Remove unrolling with tma + pipelining

stack-info: PR: #994, branch: PaulZhang12/stack/16

Author: PaulZhang12

helion/_compiler/tile_strategy.py

@@ -136,23 +136,21 @@ def get_tl_range_kwargs(config: Config, block_idx: int) -> list[str]:
         range_unroll_factor = env.config_spec.range_unroll_factors.config_get(
             config.range_unroll_factors, block_idx, 0
         )
-        if range_unroll_factor > 0:
-            kwargs.append(f"loop_unroll_factor={range_unroll_factor}")
-
         range_warp_specialize = env.config_spec.range_warp_specialize.config_get(
             config.range_warp_specializes, block_idx, None
         )
-        if range_warp_specialize is not None:
-            kwargs.append(f"warp_specialize={range_warp_specialize}")
-
         range_num_stages = env.config_spec.range_num_stages.config_get(
             config.range_num_stages, block_idx, 0
         )
+        num_stages = config.num_stages
 
-        if config.indexing == "tensor_descriptor" and range_num_stages > 0:
-            # Tensor descriptor + multi-stage tl.range pipelines tend to cause
+        if config.indexing == "tensor_descriptor":
+            # Tensor descriptor + multi-stage pipelines in addition to unrolling tend to cause
             # CUDA "misaligned address" or "unspecified launch failure" errors.
-            range_num_stages = 0
+            if range_num_stages > 0:
+                range_num_stages = 0
+            if range_unroll_factor > 0 and num_stages > 1:
+                range_unroll_factor = 0
         elif (
             range_num_stages > 1
             and range_unroll_factor > 1
@@ -170,6 +168,10 @@ def get_tl_range_kwargs(config: Config, block_idx: int) -> list[str]:
                 max(1, int(math.ceil(remainder / step))), range_num_stages
             )
 
+        if range_unroll_factor > 0:
+            kwargs.append(f"loop_unroll_factor={range_unroll_factor}")
+        if range_warp_specialize is not None:
+            kwargs.append(f"warp_specialize={range_warp_specialize}")
         if range_num_stages > 0:
             kwargs.append(f"num_stages={range_num_stages}")