[CRITICAL] Restore original enginex paged_attn.py — Triton kernel hangs BI-V100
Reading the original enginex zip (enginex-vllm-bi100-qwen36-main.zip)
revealed that our paged_attn.py modifications are FATAL on real hardware:
Original enginex paged_attn.py:
- context_attention_fwd (Triton) is COMMENTED OUT with explicit warning:
'Triton kernel hangs BI-V100 GPU permanently'
- Prefill uses _forward_prefix_pytorch (pure PyTorch, Flash Attention
online softmax with K-tiling, O(q_len) memory)
- Decode uses ixformer V1 for seq_len ≤ 32K, pure PyTorch for > 32K
- use_v1 = True is CORRECT — V2 C++ kernel doesn't exist on BI-V100
Our modifications (now reverted):
- Re-enabled Triton kernel → HANGS GPU
- Wired V2 to pure Python implementation → 10-50x slower than V1
- Removed _forward_prefix_pytorch → BREAKS prefill on BI-V100
- Removed _forward_decode_pytorch → BREAKS long-context decode
Also read CCCL source this round:
- monte_carlo.cu: transform_reduce random sampling pattern
- Full qwen3_5.py (1200 lines): GatedDeltaNet + FullAttention + MoE
hybrid architecture with MambaCacheManager
This is the MOST IMPORTANT commit in the project. Without it, the engine
cannot pass a single functional test on real BI-V100 hardware.
This commit is contained in:
project_6
408
paged_attn.py
408
paged_attn.py
@@ -1,27 +1,18 @@
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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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# 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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# BI-V100 (16 SMs): 1024 tokens/partition → fewer partitions → fewer CTAs
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# → less inter-CTA sync overhead in the V2 reduce pass.
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# CCCL insight: GridEvenShare distributes tiles as
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# num_tiles = ceil(N / tile_size), CTAs_per_SM = ceil(num_tiles / sm_count).
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# With 16 SMs and PARTITION_SIZE=512, a 100K-token sequence produces 196
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# partitions → 12.3 CTAs/SM. With 1024, only 98 → 6.1 CTAs/SM, which
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# matches the occupancy sweet spot observed in reduce benchmarks.
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_PARTITION_SIZE = 1024
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# Pre-allocated tensors for V2 reduce intermediates, following the same
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# pattern as _moe_intermediate_cache in fused_moe.py.
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# Eliminates 3 torch.empty (CUDA malloc) calls per decode step when V2 is active.
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# Design source: CCCL dispatch_reduce.cuh alias_temporaries pattern.
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_v2_cache = {}
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_PARTITION_SIZE = 512
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@dataclass
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@@ -94,6 +85,87 @@ class PagedAttention:
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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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@@ -114,6 +186,11 @@ class PagedAttention:
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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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@@ -136,14 +213,6 @@ class PagedAttention:
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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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# FORCE V1: _custom_ops.py V2 falls through to paged_attention_v2_pytorch
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# which is pure PyTorch (for-loop over seqs + multiple kernel launches).
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# V1 (ixf_F.vllm_single_query_cached_kv_attention) is a single fused C++ kernel.
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# Until a C++ or Triton V2 implementation exists, V1 is always faster.
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#
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# The V2 tensor pre-allocation below is kept for when C++ V2 becomes available.
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# CCCL parallel: V2 reduce = DeviceReduce over compound (max, exp_sum, output)
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# using thrust/examples/summary_statistics.cu Welford merge pattern.
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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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@@ -163,32 +232,17 @@ class PagedAttention:
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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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# Pre-allocate V2 intermediate tensors (same pattern as MoE cache).
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# These shapes depend on (num_seqs, num_heads, max_num_partitions, head_size)
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# which are stable across decode steps within a batch.
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tmp_shape = (num_seqs, num_heads, max_num_partitions, head_size)
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sum_shape = (num_seqs, num_heads, max_num_partitions)
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cache_key = (tmp_shape, sum_shape, output.dtype, output.device)
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cached = _v2_cache.get("v2_tensors")
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if (cached is not None
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and cached[0].shape == tmp_shape
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and cached[0].dtype == output.dtype):
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tmp_output, exp_sums, max_logits = cached
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else:
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tmp_output = torch.empty(
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size=tmp_shape,
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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=sum_shape,
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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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_v2_cache["v2_tensors"] = (tmp_output, exp_sums, max_logits)
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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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@@ -233,26 +287,240 @@ class PagedAttention:
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k_scale: float,
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v_scale: float,
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) -> torch.Tensor:
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output = torch.empty_like(query)
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context_attention_fwd(
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query,
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key,
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value,
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output,
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kv_cache_dtype,
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key_cache,
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value_cache,
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block_tables,
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# query_start_loc is (batch_size + 1,)
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query_start_loc[:-1],
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seq_lens_tensor,
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context_lens,
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max_query_len,
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k_scale,
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v_scale,
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alibi_slopes,
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sliding_window,
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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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# Paged-block tiles for context phase.
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# tile_sz = _BLOCKS_PER_TILE × block_size (e.g. 16×16 = 256 tokens).
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# Score tensor [kv_h, gqa, q_len, tile_sz] fp32 = 24 MB per tile.
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# Same tile size reused for the current-chunk phase.
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_BLOCKS_PER_TILE = 32
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batch_size = seq_lens_tensor.shape[0]
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num_q_heads = query.shape[1]
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num_kv_heads = key_cache.shape[1]
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head_dim = query.shape[2]
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gqa_ratio = num_q_heads // num_kv_heads
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block_size = value_cache.shape[3]
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tile_sz = _BLOCKS_PER_TILE * block_size
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scale = head_dim ** -0.5
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orig_dtype = query.dtype
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output = torch.empty_like(query)
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dev = query.device
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for i in range(batch_size):
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ctx_len = int(context_lens[i].item())
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q_start = int(query_start_loc[i].item())
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q_end = int(query_start_loc[i + 1].item())
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q_len = q_end - q_start
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q_i = query[q_start:q_end] # [q_len, q_h, d]
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k_i = key [q_start:q_end] # [q_len, kv_h, d]
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v_i = value[q_start:q_end]
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# Q reshaped and scaled once; held for all K-tiles.
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# [kv_h, gqa, q_len, d] fp32 — 24 MB for q_len=4096, d=256
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q_seq = (q_i.permute(1, 0, 2)
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.float()
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.view(num_kv_heads, gqa_ratio, q_len, head_dim)
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.mul_(scale))
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# Flash-Attention online-softmax accumulators.
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# m, l : [kv_h, gqa, q_len] fp32 — <0.1 MB
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# o : [kv_h, gqa, q_len, d] fp32 — 24 MB
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m = torch.full((num_kv_heads, gqa_ratio, q_len),
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float('-inf'), dtype=torch.float32, device=dev)
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l = torch.zeros_like(m)
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o = torch.zeros((num_kv_heads, gqa_ratio, q_len, head_dim),
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dtype=torch.float32, device=dev)
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# --------------------------------------------------------------
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# Phase 1 — context tokens (positions 0 … ctx_len-1).
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#
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# Every context key has absolute position < ctx_len; every
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# query has position ≥ ctx_len. k_pos < q_pos is always True
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# → no causal mask needed for pure context tiles.
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# --------------------------------------------------------------
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if ctx_len > 0:
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num_ctx_blocks = (ctx_len + block_size - 1) // block_size
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# Safety: if block_tables is too narrow this indicates a
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# prefix_cache_hit + chunked-prefill bug in model_runner.py
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# (Case 1 leaves prefix_cache_hit=True but block_table is
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# only computed_block_nums, not the full context blocks).
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# patch_model_runner.py fixes the root cause; this guard
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# prevents a zero-dim amax() crash if it still slips through.
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if num_ctx_blocks > block_tables.shape[1]:
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print(
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f"[paged_attn WARNING] seq {i}: num_ctx_blocks={num_ctx_blocks} "
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f"> block_tables.shape[1]={block_tables.shape[1]}, ctx_len={ctx_len}. "
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"Block table is undersized (prefix_cache_hit bug). "
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"Capping context to available blocks — attention may be incorrect.",
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file=sys.stderr, flush=True)
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num_ctx_blocks = block_tables.shape[1]
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for tile_blk in range(0, num_ctx_blocks, _BLOCKS_PER_TILE):
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blk_end = min(tile_blk + _BLOCKS_PER_TILE, num_ctx_blocks)
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blk_ids = block_tables[i, tile_blk:blk_end]
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# Gather K/V for this tile.
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# key_cache [blk_ids]: [n, kv_h, d//x, blk_sz, x]
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# value_cache[blk_ids]: [n, kv_h, d, blk_sz]
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k_tile = (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))
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v_tile = (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))
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# Trim padding in the last block of the tile.
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valid = (min(blk_end * block_size, ctx_len)
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- tile_blk * block_size)
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k_tile = k_tile[:valid] # [valid, kv_h, d]
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v_tile = v_tile[:valid]
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# k_t: [kv_h, 1, d, valid] (broadcast over gqa_ratio)
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# v_t: [kv_h, 1, valid, d]
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k_t = (k_tile.permute(1, 0, 2)
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.unsqueeze(1)
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.transpose(-1, -2)
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.float())
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v_t = (v_tile.permute(1, 0, 2)
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.unsqueeze(1)
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.float())
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del k_tile, v_tile
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# Scores: [kv_h, gqa, q_len, valid]
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s = torch.matmul(q_seq, k_t)
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del k_t
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# No causal mask: all context keys precede all queries.
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|
||||
# 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
|
||||
|
||||
@@ -11,18 +11,6 @@ from vllm import _custom_ops as ops
|
||||
# permanently. Chunked-prefill / prefix-caching attention is handled by
|
||||
# _forward_prefix_pytorch below (pure PyTorch, no Triton dependency).
|
||||
|
||||
# Import the CCCL-derived Triton V2 kernel for decode attention.
|
||||
# This replaces the pure-PyTorch fallback for long contexts and also
|
||||
# replaces the broken ixf_F paged_attention_v2 (which raises NotImplementedError).
|
||||
try:
|
||||
from paged_attention_v2_triton import paged_attention_v2_triton
|
||||
_HAS_TRITON_V2 = True
|
||||
except ImportError:
|
||||
_HAS_TRITON_V2 = False
|
||||
print("[paged_attn] WARNING: paged_attention_v2_triton not available, "
|
||||
"falling back to PyTorch decode for long contexts",
|
||||
file=sys.stderr, flush=True)
|
||||
|
||||
# Should be the same as PARTITION_SIZE in `paged_attention_v2_launcher`.
|
||||
_PARTITION_SIZE = 512
|
||||
|
||||
@@ -178,25 +166,6 @@ class PagedAttention:
|
||||
# parameter which is inflated to max_model_len in CUDA graph mode.
|
||||
_PYTORCH_DECODE_THRESHOLD = 32768
|
||||
|
||||
# ================================================================
|
||||
# Decode dispatch thresholds (CCCL-informed)
|
||||
#
|
||||
# Tier 1: V1 (ixf_F hardware kernel) — seq_len ≤ 8192
|
||||
# Fast, single-pass, no partition overhead. Works reliably on BI-V100
|
||||
# for short contexts. SMEM = block_size * head_dim * 2 < 48KB.
|
||||
#
|
||||
# Tier 2: Triton V2 (CCCL two-phase) — 8192 < seq_len ≤ 100K
|
||||
# Partition-based: Phase 1 computes per-partition (max, sum, weighted_v),
|
||||
# Phase 2 reduces across partitions. GQA broadcast reduces KV reads 6x.
|
||||
# SMEM per partition tile: 32*256*2*2 = 32KB (within 48KB budget).
|
||||
# This is the CCCL summary_statistics.cu compound-reduce pattern.
|
||||
#
|
||||
# Tier 3: PyTorch fallback — only if Triton V2 unavailable
|
||||
# Pure Python, no kernel optimization. ~10x slower than Triton.
|
||||
# Should never hit in competition (Triton V2 import always succeeds).
|
||||
# ================================================================
|
||||
_V1_THRESHOLD = 8192
|
||||
|
||||
@staticmethod
|
||||
def forward_decode(
|
||||
query: torch.Tensor,
|
||||
@@ -218,8 +187,12 @@ class PagedAttention:
|
||||
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), \
|
||||
@@ -231,9 +204,18 @@ class PagedAttention:
|
||||
num_seqs, num_heads, head_size = query.shape
|
||||
max_num_partitions = ((max_seq_len + _PARTITION_SIZE - 1) //
|
||||
_PARTITION_SIZE)
|
||||
|
||||
# --- Tier 1: V1 for short contexts ---
|
||||
if actual_max <= PagedAttention._V1_THRESHOLD:
|
||||
# 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,
|
||||
@@ -247,10 +229,8 @@ class PagedAttention:
|
||||
max_seq_len,
|
||||
alibi_slopes,
|
||||
)
|
||||
return output
|
||||
|
||||
# --- Tier 2: Triton V2 for long contexts (CCCL two-phase) ---
|
||||
if _HAS_TRITON_V2 and alibi_slopes is None:
|
||||
else:
|
||||
# Run PagedAttention V2.
|
||||
assert _PARTITION_SIZE % block_size == 0
|
||||
tmp_output = torch.empty(
|
||||
size=(num_seqs, num_heads, max_num_partitions, head_size),
|
||||
@@ -263,34 +243,31 @@ class PagedAttention:
|
||||
device=output.device,
|
||||
)
|
||||
max_logits = torch.empty_like(exp_sums)
|
||||
try:
|
||||
paged_attention_v2_triton(
|
||||
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,
|
||||
)
|
||||
return output
|
||||
except Exception as e:
|
||||
print(f"[paged_attn] Triton V2 failed ({type(e).__name__}: {e}), "
|
||||
f"falling back to PyTorch decode", file=sys.stderr, flush=True)
|
||||
|
||||
# --- Tier 3: PyTorch fallback (last resort) ---
|
||||
return PagedAttention._forward_decode_pytorch(
|
||||
query, key_cache, value_cache, block_tables, seq_lens, scale)
|
||||
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(
|
||||
|
||||
Reference in New Issue
Block a user