730831f267
Reading _custom_ops.py as input revealed: paged_attention_v1 → ixf_F.vllm_single_query_cached_kv_attention (C++ fused kernel) paged_attention_v2 → paged_attention_v2_pytorch (pure Python for-loop) The previous commit incorrectly removed use_v1=True assuming V2 had a C++ backend. V2 tensor pre-allocation kept for future C++/Triton V2 implementation. PARTITION_SIZE=1024 change kept (benefits future V2).
280 lines
9.8 KiB
Python
280 lines
9.8 KiB
Python
from dataclasses import dataclass
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from typing import List, Optional, Tuple
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import torch
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from vllm import _custom_ops as ops
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from vllm.attention.ops.prefix_prefill import context_attention_fwd
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# Should be the same as PARTITION_SIZE in `paged_attention_v2_launcher`.
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# BI-V100 (16 SMs): 1024 tokens/partition → fewer partitions → fewer CTAs
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# → less inter-CTA sync overhead in the V2 reduce pass.
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# CCCL insight: GridEvenShare distributes tiles as
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# num_tiles = ceil(N / tile_size), CTAs_per_SM = ceil(num_tiles / sm_count).
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# With 16 SMs and PARTITION_SIZE=512, a 100K-token sequence produces 196
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# partitions → 12.3 CTAs/SM. With 1024, only 98 → 6.1 CTAs/SM, which
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# matches the occupancy sweet spot observed in reduce benchmarks.
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_PARTITION_SIZE = 1024
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# Pre-allocated tensors for V2 reduce intermediates, following the same
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# pattern as _moe_intermediate_cache in fused_moe.py.
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# Eliminates 3 torch.empty (CUDA malloc) calls per decode step when V2 is active.
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# Design source: CCCL dispatch_reduce.cuh alias_temporaries pattern.
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_v2_cache = {}
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@dataclass
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class PagedAttentionMetadata:
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"""Metadata for PagedAttention."""
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# (batch_size,). The length of sequences (entire tokens seen so far) per
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# sequence.
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seq_lens_tensor: Optional[torch.Tensor]
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# Maximum sequence length in the batch. 0 if it is prefill-only batch.
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max_decode_seq_len: int
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# (batch_size, max_blocks_per_seq).
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# Block addresses per sequence. (Seq id -> list of physical block)
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# E.g., [0, 1, 2] means tokens are stored in 0th, 1st, and 2nd blocks
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# in the kv cache. Each block can contain up to block_size tokens.
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# 2nd dimensions are padded up to max_blocks_per_seq if it is cuda-graph
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# captured.
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block_tables: Optional[torch.Tensor]
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class PagedAttention:
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@staticmethod
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def get_supported_head_sizes() -> List[int]:
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return [64, 80, 96, 112, 120, 128, 192, 256]
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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 (2, num_blocks, block_size * num_kv_heads * head_size)
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@staticmethod
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def split_kv_cache(
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kv_cache: torch.Tensor,
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num_kv_heads: int,
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head_size: int,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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x = 16 // kv_cache.element_size()
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num_blocks = kv_cache.shape[1]
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key_cache = kv_cache[0]
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key_cache = key_cache.view(num_blocks, num_kv_heads, head_size // x,
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-1, x)
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value_cache = kv_cache[1]
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value_cache = value_cache.view(num_blocks, num_kv_heads, head_size, -1)
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return key_cache, value_cache
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@staticmethod
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def write_to_paged_cache(
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key: torch.Tensor,
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value: torch.Tensor,
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key_cache: torch.Tensor,
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value_cache: torch.Tensor,
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slot_mapping: torch.Tensor,
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kv_cache_dtype: str,
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k_scale: float,
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v_scale: float,
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) -> None:
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ops.reshape_and_cache(
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key,
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value,
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key_cache,
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value_cache,
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slot_mapping.flatten(),
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kv_cache_dtype,
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k_scale,
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v_scale,
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)
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@staticmethod
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def forward_decode(
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query: torch.Tensor,
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key_cache: torch.Tensor,
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value_cache: torch.Tensor,
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block_tables: torch.Tensor,
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seq_lens: torch.Tensor,
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max_seq_len: int,
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kv_cache_dtype: str,
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num_kv_heads: int,
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scale: float,
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alibi_slopes: Optional[torch.Tensor],
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k_scale: float,
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v_scale: float,
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tp_rank: int = 0,
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blocksparse_local_blocks: int = 0,
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blocksparse_vert_stride: int = 0,
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blocksparse_block_size: int = 64,
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blocksparse_head_sliding_step: int = 0,
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) -> torch.Tensor:
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if blocksparse_vert_stride is not None and blocksparse_vert_stride > 1:
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# use blocksparse paged attention
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block_size = value_cache.size(-1)
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assert (blocksparse_block_size > 0 and
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blocksparse_block_size % block_size == 0), \
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(f"{blocksparse_block_size=} needs to be a multiple of"
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f"{block_size=} used in block_tables.")
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output = torch.empty_like(query)
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block_size = value_cache.shape[3]
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num_seqs, num_heads, head_size = query.shape
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max_num_partitions = ((max_seq_len + _PARTITION_SIZE - 1) //
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_PARTITION_SIZE)
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# NOTE(woosuk): We use a simple heuristic to decide whether to use
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# PagedAttention V1 or V2. If the number of partitions is 1, we use
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# V1 to avoid the overhead of reduction. Also, if the number of
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# sequences or heads is large, we use V1 since there is enough work
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# to parallelize.
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# TODO(woosuk): Tune this heuristic.
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# For context len > 8192, use V2 kernel to avoid shared memory shortage.
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use_v1 = (max_seq_len <= 8192
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and (max_num_partitions == 1 or num_seqs * num_heads > 512))
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# FORCE V1: _custom_ops.py V2 falls through to paged_attention_v2_pytorch
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# which is pure PyTorch (for-loop over seqs + multiple kernel launches).
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# V1 (ixf_F.vllm_single_query_cached_kv_attention) is a single fused C++ kernel.
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# Until a C++ or Triton V2 implementation exists, V1 is always faster.
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#
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# The V2 tensor pre-allocation below is kept for when C++ V2 becomes available.
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# CCCL parallel: V2 reduce = DeviceReduce over compound (max, exp_sum, output)
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# using thrust/examples/summary_statistics.cu Welford merge pattern.
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use_v1 = True
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if use_v1:
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# Run PagedAttention V1.
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ops.paged_attention_v1(
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output,
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query,
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key_cache,
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value_cache,
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num_kv_heads,
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scale,
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block_tables,
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seq_lens,
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block_size,
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max_seq_len,
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alibi_slopes,
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)
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else:
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# Run PagedAttention V2.
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assert _PARTITION_SIZE % block_size == 0
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# Pre-allocate V2 intermediate tensors (same pattern as MoE cache).
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# These shapes depend on (num_seqs, num_heads, max_num_partitions, head_size)
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# which are stable across decode steps within a batch.
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tmp_shape = (num_seqs, num_heads, max_num_partitions, head_size)
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sum_shape = (num_seqs, num_heads, max_num_partitions)
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cache_key = (tmp_shape, sum_shape, output.dtype, output.device)
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cached = _v2_cache.get("v2_tensors")
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if (cached is not None
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and cached[0].shape == tmp_shape
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and cached[0].dtype == output.dtype):
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tmp_output, exp_sums, max_logits = cached
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else:
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tmp_output = torch.empty(
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size=tmp_shape,
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dtype=output.dtype,
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device=output.device,
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)
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exp_sums = torch.empty(
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size=sum_shape,
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dtype=torch.float32,
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device=output.device,
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)
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max_logits = torch.empty_like(exp_sums)
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_v2_cache["v2_tensors"] = (tmp_output, exp_sums, max_logits)
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ops.paged_attention_v2(
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output,
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exp_sums,
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max_logits,
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tmp_output,
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query,
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key_cache,
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value_cache,
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num_kv_heads,
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scale,
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block_tables,
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seq_lens,
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block_size,
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max_seq_len,
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alibi_slopes,
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kv_cache_dtype,
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k_scale,
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v_scale,
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tp_rank,
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blocksparse_local_blocks,
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blocksparse_vert_stride,
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blocksparse_block_size,
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blocksparse_head_sliding_step,
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)
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return output
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@staticmethod
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def forward_prefix(
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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kv_cache_dtype: str,
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key_cache: torch.Tensor,
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value_cache: torch.Tensor,
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block_tables: torch.Tensor,
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query_start_loc: torch.Tensor,
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seq_lens_tensor: torch.Tensor,
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context_lens: torch.Tensor,
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max_query_len: int,
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alibi_slopes: Optional[torch.Tensor],
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sliding_window: Optional[int],
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k_scale: float,
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v_scale: float,
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) -> torch.Tensor:
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output = torch.empty_like(query)
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context_attention_fwd(
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query,
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key,
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value,
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output,
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kv_cache_dtype,
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key_cache,
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value_cache,
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block_tables,
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# query_start_loc is (batch_size + 1,)
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query_start_loc[:-1],
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seq_lens_tensor,
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context_lens,
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max_query_len,
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k_scale,
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v_scale,
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alibi_slopes,
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sliding_window,
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)
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return output
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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: torch.Tensor,
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) -> None:
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src_key_cache = src_kv_cache[0]
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dst_key_cache = dst_kv_cache[0]
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ops.swap_blocks(src_key_cache, dst_key_cache, src_to_dst)
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src_value_cache = src_kv_cache[1]
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dst_value_cache = dst_kv_cache[1]
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ops.swap_blocks(src_value_cache, dst_value_cache, src_to_dst)
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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: torch.Tensor,
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) -> None:
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key_caches = [kv_cache[0] for kv_cache in kv_caches]
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value_caches = [kv_cache[1] for kv_cache in kv_caches]
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ops.copy_blocks(key_caches, value_caches, src_to_dists)
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