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78 lines
2.4 KiB
Python
78 lines
2.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from dataclasses import dataclass
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import torch
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from vllm.attention.backends.abstract import AttentionBackend
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from vllm.config import VllmConfig
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from vllm.v1.attention.backends.utils import (
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AttentionCGSupport,
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AttentionMetadataBuilder,
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CommonAttentionMetadata,
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split_decodes_and_prefills,
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)
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from vllm.v1.kv_cache_interface import AttentionSpec, MambaSpec
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class LinearAttentionBackend(AttentionBackend):
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@staticmethod
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def get_builder_cls() -> type["LinearAttentionMetadataBuilder"]:
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return LinearAttentionMetadataBuilder
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@dataclass
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class LinearAttentionMetadata:
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num_prefills: int
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num_prefill_tokens: int
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num_decodes: int
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num_decode_tokens: int
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query_start_loc: torch.Tensor
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seq_lens: torch.Tensor
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state_indices_tensor: torch.Tensor # shape: [batch,]
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class LinearAttentionMetadataBuilder(AttentionMetadataBuilder[LinearAttentionMetadata]):
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reorder_batch_threshold: int = 1
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_cudagraph_support = AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE
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def __init__(
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self,
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kv_cache_spec: AttentionSpec,
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layer_names: list[str],
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vllm_config: VllmConfig,
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device: torch.device,
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):
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super().__init__(kv_cache_spec, layer_names, vllm_config, device)
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assert isinstance(kv_cache_spec, MambaSpec)
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def build(
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self,
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common_prefix_len: int,
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common_attn_metadata: CommonAttentionMetadata,
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fast_build: bool = False,
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) -> LinearAttentionMetadata:
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query_start_loc = common_attn_metadata.query_start_loc
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seq_lens = common_attn_metadata.seq_lens
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state_indices_tensor = common_attn_metadata.block_table_tensor[:, 0]
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num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
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split_decodes_and_prefills(
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common_attn_metadata, decode_threshold=self.reorder_batch_threshold
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)
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)
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attn_metadata = LinearAttentionMetadata(
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num_prefills=num_prefills,
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num_prefill_tokens=num_prefill_tokens,
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num_decodes=num_decodes,
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num_decode_tokens=num_decode_tokens,
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query_start_loc=query_start_loc,
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seq_lens=seq_lens,
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state_indices_tensor=state_indices_tensor,
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)
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return attn_metadata
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