493 lines
20 KiB
Python
493 lines
20 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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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: [kv_h, head_dim, seq_len] fp32 — no GQA expansion.
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# With kv_h=1 and seq_len=100K this is 98 MB vs 586 MB if expanded.
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k_t = (key_cache[blk_ids]
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.permute(0, 3, 1, 2, 4)
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.contiguous()
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.view(-1, num_kv_heads, head_dim))[:seq_len] \
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.permute(1, 2, 0).contiguous().float() # [kv_h, d, seq_len]
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# Gather V: [kv_h, seq_len, head_dim] fp32
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v_t = (value_cache[blk_ids]
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.permute(0, 3, 1, 2)
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.contiguous()
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.view(-1, num_kv_heads, head_dim))[:seq_len] \
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.permute(1, 0, 2).contiguous().float() # [kv_h, seq_len, d]
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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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# 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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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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use_v1 = True
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if use_v1:
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# Run PagedAttention V1.
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ops.paged_attention_v1(
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output,
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query,
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key_cache,
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value_cache,
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num_kv_heads,
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scale,
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block_tables,
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seq_lens,
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block_size,
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max_seq_len,
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alibi_slopes,
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)
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else:
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# Run PagedAttention V2.
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assert _PARTITION_SIZE % block_size == 0
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tmp_output = torch.empty(
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size=(num_seqs, num_heads, max_num_partitions, head_size),
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dtype=output.dtype,
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device=output.device,
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)
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exp_sums = torch.empty(
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size=(num_seqs, num_heads, max_num_partitions),
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dtype=torch.float32,
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device=output.device,
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)
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max_logits = torch.empty_like(exp_sums)
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ops.paged_attention_v2(
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output,
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exp_sums,
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max_logits,
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tmp_output,
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query,
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key_cache,
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value_cache,
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num_kv_heads,
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scale,
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block_tables,
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seq_lens,
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block_size,
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max_seq_len,
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alibi_slopes,
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kv_cache_dtype,
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k_scale,
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v_scale,
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tp_rank,
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blocksparse_local_blocks,
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blocksparse_vert_stride,
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blocksparse_block_size,
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blocksparse_head_sliding_step,
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)
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return output
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@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 query-chunking (no Triton).
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For each sequence, gathers the context KV from the paged KV cache,
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concatenates with the current-chunk K/V, then computes scaled-dot-
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product attention with a causal mask.
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Memory optimisation — GQA-aware Q-tiling
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-----------------------------------------
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Two complementary tricks keep peak activation memory well below 1 GB
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even for 100K context on TP=4 (kv_h=1, q_h=6):
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1. No GQA pre-expansion: K/V are kept at native [kv_h, kv_len, d]
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resolution and GQA grouping is handled via 4D reshape+broadcast
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inside the matmul. With kv_h=1 and kv_len=100K this saves ~6×
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vs the old expand-then-float32 approach:
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Old: [6, 100K, 256] fp32 = 586 MB each for K and V
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New: [1, 100K, 256] fp32 = 98 MB each for K and V
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2. Q-tiling (_ATTN_Q_CHUNK=64): attn_w [kv_h, gqa, Q, kv_len] fp32
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is bounded to ~148 MB at 100K instead of growing with q_len.
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Combined peak per layer (100K): ~352 MB vs ~1200 MB previously.
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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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# Memory-efficient query-chunked attention.
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# Key optimisation: do NOT expand KV heads for GQA before materialising
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# k_t / v_t. With kv_h=1 (Qwen3.6 TP=4), keeping K/V at native kv_h
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# resolution saves ~6× memory vs expanding to q_h first:
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# Old path (expand then float32): [6, 100K, 256] fp32 = 586 MB
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# New path (keep kv_h, float32): [1, 100K, 256] fp32 = 98 MB
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# GQA grouping is handled lazily inside the Q-tile matmul via 4D
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# reshaping, so no extra tensors are created.
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try:
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_ATTN_Q_CHUNK = 64 # [kv_h, gqa, Q_CHUNK, kv_len] fp32 ≤ 150 MB
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batch_size = seq_lens_tensor.shape[0]
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num_q_heads = query.shape[1]
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num_kv_heads = key_cache.shape[1]
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head_dim = query.shape[2]
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gqa_ratio = num_q_heads // num_kv_heads
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# value_cache: [num_blocks, num_kv_heads, head_dim, block_size]
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block_size = value_cache.shape[3]
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scale = 1.0 / (head_dim ** 0.5)
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output = torch.empty_like(query)
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orig_dtype = query.dtype
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for i in range(batch_size):
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ctx_len = int(context_lens[i].item())
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q_start = int(query_start_loc[i].item())
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q_end = int(query_start_loc[i + 1].item())
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q_len = q_end - q_start
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q_i = query[q_start:q_end] # [q_len, num_q_heads, head_dim]
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k_i = key [q_start:q_end] # [q_len, num_kv_heads, head_dim]
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v_i = value[q_start:q_end]
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# --- Build full K/V (context from cache + current chunk) ----
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if ctx_len > 0:
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num_ctx_blocks = (ctx_len + block_size - 1) // block_size
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blk_ids = block_tables[i, :num_ctx_blocks]
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# key_cache[blk_ids]: [n, kv_h, d//x, blk_sz, x]
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# → permute(0,3,1,2,4) → contiguous → view → [:ctx_len]
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k_ctx = (key_cache[blk_ids]
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.permute(0, 3, 1, 2, 4)
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.contiguous()
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.view(-1, num_kv_heads, head_dim))[:ctx_len]
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# value_cache[blk_ids]: [n, kv_h, d, blk_sz]
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# → permute(0,3,1,2) → contiguous → view → [:ctx_len]
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v_ctx = (value_cache[blk_ids]
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.permute(0, 3, 1, 2)
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.contiguous()
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.view(-1, num_kv_heads, head_dim))[:ctx_len]
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k_full = torch.cat([k_ctx, k_i], dim=0) # [kv_len, kv_h, d]
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v_full = torch.cat([v_ctx, v_i], dim=0)
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del k_ctx, v_ctx
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else:
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k_full = k_i
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v_full = v_i
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kv_len = k_full.shape[0] # ctx_len + q_len
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# Transpose to [kv_h, kv_len, d], keep original dtype (fp16/bf16).
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# Do NOT cast to fp32 here — k/v stay in fp16 to halve memory.
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# attn_w is computed in fp32 (q cast to fp32 before matmul, then
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# k cast inline) so softmax precision is unaffected.
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# Do NOT expand GQA heads here either — gqa_ratio x memory savings.
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k_t = k_full.permute(1, 0, 2).contiguous() # [kv_h, kv_len, d] fp16
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del k_full
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v_t = v_full.permute(1, 0, 2).contiguous() # [kv_h, kv_len, d] fp16
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del v_full
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# k_pos used for causal mask: shape [kv_len]
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k_pos = torch.arange(kv_len, device=query.device)
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# --- Query-chunked attention with lazy GQA grouping ----------
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# q_i reshaped to [kv_h, gqa_ratio, qc, d] so matmul with
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# k_t [kv_h, kv_len, d] (broadcast over gqa_ratio dim) gives
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# attn_w [kv_h, gqa_ratio, qc, kv_len] without extra K copies.
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for qc_start in range(0, q_len, _ATTN_Q_CHUNK):
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qc_end = min(qc_start + _ATTN_Q_CHUNK, q_len)
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qc = qc_end - qc_start
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# [kv_h, gqa_ratio, qc, d]
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q_t_chunk = (q_i[qc_start:qc_end]
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.permute(1, 0, 2) # [q_h, qc, d]
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.float()
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.view(num_kv_heads, gqa_ratio, qc, head_dim))
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# [kv_h, gqa_ratio, qc, kv_len]
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# k_t unsqueezed to [kv_h, 1, kv_len, d] broadcasts over gqa_ratio.
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# Cast k slice to fp32 inline; the temporary is freed after matmul.
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attn_w = torch.matmul(q_t_chunk * scale,
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k_t.unsqueeze(1).transpose(-1, -2).float())
|
||
|
||
# Causal mask for this sub-chunk:
|
||
# query absolute position = ctx_len + qc_start..qc_end-1
|
||
qc_q_pos = torch.arange(qc_start, qc_end,
|
||
device=query.device)
|
||
# mask[j, k] = True → future key, block it
|
||
mask = k_pos.unsqueeze(0) > (ctx_len + qc_q_pos.unsqueeze(1))
|
||
attn_w.masked_fill_(
|
||
mask.unsqueeze(0).unsqueeze(0), float('-inf'))
|
||
|
||
# In-place numerically stable softmax — avoids allocating a
|
||
# new 150 MB tensor (same size as attn_w) that torch.softmax
|
||
# would create, which exhausts the fragmented GPU pool.
|
||
attn_w -= attn_w.amax(dim=-1, keepdim=True)
|
||
attn_w.exp_()
|
||
attn_w /= attn_w.sum(dim=-1, keepdim=True)
|
||
# [kv_h, gqa_ratio, qc, d]; v_t cast to fp32 inline
|
||
out_c = torch.matmul(attn_w,
|
||
v_t.unsqueeze(1).float())
|
||
# reshape to [q_h, qc, d] then [qc, q_h, d]
|
||
out_c = out_c.view(num_q_heads, qc, head_dim)
|
||
|
||
output[q_start + qc_start : q_start + qc_end] = (
|
||
out_c.to(orig_dtype).permute(1, 0, 2))
|
||
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)
|