[V0 deprecation] Remove no longer used `get_metadata_cls` (#28370)

Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com>

tests/kernels/utils.py

@@ -4,24 +4,21 @@
 
 import itertools
 import random
-import unittest
 from collections.abc import Sequence
 from numbers import Number
 from typing import Any, NamedTuple
+from unittest.mock import patch
 
 import pytest
 import torch
 from torch._prims_common import TensorLikeType
 
 from tests.kernels.quant_utils import native_w8a8_block_matmul
-from vllm.attention import AttentionBackend, AttentionMetadata, AttentionType
-from vllm.attention.backends.registry import _Backend
+from vllm.attention import AttentionType
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.fused_moe.utils import moe_kernel_quantize_input
 from vllm.utils import (
     STR_BACKEND_ENV_VAR,
-    STR_FLASH_ATTN_VAL,
-    STR_XFORMERS_ATTN_VAL,
 )
 from vllm.utils.torch_utils import make_tensor_with_pad
 
@@ -512,50 +509,6 @@ def pack_qkv(qkv: QKVInputs, device: torch.device | str) -> PackedQKVInputs:
     )
 
 
-def make_backend(backend_name: str) -> AttentionBackend:
-    """
-    Construct the backend instance determined by the backend_name string
-    argument.
-
-    Note: at time of writing the Attention wrapper automatically selects
-    its own backend for Attention.forward(); so the backend instance which
-    you generate with this function is not meant to be used for *running*
-    inference, but rather for generating compatible metadata structures
-    using backend.make_metadata()
-
-
-    Returns:
-
-    * Backend instance
-    """
-    if backend_name == STR_XFORMERS_ATTN_VAL:
-        from vllm.v1.attention.backends.xformers import XFormersAttentionBackend
-
-        return XFormersAttentionBackend()
-    if backend_name == STR_FLASH_ATTN_VAL:
-        from vllm.v1.attention.backends.flash_attn import FlashAttentionBackend
-
-        return FlashAttentionBackend()
-    if backend_name == "TRITON_ATTN":
-        from vllm.v1.attention.backends.triton_attn import TritonAttentionBackend
-
-        return TritonAttentionBackend()
-    if backend_name == "FLEX_ATTENTION":
-        from vllm.v1.attention.backends.flex_attention import FlexAttentionBackend
-
-        return FlexAttentionBackend()
-    if backend_name == "TORCH_SDPA":
-        from vllm.v1.attention.backends.cpu_attn import TorchSDPABackend
-
-        return TorchSDPABackend()
-    if backend_name == "FLASHINFER":
-        from vllm.v1.attention.backends.flashinfer import FlashInferBackend
-
-        return FlashInferBackend()
-
-    raise AssertionError(f"Unrecognized backend_name {backend_name} for unit test")
-
-
 def make_alibi_bias(
     alibi_slopes: torch.Tensor,
     num_kv_heads: int,
@@ -877,197 +830,6 @@ def make_block_tables_slot_mapping(
     return (block_tables_tensor, slot_mapping_list, max_block_idx)
 
 
-def make_test_metadata(
-    attn_backend: _Backend,
-    is_prompt: bool,
-    seq_lens: list[int] | None,
-    decoder_test_params: PhaseTestParameters | None,
-    device: torch.device | str,
-    encoder_test_params: PhaseTestParameters | None = None,
-    cross_test_params: PhaseTestParameters | None = None,
-) -> AttentionMetadata:
-    """
-    Construct fake attention metadata for a given test phase
-    (prefill-phase or decode-phase).
-
-    encoder_test_params and cross_test_params arguments allow encoder
-    attention and enc/dec cross-attention (respectively) to use distinct
-    metadata values from decoder self-attention (decoder_test_params.)
-
-    if encoder_test_params and cross_test_params are None, the attention
-    metadata will support decoder-only scenario.
-
-    Assumptions:
-
-    * No chunked prefill -> a batch is 100% prefill or 100% decode, never both
-
-    Arguments:
-
-    * attn_backend_name: Backend for sourcing attention kernels
-    * is_prompt: prefill if True, o/w decode
-    * seq_lens: list of token counts for each sequence
-    * decoder_test_params: decoder self-attention test params;
-                           this function requires
-                           kv_mmap (memory mapping) field
-    * device: CPU or CUDA device
-    * encoder_test_params: encoder attention test params;
-                           this function requires encoder query
-                           sequence lengths field. If None,
-                           encoder query sequence lengths are
-                           treated as None
-    * cross_test_params: enc/dec cross-attention test params;
-                         this function requires kv_mmap field.
-                         If None, KV cache memory map data
-                         structures are treated as None
-
-    Return:
-
-    * AttentionMetadata structure
-    """
-
-    # Decoder self-attention memory mapping
-    # decoder_test_params is None signals encoder-only
-    # scenario, so kv_mmap is None
-    kv_mmap = None if decoder_test_params is None else decoder_test_params.kv_mmap
-
-    # This function constructs metadata assuming no chunked prefill,
-    # i.e. 100% prefill tokens or 100% decode tokens
-    #
-    # - If is_prompt, num_prefills_or_decodes is the number of prefills
-    #   and num_prefill_or_decode_tokens is the number of prefill tokens
-    # - If not is_prompt, num_prefills_or_decodes is the number of decodes
-    #   and num_prefill_or_decode_tokens is the number of decode tokens
-    #
-    # seq_lens is None signals encoder-only
-    # scenario, in which case num_prefills_or_decodes and
-    # num_prefill_or_decode_tokens are unused
-    num_prefills_or_decodes = None if seq_lens is None else len(seq_lens)
-
-    num_prefill_or_decode_tokens = (
-        None if seq_lens is None else (sum(seq_lens) if is_prompt else len(seq_lens))
-    )
-
-    # Seems for non-prefix-caching scenarios context_lens
-    # is never needed
-    context_lens = None
-
-    if encoder_test_params is None:
-        encoder_seq_lens = None
-        num_encoder_tokens = None
-    else:
-        # Encoder/decoder or encoder-only models only:
-        # * Extract encoder input sequence lengths
-        assert encoder_test_params.packed_qkvo.packed_qkv is not None
-        encoder_seq_lens = encoder_test_params.packed_qkvo.packed_qkv.q_seq_lens
-        num_encoder_tokens = (
-            None if encoder_seq_lens is None else (sum(encoder_seq_lens))
-        )
-
-    # For encoder/decoder or encoder-only models only, extract *cross-attention*
-    # slot_mapping and block table (kv_mmap)
-    cross_kv_mmap = None if cross_test_params is None else cross_test_params.kv_mmap
-
-    attn_backend_obj = make_backend(attn_backend.name)
-
-    if is_prompt:
-        # Prefill-phase scenario
-
-        num_prefills = num_prefills_or_decodes
-        num_prefill_tokens = num_prefill_or_decode_tokens
-        num_decode_tokens = 0
-
-        (
-            seq_lens_tensor,
-            context_lens_tensor,
-            _,
-            _,
-            seq_start_loc,
-            encoder_seq_lens_tensor,
-            encoder_seq_start_loc,
-            max_encoder_seq_len,
-        ) = _make_metadata_tensors(
-            seq_lens, context_lens, encoder_seq_lens, device=device
-        )
-        return attn_backend_obj.make_metadata(
-            num_prefills=num_prefills,
-            slot_mapping=(None if kv_mmap is None else kv_mmap.slot_mapping),
-            enable_kv_scales_calculation=True,
-            num_prefill_tokens=num_prefill_tokens,
-            num_decode_tokens=num_decode_tokens,
-            seq_lens=seq_lens,
-            seq_lens_tensor=seq_lens_tensor,
-            seq_start_loc=seq_start_loc,
-            max_prefill_seq_len=None if seq_lens is None else max(seq_lens),
-            max_decode_seq_len=0,
-            context_lens_tensor=context_lens_tensor,
-            block_tables=(None if kv_mmap is None else kv_mmap.block_tables),
-            use_cuda_graph=False,
-            num_encoder_tokens=num_encoder_tokens,
-            encoder_seq_lens=encoder_seq_lens,
-            encoder_seq_lens_tensor=encoder_seq_lens_tensor,
-            encoder_seq_start_loc=encoder_seq_start_loc,
-            max_encoder_seq_len=max_encoder_seq_len,
-            cross_slot_mapping=(
-                None if cross_kv_mmap is None else cross_kv_mmap.slot_mapping
-            ),
-            cross_block_tables=(
-                None if cross_kv_mmap is None else cross_kv_mmap.block_tables
-            ),
-        )
-
-    else:  # not is_prompt
-        # Decode-phase scenario
-
-        assert kv_mmap is not None
-        assert num_prefill_or_decode_tokens is not None
-        assert seq_lens is not None
-
-        num_prefills = 0
-        num_prefill_tokens = 0
-        num_decode_tokens = num_prefill_or_decode_tokens
-
-        (
-            seq_lens_tensor,
-            context_lens_tensor,
-            _,
-            _,
-            seq_start_loc,
-            encoder_seq_lens_tensor,
-            encoder_seq_start_loc,
-            max_encoder_seq_len,
-        ) = _make_metadata_tensors(
-            seq_lens, context_lens, encoder_seq_lens, device=device
-        )
-
-        return attn_backend_obj.make_metadata(
-            num_prefills=num_prefills,
-            slot_mapping=kv_mmap.slot_mapping,
-            enable_kv_scales_calculation=True,
-            num_prefill_tokens=num_prefill_tokens,
-            num_decode_tokens=num_decode_tokens,
-            seq_lens=seq_lens,
-            seq_lens_tensor=seq_lens_tensor,
-            seq_start_loc=seq_start_loc,
-            max_prefill_seq_len=0,
-            max_decode_seq_len=max(seq_lens),
-            max_decode_query_len=1,
-            context_lens_tensor=context_lens_tensor,
-            block_tables=kv_mmap.block_tables,
-            use_cuda_graph=False,
-            num_encoder_tokens=num_encoder_tokens,
-            encoder_seq_lens=encoder_seq_lens,
-            encoder_seq_lens_tensor=encoder_seq_lens_tensor,
-            encoder_seq_start_loc=encoder_seq_start_loc,
-            max_encoder_seq_len=max_encoder_seq_len,
-            cross_slot_mapping=(
-                None if cross_kv_mmap is None else cross_kv_mmap.slot_mapping
-            ),
-            cross_block_tables=(
-                None if cross_kv_mmap is None else cross_kv_mmap.block_tables
-            ),
-        )
-
-
 def assert_actual_matches_ideal(
     test_params: PhaseTestParameters, output_under_test: torch.Tensor, backend: str
 ) -> None:
@@ -1308,7 +1070,7 @@ def opcheck(
     raise_exception: bool = True,
     cond: bool = True,
 ) -> dict[str, str]:
-    with unittest.mock.patch("torch.allclose", new=fp8_allclose):
+    with patch("torch.allclose", new=fp8_allclose):
         return (
             torch.library.opcheck(
                 op, args, kwargs, test_utils=test_utils, raise_exception=raise_exception

vllm/attention/backends/abstract.py

@@ -51,19 +51,10 @@ class AttentionBackend(ABC):
     def get_impl_cls() -> type["AttentionImpl"]:
         raise NotImplementedError
 
-    @staticmethod
-    @abstractmethod
-    def get_metadata_cls() -> type["AttentionMetadata"]:
-        raise NotImplementedError
-
     @classmethod
     def get_supported_kernel_block_size(cls) -> list[int | MultipleOf]:
         return cls.get_impl_cls().get_supported_kernel_block_size()
 
-    @classmethod
-    def make_metadata(cls, *args, **kwargs) -> "AttentionMetadata":
-        return cls.get_metadata_cls()(*args, **kwargs)
-
     @staticmethod
     @abstractmethod
     def get_builder_cls():  # -> Type["AttentionMetadataBuilder"]:

vllm/v1/attention/backends/cpu_attn.py

@@ -66,10 +66,6 @@ class TorchSDPABackend(AttentionBackend):
     def get_impl_cls() -> type["TorchSDPABackendImpl"]:
         return TorchSDPABackendImpl
 
-    @staticmethod
-    def get_metadata_cls() -> type["AttentionMetadata"]:
-        return TorchSDPAMetadata
-
     @staticmethod
     def get_builder_cls() -> type["TorchSDPAMetadataBuilderV1"]:
         return TorchSDPAMetadataBuilderV1

vllm/v1/attention/backends/flash_attn.py

@@ -11,7 +11,6 @@ from vllm import envs
 from vllm.attention.backends.abstract import (
     AttentionBackend,
     AttentionImpl,
-    AttentionMetadata,
     AttentionType,
     MultipleOf,
     is_quantized_kv_cache,
@@ -90,10 +89,6 @@ class FlashAttentionBackend(AttentionBackend):
     def get_impl_cls() -> type["FlashAttentionImpl"]:
         return FlashAttentionImpl
 
-    @staticmethod
-    def get_metadata_cls() -> type["AttentionMetadata"]:
-        return FlashAttentionMetadata
-
     @staticmethod
     def get_builder_cls() -> type["FlashAttentionMetadataBuilder"]:
         return FlashAttentionMetadataBuilder

vllm/v1/attention/backends/flashinfer.py

@@ -195,10 +195,6 @@ class FlashInferBackend(AttentionBackend):
     def get_impl_cls() -> type["FlashInferImpl"]:
         return FlashInferImpl
 
-    @staticmethod
-    def get_metadata_cls() -> type["FlashInferMetadata"]:
-        return FlashInferMetadata
-
     @staticmethod
     def get_builder_cls() -> type["FlashInferMetadataBuilder"]:
         return FlashInferMetadataBuilder

vllm/v1/attention/backends/flex_attention.py

@@ -20,7 +20,6 @@ from torch.nn.attention.flex_attention import (
 from vllm.attention.backends.abstract import (
     AttentionBackend,
     AttentionImpl,
-    AttentionMetadata,
     AttentionType,
     is_quantized_kv_cache,
 )
@@ -89,10 +88,6 @@ class FlexAttentionBackend(AttentionBackend):
     def get_impl_cls() -> type["FlexAttentionImpl"]:
         return FlexAttentionImpl
 
-    @staticmethod
-    def get_metadata_cls() -> type["AttentionMetadata"]:
-        return FlexAttentionMetadata
-
     @staticmethod
     def get_kv_cache_shape(
         num_blocks: int,

vllm/v1/attention/backends/mla/common.py

@@ -201,7 +201,6 @@ from vllm import _custom_ops as ops
 from vllm.attention.backends.abstract import (
     AttentionBackend,
     AttentionLayer,
-    AttentionMetadata,
     MLAAttentionImpl,
 )
 from vllm.attention.backends.utils import get_mla_dims
@@ -307,10 +306,6 @@ class MLACommonBackend(AttentionBackend):
     def get_name() -> str:
         return "TRITON_MLA"
 
-    @staticmethod
-    def get_metadata_cls() -> type["AttentionMetadata"]:
-        return MLACommonMetadata
-
     @staticmethod
     def get_builder_cls() -> type["MLACommonMetadataBuilder"]:
         return MLACommonMetadataBuilder

vllm/v1/attention/backends/mla/flashattn_mla.py

@@ -41,10 +41,6 @@ class FlashAttnMLABackend(MLACommonBackend):
     def get_name() -> str:
         return "FLASH_ATTN_MLA"
 
-    @staticmethod
-    def get_metadata_cls() -> type["FlashAttnMLAMetadata"]:
-        return FlashAttnMLAMetadata
-
     @staticmethod
     def get_builder_cls() -> type["FlashAttnMLAMetadataBuilder"]:
         return FlashAttnMLAMetadataBuilder

vllm/v1/attention/backends/mla/flashmla.py

@@ -40,10 +40,6 @@ class FlashMLABackend(MLACommonBackend):
     def get_name() -> str:
         return "FLASHMLA"
 
-    @staticmethod
-    def get_metadata_cls() -> type["FlashMLAMetadata"]:
-        return FlashMLAMetadata
-
     @staticmethod
     def get_builder_cls() -> type["FlashMLAMetadataBuilder"]:
         return FlashMLAMetadataBuilder

vllm/v1/attention/backends/mla/flashmla_sparse.py

@@ -10,7 +10,6 @@ from vllm import _custom_ops as ops
 from vllm.attention.backends.abstract import (
     AttentionBackend,
     AttentionLayer,
-    AttentionMetadata,
 )
 from vllm.attention.backends.utils import get_mla_dims
 from vllm.attention.ops.flashmla import (
@@ -57,10 +56,6 @@ class FlashMLASparseBackend(AttentionBackend):
     def get_name() -> str:
         return "FLASHMLA_SPARSE"
 
-    @staticmethod
-    def get_metadata_cls() -> type[AttentionMetadata]:
-        return FlashMLASparseMetadata
-
     @staticmethod
     def get_builder_cls() -> type["FlashMLASparseMetadataBuilder"]:
         return FlashMLASparseMetadataBuilder

vllm/v1/attention/backends/mla/indexer.py

@@ -7,7 +7,6 @@ import torch
 
 from vllm.attention.backends.abstract import (
     AttentionBackend,
-    AttentionMetadata,
     MultipleOf,
 )
 from vllm.config import VllmConfig
@@ -24,10 +23,6 @@ logger = init_logger(__name__)
 
 
 class DeepseekV32IndexerBackend(AttentionBackend):
-    @staticmethod
-    def get_metadata_cls() -> type["AttentionMetadata"]:
-        return DeepseekV32IndexerMetadata
-
     @classmethod
     def get_supported_head_sizes(cls) -> list[int]:
         return [32, 64, 128]

vllm/v1/attention/backends/mla/rocm_aiter_mla.py

@@ -35,10 +35,6 @@ class AiterMLABackend(MLACommonBackend):
     def get_impl_cls() -> type["AiterMLAImpl"]:
         return AiterMLAImpl
 
-    @staticmethod
-    def get_metadata_cls() -> type["AiterMLAMetadata"]:
-        return AiterMLAMetadata
-
     @staticmethod
     def get_builder_cls() -> type["AiterMLAMetadataBuilder"]:
         return AiterMLAMetadataBuilder

vllm/v1/attention/backends/pallas.py

@@ -108,10 +108,6 @@ class PallasAttentionBackend(AttentionBackend):
     def get_impl_cls() -> type["PallasAttentionBackendImpl"]:
         return PallasAttentionBackendImpl
 
-    @staticmethod
-    def get_metadata_cls() -> type["PallasMetadata"]:
-        return PallasMetadata
-
     @staticmethod
     def get_kv_cache_shape(
         num_blocks: int,

vllm/v1/attention/backends/rocm_aiter_fa.py

@@ -9,7 +9,6 @@ import torch
 from vllm.attention.backends.abstract import (
     AttentionBackend,
     AttentionImpl,
-    AttentionMetadata,
     AttentionType,
     MultipleOf,
 )
@@ -479,10 +478,6 @@ class AiterFlashAttentionBackend(AttentionBackend):
     def get_impl_cls() -> type["AiterFlashAttentionImpl"]:
         return AiterFlashAttentionImpl
 
-    @staticmethod
-    def get_metadata_cls() -> type["AttentionMetadata"]:
-        return AiterFlashAttentionMetadata
-
     @staticmethod
     def get_builder_cls() -> type["AiterFlashAttentionMetadataBuilder"]:
         return AiterFlashAttentionMetadataBuilder

vllm/v1/attention/backends/rocm_aiter_unified_attn.py

@@ -5,7 +5,7 @@
 import torch
 
 from vllm import _custom_ops as ops
-from vllm.attention.backends.abstract import AttentionMetadata, AttentionType
+from vllm.attention.backends.abstract import AttentionType
 from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     QuantKey,
@@ -15,7 +15,6 @@ from vllm.v1.attention.backends.flash_attn import FlashAttentionMetadata
 from vllm.v1.attention.backends.rocm_attn import (
     RocmAttentionBackend,
     RocmAttentionImpl,
-    RocmAttentionMetadata,
     RocmAttentionMetadataBuilder,
 )
 
@@ -33,10 +32,6 @@ class RocmAiterUnifiedAttentionBackend(RocmAttentionBackend):
     def get_impl_cls() -> type["RocmAiterUnifiedAttentionImpl"]:
         return RocmAiterUnifiedAttentionImpl
 
-    @staticmethod
-    def get_metadata_cls() -> type["AttentionMetadata"]:
-        return RocmAttentionMetadata
-
     @staticmethod
     def get_kv_cache_shape(
         num_blocks: int,

vllm/v1/attention/backends/rocm_attn.py

@@ -10,7 +10,6 @@ import torch
 from vllm.attention.backends.abstract import (
     AttentionBackend,
     AttentionImpl,
-    AttentionMetadata,
     AttentionType,
 )
 from vllm.attention.ops.chunked_prefill_paged_decode import chunked_prefill_paged_decode
@@ -182,10 +181,6 @@ class RocmAttentionBackend(AttentionBackend):
     def get_impl_cls() -> type["RocmAttentionImpl"]:
         return RocmAttentionImpl
 
-    @staticmethod
-    def get_metadata_cls() -> type["AttentionMetadata"]:
-        return RocmAttentionMetadata
-
     @staticmethod
     def get_kv_cache_shape(
         num_blocks: int,

vllm/v1/attention/backends/tree_attn.py

@@ -12,7 +12,6 @@ from vllm import _custom_ops as ops
 from vllm.attention.backends.abstract import (
     AttentionBackend,
     AttentionImpl,
-    AttentionMetadata,
     AttentionType,
     MultipleOf,
 )
@@ -64,10 +63,6 @@ class TreeAttentionBackend(AttentionBackend):
     def get_impl_cls() -> type["TreeAttentionImpl"]:
         return TreeAttentionImpl
 
-    @staticmethod
-    def get_metadata_cls() -> type["AttentionMetadata"]:
-        return TreeAttentionMetadata
-
     @staticmethod
     def get_kv_cache_shape(
         num_blocks: int,

vllm/v1/attention/backends/triton_attn.py

@@ -10,7 +10,6 @@ import torch
 from vllm.attention.backends.abstract import (
     AttentionBackend,
     AttentionImpl,
-    AttentionMetadata,
     AttentionType,
     MultipleOf,
 )
@@ -176,10 +175,6 @@ class TritonAttentionBackend(AttentionBackend):
     def get_impl_cls() -> type["TritonAttentionImpl"]:
         return TritonAttentionImpl
 
-    @staticmethod
-    def get_metadata_cls() -> type["AttentionMetadata"]:
-        return TritonAttentionMetadata
-
     @staticmethod
     def get_kv_cache_shape(
         num_blocks: int,

vllm/v1/attention/backends/xformers.py

@@ -10,7 +10,6 @@ import torch
 from vllm.attention.backends.abstract import (
     AttentionBackend,
     AttentionImpl,
-    AttentionMetadata,
     AttentionType,
     MultipleOf,
 )
@@ -105,10 +104,6 @@ class XFormersAttentionBackend(AttentionBackend):
     def get_impl_cls() -> type["XFormersAttentionImpl"]:
         return XFormersAttentionImpl
 
-    @staticmethod
-    def get_metadata_cls() -> type["AttentionMetadata"]:
-        return XFormersAttentionMetadata
-
     @staticmethod
     def get_kv_cache_shape(
         num_blocks: int,

vllm/v1/worker/gpu_model_runner.py

@@ -20,7 +20,11 @@ from tqdm import tqdm
 
 import vllm.envs as envs
 from vllm.attention import Attention, AttentionType
-from vllm.attention.backends.abstract import AttentionBackend, MultipleOf
+from vllm.attention.backends.abstract import (
+    AttentionBackend,
+    AttentionMetadata,
+    MultipleOf,
+)
 from vllm.compilation.counter import compilation_counter
 from vllm.compilation.cuda_graph import CUDAGraphWrapper
 from vllm.compilation.monitor import set_cudagraph_capturing_enabled
@@ -82,7 +86,6 @@ from vllm.utils.torch_utils import (
     kv_cache_dtype_str_to_dtype,
     supports_dynamo,
 )
-from vllm.v1.attention.backends.flash_attn import AttentionMetadata
 from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadataBuilder
 from vllm.v1.attention.backends.utils import (
     AttentionCGSupport,