorBase_V1,
@@ -19,6 +18,7 @@ from vllm.distributed.kv_transfer.kv_connector.v1.p2p.p2p_nccl_engine import (
 )
 from vllm.distributed.parallel_state import get_world_group
 from vllm.logger import init_logger
+from vllm.v1.attention.backend import AttentionMetadata
 from vllm.v1.attention.backends.mla.common import MLACommonMetadata
 from vllm.v1.core.sched.output import SchedulerOutput
 

vllm/engine/arg_utils.py

@@ -32,7 +32,6 @@ from pydantic.fields import FieldInfo
 from typing_extensions import TypeIs
 
 import vllm.envs as envs
-from vllm.attention.backends.registry import AttentionBackendEnum
 from vllm.config import (
     AttentionConfig,
     CacheConfig,
@@ -94,6 +93,7 @@ from vllm.utils.argparse_utils import FlexibleArgumentParser
 from vllm.utils.mem_constants import GiB_bytes
 from vllm.utils.network_utils import get_ip
 from vllm.utils.torch_utils import resolve_kv_cache_dtype_string
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
 from vllm.v1.sample.logits_processor import LogitsProcessor
 
 if TYPE_CHECKING:

vllm/envs.py

@@ -684,7 +684,7 @@ environment_variables: dict[str, Callable[[], Any]] = {
         None,
         lambda: list(
             __import__(
-                "vllm.attention.backends.registry", fromlist=["AttentionBackendEnum"]
+                "vllm.v1.attention.backends.registry", fromlist=["AttentionBackendEnum"]
             ).AttentionBackendEnum.__members__.keys()
         ),
     ),

vllm/forward_context.py

@@ -10,10 +10,10 @@ from typing import Any, NamedTuple
 import torch
 
 import vllm.envs as envs
-from vllm.attention.backends.abstract import AttentionMetadata
 from vllm.config import CUDAGraphMode, ParallelConfig, VllmConfig
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
+from vllm.v1.attention.backend import AttentionMetadata
 from vllm.v1.worker.dp_utils import coordinate_batch_across_dp
 from vllm.v1.worker.ubatch_utils import UBatchSlices
 

vllm/model_executor/layers/attention/chunked_local_attention.py

@@ -4,12 +4,11 @@ import functools
 
 import torch
 
-from vllm.attention.backends.abstract import AttentionBackend
 from vllm.attention.layer import Attention
-from vllm.attention.selector import get_attn_backend
 from vllm.config import CacheConfig
 from vllm.config.vllm import VllmConfig
 from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.v1.attention.backend import AttentionBackend
 from vllm.v1.attention.backends.utils import (
     AttentionCGSupport,
     AttentionMetadataBuilder,
@@ -17,6 +16,7 @@ from vllm.v1.attention.backends.utils import (
     make_local_attention_virtual_batches,
     subclass_attention_backend,
 )
+from vllm.v1.attention.selector import get_attn_backend
 from vllm.v1.kv_cache_interface import (
     AttentionSpec,
     ChunkedLocalAttentionSpec,

vllm/model_executor/layers/attention/cross_attention.py

@@ -6,20 +6,20 @@ from copy import copy
 import numpy as np
 import torch
 
-from vllm.attention.backends.abstract import (
+from vllm.attention.layer import Attention
+from vllm.config import CacheConfig, VllmConfig
+from vllm.logger import init_logger
+from vllm.utils.math_utils import cdiv
+from vllm.v1.attention.backend import (
     AttentionBackend,
     AttentionMetadata,
     AttentionType,
 )
-from vllm.attention.layer import Attention
-from vllm.attention.selector import get_attn_backend
-from vllm.config import CacheConfig, VllmConfig
-from vllm.logger import init_logger
-from vllm.utils.math_utils import cdiv
 from vllm.v1.attention.backends.utils import (
     CommonAttentionMetadata,
     subclass_attention_backend,
 )
+from vllm.v1.attention.selector import get_attn_backend
 from vllm.v1.kv_cache_interface import CrossAttentionSpec, KVCacheSpec
 
 logger = init_logger(__name__)

vllm/model_executor/layers/attention/encoder_only_attention.py

@@ -5,19 +5,19 @@ from copy import copy
 
 import torch
 
-from vllm.attention.backends.abstract import (
+from vllm.attention.layer import Attention
+from vllm.config import CacheConfig
+from vllm.config.vllm import VllmConfig
+from vllm.v1.attention.backend import (
     AttentionBackend,
     AttentionMetadata,
     AttentionType,
 )
-from vllm.attention.layer import Attention
-from vllm.attention.selector import get_attn_backend
-from vllm.config import CacheConfig
-from vllm.config.vllm import VllmConfig
 from vllm.v1.attention.backends.utils import (
     CommonAttentionMetadata,
     subclass_attention_backend,
 )
+from vllm.v1.attention.selector import get_attn_backend
 from vllm.v1.kv_cache_interface import KVCacheSpec
 
 

vllm/model_executor/layers/attention/mm_encoder_attention.py

@@ -4,16 +4,16 @@
 
 import torch
 
-from vllm.attention.backends.registry import AttentionBackendEnum
-from vllm.attention.ops.vit_attn_wrappers import (
-    vit_flash_attn_wrapper,
-    vit_torch_sdpa_wrapper,
-)
-from vllm.attention.utils.fa_utils import get_flash_attn_version
 from vllm.config import MultiModalConfig
 from vllm.logger import init_logger
 from vllm.model_executor.custom_op import CustomOp
 from vllm.model_executor.models.vision import get_vit_attn_backend
+from vllm.v1.attention.backends.fa_utils import get_flash_attn_version
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
+from vllm.v1.attention.ops.vit_attn_wrappers import (
+    vit_flash_attn_wrapper,
+    vit_torch_sdpa_wrapper,
+)
 
 logger = init_logger(__name__)
 

vllm/model_executor/layers/attention/static_sink_attention.py

@@ -4,26 +4,26 @@ import functools
 
 import torch
 
-from vllm.attention.backends.abstract import (
-    AttentionBackend,
-    AttentionMetadata,
-    AttentionType,
-)
 from vllm.attention.layer import Attention
-from vllm.attention.ops.triton_reshape_and_cache_flash import (
-    triton_reshape_and_cache_flash_diffkv,
-)
-from vllm.attention.selector import get_attn_backend
 from vllm.config import CacheConfig, VllmConfig
 from vllm.forward_context import ForwardContext, get_forward_context
 from vllm.logger import init_logger
 from vllm.model_executor.custom_op import CustomOp
 from vllm.utils.math_utils import cdiv
 from vllm.utils.torch_utils import direct_register_custom_op
+from vllm.v1.attention.backend import (
+    AttentionBackend,
+    AttentionMetadata,
+    AttentionType,
+)
 from vllm.v1.attention.backends.utils import (
     CommonAttentionMetadata,
     subclass_attention_backend,
 )
+from vllm.v1.attention.ops.triton_reshape_and_cache_flash import (
+    triton_reshape_and_cache_flash_diffkv,
+)
+from vllm.v1.attention.selector import get_attn_backend
 from vllm.v1.kv_cache_interface import (
     AttentionSpec,
     KVCacheSpec,

vllm/model_executor/layers/attention_layer_base.py

@@ -4,8 +4,8 @@
 
 from abc import ABC, abstractmethod
 
-from vllm.attention.backends.abstract import AttentionBackend, AttentionImpl
 from vllm.config import VllmConfig
+from vllm.v1.attention.backend import AttentionBackend, AttentionImpl
 from vllm.v1.kv_cache_interface import KVCacheSpec
 
 

vllm/model_executor/layers/batch_invariant.py

@@ -6,11 +6,11 @@ from typing import Any
 
 import torch
 
-from vllm.attention.backends.registry import AttentionBackendEnum
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
 from vllm.utils.torch_utils import is_torch_equal_or_newer
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
 logger = init_logger(__name__)
 

vllm/model_executor/layers/kda.py

@@ -5,7 +5,6 @@ import torch
 from einops import rearrange
 from torch import nn
 
-from vllm.attention.backends.abstract import AttentionMetadata
 from vllm.config import CacheConfig, ModelConfig, get_current_vllm_config
 from vllm.distributed import (
     divide,
@@ -17,6 +16,7 @@ from vllm.logger import init_logger
 from vllm.model_executor.model_loader.weight_utils import sharded_weight_loader
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.utils.torch_utils import direct_register_custom_op
+from vllm.v1.attention.backend import AttentionMetadata
 from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadata
 
 from .fla.ops.kda import (

vllm/model_executor/layers/mamba/abstract.py

@@ -5,10 +5,10 @@ from collections.abc import Iterable
 
 import torch
 
-from vllm.attention.backends.abstract import AttentionBackend
-from vllm.attention.selector import get_mamba_attn_backend
 from vllm.config import VllmConfig
 from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
+from vllm.v1.attention.backend import AttentionBackend
+from vllm.v1.attention.selector import get_mamba_attn_backend
 from vllm.v1.kv_cache_interface import KVCacheSpec, MambaSpec
 
 

vllm/model_executor/layers/mamba/linear_attn.py

@@ -8,7 +8,6 @@ import torch.nn.functional as F
 from einops import rearrange
 from torch import nn
 
-from vllm.attention.backends.abstract import AttentionMetadata
 from vllm.config import CacheConfig, ModelConfig, get_current_vllm_config
 from vllm.distributed.communication_op import tensor_model_parallel_all_reduce
 from vllm.distributed.parallel_state import (
@@ -29,6 +28,7 @@ from vllm.model_executor.layers.mamba.mamba_utils import (
 )
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.utils.torch_utils import direct_register_custom_op
+from vllm.v1.attention.backend import AttentionMetadata
 from vllm.v1.attention.backends.linear_attn import LinearAttentionMetadata
 
 

vllm/model_executor/layers/mamba/mamba_mixer2.py

@@ -5,7 +5,6 @@
 import torch
 from torch import nn
 
-from vllm.attention.backends.abstract import AttentionMetadata
 from vllm.config import CacheConfig, ModelConfig, get_current_vllm_config
 from vllm.distributed import (
     divide,
@@ -43,6 +42,7 @@ from vllm.model_executor.model_loader.weight_utils import (
 )
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.utils.torch_utils import direct_register_custom_op
+from vllm.v1.attention.backend import AttentionMetadata
 from vllm.v1.attention.backends.mamba2_attn import Mamba2AttentionMetadata
 
 # Added by the IBM Team, 2024

vllm/model_executor/layers/mamba/short_conv.py

@@ -4,7 +4,6 @@
 
 import torch
 
-from vllm.attention.backends.abstract import AttentionMetadata
 from vllm.config import CacheConfig, ModelConfig, get_current_vllm_config
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.forward_context import ForwardContext, get_forward_context
@@ -24,6 +23,7 @@ from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
     causal_conv1d_update,
 )
 from vllm.utils.torch_utils import direct_register_custom_op
+from vllm.v1.attention.backend import AttentionMetadata
 from vllm.v1.attention.backends.short_conv_attn import ShortConvAttentionMetadata
 
 

vllm/model_executor/models/afmoe.py

@@ -9,7 +9,6 @@ from itertools import islice
 import torch
 from torch import nn
 
-from vllm.attention.backends.abstract import AttentionType
 from vllm.attention.layer import Attention
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config
@@ -50,6 +49,7 @@ from vllm.model_executor.models.utils import (
     maybe_prefix,
 )
 from vllm.sequence import IntermediateTensors
+from vllm.v1.attention.backend import AttentionType
 
 logger = init_logger(__name__)
 

vllm/model_executor/models/aimv2.py

@@ -8,10 +8,10 @@ from collections.abc import Iterable
 import torch
 import torch.nn as nn
 
-from vllm.attention.layers.mm_encoder_attention import MMEncoderAttention
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.distributed.utils import divide
 from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.attention.mm_encoder_attention import MMEncoderAttention
 from vllm.model_executor.layers.conv import Conv2dLayer
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (

vllm/model_executor/models/apertus.py

@@ -32,13 +32,14 @@ import torch
 from torch import nn
 from transformers import ApertusConfig
 
-from vllm.attention.backends.abstract import AttentionType
 from vllm.attention.layer import Attention
-from vllm.attention.layers.encoder_only_attention import EncoderOnlyAttention
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import XIELU
+from vllm.model_executor.layers.attention.encoder_only_attention import (
+    EncoderOnlyAttention,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
@@ -57,6 +58,7 @@ from vllm.model_executor.model_loader.weight_utils import (
     maybe_remap_kv_scale_name,
 )
 from vllm.sequence import IntermediateTensors
+from vllm.v1.attention.backend import AttentionType
 
 from .interfaces import SupportsLoRA, SupportsPP
 from .utils import (

vllm/model_executor/models/bert.py

@@ -7,11 +7,13 @@ import torch
 from torch import nn
 from transformers import BertConfig
 
-from vllm.attention.layers.encoder_only_attention import EncoderOnlyAttention
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, PoolerConfig, VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.attention.encoder_only_attention import (
+    EncoderOnlyAttention,
+)
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
     QKVParallelLinear,

vllm/model_executor/models/bert_with_rope.py

@@ -6,7 +6,6 @@ import torch
 from torch import nn
 from transformers import PretrainedConfig
 
-from vllm.attention.layers.encoder_only_attention import EncoderOnlyAttention
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import (
@@ -16,6 +15,9 @@ from vllm.distributed import (
     tensor_model_parallel_all_reduce,
 )
 from vllm.model_executor.layers.activation import get_act_and_mul_fn, get_act_fn
+from vllm.model_executor.layers.attention.encoder_only_attention import (
+    EncoderOnlyAttention,
+)
 from vllm.model_executor.layers.fused_moe import activation_without_mul, fused_topk
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,

vllm/model_executor/models/blip.py

@@ -9,9 +9,9 @@ import torch
 import torch.nn as nn
 from transformers import Blip2VisionConfig, BlipVisionConfig
 
-from vllm.attention.layers.mm_encoder_attention import MMEncoderAttention
 from vllm.distributed import divide, get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.attention.mm_encoder_attention import MMEncoderAttention
 from vllm.model_executor.layers.conv import Conv2dLayer
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,

vllm/model_executor/models/clip.py

@@ -15,11 +15,11 @@ from transformers import (
 )
 
 from vllm.attention.layer import Attention
-from vllm.attention.layers.mm_encoder_attention import MMEncoderAttention
 from vllm.config import VllmConfig
 from vllm.config.multimodal import BaseDummyOptions, MultiModalConfig
 from vllm.distributed import divide, get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.attention.mm_encoder_attention import MMEncoderAttention
 from vllm.model_executor.layers.conv import Conv2dLayer
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,

vllm/model_executor/models/config.py

@@ -4,12 +4,12 @@ from copy import deepcopy
 from math import lcm
 from typing import TYPE_CHECKING
 
-from vllm.attention.backends.registry import AttentionBackendEnum
 from vllm.logger import init_logger
 from vllm.model_executor.models import ModelRegistry
 from vllm.platforms import current_platform
 from vllm.utils.math_utils import cdiv, round_up
 from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
 from vllm.v1.kv_cache_interface import FullAttentionSpec, MambaSpec, MLAAttentionSpec
 
 if TYPE_CHECKING:

vllm/model_executor/models/deepencoder.py

@@ -18,8 +18,8 @@ import torch.nn as nn
 import torch.nn.functional as F
 from transformers import CLIPVisionConfig
 
-from vllm.attention.layers.mm_encoder_attention import MMEncoderAttention
 from vllm.config import MultiModalConfig
+from vllm.model_executor.layers.attention.mm_encoder_attention import MMEncoderAttention
 from vllm.model_executor.layers.conv import Conv2dLayer
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader

vllm/model_executor/models/deepseek_v2.py

@@ -33,9 +33,7 @@ from torch import nn
 from transformers import DeepseekV2Config, DeepseekV3Config
 
 from vllm._aiter_ops import rocm_aiter_ops
-from vllm.attention.backends.abstract import AttentionBackend
 from vllm.attention.layer import Attention
-from vllm.attention.ops.common import pack_seq_triton, unpack_seq_triton
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, ParallelConfig, VllmConfig, get_current_vllm_config
 from vllm.distributed import (
@@ -78,10 +76,12 @@ from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors
 from vllm.utils.deep_gemm import fp8_mqa_logits, fp8_paged_mqa_logits
 from vllm.utils.torch_utils import direct_register_custom_op
+from vllm.v1.attention.backend import AttentionBackend
 from vllm.v1.attention.backends.mla.indexer import (
     DeepseekV32IndexerBackend,
     DeepseekV32IndexerMetadata,
 )
+from vllm.v1.attention.ops.common import pack_seq_triton, unpack_seq_triton
 from vllm.v1.kv_cache_interface import KVCacheSpec, MLAAttentionSpec
 from vllm.v1.worker.workspace import current_workspace_manager
 
@@ -679,7 +679,9 @@ def sparse_attn_indexer(
             )
             fp8_mqa_logits_func = fp8_mqa_logits
             if current_platform.is_rocm():
-                from vllm.attention.ops.rocm_aiter_mla_sparse import rocm_fp8_mqa_logits
+                from vllm.v1.attention.ops.rocm_aiter_mla_sparse import (
+                    rocm_fp8_mqa_logits,
+                )
 
                 fp8_mqa_logits_func = rocm_fp8_mqa_logits
             logits = fp8_mqa_logits_func(
@@ -729,7 +731,7 @@ def sparse_attn_indexer(
         num_padded_tokens = batch_size * next_n
         fp8_paged_mqa_logits_func = fp8_paged_mqa_logits
         if current_platform.is_rocm():
-            from vllm.attention.ops.rocm_aiter_mla_sparse import (
+            from vllm.v1.attention.ops.rocm_aiter_mla_sparse import (
                 rocm_fp8_paged_mqa_logits,
             )
 

vllm/model_executor/models/dots_ocr.py

@@ -8,10 +8,6 @@ import torch.nn as nn
 from torch.nn import LayerNorm
 from transformers.models.qwen2_vl import Qwen2VLProcessor
 
-from vllm.attention.backends.registry import AttentionBackendEnum
-from vllm.attention.layers.mm_encoder_attention import (
-    MMEncoderAttention,
-)
 from vllm.config import MultiModalConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
 from vllm.distributed import utils as dist_utils
@@ -20,6 +16,9 @@ from vllm.distributed.parallel_state import (
     get_tensor_model_parallel_world_size,
 )
 from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.attention.mm_encoder_attention import (
+    MMEncoderAttention,
+)
 from vllm.model_executor.layers.conv import Conv2dLayer
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
@@ -59,6 +58,7 @@ from vllm.multimodal.inputs import MultiModalDataDict
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.configs.dotsocr import DotsOCRConfig, DotsVisionConfig
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
 from .vision import run_dp_sharded_mrope_vision_model
 

vllm/model_executor/models/ernie45_vl.py

@@ -36,16 +36,15 @@ import torch.nn.functional as F
 from einops import rearrange
 from transformers import BatchFeature
 
-from vllm.attention.backends.registry import AttentionBackendEnum
-from vllm.attention.layers.mm_encoder_attention import (
-    MMEncoderAttention,
-)
 from vllm.config import MultiModalConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions, VideoDummyOptions
 from vllm.distributed import parallel_state
 from vllm.distributed import utils as dist_utils
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import QuickGELU
+from vllm.model_executor.layers.attention.mm_encoder_attention import (
+    MMEncoderAttention,
+)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
@@ -74,6 +73,7 @@ from vllm.multimodal.processing import (
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
 from .ernie45_vl_moe import Ernie4_5_VLMoeForCausalLM
 from .interfaces import (

vllm/model_executor/models/gemma3.py

@@ -22,13 +22,15 @@ import torch
 from torch import nn
 from transformers import Gemma3TextConfig
 
-from vllm.attention.backends.abstract import AttentionType
 from vllm.attention.layer import Attention
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import GeluAndMul
+from vllm.model_executor.layers.attention.encoder_only_attention import (
+    EncoderOnlyAttention,
+)
 from vllm.model_executor.layers.layernorm import GemmaRMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
@@ -47,8 +49,8 @@ from vllm.model_executor.model_loader.weight_utils import (
     maybe_remap_kv_scale_name,
 )
 from vllm.sequence import IntermediateTensors
+from vllm.v1.attention.backend import AttentionType
 
-from ...attention.layers.encoder_only_attention import EncoderOnlyAttention
 from .interfaces import SupportsLoRA, SupportsPP
 from .utils import (
     AutoWeightsLoader,

vllm/model_executor/models/glm4.py

@@ -29,7 +29,6 @@ import torch
 from torch import nn
 from transformers import Glm4Config
 
-from vllm.attention.backends.abstract import AttentionType
 from vllm.attention.layer import Attention
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, VllmConfig
@@ -41,6 +40,7 @@ from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.sequence import IntermediateTensors
+from vllm.v1.attention.backend import AttentionType
 
 from .interfaces import SupportsLoRA, SupportsPP
 from .llama import LlamaMLP as Glm4MLP

vllm/model_executor/models/glm4_1v.py

@@ -46,15 +46,14 @@ from transformers.models.glm4v.image_processing_glm4v import (
 from transformers.models.glm4v.video_processing_glm4v import Glm4vVideoProcessor
 from transformers.video_utils import VideoMetadata
 
-from vllm.attention.backends.registry import AttentionBackendEnum
-from vllm.attention.layers.mm_encoder_attention import (
-    MMEncoderAttention,
-)
 from vllm.config import MultiModalConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions, VideoDummyOptions
 from vllm.distributed import get_tensor_model_parallel_world_size, parallel_state
 from vllm.distributed import utils as dist_utils
 from vllm.logger import init_logger
+from vllm.model_executor.layers.attention.mm_encoder_attention import (
+    MMEncoderAttention,
+)
 from vllm.model_executor.layers.conv import Conv2dLayer, Conv3dLayer
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
@@ -89,6 +88,7 @@ from vllm.multimodal.processing import (
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
+from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
 from ..layers.activation import SiluAndMul
 from .interfaces import (

vllm/model_executor/models/glm4v.py

@@ -19,11 +19,11 @@ from transformers import BatchFeature, PreTrainedTokenizer, TensorType
 from transformers.image_utils import ImageInput
 from transformers.tokenization_utils_base import TextInput
 
-from vllm.attention.layers.mm_encoder_attention import MMEncoderAttention
 from vllm.config import VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import SiluAndMul, get_act_fn
+from vllm.model_executor.layers.attention.mm_encoder_attention import MMEncoderAttention
 from vllm.model_executor.layers.conv import Conv2dLayer
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,

vllm/model_executor/models/glmasr.py

@@ -11,12 +11,12 @@ from transformers import BatchFeature
 from transformers.models.glmasr import GlmAsrConfig, GlmAsrProcessor
 from transformers.models.whisper import WhisperFeatureExtractor
 
-from vllm.attention.layers.mm_encoder_attention import MMEncoderAttention
 from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
 from vllm.distributed.parallel_state import get_tensor_model_parallel_world_size
 from vllm.inputs.data import PromptType
 from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.attention.mm_encoder_attention import MMEncoderAttention
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
     QKVParallelLinear,

vllm/model_executor/models/gpt_oss.py

@@ -7,7 +7,6 @@ import torch.distributed as dist
 from torch import nn
 from transformers import GptOssConfig
 
-from vllm.attention.backends.abstract import AttentionType
 from vllm.attention.layer import Attention
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, VllmConfig
@@ -37,6 +36,7 @@ from vllm.model_executor.models.utils import sequence_parallel_chunk
 from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors
 from vllm.utils.math_utils import cdiv
+from vllm.v1.attention.backend import AttentionType
 
 from .interfaces import SupportsEagle3, SupportsLoRA, SupportsPP
 from .utils import (

vllm/model_executor/models/hunyuan_v1.py

@@ -33,7 +33,6 @@ import torch
 from torch import nn
 from transformers import PretrainedConfig
 
-from vllm.attention.backends.abstract import AttentionType
 from vllm.attention.layer import Attention
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config
@@ -65,6 +64,7 @@ from vllm.model_executor.model_loader.weight_utils import (
     maybe_remap_kv_scale_name,
 )
 from vllm.sequence import IntermediateTensors
+from vllm.v1.attention.backend import AttentionType
 
 from .interfaces import MixtureOfExperts, SupportsLoRA, SupportsPP
 from .utils import (