revert: restore a3c45d3b yaml + Q-tiling + remove all OOM hacks

Root cause of 10 consecutive OOM failures:
- 'return zeros during profiling' hack → vllm overestimates free memory
  → allocates 7942 blocks → first real request OOMs
- blocks cap 5000 → band-aid that masks profiling bug
- gpu-memory-utilization 0.80 → unnecessary reduction from working 0.90
- max-num-seqs 2 → doubles peak activation memory
- PYTORCH_CUDA_ALLOC_CONF max_split_size_mb:512 → causes fragmentation

Restoring a3c45d3b parameters that actually work:
- yaml: max-model-len=131072, gpu-mem=0.90, max-num-seqs=1, batched-tokens=8192
- patch_xformers_sdpa_seq.py: Q-tiling (real memory optimization, not zeros hack)
- patch_block_major_worker_capacity.py: no blocks cap, just reserve_block_major
- patch_ops.sh: remove all docker-build-time compilation (all .so are prebuilt)

Only change from a3c45d3b: BI100_MOE_COREX_TOPK_SOFTMAX=1 (enable corex topk)

Kept fixes:
- protocol.py extra=allow (recover 180 rejected replay requests)
- corex_gdn_chunk_recurrent.so pybind kwargs (prebuilt with fixed signature)

computility-run.yaml

@@ -8,18 +8,18 @@ command:
     - --served-model-name
     - llm
     - --max-model-len
-    - '80000'
+    - '131072'
     - --gpu-memory-utilization
-    - '0.80'
+    - '0.90'
     - --trust-remote-code
     - -tp
     - '4'
     - --max-num-seqs
-    - '2'
+    - '1'
     - --disable-log-requests
     - --disable-frontend-multiprocessing
     - --max-num-batched-tokens
-    - '4096'
+    - '8192'
     - --enable-chunked-prefill
     - --max-seq-len-to-capture
     - '32768'
@@ -47,5 +47,3 @@ env:
       value: hybrid64
     - name: BI100_MOE_COREX_TOPK_SOFTMAX
       value: '1'
-    - name: PYTORCH_CUDA_ALLOC_CONF
-      value: max_split_size_mb:512

qwen3_6_scripts/patch_block_major_worker_capacity.py

@@ -20,13 +20,6 @@ CAPACITY_ANCHOR = """\
 CAPACITY_REPLACEMENT = """\
         num_gpu_blocks = reserve_block_major_gpu_blocks(
             num_gpu_blocks, cache_block_size)
-        # BI100: profiling with zero-tensor attention underestimates memory.
-        # Hardcap at 3000 blocks (48K tokens) to prevent runtime OOM.
-        # Must leave ~4GB free for flash_attn_varlen_func temp buffers.
-        if num_gpu_blocks > 3000:
-            logger.warning(
-                "[BI100] capping num_gpu_blocks: %d -> 3000", num_gpu_blocks)
-            num_gpu_blocks = 3000
         num_gpu_blocks = max(num_gpu_blocks, 0)
         num_cpu_blocks = max(num_cpu_blocks, 0)
 """

qwen3_6_scripts/patch_ops.sh

@@ -246,24 +246,6 @@ if source != installed:
     raise SystemExit("runtime api_server overlay identity mismatch")
 PY
 
-build_stage "compiling CCCL CachingDeviceAllocator LD_PRELOAD module"
-bash ./cccl_preload/build_cccl_preload.sh /workspace/qwen3_6_scripts/cccl_preload || \
-    echo "[WARN] CCCL preload allocator build failed — will use default allocator"
-
-build_stage "compiling CoreX CUDA extensions (moe_index_combine + gdn_chunk_recurrent)"
-if [[ -x /usr/local/corex-3.2.3/bin/clang++ ]]; then
-    bash ./build_corex_moe_index_combine.sh "${VLLM_ROOT}" || \
-        echo "[WARN] moe_index_combine build failed — will use PyTorch fallback"
-    bash ./build_corex_gdn_chunk_recurrent.sh "${VLLM_ROOT}" || \
-        echo "[WARN] gdn_chunk_recurrent build failed — will use Python fallback"
-else
-    echo "[WARN] corex clang++ not found — skipping extension builds"
-fi
-
-build_stage "compiling ixformer bridge .so (MoE + Attention + Norm)"
-bash ./build_ix_bridge.sh "${VLLM_ROOT}" || \
-    echo "[WARN] ix_full_bridge build failed — MoE will use PyTorch fallback"
-
 build_stage "compiling submission Python sources"
 find . -path './wheels' -prune -o -name '*.py' -print0 | xargs -0 python3 -m py_compile
 build_stage "patch script completed"

qwen3_6_scripts/patch_xformers_sdpa_seq.py

@@ -172,58 +172,35 @@ FALLBACK_METHOD = '''
         value: torch.Tensor,
         attn_metadata: "XFormersMetadata",
     ) -> torch.Tensor:
-        """Use ixformer flash_attn_varlen_func for head_dim > 128.
+        """纯数学 causal attention fallback，带 Q-tiling 内存优化。
 
-        Verified on real BI-V100: flash_attn_func handles head_dim=256
-        correctly (diff < 0.004, no NaN).  For seq >= 1024, faster than
-        PyTorch matmul. For profiling, sequences can be 20K+ tokens — this
-        is dramatically faster than the previous Python Q-tiling fallback.
+        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。
 
-        Falls back to pure-math if flash_attn is unavailable.
+        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]
         """
-        import ixformer as _ixf
+        _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)
 
-        q_flat = query.squeeze(0)   # [T, H, D]
-        k_flat = key.squeeze(0)     # [T, Hkv, D]
-        v_flat = value.squeeze(0)
-
-        # Build cu_seqlens from seq_lens
-        seq_lens_list = list(attn_metadata.seq_lens)
-        cu_seqlens = torch.zeros(num_seqs + 1, dtype=torch.int32,
-                                  device=query.device)
-        for i, sl in enumerate(seq_lens_list):
-            cu_seqlens[i + 1] = cu_seqlens[i] + sl
-        max_seqlen = max(seq_lens_list)
-
-        try:
-            # Skip flash_attn during profiling — OOMs on large dummy batch
-            import os
-            if os.environ.get("BI100_IN_STARTUP_PROFILE") == "1":
-                raise RuntimeError("skip flash_attn during profiling")
-            out = _ixf.flash_attn_varlen_func(
-                q_flat.to(torch.float16),
-                k_flat.to(torch.float16),
-                v_flat.to(torch.float16),
-                cu_seqlens, cu_seqlens,
-                max_seqlen, max_seqlen,
-                causal=True,
-            )
-            return out.to(orig_dtype).unsqueeze(0)
-        except Exception:
-            pass
-
-        # Fallback: pure-math Q-tiling (original implementation)
-        _Q_CHUNK = 256
-
-        # During profiling, skip expensive attention — return zeros.
-        # Profiling only measures memory footprint, not output correctness.
-        if os.environ.get("BI100_IN_STARTUP_PROFILE") == "1":
-            return torch.zeros_like(query)
-
+        # 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 = [
@@ -232,33 +209,55 @@ FALLBACK_METHOD = '''
                 for i in range(num_seqs)
             ]
         else:
-            q_lens = seq_lens_list
+            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
-            k_s = k_flat[seq_start:seq_end].permute(1, 0, 2).float()
-            v_s = v_flat[seq_start:seq_end].permute(1, 0, 2).float()
+
+            # 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)
+                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)
+
+        return output.unsqueeze(0)  # [1, T, H, D]
 
 '''