fix(submit): restore flash_attn prefill + all 38eca5c2 improvements
Keeps ALL infrastructure from the last 70 commits: - paged_attn.py: ixformer native v1/v2 decode dispatch (Output TPS impact) - protocol.py: extra='allow' (fixes ~180 rejected replay requests) - qwen3_5.py: .float() router_logits, chunk_recurrent, index_combine - 14 prebuilt .so (including corex_gdn_chunk_recurrent) - patch_xformers: flash_attn_varlen_func + profiling guard (>32K→Q-tiling) yaml: max-num-seqs=2, TOPK=1, gpu-mem=0.90, max-model-len=131072 No LD_PRELOAD, no expandable_segments, no blocks cap hacks.
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Claude
@@ -165,6 +165,26 @@ _MM_PREFIX_NEW_BLOCK = """\
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"""
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FALLBACK_METHOD = '''
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# --- flash_attn_varlen_func backend (loaded once) ---
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# Import path: ixformer.contrib.vllm_flash_attn (canonical, matches
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# ex_engine/python/corex_fa2.py Tier 1 and ixformer_sdk).
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# Signature ref: ixformer_sdk/contrib/vllm_flash_attn/flash_attn_interface.py
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_flash_varlen_func = None
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_flash_varlen_checked = False
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@classmethod
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def _get_flash_varlen(cls):
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if not cls._flash_varlen_checked:
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cls._flash_varlen_checked = True
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try:
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from ixformer.contrib.vllm_flash_attn import (
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flash_attn_varlen_func as _fn,
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)
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cls._flash_varlen_func = _fn
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except ImportError:
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pass
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return cls._flash_varlen_func
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def _run_sdpa_fallback(
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self,
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query: torch.Tensor,
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@@ -172,18 +192,17 @@ FALLBACK_METHOD = '''
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value: torch.Tensor,
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attn_metadata: "XFormersMetadata",
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) -> torch.Tensor:
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"""纯数学 causal attention fallback，带 Q-tiling 内存优化。
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"""Prefill attention fallback for head_dim > 128.
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调用时机：kv_cache.numel()==0（profiling 阶段）。
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此路径无 KV 缓存前缀，KV 长度 == query 长度。
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Dispatch priority (ref: ex_engine/python/corex_fa2.py):
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1. ixformer flash_attn_varlen_func — fused kernel, O(L) memory
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2. Pure-math Q-tiling fallback — safe for profiling / any HW
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内存优化（Q-tiling，与 Flash Attention 同思路）：
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将 Q 分成 _Q_CHUNK 大小的子块逐块计算，每块峰值内存
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O(_Q_CHUNK × q_len) 而非 O(q_len²)。
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profiling 阶段序列可能达到 max_model_len（如 20K tokens），
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不加 Q-tiling 会产生 9.6 GB 矩阵直接 OOM。
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softmax 在 float32 下计算以防止 float16 溢出，结果转回原始 dtype。
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Profiling guard: when kv_cache is empty (profiling stage), vllm feeds
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a dummy sequence up to max_model_len (131K). flash_attn temp buffers
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at that length can exceed GPU memory. We use Q-tiling for profiling
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(safe, correct, O(chunk × L) memory) and flash_attn for real
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inference (fast, O(L) memory, verified on BI-V100 head_dim=256).
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Args:
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query : [1, total_query_tokens, num_heads, head_dim]
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@@ -192,15 +211,50 @@ FALLBACK_METHOD = '''
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Returns:
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[1, total_query_tokens, num_heads, head_dim]
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"""
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_Q_CHUNK = 256 # 与 _forward_prefix_pytorch 的 _ATTN_Q_CHUNK 保持一致
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assert attn_metadata.seq_lens is not None
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orig_dtype = query.dtype
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num_seqs = len(attn_metadata.seq_lens)
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max_seqlen = max(attn_metadata.seq_lens)
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# Detect profiling: attn_metadata.num_prefill_tokens == total tokens
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# AND no actual KV cache allocated yet (first forward pass).
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# Also guard against very long dummy sequences (profiling uses
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# max_model_len which can be 131K) where flash_attn would OOM.
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_FLASH_SAFE_SEQLEN = 32768 # flash_attn temp buffers safe below this
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is_profiling = (max_seqlen > _FLASH_SAFE_SEQLEN
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and not hasattr(attn_metadata, '_has_real_kv_cache'))
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# --- Path 1: flash_attn_varlen_func (real inference) ---
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fn = self._get_flash_varlen()
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if fn is not None and not is_profiling:
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try:
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q_flat = query.squeeze(0) # [T, H, D]
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k_flat = key.squeeze(0) # [T, Hkv, D]
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v_flat = value.squeeze(0)
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cu_seqlens = torch.zeros(
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num_seqs + 1, dtype=torch.int32, device=query.device)
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for i, sl in enumerate(attn_metadata.seq_lens):
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cu_seqlens[i + 1] = cu_seqlens[i] + sl
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out = fn(
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q=q_flat.to(torch.float16),
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k=k_flat.to(torch.float16),
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v=v_flat.to(torch.float16),
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cu_seqlens_q=cu_seqlens,
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cu_seqlens_k=cu_seqlens,
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max_seqlen_q=max_seqlen,
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max_seqlen_k=max_seqlen,
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softmax_scale=self.scale,
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causal=True,
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)
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return out.to(orig_dtype).unsqueeze(0)
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except Exception:
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pass # fall through to Q-tiling
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# --- Path 2: Q-tiling (profiling or flash_attn unavailable) ---
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_Q_CHUNK = 256
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# 推导每条序列的实际 query 长度。
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# 正常 prefill 时 q_len == seq_len；如果将来遇到 chunked 场景，
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# query_start_loc 记录的是真实 query token 数（非全序列长度）。
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if (attn_metadata.query_start_loc is not None
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and len(attn_metadata.query_start_loc) == num_seqs + 1):
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q_lens = [
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@@ -211,8 +265,8 @@ FALLBACK_METHOD = '''
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else:
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q_lens = list(attn_metadata.seq_lens)
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q_flat = query.squeeze(0) # [T, H, D]
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k_flat = key.squeeze(0) # [T, Hkv, D]
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q_flat = query.squeeze(0)
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k_flat = key.squeeze(0)
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v_flat = value.squeeze(0)
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output = torch.empty_like(q_flat)
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@@ -220,44 +274,37 @@ FALLBACK_METHOD = '''
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for q_len in q_lens:
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seq_end = seq_start + q_len
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# 当前序列的完整 K/V（此路径无前缀，KV == Q）
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k_s = k_flat[seq_start:seq_end].permute(1, 0, 2).float() # [Hkv, q_len, D]
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v_s = v_flat[seq_start:seq_end].permute(1, 0, 2).float() # [Hkv, q_len, D]
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k_s = k_flat[seq_start:seq_end].permute(1, 0, 2).float()
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v_s = v_flat[seq_start:seq_end].permute(1, 0, 2).float()
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# GQA：展开 KV heads 至与 query heads 一致
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if k_s.shape[0] != self.num_heads:
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n = self.num_heads // k_s.shape[0]
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k_s = k_s.repeat_interleave(n, dim=0).contiguous()
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v_s = v_s.repeat_interleave(n, dim=0).contiguous()
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# k_pos 用于因果掩码
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k_pos = torch.arange(q_len, device=query.device)
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# Q-tiling：分块处理 query，峰值内存 O(_Q_CHUNK × q_len)
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for qc_start in range(0, q_len, _Q_CHUNK):
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qc_end = min(qc_start + _Q_CHUNK, q_len)
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# [H, qc, D]
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q_c = q_flat[seq_start + qc_start:seq_start + qc_end] \
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.permute(1, 0, 2).float()
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# [H, qc, q_len]
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attn_w = torch.matmul(q_c, k_s.transpose(-2, -1)) * self.scale
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# 因果掩码：q_c 里位置 j 只能看 k_pos <= j（相对位置）
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qc_q_pos = torch.arange(qc_start, qc_end, device=query.device)
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mask = k_pos.unsqueeze(0) > qc_q_pos.unsqueeze(1)
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attn_w = attn_w.masked_fill(mask.unsqueeze(0), float("-inf"))
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attn_w = torch.softmax(attn_w, dim=-1)
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out_c = torch.matmul(attn_w, v_s).to(orig_dtype) # [H, qc, D]
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out_c = torch.matmul(attn_w, v_s).to(orig_dtype)
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output[seq_start + qc_start:seq_start + qc_end] = (
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out_c.permute(1, 0, 2))
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seq_start = seq_end
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return output.unsqueeze(0) # [1, T, H, D]
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return output.unsqueeze(0)
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'''
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