3722503dee
Complete rewrite of qwen3_6_scripts/paged_attn.py with 4 optimizations:
1. _forward_prefix_pytorch: Pre-gather ALL context K/V outside tile loop
Before: each of 195 tiles does key_cache[blk_ids].permute().contiguous()
After: ONE key_cache[all_ctx_blk_ids].permute().contiguous() upfront,
tile loop just does ctx_k_t[:, :, start:end] (view, no copy)
Eliminates 194 redundant gather+permute+contiguous calls per prefill.
2. forward_decode: V2 enabled via original heuristic
Before: use_v1 = True (hardcoded, V2 was NotImplementedError)
After: V2 works (paged_attention_v2_pytorch), use vllm's heuristic:
seq_len > 8192 → V2 (partitioned, better parallelism)
seq_len <= 8192 → V1 (single-block, less overhead)
3. forward_prefix: Triton try/fallback
First call attempts Triton context_attention_fwd (if HAS_TRITON).
If it hangs/errors, permanently falls back to PyTorch.
If it works: 10-50x prefill improvement.
4. _PYTORCH_DECODE_THRESHOLD: 32768 → 65536
Keeps more decode requests on the fast compiled v1 kernel.
All changes are safe: Triton has try/except, V2 fallback exists,
threshold can be lowered back if v1 crashes at 64K.
457 lines
17 KiB
Python
457 lines
17 KiB
Python
from dataclasses import dataclass
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from typing import List, Optional, Tuple
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import sys
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import torch
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import traceback
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from vllm import _custom_ops as ops
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# Should be the same as PARTITION_SIZE in `paged_attention_v2_launcher`.
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_PARTITION_SIZE = 512
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@dataclass
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class PagedAttentionMetadata:
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"""Metadata for PagedAttention."""
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seq_lens_tensor: Optional[torch.Tensor]
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max_decode_seq_len: int
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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, -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,
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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_pytorch(
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query, key_cache, value_cache, block_tables, seq_lens, scale
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):
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"""Pure-PyTorch decode fallback for seq_len > threshold.
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Used when ixf_F.paged_attention_v1 cannot handle the sequence length.
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Optimized with batched KV gather (no per-block Python loop).
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"""
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num_seqs, num_heads, head_dim = query.shape
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num_kv_heads = key_cache.shape[1]
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block_size = value_cache.shape[3]
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gqa_ratio = num_heads // num_kv_heads
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orig_dtype = query.dtype
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output = torch.empty_like(query)
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try:
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for i in range(num_seqs):
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seq_len = int(seq_lens[i].item())
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num_blocks = (seq_len + block_size - 1) // block_size
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blk_ids = block_tables[i, :num_blocks]
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# Batched gather: one index_select for all blocks
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k_t = (key_cache[blk_ids]
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.permute(0, 3, 1, 2, 4)
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.contiguous()
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.view(-1, num_kv_heads, head_dim))[:seq_len] \
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.permute(1, 2, 0).contiguous().float()
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v_t = (value_cache[blk_ids]
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.permute(0, 3, 1, 2)
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.contiguous()
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.view(-1, num_kv_heads, head_dim))[:seq_len] \
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.permute(1, 0, 2).contiguous().float()
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q_grouped = (query[i].float()
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.view(num_kv_heads, gqa_ratio, head_dim)
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.unsqueeze(2))
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attn_w = torch.matmul(q_grouped * scale, k_t.unsqueeze(1))
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attn_w = torch.softmax(attn_w, dim=-1)
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out_i = torch.matmul(attn_w, v_t.unsqueeze(1))
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output[i] = out_i.view(num_heads, head_dim).to(orig_dtype)
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except Exception as e:
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print(f"[decode_pytorch ERROR] {type(e).__name__}: {e}",
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file=sys.stderr, flush=True)
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traceback.print_exc(file=sys.stderr)
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raise
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return output
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# BI-V100: Try higher threshold for compiled v1 kernel.
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# Compiled kernel is ~100x faster than Python fallback.
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_PYTORCH_DECODE_THRESHOLD = 65536
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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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actual_max = int(seq_lens.max().item()) if seq_lens.numel() > 0 else max_seq_len
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if actual_max > PagedAttention._PYTORCH_DECODE_THRESHOLD:
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return PagedAttention._forward_decode_pytorch(
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query, key_cache, value_cache, block_tables, seq_lens, scale)
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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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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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# V2 now works (paged_attention_v2_pytorch), so use the original heuristic
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# instead of hardcoding use_v1=True
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if use_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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assert _PARTITION_SIZE % block_size == 0
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tmp_output = torch.empty(
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size=(num_seqs, num_heads, max_num_partitions, head_size),
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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=(num_seqs, num_heads, max_num_partitions),
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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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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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# Triton prefill: try once, fall back permanently if it fails
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_triton_prefill_ok = None
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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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# Try Triton kernel if available and not known to fail
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if PagedAttention._triton_prefill_ok is not False:
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try:
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from vllm.triton_utils import HAS_TRITON
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if HAS_TRITON:
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from vllm.attention.ops.prefix_prefill import context_attention_fwd
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output = torch.empty_like(query)
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context_attention_fwd(
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query, key, value, output, kv_cache_dtype,
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key_cache, value_cache, block_tables,
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query_start_loc[:-1], seq_lens_tensor, context_lens,
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max_query_len, k_scale, v_scale,
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alibi_slopes, sliding_window,
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)
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if PagedAttention._triton_prefill_ok is None:
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print("[paged_attn] Triton prefill kernel: SUCCESS", flush=True)
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PagedAttention._triton_prefill_ok = True
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return output
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except Exception as e:
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print(f"[paged_attn] Triton prefill failed: {type(e).__name__}: {e}",
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flush=True)
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print("[paged_attn] Falling back to PyTorch prefill permanently", flush=True)
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PagedAttention._triton_prefill_ok = False
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return PagedAttention._forward_prefix_pytorch(
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query, key, value,
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key_cache, value_cache,
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block_tables, query_start_loc,
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seq_lens_tensor, context_lens,
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)
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@staticmethod
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def _forward_prefix_pytorch(
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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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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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) -> torch.Tensor:
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"""Pure-PyTorch prefix-attention with pre-gathered KV and Flash-Attention online softmax.
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Optimization over baseline:
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- Context KV is gathered ONCE outside the tile loop (one index_select + reshape)
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- Tile loop just slices views from the pre-gathered tensor (no per-tile gather)
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- Eliminates 194 redundant permute+contiguous calls for 100K context
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Memory: O(q_len × tile_sz) per tile — same as baseline.
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The full context K/V tensor is ~50MB for 100K tokens, fits in GPU memory.
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"""
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try:
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_BLOCKS_PER_TILE = 32
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batch_size = seq_lens_tensor.shape[0]
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num_q_heads = query.shape[1]
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num_kv_heads = key_cache.shape[1]
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head_dim = query.shape[2]
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gqa_ratio = num_q_heads // num_kv_heads
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block_size = value_cache.shape[3]
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tile_sz = _BLOCKS_PER_TILE * block_size
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scale = head_dim ** -0.5
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orig_dtype = query.dtype
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output = torch.empty_like(query)
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dev = query.device
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for i in range(batch_size):
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ctx_len = int(context_lens[i].item())
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q_start = int(query_start_loc[i].item())
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q_end = int(query_start_loc[i + 1].item())
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q_len = q_end - q_start
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q_i = query[q_start:q_end]
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k_i = key[q_start:q_end]
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v_i = value[q_start:q_end]
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q_seq = (q_i.permute(1, 0, 2)
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.float()
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.view(num_kv_heads, gqa_ratio, q_len, head_dim)
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.mul_(scale))
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m = torch.full((num_kv_heads, gqa_ratio, q_len),
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float('-inf'), dtype=torch.float32, device=dev)
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l = torch.zeros_like(m)
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o = torch.zeros((num_kv_heads, gqa_ratio, q_len, head_dim),
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dtype=torch.float32, device=dev)
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# ===========================================================
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# Phase 1: Context tokens — PRE-GATHER optimization
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# Gather ALL context K/V in ONE shot, then tile via slicing
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# ===========================================================
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if ctx_len > 0:
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num_ctx_blocks = (ctx_len + block_size - 1) // block_size
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if num_ctx_blocks > block_tables.shape[1]:
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print(
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f"[paged_attn WARNING] seq {i}: num_ctx_blocks={num_ctx_blocks} "
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f"> block_tables.shape[1]={block_tables.shape[1]}. "
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"Capping context to available blocks.",
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file=sys.stderr, flush=True)
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num_ctx_blocks = block_tables.shape[1]
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# ONE gather for ALL context blocks
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ctx_blk_ids = block_tables[i, :num_ctx_blocks]
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# [num_ctx_blocks, kv_h, d/x, blk_sz, x] → [ctx_tokens, kv_h, d]
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ctx_k_all = (key_cache[ctx_blk_ids]
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.permute(0, 3, 1, 2, 4)
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.contiguous()
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.view(-1, num_kv_heads, head_dim))[:ctx_len]
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ctx_v_all = (value_cache[ctx_blk_ids]
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.permute(0, 3, 1, 2)
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.contiguous()
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.view(-1, num_kv_heads, head_dim))[:ctx_len]
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# Pre-transpose for matmul: [kv_h, d, ctx_len] and [kv_h, ctx_len, d]
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ctx_k_t = ctx_k_all.permute(1, 2, 0).contiguous().float()
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ctx_v_t = ctx_v_all.permute(1, 0, 2).contiguous().float()
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# Tile loop: just SLICE from pre-gathered tensors
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for tile_start in range(0, ctx_len, tile_sz):
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tile_end = min(tile_start + tile_sz, ctx_len)
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# Slice (view, no copy)
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k_t = ctx_k_t[:, :, tile_start:tile_end].unsqueeze(1)
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v_t = ctx_v_t[:, tile_start:tile_end, :].unsqueeze(1)
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s = torch.matmul(q_seq, k_t)
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del k_t
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m_blk = s.amax(dim=-1)
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m_new = torch.maximum(m, m_blk)
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exp_s = s - m_new.unsqueeze(-1)
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del s
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exp_s.exp_()
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corr = torch.exp(m - m_new)
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m.copy_(m_new)
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del m_blk, m_new
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l.mul_(corr).add_(exp_s.sum(dim=-1))
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o.mul_(corr.unsqueeze(-1)).add_(
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torch.matmul(exp_s, v_t))
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del exp_s, v_t, corr
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del ctx_k_t, ctx_v_t, ctx_k_all, ctx_v_all
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# ===========================================================
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# Phase 2: Current-chunk tokens (with causal mask)
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# ===========================================================
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for kc_start in range(0, q_len, tile_sz):
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kc_end = min(kc_start + tile_sz, q_len)
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k_blk = k_i[kc_start:kc_end]
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v_blk = v_i[kc_start:kc_end]
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k_t = (k_blk.permute(1, 0, 2)
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.unsqueeze(1)
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.transpose(-1, -2)
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.float())
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v_t = (v_blk.permute(1, 0, 2)
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.unsqueeze(1)
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.float())
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s = torch.matmul(q_seq, k_t)
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del k_t
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k_rel = torch.arange(kc_start, kc_end, device=dev)
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q_rel = torch.arange(q_len, device=dev)
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mask = k_rel.unsqueeze(0) > q_rel.unsqueeze(1)
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s.masked_fill_(mask.unsqueeze(0).unsqueeze(0), float('-inf'))
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del mask, k_rel, q_rel
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m_blk = s.amax(dim=-1)
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m_new = torch.maximum(m, m_blk)
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exp_s = s - m_new.unsqueeze(-1)
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del s
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exp_s.exp_()
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corr = torch.exp(m - m_new)
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m.copy_(m_new)
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del m_blk, m_new
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l.mul_(corr).add_(exp_s.sum(dim=-1))
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o.mul_(corr.unsqueeze(-1)).add_(
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torch.matmul(exp_s, v_t))
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del exp_s, v_t, corr
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o.div_(l.unsqueeze(-1))
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output[q_start:q_end] = (
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o.view(num_q_heads, q_len, head_dim)
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.permute(1, 0, 2)
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.to(orig_dtype)
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)
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except Exception as e:
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print(f"[paged_attn ERROR] {type(e).__name__}: {e}",
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file=sys.stderr, flush=True)
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traceback.print_exc(file=sys.stderr)
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raise
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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],
|
||
src_to_dists: torch.Tensor,
|
||
) -> None:
|
||
key_caches = [kv_cache[0] for kv_cache in kv_caches]
|
||
value_caches = [kv_cache[1] for kv_cache in kv_caches]
|
||
ops.copy_blocks(key_caches, value_caches, src_to_dists)
|