428 lines
15 KiB
Python
428 lines
15 KiB
Python
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"""
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策略：顺序（per-sequence）fallback — 纯 PyTorch 数学实现
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==========================================================
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逐条序列用 matmul + softmax 手写 attention，完全绕开所有硬件
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flash attention kernel（ixformer / cudnnFlashAttnForward）。
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背景：
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Iluvatar cudnnFlashAttnForward 存在两个已知问题：
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1. 不支持 is_causal=True（报错）
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2. 使用 attn_mask 路径时数值结果不正确（静默错误，输出全为"!"）
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与华为昇腾 910B4 上 llama.cpp --flash-attn off 修复同类问题的原理相同。
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纯数学路径（matmul + softmax）在任何 PyTorch 后端上结果都正确。
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优点：
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数值正确，不依赖任何硬件特定 attention kernel。
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峰值显存 = max(seq_len)² × H × dtype_size，由 --max-model-len 控制。
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缺点：
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并发请求的 prefill attention 串行执行。
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O(L²) 显存（无 flash attention 的 O(L) 优化）。
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内存参考（fp16，H_local=6）：
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max-model-len=4096 → 峰值 ~200 MB
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max-model-len=8192 → 峰值 ~800 MB
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max-model-len=16384 → 峰值 ~3.2 GB
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额外 patch（arg_utils.py）：
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vllm 0.6.3 在 max_model_len > 32K 时会自动开启 chunked prefill（无命令行
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关闭选项），原意是防止 profiling OOM。但 _run_sdpa_fallback 已通过 Q-tiling
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解决了该问题，chunked prefill 反而会把推理路径从 _run_sdpa_fallback 切换到
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_forward_prefix_pytorch，属于不必要的行为变更，因此一并禁用该自动逻辑。
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Deploy:
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python3 modified_scripts/patch_xformers_sdpa_seq.py
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"""
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from patch_utils import package_root, replace_one_of, replace_once
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VLLM_ROOT = package_root("vllm")
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XFORMERS_PATH = VLLM_ROOT / "attention" / "backends" / "xformers.py"
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ARG_UTILS_PATH = VLLM_ROOT / "engine" / "arg_utils.py"
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LOGITS_PROC_PATH = (
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VLLM_ROOT / "model_executor" / "layers" / "logits_processor.py")
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OUTLINES_DECODING_PATH = (
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VLLM_ROOT / "model_executor" / "guided_decoding" /
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"outlines_decoding.py")
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# _apply_logits_processors crashes when seq_groups is None (intermediate
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# chunked-prefill chunks on the driver rank). Add an early-return guard.
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_LP_OLD_BLOCK = """\
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def _apply_logits_processors(
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logits: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> torch.Tensor:
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found_logits_processors = False\
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"""
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_LP_NEW_BLOCK = """\
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def _apply_logits_processors(
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logits: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> torch.Tensor:
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if sampling_metadata.seq_groups is None: # intermediate chunked-prefill chunk
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return logits
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found_logits_processors = False\
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"""
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# Outlines' UNESCAPED_STRING accepts raw JSON control characters, including
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# newlines and tabs. The generated text can therefore satisfy the CFG while
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# still failing json.loads(). Use the RFC 8259 string character constraints.
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_JSON_STRING_OLD_BLOCK = """\
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| UNESCAPED_STRING
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| SIGNED_NUMBER -> number
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| "true" -> true
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| "false" -> false
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| "null" -> null
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array : "[" [value ("," value)*] "]"
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object : "{" [pair ("," pair)*] "}"
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pair : UNESCAPED_STRING ":" value
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%import common.UNESCAPED_STRING
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%import common.SIGNED_NUMBER
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%import common.WS
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%ignore WS\
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"""
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_JSON_STRING_V1_BLOCK = r'''| JSON_STRING
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| SIGNED_NUMBER -> number
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| "true" -> true
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| "false" -> false
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| "null" -> null
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array : "[" [value ("," value)*] "]"
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object : "{" [pair ("," pair)*] "}"
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pair : JSON_STRING ":" value
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JSON_STRING: /"(\\["\\\/bfnrt]|\\u[0-9a-fA-F]{4}|[^"\\\x00-\x1f])*"/
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%import common.SIGNED_NUMBER
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%import common.WS
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%ignore WS'''
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_JSON_STRING_NEW_BLOCK = r'''| JSON_STRING
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| SIGNED_NUMBER -> number
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| "true" -> true
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| "false" -> false
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| "null" -> null
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array : "[" _ws [value (_ws "," _ws value)*] _ws "]"
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object : "{" _ws [pair (_ws "," _ws pair)*] _ws "}"
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pair : JSON_STRING _ws ":" _ws value
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_ws : JSON_WS?
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JSON_STRING: /"(\\["\\\/bfnrt]|\\u[0-9a-fA-F]{4}|[^"\\\x00-\x1f])*"/
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JSON_WS: /[ \t\r\n]{1,4}/
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%import common.SIGNED_NUMBER'''
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# vllm 0.6.3 自动开启 chunked prefill 的原始块
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_ARG_OLD_BLOCK = """\
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if (is_gpu and not use_sliding_window and not use_spec_decode
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and not self.enable_lora
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and not self.enable_prompt_adapter):
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self.enable_chunked_prefill = True
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logger.warning(
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"Chunked prefill is enabled by default for models with "
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"max_model_len > 32K. Currently, chunked prefill might "
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"not work with some features or models. If you "
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"encounter any issues, please disable chunked prefill "
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"by setting --enable-chunked-prefill=False.")\
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"""
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_ARG_NEW_BLOCK = """\
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if (is_gpu and not use_sliding_window and not use_spec_decode
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and not self.enable_lora
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and not self.enable_prompt_adapter):
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pass # skip auto-enable: Q-tiling in _run_sdpa_fallback
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# handles long-context memory without chunked prefill\
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"""
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_MM_PREFIX_OLD_BLOCK = """\
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if model_config.is_multimodal_model:
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if self.enable_prefix_caching:
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logger.warning(
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"--enable-prefix-caching is currently not "
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"supported for multimodal models and has been disabled.")
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self.enable_prefix_caching = False\
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"""
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_MM_PREFIX_NEW_BLOCK = """\
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if model_config.is_multimodal_model:
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architectures = getattr(model_config.hf_config,
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"architectures", []) or []
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qwen36_native_vision = "Qwen3_5MoeForCausalLM" in architectures
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if self.enable_prefix_caching and qwen36_native_vision:
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logger.info(
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"Keeping prefix caching enabled for the Qwen3.6 native "
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"vision path.")
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elif self.enable_prefix_caching:
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logger.warning(
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"--enable-prefix-caching is currently not "
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"supported for multimodal models and has been disabled.")
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self.enable_prefix_caching = False\
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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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"""纯数学 causal attention fallback，带 Q-tiling 内存优化。
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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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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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Args:
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query : [1, total_query_tokens, num_heads, head_dim]
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key : [1, total_query_tokens, num_kv_heads, head_dim]
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value : [1, total_query_tokens, num_kv_heads, head_dim]
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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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# 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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int(attn_metadata.query_start_loc[i + 1].item()) -
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int(attn_metadata.query_start_loc[i].item())
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for i in range(num_seqs)
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]
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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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v_flat = value.squeeze(0)
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output = torch.empty_like(q_flat)
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seq_start = 0
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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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# 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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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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'''
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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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_PREFIX_CALL_OLD_BLOCK = """\
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out = PagedAttention.forward_prefix(
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query,
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key,
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value,
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self.kv_cache_dtype,
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key_cache,
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|||
|
|
value_cache,
|
|||
|
|
prefill_meta.block_tables,
|
|||
|
|
prefill_meta.query_start_loc,
|
|||
|
|
prefill_meta.seq_lens_tensor,
|
|||
|
|
prefill_meta.context_lens_tensor,
|
|||
|
|
prefill_meta.max_query_len,
|
|||
|
|
self.alibi_slopes,
|
|||
|
|
self.sliding_window,
|
|||
|
|
k_scale,
|
|||
|
|
v_scale,
|
|||
|
|
)\
|
|||
|
|
"""
|
|||
|
|
|
|||
|
|
_PREFIX_CALL_NEW_BLOCK = """\
|
|||
|
|
out = PagedAttention.forward_prefix(
|
|||
|
|
query,
|
|||
|
|
key,
|
|||
|
|
value,
|
|||
|
|
self.kv_cache_dtype,
|
|||
|
|
key_cache,
|
|||
|
|
value_cache,
|
|||
|
|
prefill_meta.block_tables,
|
|||
|
|
prefill_meta.query_start_loc,
|
|||
|
|
prefill_meta.seq_lens_tensor,
|
|||
|
|
prefill_meta.context_lens_tensor,
|
|||
|
|
prefill_meta.max_query_len,
|
|||
|
|
self.alibi_slopes,
|
|||
|
|
self.sliding_window,
|
|||
|
|
k_scale,
|
|||
|
|
v_scale,
|
|||
|
|
is_causal_decoder=(attn_type == AttentionType.DECODER),
|
|||
|
|
)\
|
|||
|
|
"""
|
|||
|
|
|
|||
|
|
|
|||
|
|
def patch_file(path):
|
|||
|
|
replace_once(
|
|||
|
|
path,
|
|||
|
|
INJECT_ANCHOR,
|
|||
|
|
FALLBACK_METHOD + INJECT_ANCHOR,
|
|||
|
|
required=True,
|
|||
|
|
already_contains="def _run_sdpa_fallback(")
|
|||
|
|
replace_once(
|
|||
|
|
path,
|
|||
|
|
OLD_XFORMER_BLOCK,
|
|||
|
|
NEW_XFORMER_BLOCK,
|
|||
|
|
required=True,
|
|||
|
|
already_contains="out = self._run_sdpa_fallback(query, key, value, attn_metadata)")
|
|||
|
|
replace_once(
|
|||
|
|
path,
|
|||
|
|
_PREFIX_CALL_OLD_BLOCK,
|
|||
|
|
_PREFIX_CALL_NEW_BLOCK,
|
|||
|
|
required=True,
|
|||
|
|
already_contains=(
|
|||
|
|
"is_causal_decoder=(attn_type == AttentionType.DECODER)"))
|
|||
|
|
|
|||
|
|
|
|||
|
|
def patch_arg_utils(path):
|
|||
|
|
replace_once(
|
|||
|
|
path,
|
|||
|
|
_ARG_OLD_BLOCK,
|
|||
|
|
_ARG_NEW_BLOCK,
|
|||
|
|
required=True,
|
|||
|
|
already_contains="skip auto-enable: Q-tiling")
|
|||
|
|
replace_once(
|
|||
|
|
path,
|
|||
|
|
_MM_PREFIX_OLD_BLOCK,
|
|||
|
|
_MM_PREFIX_NEW_BLOCK,
|
|||
|
|
required=True,
|
|||
|
|
already_contains="Keeping prefix caching enabled for the Qwen3.6")
|
|||
|
|
|
|||
|
|
|
|||
|
|
def patch_logits_processor(path):
|
|||
|
|
replace_once(
|
|||
|
|
path,
|
|||
|
|
_LP_OLD_BLOCK,
|
|||
|
|
_LP_NEW_BLOCK,
|
|||
|
|
required=True,
|
|||
|
|
already_contains="intermediate chunked-prefill chunk")
|
|||
|
|
|
|||
|
|
|
|||
|
|
def patch_outlines_json_grammar(path):
|
|||
|
|
replace_one_of(
|
|||
|
|
path,
|
|||
|
|
[
|
|||
|
|
(_JSON_STRING_V1_BLOCK, _JSON_STRING_NEW_BLOCK),
|
|||
|
|
(_JSON_STRING_OLD_BLOCK, _JSON_STRING_NEW_BLOCK),
|
|||
|
|
],
|
|||
|
|
required=True,
|
|||
|
|
already_contains="JSON_WS:")
|
|||
|
|
|
|||
|
|
|
|||
|
|
def main():
|
|||
|
|
print("=== patch_xformers_sdpa_seq (sequential, pure-math) ===")
|
|||
|
|
print(f"Target: {XFORMERS_PATH}")
|
|||
|
|
patch_file(XFORMERS_PATH)
|
|||
|
|
|
|||
|
|
print("\n=== patch_arg_utils (disable chunked-prefill auto-enable) ===")
|
|||
|
|
print(f"Target: {ARG_UTILS_PATH}")
|
|||
|
|
patch_arg_utils(ARG_UTILS_PATH)
|
|||
|
|
|
|||
|
|
print("\n=== patch_logits_processor (seq_groups=None guard for chunked prefill) ===")
|
|||
|
|
print(f"Target: {LOGITS_PROC_PATH}")
|
|||
|
|
patch_logits_processor(LOGITS_PROC_PATH)
|
|||
|
|
|
|||
|
|
print("\n=== patch_outlines_json_grammar (reject raw control chars) ===")
|
|||
|
|
print(f"Target: {OUTLINES_DECODING_PATH}")
|
|||
|
|
patch_outlines_json_grammar(OUTLINES_DECODING_PATH)
|
|||
|
|
|
|||
|
|
print("\nDone.")
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == "__main__":
|
|||
|
|
main()
|