[Enhancement] Improve iterator handling in layout utilities and parallel operations

src/layout/utils.cc

@@ -115,6 +115,10 @@ Array<IterSplitExpr> get_unused_iters(const IterMark &mark,
   return results;
 }
 
+// Heuristic: detect per-iterator gaps ("unused" pieces) even when the iterator
+// appears in fused forms across multiple index expressions. We first normalize
+// every index into IterSumExpr, collect all splits per source Var, then
+// consolidate them to avoid misclassifying a used split as unused.
 Array<IterSplitExpr> DivideUnusedIterators(const Array<PrimExpr> &exprs,
                                            const Array<IterVar> input_iters,
                                            Analyzer *analyzer) {
@@ -134,17 +138,25 @@ Array<IterSplitExpr> DivideUnusedIterators(const Array<PrimExpr> &exprs,
   }
 
   for (const IterVar &iter : input_iters) {
-    IterMark iv_mark;
+    // Merge splits from all IterMark that share the same source Var as `iter`.
+    std::vector<IterSplitExpr> merged_splits;
     for (const IterMark &mark : collector.visited_) {
-      if (mark->source.as<Var>()->same_as(iter->var)) { // NOLINT(*)
-        iv_mark = mark;
-        break;
+      auto vexpr = mark->source.as<Var>();
+      if (vexpr && vexpr.value().same_as(iter->var)) {
+        auto it = collector.mark2splits_.find(mark);
+        if (it != collector.mark2splits_.end()) {
+          const auto &vec = it->second;
+          merged_splits.insert(merged_splits.end(), vec.begin(), vec.end());
+        }
       }
     }
-    if (iv_mark.defined()) {
-      auto splits =
-          get_unused_iters(iv_mark, collector.mark2splits_[iv_mark], analyzer);
-      // Put the small axis last
+
+    if (!merged_splits.empty()) {
+      // Use a unified mark (Var + full extent) to compute the missing pieces
+      // so that fused usages are honored as "used" and not reintroduced.
+      IterMark unified_mark(iter->var, iter->dom->extent);
+      auto splits = get_unused_iters(unified_mark, merged_splits, analyzer);
+      // Put the small axis last for a flattened ordering.
       results.insert(results.end(), splits.rbegin(), splits.rend());
     } else if (!is_one(iter->dom->extent)) {
       auto mark = IterMark(iter->var, iter->dom->extent);

src/op/parallel.cc

@@ -620,11 +620,66 @@ Fragment ParallelOpNode::CompleteBufferFragment(const Buffer &buffer) const {
   if (IsCommonAccessIndice(buffer)) {
     return loop_layout_;
   }
+  // Prefer a simple path: if original 2D indices form a bijective map, invert
+  // them directly and avoid introducing a synthetic replicate dimension.
+  {
+    auto res2d =
+        arith::DetectIterMap(indice_map_[buffer], ToVMap(loop_vars_), 1,
+                             arith::IterMapLevel::Bijective,
+                             const_cast<arith::Analyzer *>(&analyzer_));
+    if (res2d->errors.empty()) {
+      Layout ind_inv2d = Layout(loop_vars_, indice_map_[buffer])->Inverse();
+      PrimExpr indice_rep_extent = 1;
+      PrimExpr loop_rep_extent = loop_layout_->ReplicateExtent();
+      PrimExpr dest_buffer_rep_extent = indice_rep_extent * loop_rep_extent;
+      Array<PrimExpr> fwd2;
+      for (size_t i = 0; i < buffer->shape.size(); i++) {
+        fwd2.push_back(InputPlaceholder(i));
+      }
+      PrimExpr thd_b2 =
+          loop_layout_->ForwardThread(ind_inv2d->Forward(fwd2), std::nullopt);
+      return Fragment(buffer->shape, {}, thd_b2, dest_buffer_rep_extent,
+                      std::nullopt)
+          ->CondenseReplicateVar();
+    }
+  }
+  // Otherwise, infer an extra flattened iterator that captures truly-unused
+  // pieces of the loop space (if any), then try inversion with it.
   PrimExpr rep_b = MakeFlattenedExpression(
       DivideUnusedIterators(indice_map_[buffer], loop_vars_, &analyzer_));
   auto bijective_indice = indice_map_[buffer];
   bijective_indice.push_back(rep_b);
-  Layout ind_inv = Layout(loop_vars_, bijective_indice)->Inverse();
+  Layout layout_before_inv = Layout(loop_vars_, bijective_indice);
+
+  // Pre-check cardinality to guard non-bijective combinations after adding
+  // rep_b.
+  PrimExpr in_prod = 1;
+  for (const auto &iv : loop_vars_)
+    in_prod *= iv->dom->extent;
+  PrimExpr out_prod = 1;
+  for (const auto &d : layout_before_inv->OutputShape())
+    out_prod *= d;
+
+  if (!analyzer_.CanProveEqual(in_prod, out_prod)) {
+    DLOG(WARNING) << "  Non-bijective mapping after appending rep_b; falling "
+                     "back to no-rep inversion.";
+    Layout ind_inv_fallback =
+        Layout(loop_vars_, indice_map_[buffer])->Inverse();
+    PrimExpr indice_rep_extent = 1;
+    PrimExpr loop_rep_extent = loop_layout_->ReplicateExtent();
+    PrimExpr dest_buffer_rep_extent = indice_rep_extent * loop_rep_extent;
+    Array<PrimExpr> fwd2;
+    for (size_t i = 0; i < buffer->shape.size(); i++) {
+      fwd2.push_back(InputPlaceholder(i));
+    }
+    PrimExpr thd_b = loop_layout_->ForwardThread(
+        ind_inv_fallback->Forward(fwd2), std::nullopt);
+    return Fragment(buffer->shape, {}, thd_b, dest_buffer_rep_extent,
+                    std::nullopt)
+        ->CondenseReplicateVar();
+  }
+
+  Layout ind_inv = layout_before_inv->Inverse();
   PrimExpr indice_rep_extent =
       ind_inv->InputShape().back(); // this is the size of rep_b
   PrimExpr loop_rep_extent = loop_layout_->ReplicateExtent();

testing/python/layout/test_tilelang_layout_fused_replicate.py

@@ -0,0 +1,63 @@
+import pytest
+import torch
+
+import tilelang
+import tilelang.testing
+import tilelang.language as T
+
+tilelang.testing.set_random_seed()
+
+VEC_SIZE = 32
+
+
+@tilelang.jit
+def fused_index_kernel(B: int, M: int, N: int, BLOCK_MN: int, BLOCK_K: int):
+
+    @T.prim_func
+    def main(
+            a: T.Buffer((B, M, N), "bfloat16"),
+            a_out: T.Buffer((B, M, N), "float32"),
+    ):
+        with T.Kernel(
+                T.ceildiv(M, BLOCK_MN),
+                T.ceildiv(N, BLOCK_K),
+                B,
+                threads=128,
+        ) as (pid_m, pid_n, pid_b):
+            a_fp32_local = T.alloc_fragment((BLOCK_MN * BLOCK_K // VEC_SIZE, VEC_SIZE), "float32")
+            offs_m = pid_m * BLOCK_MN
+            offs_n = pid_n * BLOCK_K
+
+            for i, j in T.Parallel(BLOCK_MN, BLOCK_K):
+                idx = i * BLOCK_K + j
+                a_out[pid_b, offs_m + i, offs_n + j] = a_fp32_local[idx // VEC_SIZE, idx % VEC_SIZE]
+
+    return main
+
+
+def _require_cuda_tensor(shape, dtype):
+    if not torch.cuda.is_available():
+        pytest.skip("CUDA not available")
+    try:
+        return torch.randn(*shape, device="cuda", dtype=dtype)
+    except RuntimeError as err:
+        pytest.skip(f"CUDA runtime unavailable: {err}")
+
+
+def test_layout_infer_compiles_and_runs():
+    B, M, N = 1, 32, 64
+    BLOCK_MN, BLOCK_K = 32, 64
+    kernel = fused_index_kernel(B, M, N, BLOCK_MN, BLOCK_K)
+
+    a = _require_cuda_tensor((B, M, N), torch.bfloat16)
+    a_out = torch.empty((B, M, N), dtype=torch.float32, device=a.device)
+
+    # Ensure kernel compiles and executes without layout inversion failure
+    kernel(a, a_out)
+
+    assert a_out.shape == a.shape
+    assert a_out.dtype == torch.float32
+
+
+if __name__ == "__main__":
+    tilelang.testing.main()