feat: sparse_pool 选项 — (段×唯一)稀疏矩阵乘做池化,避免materialize[M,emb]

针对 profile 的 dedup展开(15%)+segment_reduce(6.6%)。段内高重复(slot19)塌缩
为单个带权项。CONFIG.sparse_pool;bench --sparse-pool;等价测试已加。默认关,待验证。

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

代码/code/infer.py

@@ -53,6 +53,7 @@ CONFIG = {
     "syncfree_mask": True,    # True=用searchsorted构造因果mask(无同步)；False=repeat_interleave(同步)
     "emb_fp16": True,         # True=Embedding表转FP16(查表带宽减半,实测AUC 0.75932≈无损)
     "dedup_embedding": True,  # True=查表前对sign去重(只查唯一值再展开),本地7.80->6.49s,AUC逐位等价
+    "sparse_pool": False,     # True=用(段×唯一)稀疏矩阵乘做池化,避免materialize整个[M,512](段内高重复时省)
     "compile": False,         # 是否 torch.compile（实测慢5×，勿开）
 }
 
@@ -381,13 +382,27 @@ class RepEncoder(nn.Module):
         cat_values = self._signid(torch.cat(parts), max_idx)
         seg = torch.cat([torch.zeros(1, dtype=torch.long, device=cat_values.device),
                          torch.cat(ends)])   # [28*N + 1]
-        if CONFIG.get("dedup_embedding", False):
-            # 去重：只对唯一 sign 查大表，再按逆索引展开（数学逐位等价，省随机访存）
+        if CONFIG.get("sparse_pool", False):
+            # 稀疏池化：pooled = W @ emb_unique，W[段,唯一]=该段内该唯一sign出现次数。
+            # 段内高重复(slot19)塌缩成单个带权项，避免 materialize 整个 [M,emb]。
             uniq, inv = torch.unique(cat_values, return_inverse=True)
-            emb = self.emb(uniq).to(target_dtype)[inv]
+            emb_unique = self.emb(uniq).float()  # 小表；sparse.mm 用 fp32 稳
+            M = cat_values.numel()
+            num_seg = seg.numel() - 1
+            seg_id = torch.searchsorted(
+                seg, torch.arange(M, device=cat_values.device), right=True) - 1
+            W = torch.sparse_coo_tensor(
+                torch.stack([seg_id, inv]),
+                torch.ones(M, device=cat_values.device, dtype=torch.float32),
+                size=(num_seg, uniq.numel())).coalesce()
+            pooled = torch.sparse.mm(W, emb_unique).to(target_dtype)  # [28*N, emb]
         else:
-            emb = self.emb(cat_values).to(target_dtype)
-        pooled = torch.segment_reduce(emb, reduce='sum', offsets=seg, initial=0)  # [28*N, emb]
+            if CONFIG.get("dedup_embedding", False):
+                uniq, inv = torch.unique(cat_values, return_inverse=True)
+                emb = self.emb(uniq).to(target_dtype)[inv]
+            else:
+                emb = self.emb(cat_values).to(target_dtype)
+            pooled = torch.segment_reduce(emb, reduce='sum', offsets=seg, initial=0)  # [28*N, emb]
         pooled = pooled.view(self.slot_num, N, self.emb_dim).permute(1, 0, 2).reshape(
             N, self.slot_num * self.emb_dim)
         return self.linear(self.input_norm(pooled))