[BugFix][MetaSchedule] MultiLevelTilingTensorCore generates inconsistent thread-binding sketch for batched matmul (#17012)

* [BugFix][MetaSchedule] MultiLevelTilingTensorCore generates inconsistent thread-binding sketch for batched matmul

* Update testcase test_meta_schedule_schedule_rule_mlt_tc.py::test_conv_1x1

---------

Co-authored-by: tsu-bin <[email protected]>

Author: tsu-bin

src/meta_schedule/schedule_rule/multi_level_tiling.cc

@@ -190,7 +190,8 @@ std::pair<Array<tir::ExprRV>, Array<tir::LoopRV>> MultiLevelTilingNode::SplitLoo
   return {factors, splits};
 }
 
-std::vector<State> MultiLevelTilingNode::TileLoopNest(State state) const {
+std::vector<State> MultiLevelTilingNode::TileLoopNest(State state,
+                                                      int tile_inner_most_space_loop_num) const {
   Schedule& sch = state->sch;
   const BlockRV& block_rv = state->block_rv;
   // Step 1. Assuming trivial binding, pair the loops and their iter-var-types
@@ -199,6 +200,16 @@ std::vector<State> MultiLevelTilingNode::TileLoopNest(State state) const {
   ICHECK_EQ(loops.size(), iter_types.size());
   // Step 2. For each loop axis, tile it
   int64_t spatial_loop_product = 1;
+
+  int total_spatial_loop_num = 0;
+  std::for_each(iter_types.begin(), iter_types.end(), [&](const auto& iter_type) {
+    if (iter_type == IterVarType::kDataPar) total_spatial_loop_num++;
+  });
+  CHECK_GE(total_spatial_loop_num, tile_inner_most_space_loop_num);
+  if (tile_inner_most_space_loop_num < 0) tile_inner_most_space_loop_num = total_spatial_loop_num;
+  int outer_most_spatial_loop_skipped_num = total_spatial_loop_num - tile_inner_most_space_loop_num;
+
+  Array<LoopRV> skipped_outer_spatial_loops;
   std::vector<Array<LoopRV>> tiles(s_indices_.size() + r_indices_.size());
   state->tile_factors.resize(tiles.size());
   std::vector<Array<tir::ExprRV>> tile_factors;
@@ -208,6 +219,11 @@ std::vector<State> MultiLevelTilingNode::TileLoopNest(State state) const {
     const std::vector<int>* idx = nullptr;
 
     if (iter_types[i] == IterVarType::kDataPar) {
+      if (outer_most_spatial_loop_skipped_num > 0) {
+        skipped_outer_spatial_loops.push_back(loop);
+        outer_most_spatial_loop_skipped_num--;
+        continue;
+      }
       idx = &s_indices_;
       if (spatial_loop_product != -1) {
         if (const int64_t* extent = tir::GetLoopIntExtent(sch->Get(loop).get())) {
@@ -241,6 +257,11 @@ std::vector<State> MultiLevelTilingNode::TileLoopNest(State state) const {
   sch->Reorder(support::ConcatArrayList<LoopRV>(tiles.begin(), tiles.end()));
   // Step 4. Bind the tiles to threads
   int n_binds = std::min(tile_binds.size(), tiles.size());
+  if (skipped_outer_spatial_loops.size() && n_binds) {
+    auto& the_first_tile = tiles[0];
+    the_first_tile.insert(the_first_tile.begin(), skipped_outer_spatial_loops.begin(),
+                          skipped_outer_spatial_loops.end());
+  }
   for (int i = 0; i < n_binds; ++i) {
     LoopRV fused = sch->Fuse(tiles[i]);
     sch->Bind(fused, tile_binds[i]);

src/meta_schedule/schedule_rule/multi_level_tiling.h

@@ -162,7 +162,7 @@ class MultiLevelTilingNode : public ScheduleRuleNode {
   // SubRule 1. add write cache
   std::vector<State> AddWriteReuse(State state) const;
   // SubRule 2. tile the loop nest
-  std::vector<State> TileLoopNest(State state) const;
+  std::vector<State> TileLoopNest(State state, int tile_inner_most_space_loop_num = -1) const;
   // SubRule 3. add read cache
   std::vector<State> AddReadReuse(State state) const;
   // SubRule 4. add async pipeline

src/meta_schedule/schedule_rule/multi_level_tiling_tensor_core.cc

@@ -251,7 +251,7 @@ std::vector<State> MultiLevelTilingTensorCoreNode::ApplySubRules(std::vector<Sta
   });
   states = SubRule(std::move(states), [&](State state) {
     TensorCoreState tc_state = Downcast<TensorCoreState>(state);
-    return tc_state->is_mma ? MMATileLoopNest(tc_state) : TileLoopNest(state);
+    return tc_state->is_mma ? MMATileLoopNest(tc_state) : TileLoopNest(state, 2);
   });
   states = SubRule(std::move(states), [&](State state) {
     return TransformIntermediateOutputLayout(Downcast<TensorCoreState>(state));

tests/python/meta_schedule/test_meta_schedule_schedule_rule_mlt_tc.py

@@ -903,39 +903,39 @@ def test_conv_1x1():
     def conv2d_1x1_0(inputs: T.Buffer((1, 16, 16, 64), "float16"), weight: T.Buffer((1, 1, 64, 64), "float16"), conv2d_nhwc: T.Buffer((1, 16, 16, 64), "float32")):
         T.func_attr({"global_symbol": "main", "tir.noalias": T.bool(True)})
         # with T.block("root"):
-        conv2d_nhwc_reindex_shared = T.alloc_buffer((2, 2, 8, 2, 16, 16), scope="shared")
-        conv2d_nhwc_reindex_shared_wmma_accumulator = T.alloc_buffer((2, 2, 8, 2, 16, 16), scope="wmma.accumulator")
+        conv2d_nhwc_reindex_shared = T.alloc_buffer((2, 1, 8, 4, 16, 16), scope="shared")
+        conv2d_nhwc_reindex_shared_wmma_accumulator = T.alloc_buffer((2, 1, 8, 4, 16, 16), scope="wmma.accumulator")
         PadInput_reindex_shared = T.alloc_buffer((256, 64), "float16", scope="shared")
         weight_reindex_shared = T.alloc_buffer((1, 1, 64, 64), "float16", scope="shared")
         PadInput_reindex_shared_wmma_matrix_a = T.alloc_buffer((256, 64), "float16", scope="wmma.matrix_a")
         weight_reindex_shared_wmma_matrix_b = T.alloc_buffer((1, 1, 64, 64), "float16", scope="wmma.matrix_b")
-        for ax0_0_ax1_0_ax2_0_0_ax3_0_0_fused in T.thread_binding(4, thread="blockIdx.y"):
-            for ax0_1_ax1_1_ax2_0_1_ax3_0_1_fused in T.thread_binding(1, thread="blockIdx.x"):
-                for ax0_2_ax1_2_ax2_0_2_ax3_0_2_fused in T.thread_binding(1, thread="threadIdx.y"):
-                    for ax4_0_0 in range(1):
+        for ax0_ax1_ax2_0_0_ax3_0_0_fused in T.thread_binding(1, thread="blockIdx.y"):
+            for ax2_0_1_ax3_0_1_fused in T.thread_binding(1, thread="blockIdx.x"):
+                for ax2_0_2_ax3_0_2_fused in T.thread_binding(2, thread="threadIdx.y"):
+                    for ax4_0_0 in range(2):
                         for ax0_ax1_fused in range(8192):
                             with T.block("PadInput_reindex_shared"):
-                                v0 = T.axis.spatial(256, ax0_0_ax1_0_ax2_0_0_ax3_0_0_fused // 2 * 128 + ax0_ax1_fused // 64)
-                                v1 = T.axis.spatial(64, ax0_ax1_fused % 64)
+                                v0 = T.axis.spatial(256, ax0_ax1_fused // 32)
+                                v1 = T.axis.spatial(64, ax4_0_0 * 32 + ax0_ax1_fused % 32)
                                 T.reads(inputs[0, v0 // 16, v0 % 16, v1])
                                 T.writes(PadInput_reindex_shared[v0, v1])
-                                T.block_attr({"buffer_dim_align": [[0, 0, 32, 8]], "meta_schedule.cooperative_fetch": 2})
+                                T.block_attr({"buffer_dim_align": [[0, 0, 32, 8]], "meta_schedule.cooperative_fetch": 8})
                                 PadInput_reindex_shared[v0, v1] = inputs[0, v0 // 16, v0 % 16, v1]
                         for ax0_ax1_ax2_ax3_fused in range(2048):
                             with T.block("weight_reindex_shared"):
                                 v0 = T.axis.spatial(1, 0)
                                 v1 = T.axis.spatial(1, 0)
-                                v2 = T.axis.spatial(64, ax0_ax1_ax2_ax3_fused // 32)
-                                v3 = T.axis.spatial(64, ax0_0_ax1_0_ax2_0_0_ax3_0_0_fused % 2 * 32 + ax0_ax1_ax2_ax3_fused % 32)
+                                v2 = T.axis.spatial(64, ax4_0_0 * 32 + ax0_ax1_ax2_ax3_fused // 64)
+                                v3 = T.axis.spatial(64, ax0_ax1_ax2_ax3_fused % 64)
                                 T.reads(weight[v0, v1, v2, v3])
                                 T.writes(weight_reindex_shared[v0, v1, v2, v3])
-                                T.block_attr({"buffer_dim_align": [[0, 2, 32, 8]], "meta_schedule.cooperative_fetch": 8})
+                                T.block_attr({"buffer_dim_align": [[0, 2, 32, 8]], "meta_schedule.cooperative_fetch": 4})
                                 weight_reindex_shared[v0, v1, v2, v3] = weight[v0, v1, v2, v3]
                         for ax4_0_1 in range(1):
-                            for ax0_0, ax1_0 in T.grid(8, 4):
+                            for ax0_0, ax1_0 in T.grid(8, 2):
                                 with T.block("PadInput_reindex_shared_wmma.matrix_a_o"):
-                                    v0_o = T.axis.spatial(16, ax0_0_ax1_0_ax2_0_0_ax3_0_0_fused // 2 * 8 + ax0_0)
-                                    v1_o = T.axis.spatial(4, ax1_0)
+                                    v0_o = T.axis.spatial(16, ax2_0_2_ax3_0_2_fused * 8 + ax0_0)
+                                    v1_o = T.axis.spatial(4, ax4_0_0 * 2 + ax1_0)
                                     T.reads(PadInput_reindex_shared[v0_o * 16:v0_o * 16 + 16, v1_o * 16:v1_o * 16 + 16])
                                     T.writes(PadInput_reindex_shared_wmma_matrix_a[v0_o * 16:v0_o * 16 + 16, v1_o * 16:v1_o * 16 + 16])
                                     T.block_attr({"meta_schedule.auto_tensorize": "wmma_load_16x16x16_f16_a_shared"})
@@ -945,10 +945,11 @@ def conv2d_1x1_0(inputs: T.Buffer((1, 16, 16, 64), "float16"), weight: T.Buffer(
                                             T.reads(PadInput_reindex_shared[v0_o * 16 + v0_i, v1_o * 16 + v1_i])
                                             T.writes(PadInput_reindex_shared_wmma_matrix_a[v0_o * 16 + v0_i, v1_o * 16 + v1_i])
                                             PadInput_reindex_shared_wmma_matrix_a[v0_o * 16 + v0_i, v1_o * 16 + v1_i] = PadInput_reindex_shared[v0_o * 16 + v0_i, v1_o * 16 + v1_i]
-                            for ax0, ax1, ax2_0, ax3_0 in T.grid(1, 1, 4, 2):
+                            for ax0, ax1, ax2_0, ax3_0 in T.grid(1, 1, 2, 4):
                                 with T.block("weight_reindex_shared_wmma.matrix_b_o"):
-                                    v0_o, v1_o, v2_o = T.axis.remap("SSS", [ax0, ax1, ax2_0])
-                                    v3_o = T.axis.spatial(4, ax0_0_ax1_0_ax2_0_0_ax3_0_0_fused % 2 * 2 + ax3_0)
+                                    v0_o, v1_o = T.axis.remap("SS", [ax0, ax1])
+                                    v2_o = T.axis.spatial(4, ax4_0_0 * 2 + ax2_0)
+                                    v3_o = T.axis.spatial(4, ax3_0)
                                     T.reads(weight_reindex_shared[v0_o, v1_o, v2_o * 16:v2_o * 16 + 16, v3_o * 16:v3_o * 16 + 16])
                                     T.writes(weight_reindex_shared_wmma_matrix_b[v0_o, v1_o, v2_o * 16:v2_o * 16 + 16, v3_o * 16:v3_o * 16 + 16])
                                     T.block_attr({"meta_schedule.auto_tensorize": "wmma_load_16x16x16_f16_b_shared"})
@@ -958,38 +959,38 @@ def conv2d_1x1_0(inputs: T.Buffer((1, 16, 16, 64), "float16"), weight: T.Buffer(
                                             T.reads(weight_reindex_shared[v0_o, v1_o, v2_o * 16 + v2_i, v3_o * 16 + v3_i])
                                             T.writes(weight_reindex_shared_wmma_matrix_b[v0_o, v1_o, v2_o * 16 + v2_i, v3_o * 16 + v3_i])
                                             weight_reindex_shared_wmma_matrix_b[v0_o, v1_o, v2_o * 16 + v2_i, v3_o * 16 + v3_i] = weight_reindex_shared[v0_o, v1_o, v2_o * 16 + v2_i, v3_o * 16 + v3_i]
-                            for ax0_3, ax1_3, ax2_0_3, ax3_0_3, ax4_0_2, ax0_4, ax1_4, ax2_0_4, ax3_0_4 in T.grid(1, 1, 8, 2, 4, 1, 1, 1, 1):
+                            for ax2_0_3, ax3_0_3, ax4_0_2, ax2_0_4, ax3_0_4 in T.grid(8, 1, 2, 1, 4):
                                 with T.block("conv2d_nhwc_o"):
-                                    v0_o = T.axis.spatial(1, ax0_3 + ax0_4)
-                                    v1_o = T.axis.spatial(1, ax1_3 + ax1_4)
-                                    v2_o = T.axis.spatial(16, ax0_0_ax1_0_ax2_0_0_ax3_0_0_fused // 2 * 8 + ax2_0_3 + ax2_0_4)
-                                    v3_o = T.axis.spatial(4, ax0_0_ax1_0_ax2_0_0_ax3_0_0_fused % 2 * 2 + ax3_0_3 + ax3_0_4)
-                                    v4_o = T.axis.reduce(4, ax4_0_0 * 4 + ax4_0_1 * 4 + ax4_0_2)
+                                    v0_o = T.axis.spatial(1, 0)
+                                    v1_o = T.axis.spatial(1, 0)
+                                    v2_o = T.axis.spatial(16, ax2_0_2_ax3_0_2_fused * 8 + ax2_0_3 + ax2_0_4)
+                                    v3_o = T.axis.spatial(4, ax3_0_3 * 4 + ax3_0_4)
+                                    v4_o = T.axis.reduce(4, ax4_0_0 * 2 + ax4_0_1 * 2 + ax4_0_2)
                                     T.reads(PadInput_reindex_shared_wmma_matrix_a[v2_o * 16:v2_o * 16 + 16, v4_o * 16:v4_o * 16 + 16], weight_reindex_shared_wmma_matrix_b[v0_o, v1_o, v4_o * 16:v4_o * 16 + 16, v3_o * 16:v3_o * 16 + 16])
-                                    T.writes(conv2d_nhwc_reindex_shared_wmma_accumulator[v2_o // 8, v3_o // 2, v2_o % 8, v3_o % 2, 0:16, 0:16])
+                                    T.writes(conv2d_nhwc_reindex_shared_wmma_accumulator[v2_o // 8, 0, v2_o % 8, v3_o, 0:16, 0:16])
                                     T.block_attr({"meta_schedule.auto_tensorize": "wmma_sync_16x16x16_f16f16f32", "meta_schedule.auto_tensorize_init": "wmma_fill_16x16x16_f32", "warp_execution": 1})
                                     with T.init():
                                         for ax2_1, ax3_1 in T.grid(16, 16):
                                             with T.block("conv2d_nhwc_init"):
                                                 v2_i_init, v3_i_init = T.axis.remap("SS", [ax2_1, ax3_1])
                                                 T.reads()
-                                                T.writes(conv2d_nhwc_reindex_shared_wmma_accumulator[v2_o // 8, v3_o // 2, v2_o % 8, v3_o % 2, v2_i_init, v3_i_init])
-                                                conv2d_nhwc_reindex_shared_wmma_accumulator[v2_o // 8, v3_o // 2, v2_o % 8, v3_o % 2, v2_i_init, v3_i_init] = T.float32(0)
+                                                T.writes(conv2d_nhwc_reindex_shared_wmma_accumulator[v2_o // 8, 0, v2_o % 8, v3_o, v2_i_init, v3_i_init])
+                                                conv2d_nhwc_reindex_shared_wmma_accumulator[v2_o // 8, 0, v2_o % 8, v3_o, v2_i_init, v3_i_init] = T.float32(0)
                                     for ax2_1, ax3_1, ax4_1 in T.grid(16, 16, 16):
                                         with T.block("conv2d_nhwc"):
                                             v2_i, v3_i, v4_i = T.axis.remap("SSR", [ax2_1, ax3_1, ax4_1])
-                                            T.reads(conv2d_nhwc_reindex_shared_wmma_accumulator[v2_o // 8, v3_o // 2, v2_o % 8, v3_o % 2, v2_i, v3_i], PadInput_reindex_shared_wmma_matrix_a[v2_o * 16 + v2_i, v4_o * 16 + v4_i], weight_reindex_shared_wmma_matrix_b[v0_o, v1_o, v4_o * 16 + v4_i, v3_o * 16 + v3_i])
-                                            T.writes(conv2d_nhwc_reindex_shared_wmma_accumulator[v2_o // 8, v3_o // 2, v2_o % 8, v3_o % 2, v2_i, v3_i])
+                                            T.reads(conv2d_nhwc_reindex_shared_wmma_accumulator[v2_o // 8, 0, v2_o % 8, v3_o, v2_i, v3_i], PadInput_reindex_shared_wmma_matrix_a[v2_o * 16 + v2_i, v4_o * 16 + v4_i], weight_reindex_shared_wmma_matrix_b[v0_o, v1_o, v4_o * 16 + v4_i, v3_o * 16 + v3_i])
+                                            T.writes(conv2d_nhwc_reindex_shared_wmma_accumulator[v2_o // 8, 0, v2_o % 8, v3_o, v2_i, v3_i])
                                             T.block_attr({"meta_schedule.tiling_structure": "SSSRRSRS"})
-                                            conv2d_nhwc_reindex_shared_wmma_accumulator[v2_o // 8, v3_o // 2, v2_o % 8, v3_o % 2, v2_i, v3_i] = conv2d_nhwc_reindex_shared_wmma_accumulator[v2_o // 8, v3_o // 2, v2_o % 8, v3_o % 2, v2_i, v3_i] + T.Cast("float32", PadInput_reindex_shared_wmma_matrix_a[v2_o * 16 + v2_i, v4_o * 16 + v4_i]) * T.Cast("float32", weight_reindex_shared_wmma_matrix_b[v0_o, v1_o, v4_o * 16 + v4_i, v3_o * 16 + v3_i])
+                                            conv2d_nhwc_reindex_shared_wmma_accumulator[v2_o // 8, 0, v2_o % 8, v3_o, v2_i, v3_i] = conv2d_nhwc_reindex_shared_wmma_accumulator[v2_o // 8, 0, v2_o % 8, v3_o, v2_i, v3_i] + T.Cast("float32", PadInput_reindex_shared_wmma_matrix_a[v2_o * 16 + v2_i, v4_o * 16 + v4_i]) * T.Cast("float32", weight_reindex_shared_wmma_matrix_b[v0_o, v1_o, v4_o * 16 + v4_i, v3_o * 16 + v3_i])
                 for ax2 in range(8):
-                    for ax0_ax1_fused in T.thread_binding(1, thread="threadIdx.y"):
-                        for ax2_1, ax3 in T.grid(1, 2):
+                    for ax0_ax1_fused in T.thread_binding(2, thread="threadIdx.y"):
+                        for ax2_1, ax3 in T.grid(1, 4):
                             with T.block("conv2d_nhwc_reindex_shared_wmma.accumulator_o"):
-                                v0_o = T.axis.spatial(2, ax0_0_ax1_0_ax2_0_0_ax3_0_0_fused // 2)
-                                v1_o = T.axis.spatial(2, ax0_0_ax1_0_ax2_0_0_ax3_0_0_fused % 2)
+                                v0_o = T.axis.spatial(2, ax0_ax1_fused)
+                                v1_o = T.axis.spatial(1, 0)
                                 v2_o = T.axis.spatial(8, ax2 + ax2_1)
-                                v3_o = T.axis.spatial(2, ax3)
+                                v3_o = T.axis.spatial(4, ax3)
                                 v4_o = T.axis.spatial(1, 0)
                                 v5_o = T.axis.spatial(1, 0)
                                 T.reads(conv2d_nhwc_reindex_shared_wmma_accumulator[v0_o, v1_o, v2_o, v3_o, 0:16, 0:16])
@@ -1001,29 +1002,27 @@ def conv2d_1x1_0(inputs: T.Buffer((1, 16, 16, 64), "float16"), weight: T.Buffer(
                                         T.reads(conv2d_nhwc_reindex_shared_wmma_accumulator[v0_o, v1_o, v2_o, v3_o, v4_i, v5_i])
                                         T.writes(conv2d_nhwc_reindex_shared[v0_o, v1_o, v2_o, v3_o, v4_i, v5_i])
                                         conv2d_nhwc_reindex_shared[v0_o, v1_o, v2_o, v3_o, v4_i, v5_i] = conv2d_nhwc_reindex_shared_wmma_accumulator[v0_o, v1_o, v2_o, v3_o, v4_i, v5_i]
-                    for ax0_ax1_ax3_ax4_ax5_fused in range(512):
+                    for ax0_ax1_ax3_ax4_ax5_fused in range(2048):
                         with T.block("conv2d_nhwc_reindex_shared"):
-                            v0 = T.axis.spatial(2, ax0_0_ax1_0_ax2_0_0_ax3_0_0_fused // 2)
-                            v1 = T.axis.spatial(2, ax0_0_ax1_0_ax2_0_0_ax3_0_0_fused % 2)
+                            v0 = T.axis.spatial(2, ax0_ax1_ax3_ax4_ax5_fused // 1024)
+                            v1 = T.axis.spatial(1, 0)
                             v2 = T.axis.spatial(8, ax2)
-                            v3 = T.axis.spatial(2, ax0_ax1_ax3_ax4_ax5_fused // 256)
+                            v3 = T.axis.spatial(4, ax0_ax1_ax3_ax4_ax5_fused % 1024 // 256)
                             v4 = T.axis.spatial(16, ax0_ax1_ax3_ax4_ax5_fused % 256 // 16)
                             v5 = T.axis.spatial(16, ax0_ax1_ax3_ax4_ax5_fused % 16)
                             T.reads(conv2d_nhwc_reindex_shared[v0, v1, v2, v3, v4, v5])
-                            T.writes(conv2d_nhwc[0, (v4 + v2 * 16 + v0 * 128) // 16, (v4 + v2 * 16 + v0 * 128) % 16, v5 + v3 * 16 + v1 * 32])
+                            T.writes(conv2d_nhwc[0, (v4 + v2 * 16 + v0 * 128) // 16, (v4 + v2 * 16 + v0 * 128) % 16, v5 + v3 * 16])
                             T.block_attr({"meta_schedule.cooperative_fetch": 1})
-                            conv2d_nhwc[0, (v4 + v2 * 16 + v0 * 128) // 16, (v4 + v2 * 16 + v0 * 128) % 16, v5 + v3 * 16 + v1 * 32] = conv2d_nhwc_reindex_shared[v0, v1, v2, v3, v4, v5]
+                            conv2d_nhwc[0, (v4 + v2 * 16 + v0 * 128) // 16, (v4 + v2 * 16 + v0 * 128) % 16, v5 + v3 * 16] = conv2d_nhwc_reindex_shared[v0, v1, v2, v3, v4, v5]
     # fmt: on
 
     decision_0 = [
-        ("SamplePerfectTile", [1, 1, 1, 1, 1]),
-        ("SamplePerfectTile", [1, 1, 1, 1, 1]),
-        ("SamplePerfectTile", [2, 1, 1, 8, 1]),
-        ("SamplePerfectTile", [2, 1, 1, 2, 1]),
-        ("SamplePerfectTile", [1, 1, 4]),
+        ("SamplePerfectTile", [1, 1, 2, 8, 1]),
+        ("SamplePerfectTile", [1, 1, 1, 1, 4]),
+        ("SamplePerfectTile", [2, 1, 2]),
         ("SampleCategorical", 0),
-        ("SampleCategorical", 1),
         ("SampleCategorical", 3),
+        ("SampleCategorical", 2),
     ]
 
     mod = te.create_prim_func(