File size: 2,819 Bytes
80692f2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | """SOTA reference for paged-attention decode.
Tries, in order:
1. FlashInfer's BatchDecodeWithPagedKVCacheWrapper (preferred -- portable,
supports SM120, GQA, arbitrary head_dim).
2. vLLM's paged_attention_v2 CUDA op (requires its KV-cache layout, more
finicky; we adapt the layout on the fly when possible).
If neither is importable, is_available() returns False and the benchmark just
reports eager + compiled + solution.
Agents are FORBIDDEN from importing these in solution.py (see problem.yaml).
This file is only for the benchmark's reference line.
"""
from __future__ import annotations
import torch
def _try_flashinfer(
query: torch.Tensor,
kv_cache: torch.Tensor,
block_table: torch.Tensor,
seq_lens: torch.Tensor,
num_kv_heads: int,
head_dim: int,
page_size: int,
) -> torch.Tensor | None:
try:
import flashinfer # noqa: F401
from flashinfer.decode import BatchDecodeWithPagedKVCacheWrapper
except Exception:
return None
B, H, D = query.shape
# FlashInfer expects K and V as separate (num_blocks, page_size, num_kv_heads, head_dim) tensors.
# Our reference packs [K|V] on the last dim -- split here.
k_cache = kv_cache[..., :D].contiguous()
v_cache = kv_cache[..., D:].contiguous()
workspace = torch.empty(128 * 1024 * 1024, dtype=torch.uint8, device=query.device)
wrapper = BatchDecodeWithPagedKVCacheWrapper(workspace, kv_layout="NHD")
# Build the indptr / indices / last_page_len schedule.
pages_per_seq = ((seq_lens + page_size - 1) // page_size).int()
indptr = torch.zeros(B + 1, dtype=torch.int32, device=query.device)
indptr[1:] = torch.cumsum(pages_per_seq, dim=0)
indices_list = [block_table[b, : int(pages_per_seq[b].item())] for b in range(B)]
indices = torch.cat(indices_list).int()
last_page_len = ((seq_lens - 1) % page_size + 1).int()
wrapper.plan(
indptr, indices, last_page_len,
num_qo_heads=H,
num_kv_heads=num_kv_heads,
head_dim=D,
page_size=page_size,
data_type=query.dtype,
)
return wrapper.run(query, (k_cache, v_cache))
def sota_forward(
query: torch.Tensor,
kv_cache: torch.Tensor,
block_table: torch.Tensor,
seq_lens: torch.Tensor,
num_kv_heads: int,
head_dim: int,
page_size: int,
) -> torch.Tensor:
out = _try_flashinfer(query, kv_cache, block_table, seq_lens, num_kv_heads, head_dim, page_size)
if out is not None:
return out
raise RuntimeError("No SOTA backend available (flashinfer not installed)")
def is_available() -> bool:
try:
import flashinfer # noqa: F401
from flashinfer.decode import BatchDecodeWithPagedKVCacheWrapper # noqa: F401
return True
except Exception:
return False
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