221 lines
8.2 KiB
Python
221 lines
8.2 KiB
Python
from dataclasses import dataclass
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from typing import Any, Dict, List, Optional, Set, Tuple, Type
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try:
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import flashinfer
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from flash_attn import flash_attn_varlen_func
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from flashinfer import BatchDecodeWithPagedKVCacheWrapper
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except ImportError:
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flashinfer = None
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flash_attn_varlen_func = None
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BatchDecodeWithPagedKVCacheWrapper = None
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import torch
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from vllm import _custom_ops as ops
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from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
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AttentionMetadata,
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AttentionMetadataPerStage)
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class FlashInferBackend(AttentionBackend):
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@staticmethod
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def get_impl_cls() -> Type["FlashInferImpl"]:
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return FlashInferImpl
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@staticmethod
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def make_metadata(*args, **kwargs) -> "FlashInferMetadata":
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return FlashInferMetadata(*args, **kwargs)
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@staticmethod
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def get_kv_cache_shape(
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num_blocks: int,
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block_size: int,
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num_kv_heads: int,
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head_size: int,
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) -> Tuple[int, ...]:
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return (num_blocks, 2, block_size, num_kv_heads, head_size)
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@staticmethod
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def swap_blocks(
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src_kv_cache: torch.Tensor,
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dst_kv_cache: torch.Tensor,
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src_to_dst: Dict[int, int],
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) -> None:
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raise NotImplementedError
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@staticmethod
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def copy_blocks(
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kv_caches: List[torch.Tensor],
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src_to_dists: Dict[int, List[int]],
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) -> None:
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raise NotImplementedError
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@staticmethod
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def get_supported_head_sizes() -> List[int]:
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return [64, 128, 256]
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@dataclass
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class FlashInferMetadata(AttentionMetadataPerStage):
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is_prompt: bool
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use_cuda_graph: bool = False
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decode_wrapper: Optional[BatchDecodeWithPagedKVCacheWrapper] = None
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# Metadata for the prefill stage since we still
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# use flash attention for prefill.
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seq_start_loc: Optional[torch.Tensor] = None
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max_seq_len: Optional[int] = None
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block_tables: Optional[torch.Tensor] = None
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# Metadata for the decode stage
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# Workspace buffer required by the kernel, the buffer should not
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# be allocated/deacollated by the FalshInfermetadata object.
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workspace_buffer: Optional[torch.Tensor] = None
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# An example for paged_kv_indices, paged_kv_indptr:
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# request 1, page indices [0, 5, 8]
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# request 2, page indices [1, 6, 7]
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# request 3, page indices [3, 4]
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# paged_kv_indices is a concatenation of page indices of all requests:
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# [0, 5, 8, 1, 6, 7, 3, 4]
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# paged_kv_indptr is used to index into paged_kv_indices:
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# [0, 3, 6, 8]
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# The indptr of the paged kv cache, shape: [batch_size + 1]
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paged_kv_indptr: Optional[torch.Tensor] = None
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# The page indices of the paged kv cache
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paged_kv_indices: Optional[torch.Tensor] = None
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# The number of entries in the last page of each request in
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# the paged kv cache, shape: [batch_size]
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paged_kv_last_page_len: Optional[torch.Tensor] = None
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# The number of query/output heads
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num_qo_heads: Optional[int] = None
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# The number of key/value heads
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num_kv_heads: Optional[int] = None
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# The dimension of the attention heads
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head_dim: Optional[int] = None
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# Block size of vllm
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page_size: Optional[int] = None
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# The data type of the paged kv cache
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data_type: torch.dtype = None
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def __post_init__(self):
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# Refer to
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# https://github.com/flashinfer-ai/flashinfer/blob/3d55c71a62052c590c130897d3a3db49b14fcc34/include/flashinfer/utils.cuh#L157
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supported_head_sizes = FlashInferBackend.get_supported_head_sizes()
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if self.head_dim is not None and self.head_dim \
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not in supported_head_sizes:
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raise ValueError(
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f"Only {supported_head_sizes} are supported for head_dim,",
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f"received {self.head_dim}.")
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# When using flashinfer, we are also creating the FlashInferMetadata,
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# which will also call post_init by default, here we want to skip the
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# post_init if it's the prefill phase.
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if not self.is_prompt:
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self.decode_wrapper = flashinfer.BatchDecodeWithPagedKVCacheWrapper(
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self.workspace_buffer, "NHD")
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self.decode_wrapper.begin_forward(
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self.paged_kv_indptr,
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self.paged_kv_indices,
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self.paged_kv_last_page_len,
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self.num_qo_heads,
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self.num_kv_heads,
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self.head_dim,
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self.page_size,
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# Disable flashinfer's pos encoding and use vllm's rope.
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pos_encoding_mode="NONE",
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data_type=self.data_type)
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def asdict_zerocopy(self,
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skip_fields: Optional[Set[str]] = None
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) -> Dict[str, Any]:
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if skip_fields is None:
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skip_fields = set()
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# We need to skip the decode_wrapper field since it cannot be
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# broadcasted with nccl when TP is enabled.
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skip_fields.add('decode_wrapper')
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return super().asdict_zerocopy(skip_fields)
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class FlashInferImpl(AttentionImpl):
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def __init__(
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self,
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num_heads: int,
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head_size: int,
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scale: float,
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num_kv_heads: Optional[int] = None,
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alibi_slopes: Optional[List[float]] = None,
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sliding_window: Optional[int] = None,
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) -> None:
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if sliding_window is not None:
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raise ValueError("Sliding window is not supported in FlashInfer.")
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self.sliding_window = (-1, -1)
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self.alibi_slopes = alibi_slopes
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self.scale = scale
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self.num_heads = num_heads
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self.head_size = head_size
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self.num_kv_heads = num_heads if num_kv_heads is None else num_kv_heads
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def forward(self, query: torch.Tensor, key: torch.Tensor,
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value: torch.Tensor, kv_cache: Optional[torch.Tensor],
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attn_metadata: AttentionMetadata[FlashInferMetadata],
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kv_scale: float):
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num_tokens, hidden_size = query.shape
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query = query.view(-1, self.num_heads, self.head_size)
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key = key.view(-1, self.num_kv_heads, self.head_size)
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value = value.view(-1, self.num_kv_heads, self.head_size)
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if attn_metadata.num_prefill_tokens > 0:
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assert attn_metadata.num_decode_tokens == 0, (
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"Chunked prefill is not supported with flashinfer yet.")
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if attn_metadata.num_decode_tokens > 0:
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assert attn_metadata.num_prefill_tokens == 0, (
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"Chunked prefill is not supported with flashinfer yet.")
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if kv_cache is not None:
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# Use the same reshape and cache kernel as flash attention.
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ops.reshape_and_cache_flash(
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key,
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value,
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kv_cache[:, 0],
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kv_cache[:, 1],
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attn_metadata.slot_mapping.flatten(),
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attn_metadata.kv_cache_dtype,
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)
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if prefill_meta := attn_metadata.prefill_metadata:
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assert prefill_meta.block_tables is not None
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if kv_cache is None or prefill_meta.block_tables.numel() == 0:
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output = flash_attn_varlen_func(
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q=query,
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k=key,
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v=value,
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cu_seqlens_q=prefill_meta.seq_start_loc,
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cu_seqlens_k=prefill_meta.seq_start_loc,
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max_seqlen_q=prefill_meta.max_seq_len,
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max_seqlen_k=prefill_meta.max_seq_len,
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softmax_scale=self.scale,
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causal=True,
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window_size=self.sliding_window,
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alibi_slopes=self.alibi_slopes,
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)
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else:
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raise NotImplementedError(
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"Prefix caching is not supported with flashinfer yet.")
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else:
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assert attn_metadata.decode_metadata is not None
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assert attn_metadata.decode_metadata.decode_wrapper is not None
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query = query.contiguous(
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) # Flashinfer requires query to be contiguous
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output = attn_metadata.decode_metadata.decode_wrapper.forward(
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query,
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kv_cache,
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sm_scale=self.scale,
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)
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return output.view(num_tokens, hidden_size)
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