enginex-vllm-bi100-qwen36/qwen3_6_scripts/patch_xformers_sdpa_seq.py

"""
策略：顺序（per-sequence）fallback — 纯 PyTorch 数学实现
==========================================================
逐条序列用 matmul + softmax 手写 attention，完全绕开所有硬件
flash attention kernel（ixformer / cudnnFlashAttnForward）。

背景：
  Iluvatar cudnnFlashAttnForward 存在两个已知问题：
    1. 不支持 is_causal=True（报错）
    2. 使用 attn_mask 路径时数值结果不正确（静默错误，输出全为"!"）
  与华为昇腾 910B4 上 llama.cpp --flash-attn off 修复同类问题的原理相同。
  纯数学路径（matmul + softmax）在任何 PyTorch 后端上结果都正确。

优点：
  数值正确，不依赖任何硬件特定 attention kernel。
  峰值显存 = max(seq_len)² × H × dtype_size，由 --max-model-len 控制。

缺点：
  并发请求的 prefill attention 串行执行。
  O(L²) 显存（无 flash attention 的 O(L) 优化）。

内存参考（fp16，H_local=6）：
  max-model-len=4096  → 峰值 ~200 MB
  max-model-len=8192  → 峰值 ~800 MB
  max-model-len=16384 → 峰值 ~3.2 GB

Deploy:
  python3 modified_scripts/patch_xformers_sdpa_seq.py
"""

XFORMERS_PATH = (
    "/usr/local/corex/lib64/python3/dist-packages/"
    "vllm/attention/backends/xformers.py"
)

FALLBACK_METHOD = '''
    def _run_sdpa_fallback(
        self,
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        attn_metadata: "XFormersMetadata",
    ) -> torch.Tensor:
        """纯数学 causal attention fallback，带 Q-tiling 内存优化。

        调用时机：kv_cache.numel()==0（profiling 阶段）。
        此路径无 KV 缓存前缀，KV 长度 == query 长度。

        内存优化（Q-tiling，与 Flash Attention 同思路）：
          将 Q 分成 _Q_CHUNK 大小的子块逐块计算，每块峰值内存
          O(_Q_CHUNK × q_len) 而非 O(q_len²)。
          profiling 阶段序列可能达到 max_model_len（如 20K tokens），
          不加 Q-tiling 会产生 9.6 GB 矩阵直接 OOM。

        softmax 在 float32 下计算以防止 float16 溢出，结果转回原始 dtype。

        Args:
            query : [1, total_query_tokens, num_heads,    head_dim]
            key   : [1, total_query_tokens, num_kv_heads, head_dim]
            value : [1, total_query_tokens, num_kv_heads, head_dim]
        Returns:
            [1, total_query_tokens, num_heads, head_dim]
        """
        _Q_CHUNK = 256  # 与 _forward_prefix_pytorch 的 _ATTN_Q_CHUNK 保持一致

        assert attn_metadata.seq_lens is not None
        orig_dtype = query.dtype
        num_seqs = len(attn_metadata.seq_lens)

        # 推导每条序列的实际 query 长度。
        # 正常 prefill 时 q_len == seq_len；如果将来遇到 chunked 场景，
        # query_start_loc 记录的是真实 query token 数（非全序列长度）。
        if (attn_metadata.query_start_loc is not None
                and len(attn_metadata.query_start_loc) == num_seqs + 1):
            q_lens = [
                int(attn_metadata.query_start_loc[i + 1].item()) -
                int(attn_metadata.query_start_loc[i].item())
                for i in range(num_seqs)
            ]
        else:
            q_lens = list(attn_metadata.seq_lens)

        q_flat = query.squeeze(0)   # [T, H,   D]
        k_flat = key.squeeze(0)     # [T, Hkv, D]
        v_flat = value.squeeze(0)

        output = torch.empty_like(q_flat)
        seq_start = 0
        for q_len in q_lens:
            seq_end = seq_start + q_len

            # 当前序列的完整 K/V（此路径无前缀，KV == Q）
            k_s = k_flat[seq_start:seq_end].permute(1, 0, 2).float()  # [Hkv, q_len, D]
            v_s = v_flat[seq_start:seq_end].permute(1, 0, 2).float()  # [Hkv, q_len, D]

            # GQA：展开 KV heads 至与 query heads 一致
            if k_s.shape[0] != self.num_heads:
                n = self.num_heads // k_s.shape[0]
                k_s = k_s.repeat_interleave(n, dim=0).contiguous()
                v_s = v_s.repeat_interleave(n, dim=0).contiguous()

            # k_pos 用于因果掩码
            k_pos = torch.arange(q_len, device=query.device)

            # Q-tiling：分块处理 query，峰值内存 O(_Q_CHUNK × q_len)
            for qc_start in range(0, q_len, _Q_CHUNK):
                qc_end = min(qc_start + _Q_CHUNK, q_len)

                # [H, qc, D]
                q_c = q_flat[seq_start + qc_start:seq_start + qc_end] \
                      .permute(1, 0, 2).float()

                # [H, qc, q_len]
                attn_w = torch.matmul(q_c, k_s.transpose(-2, -1)) * self.scale

                # 因果掩码：q_c 里位置 j 只能看 k_pos <= j（相对位置）
                qc_q_pos = torch.arange(qc_start, qc_end, device=query.device)
                mask = k_pos.unsqueeze(0) > qc_q_pos.unsqueeze(1)
                attn_w = attn_w.masked_fill(mask.unsqueeze(0), float("-inf"))

                attn_w = torch.softmax(attn_w, dim=-1)
                out_c = torch.matmul(attn_w, v_s).to(orig_dtype)  # [H, qc, D]

                output[seq_start + qc_start:seq_start + qc_end] = (
                    out_c.permute(1, 0, 2))

            seq_start = seq_end

        return output.unsqueeze(0)  # [1, T, H, D]

'''

OLD_XFORMER_BLOCK = """\
        self.attn_op = xops.fmha.flash.FwOp()
        if self.alibi_slopes is None:
            # Add the batch dimension.
            query = query.unsqueeze(0)
            key = key.unsqueeze(0)
            value = value.unsqueeze(0)
            out = xops.memory_efficient_attention_forward(
                query,
                key,
                value,
                attn_bias=attn_bias[0],
                p=0.0,
                scale=self.scale,
                op = self.attn_op
                )
            return out.view_as(original_query)\
"""

NEW_XFORMER_BLOCK = """\
        self.attn_op = xops.fmha.flash.FwOp()
        if self.alibi_slopes is None:
            # Add the batch dimension.
            query = query.unsqueeze(0)
            key = key.unsqueeze(0)
            value = value.unsqueeze(0)
            if self.head_size > 128:
                out = self._run_sdpa_fallback(query, key, value, attn_metadata)
            else:
                out = xops.memory_efficient_attention_forward(
                    query,
                    key,
                    value,
                    attn_bias=attn_bias[0],
                    p=0.0,
                    scale=self.scale,
                    op=self.attn_op,
                )
            return out.view_as(original_query)\
"""

INJECT_ANCHOR = "    def _run_memory_efficient_xformers_forward("


def patch_file(path):
    with open(path, "r") as f:
        content = f.read()
    changed = False

    if "_run_sdpa_fallback" in content:
        print("  [skip] _run_sdpa_fallback already present")
    elif INJECT_ANCHOR not in content:
        print("  [warn] inject anchor not found")
    else:
        content = content.replace(INJECT_ANCHOR, FALLBACK_METHOD + INJECT_ANCHOR, 1)
        print("  [ok]   injected _run_sdpa_fallback (sequential, pure-math)")
        changed = True

    if NEW_XFORMER_BLOCK in content:
        print("  [skip] dispatch block already patched")
    elif OLD_XFORMER_BLOCK in content:
        content = content.replace(OLD_XFORMER_BLOCK, NEW_XFORMER_BLOCK, 1)
        print("  [ok]   patched dispatch block")
        changed = True
    else:
        print("  [warn] dispatch block anchor not found")

    if changed:
        with open(path, "w") as f:
            f.write(content)
        print(f"  Written: {path}")


def main():
    print("=== patch_xformers_sdpa_seq (sequential, pure-math) ===")
    print(f"Target: {XFORMERS_PATH}")
    patch_file(XFORMERS_PATH)
    print("\nDone.")


if __name__ == "__main__":
    main()