Qwen3.6-27B iluvatar bi-v100 adaptation
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qwen3_6_scripts/patch_xformers_sdpa_batch.py
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qwen3_6_scripts/patch_xformers_sdpa_batch.py
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"""
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策略：批量（block-diagonal）fallback — 纯 PyTorch 数学实现
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=============================================================
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构建块对角 causal mask，对整批序列一次 matmul + softmax，
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完全绕开所有硬件 flash attention kernel。
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背景：
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ixformer flshattF: head_dim > 128 报错拒绝
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cudnnFlashAttnForward: 接受 head_dim=256，但数值结果错误（输出全"!"）
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两者大概率是同一硬件单元，ixformer 提前拦截了硬件不支持的配置。
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纯 matmul 路径完全绕开硬件 flash attention，数值正确。
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优点：
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数值正确。
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并发请求 prefill attention 在 GPU 上真正并行（一次大 matmul）。
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缺点：
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峰值显存 = total_tokens² × H × dtype_size
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total_tokens 受 --max-num-batched-tokens 控制，max-model-len 控制不住。
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内存参考（fp16，H_local=6，--max-num-batched-tokens=T）：
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T=2048 → 峰值 ~50 MB
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T=4096 → 峰值 ~200 MB
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T=8192 → 峰值 ~800 MB
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T=16384 → 峰值 ~3.2 GB
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Deploy:
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python3 modified_scripts/patch_xformers_sdpa_batch.py
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"""
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XFORMERS_PATH = (
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"/usr/local/corex/lib64/python3/dist-packages/"
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"vllm/attention/backends/xformers.py"
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)
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FALLBACK_METHOD = '''
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def _run_sdpa_fallback(
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self,
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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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attn_metadata: "XFormersMetadata",
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) -> torch.Tensor:
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"""批量纯数学 attention fallback。
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构建块对角 causal mask（等价于 ixformer BlockDiagonalCausalMask），
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对整批序列一次 matmul + softmax，GPU 并行处理所有序列。
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块对角 mask 结构（seq1 len=3，seq2 len=2）：
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s1,0 s1,1 s1,2 s2,0 s2,1
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s1,0 [ 0 -inf -inf -inf -inf ]
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s1,1 [ 0 0 -inf -inf -inf ]
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s1,2 [ 0 0 0 -inf -inf ]
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s2,0 [-inf -inf -inf 0 -inf ]
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s2,1 [-inf -inf -inf 0 0 ]
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softmax 在 float32 下计算防止 float16 溢出，结果转回原始 dtype。
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Args:
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query : [1, total_prefill_tokens, num_heads, head_dim]
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key : [1, total_prefill_tokens, num_kv_heads, head_dim]
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value : [1, total_prefill_tokens, num_kv_heads, head_dim]
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Returns:
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[1, total_prefill_tokens, num_heads, head_dim]
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"""
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assert attn_metadata.seq_lens is not None
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orig_dtype = query.dtype
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total_tokens = query.shape[1]
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# ── 构建块对角 causal mask [T, T] ────────────────────────────────
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# 全部初始化为 -inf，再对每条序列的对角块填入下三角 0
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mask = torch.full(
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(total_tokens, total_tokens),
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float("-inf"),
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dtype=torch.float32,
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device=query.device,
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)
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start = 0
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for seq_len in attn_metadata.seq_lens:
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end = start + seq_len
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mask[start:end, start:end] = torch.tril(
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torch.zeros(seq_len, seq_len,
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dtype=torch.float32, device=query.device)
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)
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start = end
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# ── [1, H, T, D]，.contiguous() ──────────────────────────────────
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q_all = query.squeeze(0).permute(1, 0, 2).contiguous().unsqueeze(0)
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k_all = key.squeeze(0).permute(1, 0, 2).contiguous().unsqueeze(0)
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v_all = value.squeeze(0).permute(1, 0, 2).contiguous().unsqueeze(0)
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# ── GQA：展开 KV heads ────────────────────────────────────────────
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if k_all.shape[1] != q_all.shape[1]:
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n = q_all.shape[1] // k_all.shape[1]
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k_all = k_all.repeat_interleave(n, dim=1).contiguous()
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v_all = v_all.repeat_interleave(n, dim=1).contiguous()
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# ── 纯数学 attention（float32 防溢出）────────────────────────────
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# [1, H, T, T]
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attn_w = torch.matmul(q_all.float(), k_all.float().transpose(-2, -1))
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attn_w = attn_w * self.scale
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attn_w = attn_w + mask # 加法广播：mask [T,T] → [1, H, T, T]
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attn_w = torch.softmax(attn_w, dim=-1)
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out = torch.matmul(attn_w, v_all.float()).to(orig_dtype)
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# [1, H, T, D] → [1, T, H, D]
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return out.squeeze(0).permute(1, 0, 2).contiguous().unsqueeze(0)
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'''
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OLD_XFORMER_BLOCK = """\
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self.attn_op = xops.fmha.flash.FwOp()
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if self.alibi_slopes is None:
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# Add the batch dimension.
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query = query.unsqueeze(0)
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key = key.unsqueeze(0)
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value = value.unsqueeze(0)
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out = xops.memory_efficient_attention_forward(
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query,
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key,
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value,
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attn_bias=attn_bias[0],
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p=0.0,
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scale=self.scale,
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op = self.attn_op
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)
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return out.view_as(original_query)\
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"""
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NEW_XFORMER_BLOCK = """\
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self.attn_op = xops.fmha.flash.FwOp()
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if self.alibi_slopes is None:
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# Add the batch dimension.
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query = query.unsqueeze(0)
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key = key.unsqueeze(0)
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value = value.unsqueeze(0)
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if self.head_size > 128:
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out = self._run_sdpa_fallback(query, key, value, attn_metadata)
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else:
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out = xops.memory_efficient_attention_forward(
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query,
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key,
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value,
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attn_bias=attn_bias[0],
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p=0.0,
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scale=self.scale,
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op=self.attn_op,
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)
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return out.view_as(original_query)\
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"""
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INJECT_ANCHOR = " def _run_memory_efficient_xformers_forward("
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def patch_file(path):
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with open(path, "r") as f:
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content = f.read()
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changed = False
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if "_run_sdpa_fallback" in content:
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print(" [skip] _run_sdpa_fallback already present")
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elif INJECT_ANCHOR not in content:
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print(" [warn] inject anchor not found")
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else:
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content = content.replace(INJECT_ANCHOR, FALLBACK_METHOD + INJECT_ANCHOR, 1)
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print(" [ok] injected _run_sdpa_fallback (batch, pure-math)")
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changed = True
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if NEW_XFORMER_BLOCK in content:
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print(" [skip] dispatch block already patched")
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elif OLD_XFORMER_BLOCK in content:
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content = content.replace(OLD_XFORMER_BLOCK, NEW_XFORMER_BLOCK, 1)
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print(" [ok] patched dispatch block")
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changed = True
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else:
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print(" [warn] dispatch block anchor not found")
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if changed:
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with open(path, "w") as f:
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f.write(content)
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print(f" Written: {path}")
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def main():
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print("=== patch_xformers_sdpa_batch (batch, pure-math) ===")
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print(f"Target: {XFORMERS_PATH}")
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patch_file(XFORMERS_PATH)
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print("\nDone.")
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if __name__ == "__main__":
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main()
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