025059d78e
CCCL GridEvenShare principle: each work unit must complete within bounded time. Python fallback decode was O(seq_len) per step — at seq_len > 32K, each decode step took seconds, causing HTTP timeout and service crash during case_truncation (max_tokens=8192). Raised _PYTORCH_DECODE_THRESHOLD from 32768 to 999999 to force all decode through ixformer native paged_attention_v1 kernel, which is O(1) per decode step regardless of sequence length. Competition submission Job 101 crashed at case_truncation phase with RemoteDisconnected. Job 66 (competitor) passed this phase using native kernel at all lengths. Root cause confirmed: Python fallback too slow for production use. Also derived from CCCL grid_even_share.cuh DispatchInit: big_share_items = normal_share_items + tile_items (at most +1 tile) Never let any block take unbounded work.
828 lines
38 KiB
Python
828 lines
38 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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# from vllm.attention.ops.prefix_prefill import context_attention_fwd
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# NOTE: context_attention_fwd (Triton kernel from prefix_prefill.py) is NOT
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# imported here. On Iluvatar BI-V100 that kernel hangs the GPU card
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# permanently. Chunked-prefill / prefix-caching attention is handled by
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# _forward_prefix_pytorch below (pure PyTorch, no Triton dependency).
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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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# (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_pytorch(
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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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scale: float,
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) -> torch.Tensor:
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"""Pure-PyTorch decode attention for long contexts (no hardware kernel).
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Architecture mirrors CCCL's three-layer reduce:
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dispatch_reduce.cuh → kernel_reduce.cuh → agent_reduce.cuh
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(work distribution) (kernel entry) (tile consumption)
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CCCL agent_reduce.cuh has two key patterns we translate here:
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1. ConsumeFullTile vectorized path: data loaded as VectorT in striped
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access (no BlockLoad staging → no SMEM for data, only for BlockReduce
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scratch). PyTorch equivalent: single reshape+view without .contiguous()
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when possible; fall back to one .contiguous() per K/V gather.
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2. ConsumeTiles with GridEvenShare STRIP_MINE: each CTA strides across
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the input with stride = grid_size * tile_items. For decode (q_len=1),
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we tile over KV blocks with adaptive tile_sz per the same
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GridEvenShare formula: max_tiles = sm_count * subscription_factor.
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3. summary_statistics.cu compound reduce: accumulator = {m, l, o}.
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unary_op: score_tile → (max, sum_exp, weighted_V).
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binary_op: online softmax merge with correction factor.
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This is the Flash Attention online softmax — identical structure.
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For decode, q_len=1 per sequence. The attention weight is [H, 1, seq_len]
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which is small (~5 MB at 50K tokens). We tile over KV blocks to control
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peak memory and apply online softmax (Flash Attention Algorithm 1) per tile.
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Shapes
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------
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query : [num_seqs, num_heads, head_dim]
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key_cache : [num_blocks, num_kv_heads, head_dim//x, block_size, x]
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value_cache : [num_blocks, num_kv_heads, head_dim, block_size]
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block_tables: [num_seqs, max_blocks_per_seq]
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seq_lens : [num_seqs]
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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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dev = query.device
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output = torch.empty_like(query)
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# ================================================================
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# CCCL spread_out_items_per_thread adaptive tile sizing for decode
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#
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# Ported from dispatch_transform.cuh::spread_out_items_per_thread
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# and dispatch_reduce.cuh::InvokePasses GridEvenShare.
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#
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# CCCL formula (dispatch_transform.cuh line 183):
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# items = min(max_items,
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# ceil_div(num_items, sm_count * threads * max_occupancy))
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# items = clamp(items, min_items, max_items)
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#
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# Our translation for PyTorch decode:
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# "items" = KV blocks per tile (how much work per matmul call)
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# "num_items" = total KV blocks in the sequence
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# "sm_count * max_occupancy" = target number of tiles (~4-8)
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# Fewer tiles = fewer Python loop iterations = less launch overhead
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#
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# For decode (q_len=1), score tensor per tile is tiny:
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# kv_h × gqa × 1 × (tile_blocks × block_size) × 4 bytes
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# = 4 × 6 × 1 × 16384 × 4 = 1.5 MB (even at kv_h=4, safe)
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# So the constraint is NOT memory — it's minimizing loop iterations.
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#
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# CCCL grid_even_share.cuh DispatchInit logic:
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# total_tiles = ceil_div(num_items, tile_size)
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# grid_size = min(total_tiles, max_grid_size)
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# big_shares = total_tiles - (avg_tiles * grid_size)
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# Our target: ~4 tiles max (Python overhead >> kernel launch overhead)
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# ================================================================
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# CCCL GridEvenShare: max_blocks = sm_occupancy * sm_count * subscription_factor
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# BI-V100: 1 * 16 * 5 = 80 max CTAs for CUDA kernels.
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# But this is Python (PyTorch ops), not CUDA launches — Python loop
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# overhead dominates. Each iteration = 1 torch.matmul launch + online
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# softmax update. Target 2 iterations (not 4): the matmul itself is
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# already parallelized across SMs, so fewer Python loops = less overhead.
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# For seq_len=100K with block_size=16: 6250 blocks / 2 = 3125 blocks/tile.
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# Score tensor: 4 kv_heads × 6 gqa × 1 × 50000 × 4B = 4.8 MB — fits.
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_BI100_TARGET_TILES = 2 # 2 iterations: minimize Python loop overhead
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_MIN_TILE_BLOCKS = 128 # floor: ensure matmul is large enough to saturate 16 SMs
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_MAX_TILE_BLOCKS = 8192 # ceiling: 8192 × 16 = 128K tokens per tile — fits in memory
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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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if seq_len == 0:
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output[i].zero_()
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continue
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num_blocks_i = (seq_len + block_size - 1) // block_size
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blk_ids = block_tables[i, :num_blocks_i]
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# Q reshaped once: [kv_h, gqa, 1, d] fp32 — tiny for decode
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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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.mul_(scale))
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# Online softmax accumulators (CCCL summary_stats_data pattern)
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# accumulator = {m (running max), l (running sum_exp), o (running output)}
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m = torch.full((num_kv_heads, gqa_ratio, 1),
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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, 1, head_dim),
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dtype=torch.float32, device=dev)
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# Tile over KV blocks — CCCL spread_out_items_per_thread pattern
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# Adaptive: tile_blocks = ceil(num_blocks / target_tiles)
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# clamped to [_MIN_TILE_BLOCKS, _MAX_TILE_BLOCKS]
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tile_blocks = max(_MIN_TILE_BLOCKS,
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min(_MAX_TILE_BLOCKS,
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(num_blocks_i + _BI100_TARGET_TILES - 1)
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// _BI100_TARGET_TILES))
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for tile_start in range(0, num_blocks_i, tile_blocks):
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tile_end = min(tile_start + tile_blocks, num_blocks_i)
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tile_blk_ids = blk_ids[tile_start:tile_end]
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# Valid tokens in this tile
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tile_token_start = tile_start * block_size
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tile_token_end = min(tile_end * block_size, seq_len)
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valid_tokens = tile_token_end - tile_token_start
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# --------------------------------------------------------
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# KV gather — agent_reduce.cuh ConsumeFullTile pattern
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#
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# agent_reduce loads VectorT in striped access when possible.
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# PyTorch equivalent: reshape the 5D cache layout to 3D in
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# one permute+contiguous, avoiding the double-contiguous
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# pattern of the old code.
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#
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# key_cache shape: [num_blocks, kv_h, d//x, blk_sz, x]
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# Target: [kv_h, d, valid_tokens] for Q@K^T
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#
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# Optimized path: permute(1,2,4,0,3) → [kv_h, d//x, x, n_blk, blk_sz]
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# → reshape to [kv_h, d, n_blk*blk_sz] → slice [:valid_tokens]
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# This is ONE contiguous() call instead of TWO.
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# --------------------------------------------------------
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k_gathered = key_cache[tile_blk_ids] # [n, kv_h, d//x, blk_sz, x]
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k_t = (k_gathered
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.permute(1, 2, 4, 0, 3) # [kv_h, d//x, x, n, blk_sz]
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.contiguous()
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.view(num_kv_heads, head_dim, -1) # [kv_h, d, n*blk_sz]
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[:, :, :valid_tokens]
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.unsqueeze(1) # [kv_h, 1, d, valid]
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.float())
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del k_gathered
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v_gathered = value_cache[tile_blk_ids] # [n, kv_h, d, blk_sz]
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v_t = (v_gathered
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.permute(1, 2, 0, 3) # [kv_h, d, n, blk_sz]
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.contiguous()
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.view(num_kv_heads, head_dim, -1) # [kv_h, d, n*blk_sz]
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[:, :, :valid_tokens]
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.transpose(1, 2) # [kv_h, valid, d]
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.unsqueeze(1) # [kv_h, 1, valid, d]
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.float())
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del v_gathered
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# --------------------------------------------------------
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# Scores + online softmax — summary_statistics.cu pattern
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#
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# unary_op: score_tile → (max, sum_exp, weighted_V)
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# binary_op: merge with correction factor
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#
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# CCCL summary_stats_binary_op merges:
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# result.mean = x.mean + delta * y.n / n
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# result.M2 = x.M2 + y.M2 + delta² * x.n * y.n / n
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#
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# Online softmax merge:
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# m_new = max(m_old, m_tile)
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# corr = exp(m_old - m_new) ← rescale factor
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# l_new = l_old * corr + l_tile
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# o_new = o_old * corr + tile_exp @ V
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#
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# Structurally identical: m↔max, l↔n, o↔mean×n.
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# --------------------------------------------------------
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# [kv_h, gqa, 1, valid_tokens]
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s = torch.matmul(q_grouped, k_t)
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del k_t
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# Online softmax update (Flash Attention Algorithm 1)
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m_tile = s.amax(dim=-1, keepdim=True) # [kv_h, gqa, 1, 1]
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m_new = torch.maximum(m, m_tile.squeeze(-1))
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corr = torch.exp(m - m_new) # rescale old accum
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exp_s = torch.exp(s - m_new.unsqueeze(-1))
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del s
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m.copy_(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_(torch.matmul(exp_s, v_t))
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del exp_s, v_t, corr, m_new, m_tile
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# Finalize: normalize
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o.div_(l.unsqueeze(-1))
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output[i] = (o.view(num_heads, head_dim)
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.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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# ================================================================
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# CCCL Design Pattern: summary_statistics.cu transform_reduce
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#
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# CCCL packs {n, min, max, mean, M2, M3, M4} into one struct and
|
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# computes ALL statistics in a single pass via transform_reduce.
|
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# The binary_op merges two partial results (Welford parallel algo).
|
||
#
|
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# Our online softmax is the same pattern:
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# accumulator = {m (running max), l (running sum_exp), o (running output)}
|
||
# unary_op: score_tile → {max(tile), sum(exp(tile-max)), exp(tile-max) @ V}
|
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# binary_op: merge two accumulators with correction factor
|
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#
|
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# Key insight: kv_heads are INDEPENDENT — no cross-head dependency.
|
||
# Current code already batches via [kv_h, gqa, q_len, tile_sz] tensor ops.
|
||
# The CCCL pattern validates this is optimal: one matmul per tile across
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# all heads simultaneously, not per-head iteration.
|
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#
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# Future optimization: if we ever get Triton/CUDA access, the binary_op
|
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# merge step ({m,l,o} update) could be fused with the matmul via a
|
||
# custom epilogue — this is what FlashAttention-2/3 does at the CUDA level.
|
||
# ================================================================
|
||
|
||
# paged_attention_v1 on BI-V100 fails for long contexts.
|
||
# Route on actual sequence length (seq_lens.max()), not the max_seq_len
|
||
# parameter which is inflated to max_model_len in CUDA graph mode.
|
||
_PYTORCH_DECODE_THRESHOLD = 999999
|
||
|
||
@staticmethod
|
||
def forward_decode(
|
||
query: torch.Tensor,
|
||
key_cache: torch.Tensor,
|
||
value_cache: torch.Tensor,
|
||
block_tables: torch.Tensor,
|
||
seq_lens: torch.Tensor,
|
||
max_seq_len: int,
|
||
kv_cache_dtype: str,
|
||
num_kv_heads: int,
|
||
scale: float,
|
||
alibi_slopes: Optional[torch.Tensor],
|
||
k_scale: float,
|
||
v_scale: float,
|
||
tp_rank: int = 0,
|
||
blocksparse_local_blocks: int = 0,
|
||
blocksparse_vert_stride: int = 0,
|
||
blocksparse_block_size: int = 64,
|
||
blocksparse_head_sliding_step: int = 0,
|
||
) -> torch.Tensor:
|
||
actual_max = int(seq_lens.max().item()) if seq_lens.numel() > 0 else max_seq_len
|
||
if actual_max > PagedAttention._PYTORCH_DECODE_THRESHOLD:
|
||
return PagedAttention._forward_decode_pytorch(
|
||
query, key_cache, value_cache, block_tables, seq_lens, scale)
|
||
|
||
if blocksparse_vert_stride is not None and blocksparse_vert_stride > 1:
|
||
# use blocksparse paged attention
|
||
block_size = value_cache.size(-1)
|
||
assert (blocksparse_block_size > 0 and
|
||
blocksparse_block_size % block_size == 0), \
|
||
(f"{blocksparse_block_size=} needs to be a multiple of"
|
||
f"{block_size=} used in block_tables.")
|
||
|
||
output = torch.empty_like(query)
|
||
block_size = value_cache.shape[3]
|
||
num_seqs, num_heads, head_size = query.shape
|
||
max_num_partitions = ((max_seq_len + _PARTITION_SIZE - 1) //
|
||
_PARTITION_SIZE)
|
||
# NOTE(woosuk): We use a simple heuristic to decide whether to use
|
||
# PagedAttention V1 or V2. If the number of partitions is 1, we use
|
||
# V1 to avoid the overhead of reduction. Also, if the number of
|
||
# sequences or heads is large, we use V1 since there is enough work
|
||
# to parallelize.
|
||
# TODO(woosuk): Tune this heuristic.
|
||
# For context len > 8192, use V2 kernel to avoid shared memory shortage.
|
||
# CCCL dispatch_reduce.cuh two-path dispatch architecture:
|
||
# single-tile: num_items ≤ threads × items → one CTA, zero temp buffer
|
||
# multi-tile: GridEvenShare partitions across sm_count × occupancy CTAs
|
||
#
|
||
# Paged attention equivalent:
|
||
# V1 = single-pass: one CTA iterates ALL KV blocks (like DeviceReduceSingleTileKernel)
|
||
# V2 = partitioned: KV blocks split into PARTITION_SIZE chunks across CTAs,
|
||
# then a second kernel merges partition results (like InvokePasses two-phase)
|
||
#
|
||
# V1 is optimal when seq_len fits in one CTA's tile (small context).
|
||
# V2 is optimal when seq_len >> PARTITION_SIZE (long context) — parallelism
|
||
# across partitions compensates for the merge overhead.
|
||
#
|
||
# CCCL's GridEvenShare formula:
|
||
# max_blocks = sm_occupancy × sm_count × subscription_factor
|
||
# BI-V100: ~1 × 16 × 5 = 80 max blocks
|
||
# V2 becomes worthwhile when max_num_partitions > 1 AND the partition
|
||
# parallelism exceeds the sequence×head parallelism.
|
||
#
|
||
# Original heuristic (before hardcode): V1 when max_seq_len ≤ 8192 OR
|
||
# when batch×heads already saturates the GPU (num_seqs*num_heads > 512).
|
||
# Restored with BI-V100 SM count awareness.
|
||
# ──── CCCL GridEvenShare dispatch (from dispatch_reduce.cuh) ────
|
||
# CCCL formula: max_blocks = sm_occupancy × sm_count × subscription_factor
|
||
# Then: grid_size = min(total_tiles, max_blocks)
|
||
# If grid_size == 1 → single-tile (V1). If grid_size > 1 → multi-tile (V2).
|
||
#
|
||
# BI-V100 hardware (confirmed):
|
||
# sm_count = 16, sm_occupancy ≈ 1 CTA/SM (conservative for attention),
|
||
# subscription_factor = 5 (CCCL default from util_arch.cuh)
|
||
#
|
||
# Tile size = _PARTITION_SIZE (512 tokens per partition)
|
||
# total_tiles = ceil_div(max_seq_len, _PARTITION_SIZE)
|
||
# max_blocks = 1 × 16 × 5 = 80
|
||
#
|
||
# This replaces the ad-hoc "num_seqs * num_heads > 512" heuristic
|
||
# with CCCL's precise GridEvenShare work distribution.
|
||
bi100_sm_count = 16
|
||
bi100_sm_occupancy = 1 # conservative: 1 attention CTA per SM
|
||
bi100_subscription = 5 # CCCL default subscription_factor
|
||
bi100_max_blocks = bi100_sm_occupancy * bi100_sm_count * bi100_subscription # 80
|
||
|
||
total_tiles = (max_seq_len + _PARTITION_SIZE - 1) // _PARTITION_SIZE
|
||
grid_size = min(total_tiles, bi100_max_blocks)
|
||
|
||
# CCCL single-tile vs multi-tile decision:
|
||
# V1 (single-tile) when problem fits in one CTA's work,
|
||
# OR when sequence×head parallelism already saturates the GPU
|
||
# (no benefit from partitioning — each sequence already has its own CTA)
|
||
seq_head_parallelism = num_seqs * num_heads
|
||
use_v1 = (grid_size == 1
|
||
or seq_head_parallelism >= bi100_max_blocks)
|
||
if use_v1:
|
||
# Run PagedAttention V1.
|
||
ops.paged_attention_v1(
|
||
output,
|
||
query,
|
||
key_cache,
|
||
value_cache,
|
||
num_kv_heads,
|
||
scale,
|
||
block_tables,
|
||
seq_lens,
|
||
block_size,
|
||
max_seq_len,
|
||
alibi_slopes,
|
||
)
|
||
else:
|
||
# Run PagedAttention V2.
|
||
assert _PARTITION_SIZE % block_size == 0
|
||
# CCCL agent_merge_sort.cuh union _TempStorage pattern:
|
||
# agent_merge_sort shares a single SMEM allocation across
|
||
# load_keys, load_items, store_keys, and block_merge ops
|
||
# (they don't execute concurrently, so one buffer suffices).
|
||
# Our equivalent: cache V2 temp tensors across decode steps.
|
||
# For max_num_seqs=1 (competition config), these shapes are
|
||
# stable across all decode steps for the same sequence.
|
||
_v2_key = ("v2_tmp", num_seqs, num_heads, max_num_partitions,
|
||
head_size, output.dtype, output.device)
|
||
_v2_cached = getattr(PagedAttention, '_v2_cache', {}).get(_v2_key)
|
||
if _v2_cached is not None:
|
||
tmp_output, exp_sums, max_logits = _v2_cached
|
||
else:
|
||
tmp_output = torch.empty(
|
||
size=(num_seqs, num_heads, max_num_partitions, head_size),
|
||
dtype=output.dtype,
|
||
device=output.device,
|
||
)
|
||
exp_sums = torch.empty(
|
||
size=(num_seqs, num_heads, max_num_partitions),
|
||
dtype=torch.float32,
|
||
device=output.device,
|
||
)
|
||
max_logits = torch.empty_like(exp_sums)
|
||
if not hasattr(PagedAttention, '_v2_cache'):
|
||
PagedAttention._v2_cache = {}
|
||
PagedAttention._v2_cache[_v2_key] = (tmp_output, exp_sums, max_logits)
|
||
ops.paged_attention_v2(
|
||
output,
|
||
exp_sums,
|
||
max_logits,
|
||
tmp_output,
|
||
query,
|
||
key_cache,
|
||
value_cache,
|
||
num_kv_heads,
|
||
scale,
|
||
block_tables,
|
||
seq_lens,
|
||
block_size,
|
||
max_seq_len,
|
||
alibi_slopes,
|
||
kv_cache_dtype,
|
||
k_scale,
|
||
v_scale,
|
||
tp_rank,
|
||
blocksparse_local_blocks,
|
||
blocksparse_vert_stride,
|
||
blocksparse_block_size,
|
||
blocksparse_head_sliding_step,
|
||
)
|
||
return output
|
||
|
||
@staticmethod
|
||
def forward_prefix(
|
||
query: torch.Tensor,
|
||
key: torch.Tensor,
|
||
value: torch.Tensor,
|
||
kv_cache_dtype: str,
|
||
key_cache: torch.Tensor,
|
||
value_cache: torch.Tensor,
|
||
block_tables: torch.Tensor,
|
||
query_start_loc: torch.Tensor,
|
||
seq_lens_tensor: torch.Tensor,
|
||
context_lens: torch.Tensor,
|
||
max_query_len: int,
|
||
alibi_slopes: Optional[torch.Tensor],
|
||
sliding_window: Optional[int],
|
||
k_scale: float,
|
||
v_scale: float,
|
||
) -> torch.Tensor:
|
||
# NOTE: The Triton context_attention_fwd kernel hangs on Iluvatar
|
||
# BI-V100 hardware (same class of issue as cudnnFlashAttnForward).
|
||
# Use a pure-PyTorch fallback that reads the paged KV cache directly.
|
||
return PagedAttention._forward_prefix_pytorch(
|
||
query, key, value,
|
||
key_cache, value_cache,
|
||
block_tables, query_start_loc,
|
||
seq_lens_tensor, context_lens,
|
||
)
|
||
|
||
@staticmethod
|
||
def _forward_prefix_pytorch(
|
||
query: torch.Tensor,
|
||
key: torch.Tensor,
|
||
value: torch.Tensor,
|
||
key_cache: torch.Tensor,
|
||
value_cache: torch.Tensor,
|
||
block_tables: torch.Tensor,
|
||
query_start_loc: torch.Tensor,
|
||
seq_lens_tensor: torch.Tensor,
|
||
context_lens: torch.Tensor,
|
||
) -> torch.Tensor:
|
||
"""Pure-PyTorch prefix-attention with K-tiling (Flash-Attention online softmax).
|
||
|
||
Memory complexity: O(q_len), independent of kv_len.
|
||
With chunked prefill (q_len ≤ max_num_batched_tokens = 4096) peak
|
||
per layer ≈ 96 MB regardless of context length.
|
||
|
||
Algorithm: Flash Attention online softmax.
|
||
Q is reshaped once to [kv_h, gqa, q_len, d] (24 MB) and held for all
|
||
K-tiles. For each tile a running (m, l, o) accumulator is updated —
|
||
the [q_len × kv_len] attention matrix is NEVER materialised in full.
|
||
|
||
Tile budget (kv_h=1, gqa=6, q_len=4096, tile=256 tokens):
|
||
q_seq [1, 6, 4096, 256] fp32 24 MB (held all tiles)
|
||
o_acc same shape 24 MB (held all tiles)
|
||
s same shape 24 MB (per tile, freed before exp_s)
|
||
exp_s same shape 24 MB (per tile, brief overlap with s)
|
||
Peak ≈ 96 MB (s and exp_s briefly coexist during update).
|
||
|
||
Shapes
|
||
------
|
||
query : [total_q_tokens, num_q_heads, head_dim]
|
||
key : [total_q_tokens, num_kv_heads, head_dim]
|
||
value : [total_q_tokens, num_kv_heads, head_dim]
|
||
key_cache : [num_blocks, num_kv_heads, head_dim//x, block_size, x]
|
||
value_cache : [num_blocks, num_kv_heads, head_dim, block_size]
|
||
block_tables : [batch_size, max_blocks_per_seq]
|
||
query_start_loc: [batch_size + 1]
|
||
seq_lens_tensor: [batch_size] total length (context + query)
|
||
context_lens : [batch_size] tokens already in KV cache
|
||
"""
|
||
try:
|
||
# ================================================================
|
||
# Tile sizing strategy — ported from CCCL dispatch_reduce.cuh
|
||
#
|
||
# CCCL's GridEvenShare computes:
|
||
# max_blocks = sm_occupancy × sm_count × subscription_factor
|
||
# tile_size = num_items / max_blocks (evenly distributed)
|
||
#
|
||
# For BI-V100 (16 SMs), fixed _BLOCKS_PER_TILE=32 wastes memory
|
||
# on short contexts and underutilizes on long ones.
|
||
#
|
||
# Key insight from kernel_reduce.cuh:
|
||
# StableReductionOrder=false uses atomicAdd → single kernel pass.
|
||
# For online softmax (our case), we accumulate (m, l, o) per tile
|
||
# then merge — this IS a multi-pass reduce. Larger tiles = fewer
|
||
# merge steps = less numerical drift + less Python loop overhead.
|
||
#
|
||
# CCCL subscription_factor = CUB_SUBSCRIPTION_FACTOR(0) = 5
|
||
# Effective: 16 SM × 1 CTA/SM × 5 = 80 concurrent tiles max.
|
||
# But Python loop overhead dominates, so we want FEWER, LARGER tiles.
|
||
#
|
||
# Strategy: target ~4-8 tiles per context phase.
|
||
# Fewer tiles → fewer matmul calls → less launch overhead.
|
||
# SMEM constraint: score tensor [kv_h, gqa, q_len, tile_sz] fp32
|
||
# must not cause OOM. With q_len=4096, kv_h=1, gqa=6:
|
||
# tile_sz=1024 → 1×6×4096×1024×4 = 96 MB (too much)
|
||
# tile_sz=512 → 48 MB (borderline)
|
||
# tile_sz=256 → 24 MB (safe)
|
||
# For decode (q_len=1): tile_sz=4096 → only 96 KB (always safe)
|
||
# ================================================================
|
||
_SMEM_BUDGET_BYTES = 96 * 1024 * 1024 # 96 MB score tensor budget
|
||
|
||
batch_size = seq_lens_tensor.shape[0]
|
||
num_q_heads = query.shape[1]
|
||
num_kv_heads = key_cache.shape[1]
|
||
head_dim = query.shape[2]
|
||
gqa_ratio = num_q_heads // num_kv_heads
|
||
block_size = value_cache.shape[3]
|
||
scale = head_dim ** -0.5
|
||
orig_dtype = query.dtype
|
||
output = torch.empty_like(query)
|
||
dev = query.device
|
||
|
||
for i in range(batch_size):
|
||
ctx_len = int(context_lens[i].item())
|
||
q_start = int(query_start_loc[i].item())
|
||
q_end = int(query_start_loc[i + 1].item())
|
||
q_len = q_end - q_start
|
||
|
||
q_i = query[q_start:q_end] # [q_len, q_h, d]
|
||
k_i = key [q_start:q_end] # [q_len, kv_h, d]
|
||
v_i = value[q_start:q_end]
|
||
|
||
# CCCL spread_out_items_per_thread adaptive tile sizing.
|
||
#
|
||
# Two constraints compete:
|
||
# 1. Memory: score tensor [kv_h, gqa, q_len, tile_sz] × 4 ≤ budget
|
||
# 2. Iteration count: want ~4-8 tiles to minimize Python overhead
|
||
#
|
||
# CCCL dispatch_transform.cuh::spread_out_items_per_thread:
|
||
# items = ceil_div(num_items, sm_count * threads * occupancy)
|
||
# items = clamp(items, min_items, max_items)
|
||
#
|
||
# Our translation: tile_sz = max context tokens / target_tiles,
|
||
# then clamp by memory budget.
|
||
score_row_bytes = num_kv_heads * gqa_ratio * q_len * 4
|
||
if score_row_bytes > 0:
|
||
mem_max_tokens = _SMEM_BUDGET_BYTES // score_row_bytes
|
||
mem_max_tokens = (mem_max_tokens // block_size) * block_size
|
||
else:
|
||
mem_max_tokens = block_size * 256
|
||
|
||
total_kv_tokens = ctx_len + q_len
|
||
# spread_out: target 4 tiles for context, 4 for current chunk
|
||
spread_tile = max(block_size,
|
||
(total_kv_tokens + 3) // 4)
|
||
# Round to block_size
|
||
spread_tile = (spread_tile // block_size) * block_size
|
||
spread_tile = max(spread_tile, block_size)
|
||
# Clamp by memory budget
|
||
tile_sz = min(spread_tile, mem_max_tokens)
|
||
tile_sz = max(tile_sz, block_size) # floor
|
||
|
||
# Q reshaped and scaled once; held for all K-tiles.
|
||
# [kv_h, gqa, q_len, d] fp32 — 24 MB for q_len=4096, d=256
|
||
q_seq = (q_i.permute(1, 0, 2)
|
||
.float()
|
||
.view(num_kv_heads, gqa_ratio, q_len, head_dim)
|
||
.mul_(scale))
|
||
|
||
# Flash-Attention online-softmax accumulators.
|
||
# m, l : [kv_h, gqa, q_len] fp32 — <0.1 MB
|
||
# o : [kv_h, gqa, q_len, d] fp32 — 24 MB
|
||
m = torch.full((num_kv_heads, gqa_ratio, q_len),
|
||
float('-inf'), dtype=torch.float32, device=dev)
|
||
l = torch.zeros_like(m)
|
||
o = torch.zeros((num_kv_heads, gqa_ratio, q_len, head_dim),
|
||
dtype=torch.float32, device=dev)
|
||
|
||
# --------------------------------------------------------------
|
||
# Phase 1 — context tokens (positions 0 … ctx_len-1).
|
||
#
|
||
# Every context key has absolute position < ctx_len; every
|
||
# query has position ≥ ctx_len. k_pos < q_pos is always True
|
||
# → no causal mask needed for pure context tiles.
|
||
# --------------------------------------------------------------
|
||
# Convert token-based tile_sz to block count for iteration
|
||
blocks_per_tile = tile_sz // block_size
|
||
|
||
if ctx_len > 0:
|
||
num_ctx_blocks = (ctx_len + block_size - 1) // block_size
|
||
if num_ctx_blocks > block_tables.shape[1]:
|
||
print(
|
||
f"[paged_attn WARNING] seq {i}: num_ctx_blocks={num_ctx_blocks} "
|
||
f"> block_tables.shape[1]={block_tables.shape[1]}, ctx_len={ctx_len}. "
|
||
"Block table is undersized (prefix_cache_hit bug). "
|
||
"Capping context to available blocks — attention may be incorrect.",
|
||
file=sys.stderr, flush=True)
|
||
num_ctx_blocks = block_tables.shape[1]
|
||
for tile_blk in range(0, num_ctx_blocks, blocks_per_tile):
|
||
blk_end = min(tile_blk + blocks_per_tile, num_ctx_blocks)
|
||
blk_ids = block_tables[i, tile_blk:blk_end]
|
||
|
||
# Gather K/V for this tile.
|
||
# key_cache [blk_ids]: [n, kv_h, d//x, blk_sz, x]
|
||
# value_cache[blk_ids]: [n, kv_h, d, blk_sz]
|
||
k_tile = (key_cache[blk_ids]
|
||
.permute(0, 3, 1, 2, 4)
|
||
.contiguous()
|
||
.view(-1, num_kv_heads, head_dim))
|
||
v_tile = (value_cache[blk_ids]
|
||
.permute(0, 3, 1, 2)
|
||
.contiguous()
|
||
.view(-1, num_kv_heads, head_dim))
|
||
|
||
# Trim padding in the last block of the tile.
|
||
valid = (min(blk_end * block_size, ctx_len)
|
||
- tile_blk * block_size)
|
||
k_tile = k_tile[:valid] # [valid, kv_h, d]
|
||
v_tile = v_tile[:valid]
|
||
|
||
# k_t: [kv_h, 1, d, valid] (broadcast over gqa_ratio)
|
||
# v_t: [kv_h, 1, valid, d]
|
||
k_t = (k_tile.permute(1, 0, 2)
|
||
.unsqueeze(1)
|
||
.transpose(-1, -2)
|
||
.float())
|
||
v_t = (v_tile.permute(1, 0, 2)
|
||
.unsqueeze(1)
|
||
.float())
|
||
del k_tile, v_tile
|
||
|
||
# Scores: [kv_h, gqa, q_len, valid]
|
||
s = torch.matmul(q_seq, k_t)
|
||
del k_t
|
||
# No causal mask: all context keys precede all queries.
|
||
|
||
# Online softmax update — Flash-Attention Algorithm 1.
|
||
# exp_s = s - new_max (in-place exp after del s)
|
||
m_blk = s.amax(dim=-1)
|
||
m_new = torch.maximum(m, m_blk)
|
||
exp_s = s - m_new.unsqueeze(-1)
|
||
del s
|
||
exp_s.exp_()
|
||
corr = torch.exp(m - m_new)
|
||
m.copy_(m_new)
|
||
del m_blk, m_new
|
||
l.mul_(corr).add_(exp_s.sum(dim=-1))
|
||
o.mul_(corr.unsqueeze(-1)).add_(
|
||
torch.matmul(exp_s, v_t))
|
||
del exp_s, v_t, corr
|
||
|
||
# --------------------------------------------------------------
|
||
# Phase 2 — current-chunk tokens (positions ctx_len … ctx_len+q_len-1).
|
||
#
|
||
# Causal mask: query at relative position j sees key at relative
|
||
# position k only when k ≤ j. Tiles of tile_sz tokens each.
|
||
# --------------------------------------------------------------
|
||
for kc_start in range(0, q_len, tile_sz):
|
||
kc_end = min(kc_start + tile_sz, q_len)
|
||
kc_len = kc_end - kc_start
|
||
|
||
k_blk = k_i[kc_start:kc_end] # [kc_len, kv_h, d]
|
||
v_blk = v_i[kc_start:kc_end]
|
||
|
||
k_t = (k_blk.permute(1, 0, 2)
|
||
.unsqueeze(1)
|
||
.transpose(-1, -2)
|
||
.float()) # [kv_h, 1, d, kc_len]
|
||
v_t = (v_blk.permute(1, 0, 2)
|
||
.unsqueeze(1)
|
||
.float()) # [kv_h, 1, kc_len, d]
|
||
|
||
s = torch.matmul(q_seq, k_t) # [kv_h, gqa, q_len, kc_len]
|
||
del k_t
|
||
|
||
# Causal mask: key at (kc_start+k) must not exceed query j.
|
||
k_rel = torch.arange(kc_start, kc_end, device=dev)
|
||
q_rel = torch.arange(q_len, device=dev)
|
||
mask = k_rel.unsqueeze(0) > q_rel.unsqueeze(1) # [q_len, kc_len]
|
||
s.masked_fill_(mask.unsqueeze(0).unsqueeze(0), float('-inf'))
|
||
del mask, k_rel, q_rel
|
||
|
||
# Online softmax update (identical to context phase).
|
||
m_blk = s.amax(dim=-1)
|
||
m_new = torch.maximum(m, m_blk)
|
||
exp_s = s - m_new.unsqueeze(-1)
|
||
del s
|
||
exp_s.exp_()
|
||
corr = torch.exp(m - m_new)
|
||
m.copy_(m_new)
|
||
del m_blk, m_new
|
||
l.mul_(corr).add_(exp_s.sum(dim=-1))
|
||
o.mul_(corr.unsqueeze(-1)).add_(
|
||
torch.matmul(exp_s, v_t))
|
||
del exp_s, v_t, corr
|
||
|
||
# --------------------------------------------------------------
|
||
# Finalize: normalize running output by normalization factor.
|
||
# o: [kv_h, gqa, q_len, d] → [q_len, q_h, d]
|
||
# --------------------------------------------------------------
|
||
o.div_(l.unsqueeze(-1))
|
||
output[q_start:q_end] = (
|
||
o.view(num_q_heads, q_len, head_dim)
|
||
.permute(1, 0, 2)
|
||
.to(orig_dtype)
|
||
)
|
||
|
||
except Exception as e:
|
||
print(f"[paged_attn ERROR] {type(e).__name__}: {e}",
|
||
file=sys.stderr, flush=True)
|
||
traceback.print_exc(file=sys.stderr)
|
||
raise
|
||
return output
|
||
|
||
@staticmethod
|
||
def swap_blocks(
|
||
src_kv_cache: torch.Tensor,
|
||
dst_kv_cache: torch.Tensor,
|
||
src_to_dst: torch.Tensor,
|
||
) -> None:
|
||
src_key_cache = src_kv_cache[0]
|
||
dst_key_cache = dst_kv_cache[0]
|
||
ops.swap_blocks(src_key_cache, dst_key_cache, src_to_dst)
|
||
|
||
src_value_cache = src_kv_cache[1]
|
||
dst_value_cache = dst_kv_cache[1]
|
||
ops.swap_blocks(src_value_cache, dst_value_cache, src_to_dst)
|
||
|
||
@staticmethod
|
||
def copy_blocks(
|
||
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)
|