81972a05c6
CCCL source read: cub/device/dispatch/kernels/kernel_segmented_reduce.cuh
Three agent tiers based on segment size:
Small (≤ small_items_per_tile) → 1 thread per segment (AgentSmallReduce)
Medium (≤ medium_items_per_tile) → 1 warp per segment (AgentMediumReduce)
Large (> medium) → 1 block per segment (AgentReduce)
All three share a union __shared__ memory — only one tier active at a time.
Applied to paged_attention forward_decode:
OLD: use_v1=True forced V1 for all sequence lengths.
V2's partitioned execution was never attempted on BI-V100.
NEW: Three-tier dispatch mirroring CCCL's segmented_reduce:
Small (seq_len ≤ 8192) → V1 native (single CTA, optimal for short seqs)
Medium (8192 < seq ≤ 32K) → V2 native attempt with try/except fallback to V1
V2 partitions work across multiple CTAs, better
for 16-SM BI-V100 on medium sequences
Large (seq > 32K) → PyTorch fallback (V1 SMEM overflow)
Also added CCCL CachingDeviceAllocator buffer reuse pattern to prefix attention:
Pre-allocated _m_blk, _m_new, _corr buffers outside tile loops,
reused via torch.amax(out=), torch.maximum(out=), torch.exp(out=).
677 lines
30 KiB
Python
677 lines
30 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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paged_attention_v1 hangs on BI-V100 when max_seq_len > ~32K due to
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shared memory limits. For decode, q_len=1 per sequence so no Q-tiling
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is needed — the attention weight tensor is [H, 1, seq_len] which is
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trivially small (~5 MB at 50K).
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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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output = torch.empty_like(query)
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# ================================================================
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# KV cache gather strategy — from CCCL agent_reduce.cuh
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#
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# agent_reduce has two load paths:
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# 1. Vectorized: aligned, contiguous, trivially relocatable, sizeof ≤ 8
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# → loads VectorT (e.g. float4) in striped access
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# 2. Scalar: fallback with CacheModifiedInputIterator
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#
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# PyTorch equivalent: .contiguous() ensures vectorized GPU memory access.
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# The key optimization from agent_reduce is to minimize the number of
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# .contiguous() calls — each one is a full memcpy on GPU.
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#
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# Current code does: index → permute → contiguous → view → slice →
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# permute → contiguous → float
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# That's 2 contiguous() calls per K and V = 4 GPU memcpy per sequence.
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#
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# Optimization: reshape key_cache layout knowledge to reduce copies.
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# key_cache shape: [num_blocks, kv_h, d//x, blk_sz, x]
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# After index + reshape: [n_blk, blk_sz, kv_h, d] via one permute+reshape
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# Then slice + transpose: [kv_h, d, seq_len]
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# This is still 2 contiguous(), but the first reshape can be fused.
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# ================================================================
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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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# Gather K: single permute+contiguous → view → slice → transpose
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# key_cache[blk_ids]: [n, kv_h, d//x, blk_sz, x]
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k_gathered = key_cache[blk_ids]
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k_t = (k_gathered
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.permute(0, 3, 1, 2, 4) # [n, blk_sz, kv_h, d//x, x]
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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() # [kv_h, d, seq_len]
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del k_gathered
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# Gather V: same pattern
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v_gathered = value_cache[blk_ids]
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v_t = (v_gathered
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.permute(0, 3, 1, 2) # [n, blk_sz, kv_h, d]
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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() # [kv_h, seq_len, d]
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del v_gathered
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# Reshape Q for lazy GQA: [kv_h, gqa_ratio, 1, d]
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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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# [kv_h, gqa_ratio, 1, seq_len]
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attn_w = torch.matmul(
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q_grouped * scale, # [kv_h, gqa, 1, d]
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k_t.unsqueeze(1)) # [kv_h, 1, d, seq_len]
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attn_w = torch.softmax(attn_w, dim=-1)
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# [kv_h, gqa_ratio, 1, d] → [num_heads, head_dim]
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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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# ================================================================
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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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#
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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)}
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# 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.
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# Current code already batches via [kv_h, gqa, q_len, tile_sz] tensor ops.
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# 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
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# custom epilogue — this is what FlashAttention-2/3 does at the CUDA level.
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# ================================================================
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# paged_attention_v1 on BI-V100 fails for long contexts.
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# Route on actual sequence length (seq_lens.max()), not the max_seq_len
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# parameter which is inflated to max_model_len in CUDA graph mode.
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_PYTORCH_DECODE_THRESHOLD = 32768
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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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# ================================================================
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# CCCL kernel_segmented_reduce.cuh three-tier dispatch:
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# Small (≤ small_items_per_tile) → 1 thread per segment
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# Medium (≤ medium_items_per_tile) → 1 warp per segment
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# Large (> medium_items_per_tile) → 1 block per segment
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#
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# Applied to paged attention decode:
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# Small (≤ 8192) → V1 native (single CTA, no partitioning)
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# Medium (8192..32K) → V2 native attempt (partitioned, two-phase)
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# Large (> 32K) → PyTorch fallback (V1 SMEM overflow on BI-V100)
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#
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# The previous use_v1=True forced V1 for all lengths, wasting V2's
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# partitioned execution for medium-length sequences.
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# ================================================================
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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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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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# CCCL segmented_reduce three-tier: don't force V1 for all lengths.
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# V2 partitioned execution is better for medium-length sequences
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# (8K-32K) where V1's single-CTA approach underutilizes 16 SMs.
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# But V2 native may fail on BI-V100 — catch and fallback to V1.
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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 (partitioned, CCCL two-phase pattern).
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# Try V2 native; if it fails, fallback to V1.
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try:
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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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except Exception:
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# V2 native failed on BI-V100 — fallback to V1
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ops.paged_attention_v1(
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output, query, key_cache, value_cache, num_kv_heads,
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scale, block_tables, seq_lens, block_size, max_seq_len,
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alibi_slopes,
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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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# NOTE: The Triton context_attention_fwd kernel hangs on Iluvatar
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# BI-V100 hardware (same class of issue as cudnnFlashAttnForward).
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# Use a pure-PyTorch fallback that reads the paged KV cache directly.
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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 K-tiling (Flash-Attention online softmax).
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Memory complexity: O(q_len), independent of kv_len.
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With chunked prefill (q_len ≤ max_num_batched_tokens = 4096) peak
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per layer ≈ 96 MB regardless of context length.
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Algorithm: Flash Attention online softmax.
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Q is reshaped once to [kv_h, gqa, q_len, d] (24 MB) and held for all
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K-tiles. For each tile a running (m, l, o) accumulator is updated —
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the [q_len × kv_len] attention matrix is NEVER materialised in full.
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Tile budget (kv_h=1, gqa=6, q_len=4096, tile=256 tokens):
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q_seq [1, 6, 4096, 256] fp32 24 MB (held all tiles)
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o_acc same shape 24 MB (held all tiles)
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s same shape 24 MB (per tile, freed before exp_s)
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exp_s same shape 24 MB (per tile, brief overlap with s)
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Peak ≈ 96 MB (s and exp_s briefly coexist during update).
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Shapes
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------
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query : [total_q_tokens, num_q_heads, head_dim]
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key : [total_q_tokens, num_kv_heads, head_dim]
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value : [total_q_tokens, num_kv_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 : [batch_size, max_blocks_per_seq]
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query_start_loc: [batch_size + 1]
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seq_lens_tensor: [batch_size] total length (context + query)
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context_lens : [batch_size] tokens already in KV cache
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"""
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try:
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# ================================================================
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# Tile sizing strategy — ported from CCCL dispatch_reduce.cuh
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#
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# CCCL's GridEvenShare computes:
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# max_blocks = sm_occupancy × sm_count × subscription_factor
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# tile_size = num_items / max_blocks (evenly distributed)
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#
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# For BI-V100 (16 SMs), fixed _BLOCKS_PER_TILE=32 wastes memory
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# on short contexts and underutilizes on long ones.
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#
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# Key insight from kernel_reduce.cuh:
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||
# 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-style adaptive tile sizing per sequence.
|
||
# Score tensor = [kv_h, gqa, q_len, tile_sz] × 4 bytes
|
||
# Solve: kv_h × gqa × q_len × tile_sz × 4 ≤ budget
|
||
score_row_bytes = num_kv_heads * gqa_ratio * q_len * 4
|
||
if score_row_bytes > 0:
|
||
max_tile_tokens = _SMEM_BUDGET_BYTES // score_row_bytes
|
||
# Round down to block_size boundary
|
||
max_tile_tokens = (max_tile_tokens // block_size) * block_size
|
||
# Clamp: at least 1 block, at most what context needs
|
||
tile_sz = max(block_size, min(max_tile_tokens, 2048))
|
||
else:
|
||
tile_sz = block_size * 32 # fallback
|
||
|
||
# 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
|
||
|
||
# ============================================================
|
||
# CCCL CachingDeviceAllocator pattern (util_allocator.cuh):
|
||
# Pre-allocate buffers once, reuse across all tile iterations.
|
||
# CCCL's allocator uses geometric bin sizing to avoid repeated
|
||
# cudaMalloc/cudaFree. PyTorch equivalent: allocate the max-size
|
||
# tensors once outside the loop, reuse with slicing.
|
||
#
|
||
# Tensors that are the SAME size every iteration:
|
||
# m_blk, m_new, corr: [kv_h, gqa, q_len] — from softmax update
|
||
# Tensors that vary by last dim (valid tokens per tile):
|
||
# s: [kv_h, gqa, q_len, valid] — score matrix
|
||
# But torch.matmul with out= requires exact shape match,
|
||
# so we pre-alloc at max tile_sz and slice.
|
||
# ============================================================
|
||
_m_blk = torch.empty((num_kv_heads, gqa_ratio, q_len),
|
||
dtype=torch.float32, device=dev)
|
||
_m_new = torch.empty_like(_m_blk)
|
||
_corr = torch.empty_like(_m_blk)
|
||
|
||
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.
|
||
# CCCL CachingDeviceAllocator: reuse pre-allocated buffers
|
||
# instead of allocating m_blk, m_new, corr each iteration.
|
||
torch.amax(s, dim=-1, out=_m_blk)
|
||
torch.maximum(m, _m_blk, out=_m_new)
|
||
exp_s = s - _m_new.unsqueeze(-1)
|
||
del s
|
||
exp_s.exp_()
|
||
torch.exp(m - _m_new, out=_corr)
|
||
m.copy_(_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
|
||
|
||
# --------------------------------------------------------------
|
||
# 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 — reuse pre-allocated buffers.
|
||
# CCCL CachingDeviceAllocator: same buffers as Phase 1.
|
||
torch.amax(s, dim=-1, out=_m_blk)
|
||
torch.maximum(m, _m_blk, out=_m_new)
|
||
exp_s = s - _m_new.unsqueeze(-1)
|
||
del s
|
||
exp_s.exp_()
|
||
torch.exp(m - _m_new, out=_corr)
|
||
m.copy_(_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
|
||
|
||
# --------------------------------------------------------------
|
||
# 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)
|