forked from EngineX-Iluvatar/enginex-vllm-bi100-qwen36
192 lines
7.2 KiB
Python
192 lines
7.2 KiB
Python
"""
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策略：批量（block-diagonal）— F.scaled_dot_product_attention，可走硬件 kernel
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=============================================================================
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构建块对角 causal mask，对整批序列一次 F.scaled_dot_product_attention。
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与 patch_xformers_sdpa_batch.py（纯 matmul）的区别：
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SDPA 会根据 PyTorch/驱动能力分发到最优 kernel（Flash Attention /
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mem-efficient attention / math fallback），而不是固定走 cublas matmul。
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历史说明：
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该方案最早因输出全"!"而被弃用，后续排查确认"!"由 mamba_cache.py bug
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引起，与 attention 实现无关。当前恢复此方案用于性能对比测试。
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已知硬件限制（BI-V100）：
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cudnnFlashAttnForward 不支持 is_causal=True（报错）。
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本实现使用 is_causal=False + 显式块对角 additive mask 规避此限制。
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若 SDPA 仍分发到有问题的 kernel，回退到 patch_xformers_sdpa_batch.py。
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优点（vs 纯 matmul）：
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SDPA 可分发到 Flash Attention kernel → O(L) 显存、更快的 CUDA kernel。
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缺点：
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依赖硬件 kernel 行为，若 kernel 有 bug 则数值错误（需与 matmul 版对比验证）。
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Deploy:
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python3 modified_scripts/patch_xformers_sdpa_batch_kernel.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 F.scaled_dot_product_attention fallback（可走硬件 kernel）。
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构建块对角 causal mask，对整批序列一次 SDPA 调用。
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SDPA 可分发到 Flash Attention / mem-efficient attention kernel。
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is_causal=False + 显式 additive mask，规避 cudnnFlashAttnForward
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不支持 is_causal=True 的限制。
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块对角 mask（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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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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import torch.nn.functional as F
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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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# ── 块对角 causal mask [T, T] ─────────────────────────────────────
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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=orig_dtype,
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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, dtype=orig_dtype, device=query.device)
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)
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start = end
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# ── [1, H, T, D] ──────────────────────────────────────────────────
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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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# ── F.scaled_dot_product_attention（可走硬件 kernel）─────────────
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# is_causal=False：避免 cudnnFlashAttnForward "not support causal mode"
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# attn_mask 传 additive float mask（非 bool），SDPA 选择 math/kernel 路径
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out = F.scaled_dot_product_attention(
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q_all, k_all, v_all,
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attn_mask=mask,
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dropout_p=0.0,
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is_causal=False,
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scale=self.scale,
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)
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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, F.sdpa kernel)")
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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_kernel (batch, F.sdpa + kernel dispatch) ===")
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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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