664 lines
28 KiB
Python
664 lines
28 KiB
Python
from dataclasses import dataclass
|
||
from typing import List, Optional, Tuple
|
||
import sys
|
||
import torch
|
||
import traceback
|
||
from vllm import _custom_ops as ops
|
||
|
||
# from vllm.attention.ops.prefix_prefill import context_attention_fwd
|
||
# NOTE: context_attention_fwd (Triton kernel from prefix_prefill.py) is NOT
|
||
# imported here. On Iluvatar BI-V100 that kernel hangs the GPU card
|
||
# permanently. Chunked-prefill / prefix-caching attention is handled by
|
||
# _forward_prefix_pytorch below (pure PyTorch, no Triton dependency).
|
||
|
||
# Should be the same as PARTITION_SIZE in `paged_attention_v2_launcher`.
|
||
_PARTITION_SIZE = 512
|
||
|
||
|
||
@dataclass
|
||
class PagedAttentionMetadata:
|
||
"""Metadata for PagedAttention."""
|
||
# (batch_size,). The length of sequences (entire tokens seen so far) per
|
||
# sequence.
|
||
seq_lens_tensor: Optional[torch.Tensor]
|
||
# Maximum sequence length in the batch. 0 if it is prefill-only batch.
|
||
max_decode_seq_len: int
|
||
# (batch_size, max_blocks_per_seq).
|
||
# Block addresses per sequence. (Seq id -> list of physical block)
|
||
# E.g., [0, 1, 2] means tokens are stored in 0th, 1st, and 2nd blocks
|
||
# in the kv cache. Each block can contain up to block_size tokens.
|
||
# 2nd dimensions are padded up to max_blocks_per_seq if it is cuda-graph
|
||
# captured.
|
||
block_tables: Optional[torch.Tensor]
|
||
|
||
|
||
class PagedAttention:
|
||
|
||
@staticmethod
|
||
def get_supported_head_sizes() -> List[int]:
|
||
return [64, 80, 96, 112, 120, 128, 192, 256]
|
||
|
||
@staticmethod
|
||
def get_kv_cache_shape(
|
||
num_blocks: int,
|
||
block_size: int,
|
||
num_kv_heads: int,
|
||
head_size: int,
|
||
) -> Tuple[int, ...]:
|
||
return (2, num_blocks, block_size * num_kv_heads * head_size)
|
||
|
||
@staticmethod
|
||
def split_kv_cache(
|
||
kv_cache: torch.Tensor,
|
||
num_kv_heads: int,
|
||
head_size: int,
|
||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||
x = 16 // kv_cache.element_size()
|
||
num_blocks = kv_cache.shape[1]
|
||
|
||
key_cache = kv_cache[0]
|
||
key_cache = key_cache.view(num_blocks, num_kv_heads, head_size // x,
|
||
-1, x)
|
||
value_cache = kv_cache[1]
|
||
value_cache = value_cache.view(num_blocks, num_kv_heads, head_size, -1)
|
||
return key_cache, value_cache
|
||
|
||
@staticmethod
|
||
def write_to_paged_cache(
|
||
key: torch.Tensor,
|
||
value: torch.Tensor,
|
||
key_cache: torch.Tensor,
|
||
value_cache: torch.Tensor,
|
||
slot_mapping: torch.Tensor,
|
||
kv_cache_dtype: str,
|
||
k_scale: float,
|
||
v_scale: float,
|
||
) -> None:
|
||
ops.reshape_and_cache(
|
||
key,
|
||
value,
|
||
key_cache,
|
||
value_cache,
|
||
slot_mapping.flatten(),
|
||
kv_cache_dtype,
|
||
k_scale,
|
||
v_scale,
|
||
)
|
||
|
||
@staticmethod
|
||
def _forward_decode_pytorch(
|
||
query: torch.Tensor,
|
||
key_cache: torch.Tensor,
|
||
value_cache: torch.Tensor,
|
||
block_tables: torch.Tensor,
|
||
seq_lens: torch.Tensor,
|
||
scale: float,
|
||
) -> torch.Tensor:
|
||
"""Pure-PyTorch decode attention for long contexts (no hardware kernel).
|
||
|
||
paged_attention_v1 hangs on BI-V100 when max_seq_len > ~32K due to
|
||
shared memory limits. For decode, q_len=1 per sequence so no Q-tiling
|
||
is needed — the attention weight tensor is [H, 1, seq_len] which is
|
||
trivially small (~5 MB at 50K).
|
||
|
||
Shapes
|
||
------
|
||
query : [num_seqs, num_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: [num_seqs, max_blocks_per_seq]
|
||
seq_lens : [num_seqs]
|
||
"""
|
||
num_seqs, num_heads, head_dim = query.shape
|
||
num_kv_heads = key_cache.shape[1]
|
||
block_size = value_cache.shape[3]
|
||
gqa_ratio = num_heads // num_kv_heads
|
||
orig_dtype = query.dtype
|
||
|
||
output = torch.empty_like(query)
|
||
|
||
try:
|
||
for i in range(num_seqs):
|
||
seq_len = int(seq_lens[i].item())
|
||
num_blocks = (seq_len + block_size - 1) // block_size
|
||
blk_ids = block_tables[i, :num_blocks]
|
||
|
||
# Gather K: [kv_h, head_dim, seq_len] fp32 — no GQA expansion.
|
||
# With kv_h=1 and seq_len=100K this is 98 MB vs 586 MB if expanded.
|
||
k_t = (key_cache[blk_ids]
|
||
.permute(0, 3, 1, 2, 4)
|
||
.contiguous()
|
||
.view(-1, num_kv_heads, head_dim))[:seq_len] \
|
||
.permute(1, 2, 0).contiguous().float() # [kv_h, d, seq_len]
|
||
|
||
# Gather V: [kv_h, seq_len, head_dim] fp32
|
||
v_t = (value_cache[blk_ids]
|
||
.permute(0, 3, 1, 2)
|
||
.contiguous()
|
||
.view(-1, num_kv_heads, head_dim))[:seq_len] \
|
||
.permute(1, 0, 2).contiguous().float() # [kv_h, seq_len, d]
|
||
|
||
# Reshape Q for lazy GQA: [kv_h, gqa_ratio, 1, d]
|
||
q_grouped = (query[i].float()
|
||
.view(num_kv_heads, gqa_ratio, head_dim)
|
||
.unsqueeze(2))
|
||
|
||
# [kv_h, gqa_ratio, 1, seq_len]
|
||
attn_w = torch.matmul(
|
||
q_grouped * scale, # [kv_h, gqa, 1, d]
|
||
k_t.unsqueeze(1)) # [kv_h, 1, d, seq_len]
|
||
attn_w = torch.softmax(attn_w, dim=-1)
|
||
|
||
# [kv_h, gqa_ratio, 1, d] → [num_heads, head_dim]
|
||
out_i = torch.matmul(attn_w, v_t.unsqueeze(1))
|
||
output[i] = out_i.view(num_heads, head_dim).to(orig_dtype)
|
||
|
||
except Exception as e:
|
||
print(f"[decode_pytorch ERROR] {type(e).__name__}: {e}",
|
||
file=sys.stderr, flush=True)
|
||
traceback.print_exc(file=sys.stderr)
|
||
raise
|
||
|
||
return output
|
||
|
||
# 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 = 32768
|
||
|
||
@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.
|
||
use_v1 = (max_seq_len <= 8192
|
||
and (max_num_partitions == 1 or num_seqs * num_heads > 512))
|
||
use_v1 = True
|
||
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
|
||
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)
|
||
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).
|
||
# 单次 pad-方阵原生 flash(verified) when possible; on any anomaly or
|
||
# when disabled via env, fall back to the pure-PyTorch path.
|
||
import os as _os
|
||
if _os.environ.get("PREFIX_FLASH", "1") != "0":
|
||
try:
|
||
return PagedAttention._forward_prefix_flash(
|
||
query, key, value,
|
||
key_cache, value_cache,
|
||
block_tables, query_start_loc,
|
||
seq_lens_tensor, context_lens,
|
||
)
|
||
except Exception as _e:
|
||
import sys as _sys, traceback as _tb
|
||
print(f"[prefix_flash FALLBACK] {type(_e).__name__}: {_e}",
|
||
file=_sys.stderr, flush=True)
|
||
_tb.print_exc(file=_sys.stderr)
|
||
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_flash(
|
||
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:
|
||
"""单次 pad-方阵原生 flash 的 prefix/chunked-prefill attention。
|
||
|
||
对每个 sequence:
|
||
1. gather 历史 KV(paged cache) → 连续张量,拼上当前 chunk 的 KV
|
||
→ K/V = [ctx_len + q_len, kv_h, d]。
|
||
2. 把 query 前面 pad ctx_len 个零行 → [ctx_len+q_len, q_h, d](方阵)。
|
||
3. 单次 flash_attn_varlen_func(causal=True) 方阵 —— causal 在方阵下语义正确,
|
||
query 第 (ctx_len+j) 行只看到 key [0, ctx_len+j], 正是 prefix 语义。
|
||
4. 丢弃前 ctx_len 行(pad 产生的垃圾输出),返回后 q_len 行。
|
||
|
||
比两段式(flash+lse归merge)简单且快 1.3~3.5x —— 无需 lse 重算(ixformer 推理版
|
||
flash 不暴露 softmax_lse, 重算 lse 占两段式 77% 成本)。方阵 causal 已验证精确
|
||
(verify_single_correct.py, max_abs≤2e-3, GQA 4Q/1KV, ctx≤80K)。
|
||
|
||
显存兜底:总长 (ctx+q) 超阈值时抛异常, 由 forward_prefix 回退纯 PyTorch。
|
||
pad 浪费兜底:ctx_len 远大于 q_len(缓存命中态)时 pad 方阵浪费大, 抛异常回退
|
||
纯 PyTorch(O(q·k) 在大 ctx 反而更省)。只有冷 prefill 的 chunked(ctx≲q_len)吃单次 pad。
|
||
"""
|
||
from ixformer.functions.flash_attn_lib import flash_attn_varlen_func
|
||
|
||
# pad 方阵总长上限。方阵 flash 是 O(tk^2), ctx 远大于 q_len 时 pad 浪费大;
|
||
# 超阈值交给纯 PyTorch(O(q·k), 大 ctx 反而相对省)。
|
||
_FLASH_TOTAL_LIMIT = 131072
|
||
# pad 浪费比例上限: ctx_len > q_len * R 时该 seq 退回纯 PyTorch。
|
||
# 冷 prefill 的后续 chunk: ctx/q ≤ ~4 (65K/16K); 缓存命中: ctx/q ≫ 10 → 退回。
|
||
_FLASH_PAD_RATIO = 4
|
||
|
||
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]
|
||
block_size = value_cache.shape[3]
|
||
scale = head_dim ** -0.5
|
||
orig_dtype = query.dtype
|
||
dev = query.device
|
||
output = torch.empty_like(query)
|
||
|
||
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
|
||
total_k = ctx_len + q_len
|
||
|
||
if total_k > _FLASH_TOTAL_LIMIT:
|
||
raise RuntimeError(
|
||
f"total_k={total_k} > flash limit {_FLASH_TOTAL_LIMIT}; "
|
||
"fall back to pytorch for memory safety")
|
||
if ctx_len > q_len * _FLASH_PAD_RATIO:
|
||
raise RuntimeError(
|
||
f"pad ratio too high (ctx={ctx_len} q={q_len}); "
|
||
"fall back to pytorch (single-pad wasteful for cache-hit)")
|
||
|
||
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]
|
||
|
||
if ctx_len > 0:
|
||
num_ctx_blocks = (ctx_len + block_size - 1) // block_size
|
||
if num_ctx_blocks > block_tables.shape[1]:
|
||
# 与 pytorch 版一致的 undersized 保护(prefix_cache_hit bug)
|
||
num_ctx_blocks = block_tables.shape[1]
|
||
blk_ids = block_tables[i, :num_ctx_blocks]
|
||
# gather K/V: paged 布局 → 连续 [ctx_len, kv_h, d]
|
||
k_ctx = (key_cache[blk_ids]
|
||
.permute(0, 3, 1, 2, 4)
|
||
.contiguous()
|
||
.view(-1, num_kv_heads, head_dim))[:ctx_len]
|
||
v_ctx = (value_cache[blk_ids]
|
||
.permute(0, 3, 1, 2)
|
||
.contiguous()
|
||
.view(-1, num_kv_heads, head_dim))[:ctx_len]
|
||
k_full = torch.cat([k_ctx, k_i], dim=0).contiguous() # [tk, kv_h, d]
|
||
v_full = torch.cat([v_ctx, v_i], dim=0).contiguous()
|
||
# query pad 到方阵: 前 ctx_len 行零(输出丢弃)
|
||
q_pad = torch.zeros(total_k, num_q_heads, head_dim,
|
||
device=dev, dtype=q_i.dtype)
|
||
q_pad[ctx_len:] = q_i
|
||
else:
|
||
k_full, v_full, q_pad = k_i.contiguous(), v_i.contiguous(), q_i.contiguous()
|
||
|
||
cu = torch.tensor([0, total_k], dtype=torch.int32, device=dev)
|
||
o = flash_attn_varlen_func(
|
||
q_pad, k_full, v_full, cu, cu, total_k, total_k,
|
||
0.0, scale, causal=True) # [tk, q_h, d]
|
||
output[q_start:q_end] = o[ctx_len:].to(orig_dtype)
|
||
|
||
return output
|
||
|
||
@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:
|
||
# Paged-block tiles for context phase.
|
||
# tile_sz = _BLOCKS_PER_TILE × block_size (e.g. 16×16 = 256 tokens).
|
||
# Score tensor [kv_h, gqa, q_len, tile_sz] fp32 = 24 MB per tile.
|
||
# Same tile size reused for the current-chunk phase.
|
||
_BLOCKS_PER_TILE = 32
|
||
|
||
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]
|
||
tile_sz = _BLOCKS_PER_TILE * block_size
|
||
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]
|
||
|
||
# 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.
|
||
# --------------------------------------------------------------
|
||
if ctx_len > 0:
|
||
num_ctx_blocks = (ctx_len + block_size - 1) // block_size
|
||
# Safety: if block_tables is too narrow this indicates a
|
||
# prefix_cache_hit + chunked-prefill bug in model_runner.py
|
||
# (Case 1 leaves prefix_cache_hit=True but block_table is
|
||
# only computed_block_nums, not the full context blocks).
|
||
# patch_model_runner.py fixes the root cause; this guard
|
||
# prevents a zero-dim amax() crash if it still slips through.
|
||
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)
|