Add autotuning for range() unrolling

helion/_compiler/device_ir.py

@@ -529,6 +529,8 @@ def visit_For(self, node: ast.For) -> None:
             )
             outputs: LiftTensorArgs | None = None
             begin, end = self._extract_tile_begin_end(node)
+            if (begin is None or isinstance(begin, int)) and isinstance(end, int):
+                CompileEnvironment.current().config_spec.allow_unroll_loops = True
             if isinstance(inner_type, SequenceType):
                 iter_vars = inner_type.unpack()
                 if begin is None:

helion/_compiler/generate_ast.py

@@ -56,12 +56,16 @@ def mask_var(self, block_idx: int) -> str | None:
             return loops[-1].strategy.mask_var(block_idx)
         return None
 
-    def add_statement(self, stmt: ast.AST | str | None) -> None:
+    def add_statement(self, stmt: ast.AST | list[ast.AST] | str | None) -> None:
         if stmt is None:
             return
         if isinstance(stmt, str):
             stmt = statement_from_string(stmt)
-        self.statements_stack[-1].append(stmt)
+        if isinstance(stmt, list):
+            for s in stmt:
+                self.statements_stack[-1].append(s)
+        else:
+            self.statements_stack[-1].append(stmt)
 
     def tmpvar(self, *, dce: bool = False, prefix: str = "v") -> str:
         return self.device_function.unique_name(prefix, dce=dce)
@@ -116,7 +120,12 @@ def add_device_loop(self, device_loop: DeviceLoopState) -> Iterator[None]:
                 for idx in device_loop.block_ids:
                     self.active_device_loops[idx].pop()
         self.statements_stack[-1].extend(device_loop.outer_prefix)
-        self.add_statement(device_loop.for_node)
+        stmt = device_loop.for_node
+        if self.device_function.config.unroll_loops:
+            from .static_loop_unroller import unroll_loop
+
+            stmt = unroll_loop(node=device_loop.for_node, allow_range=True)
+        self.add_statement(stmt)
         self.statements_stack[-1].extend(device_loop.outer_suffix)
 
     def set_active_loops(self, device_grid: DeviceLoopOrGridState) -> None:

helion/_compiler/host_function.py

@@ -103,7 +103,7 @@ def __init__(
             from .static_loop_unroller import unroll_static_loops
             from .type_propagation import propagate_types
 
-            unroll_static_loops(self)
+            unroll_static_loops(func=self, allow_range=False)
             propagate_types(self, fake_args)
             env.finalize_config_spec()
             self.device_ir = lower_to_device_ir(self)

helion/_compiler/static_loop_unroller.py

@@ -23,6 +23,9 @@ class StaticLoopUnroller(ast.NodeTransformer):
     TODO(oulgen): This pass is primitive, does not handle for.orelse, break, continue etc
     """
 
+    def __init__(self, allow_range: bool) -> None:
+        self.allow_range = allow_range
+
     def visit_For(self, node: ast.For) -> ast.AST | list[ast.AST]:
         # Generic visit to handle nested loops
         node = self.generic_visit(node)  # pyre-ignore[9]
@@ -45,6 +48,35 @@ def _extract_static_values(self, iter_node: ast.expr) -> list[ast.expr] | None:
         """
         if isinstance(iter_node, (ast.List, ast.Tuple)):
             return iter_node.elts
+        if (
+            self.allow_range
+            and isinstance(iter_node, ast.Call)
+            and isinstance(iter_node.func, ast.Name)
+            and iter_node.func.id == "range"
+        ):
+            range_values = self._extract_range_values(iter_node)
+            if range_values is not None:
+                return [create(ast.Constant, value=val) for val in range_values]
+
+        return None
+
+    def _extract_range_values(self, range_call: ast.Call) -> list[int] | None:
+        """
+        Extract values from a range() call if all arguments are constants.
+        """
+        args = range_call.args
+
+        for arg in args:
+            if not isinstance(arg, ast.Constant) or not isinstance(arg.value, int):
+                return None
+
+        if len(args) == 1:
+            return list(range(args[0].value))  # pyre-ignore[16]
+        if len(args) == 2:
+            return list(range(args[0].value, args[1].value))
+        if len(args) == 3:
+            return list(range(args[0].value, args[1].value, args[2].value))
+
         return None
 
     def _unroll_loop(
@@ -68,14 +100,17 @@ def _unroll_loop(
         return unrolled_statements
 
 
-def unroll_static_loops(func: HostFunction) -> None:
-    new_body = []
+def unroll_loop(*, node: ast.AST, allow_range: bool) -> ast.AST | list[ast.AST]:
+    try:
+        return StaticLoopUnroller(allow_range).visit(node)
+    except CannotUnrollLoop:
+        return node
+
+
+def unroll_static_loops(*, func: HostFunction, allow_range: bool) -> None:
+    new_body: list[ast.stmt] = []
     for stmt in func.body:
-        try:
-            unrolled_stmts = StaticLoopUnroller().visit(stmt)
-        except CannotUnrollLoop:
-            new_body.append(stmt)
-        else:
-            assert isinstance(unrolled_stmts, ast.stmt)
-            new_body.append(unrolled_stmts)
+        maybe_unrolled = unroll_loop(node=stmt, allow_range=allow_range)
+        assert isinstance(maybe_unrolled, ast.stmt)
+        new_body.append(maybe_unrolled)
     func.body = new_body

helion/autotuner/config_spec.py

@@ -39,6 +39,7 @@
         "num_warps",
         "num_stages",
         "use_yz_grid",
+        "unroll_loops",
         "indexing",
     ]
 )
@@ -65,6 +66,7 @@ class ConfigSpec:
         default_factory=dict
     )
     allow_use_yz_grid: bool | None = None
+    allow_unroll_loops: bool | None = None
 
     def _remove_duplicates(self) -> None:
         self.loop_orders._remove_duplicates()
@@ -111,6 +113,8 @@ def normalize(self, config: helion.Config | dict[str, object]) -> None:
 
         if self.allow_use_yz_grid:
             config.setdefault("use_yz_grid", False)
+        if self.allow_unroll_loops:
+            config.setdefault("unroll_loops", False)
 
         config.setdefault("indexing", "pointer")
 
@@ -151,6 +155,9 @@ def flat_config(self, fn: Callable[[ConfigSpecFragment], object]) -> helion.Conf
                 not config["flatten_loops"] or not config["flatten_loops"][0]
             ):
                 config["use_yz_grid"] = use_yz_grid
+        if self.allow_unroll_loops:
+            config["unroll_loops"] = fn(BooleanFragment())
+
         for name in ("loop_orders", "flatten_loops", "reduction_loops", "l2_groupings"):
             if not config[name]:
                 config.pop(name)

helion/runtime/config.py

@@ -28,6 +28,7 @@ def __init__(
         num_warps: int | None = None,
         num_stages: int | None = None,
         use_yz_grid: bool | None = None,
+        unroll_loops: bool | None = None,
         indexing: IndexingLiteral | None = None,
         # For user-defined properties
         **kwargs: object,
@@ -43,6 +44,7 @@ def __init__(
             num_warps: Number of warps per block.
             num_stages: Number of stages for software pipelining.
             use_yz_grid: Whether to use yz grid dimensions.
+            unroll_loops: Whether to unroll loops.
             indexing: Indexing strategy ("pointer", "tensor_descriptor", "block_ptr").
             **kwargs: Additional user-defined configuration parameters.
         """
@@ -57,6 +59,7 @@ def __init__(
             "num_stages": num_stages,
             "indexing": indexing,
             "use_yz_grid": use_yz_grid,
+            "unroll_loops": unroll_loops,
         }
         for key, value in core_props.items():
             if value is not None:
@@ -138,6 +141,10 @@ def l2_groupings(self) -> list[int]:
     def use_yz_grid(self) -> bool:
         return cast("bool", self.config.get("use_yz_grid", False))
 
+    @property
+    def unroll_loops(self) -> bool:
+        return cast("bool", self.config.get("unroll_loops", False))
+
     @property
     def indexing(self) -> IndexingLiteral:
         return self.config.get("indexing", "pointer")  # type: ignore

test/test_autotuner.py

@@ -44,16 +44,16 @@ def test_config_fragment0(self):
         self.assertExpectedInline(
             "\n".join(map(repr, configs)),
             """\
-helion.Config(block_sizes=[16, 16, 16], loop_orders=[[0, 1]], l2_groupings=[1], num_warps=4, num_stages=3, indexing='pointer')
-helion.Config(block_sizes=[16, 32, 16], loop_orders=[[1, 0]], l2_groupings=[8], num_warps=32, num_stages=3, indexing='block_ptr')
-helion.Config(block_sizes=[32, 16, 16], loop_orders=[[1, 0]], l2_groupings=[32], num_warps=8, num_stages=8, indexing='block_ptr')
-helion.Config(block_sizes=[16, 16, 32], loop_orders=[[0, 1]], l2_groupings=[16], num_warps=4, num_stages=7, indexing='tensor_descriptor')
-helion.Config(block_sizes=[16, 16, 16], loop_orders=[[0, 1]], l2_groupings=[4], num_warps=8, num_stages=2, indexing='tensor_descriptor')
-helion.Config(block_sizes=[16, 16, 16], loop_orders=[[1, 0]], l2_groupings=[64], num_warps=4, num_stages=7, indexing='tensor_descriptor')
-helion.Config(block_sizes=[32, 128, 64], loop_orders=[[0, 1]], l2_groupings=[2], num_warps=16, num_stages=5, indexing='pointer')
-helion.Config(block_sizes=[16, 16, 16], loop_orders=[[1, 0]], l2_groupings=[2], num_warps=16, num_stages=3, indexing='tensor_descriptor')
-helion.Config(block_sizes=[16, 16, 16], loop_orders=[[0, 1]], l2_groupings=[16], num_warps=4, num_stages=2, indexing='block_ptr')
-helion.Config(block_sizes=[16, 16, 16], loop_orders=[[0, 1]], l2_groupings=[1], num_warps=1, num_stages=1, indexing='tensor_descriptor')""",
+helion.Config(block_sizes=[16, 16, 16], loop_orders=[[0, 1]], l2_groupings=[1], num_warps=4, num_stages=3, indexing='pointer', unroll_loops=False)
+helion.Config(block_sizes=[32, 128, 64], loop_orders=[[1, 0]], l2_groupings=[8], num_warps=32, num_stages=3, indexing='block_ptr', unroll_loops=False)
+helion.Config(block_sizes=[128, 16, 128], loop_orders=[[0, 1]], l2_groupings=[8], num_warps=4, num_stages=6, indexing='pointer', unroll_loops=False)
+helion.Config(block_sizes=[16, 16, 16], loop_orders=[[0, 1]], l2_groupings=[16], num_warps=4, num_stages=7, indexing='tensor_descriptor', unroll_loops=True)
+helion.Config(block_sizes=[16, 16, 16], loop_orders=[[0, 1]], l2_groupings=[8], num_warps=32, num_stages=2, indexing='tensor_descriptor', unroll_loops=False)
+helion.Config(block_sizes=[16, 16, 16], loop_orders=[[1, 0]], l2_groupings=[64], num_warps=4, num_stages=7, indexing='tensor_descriptor', unroll_loops=False)
+helion.Config(block_sizes=[32, 16, 16], loop_orders=[[0, 1]], l2_groupings=[16], num_warps=4, num_stages=4, indexing='tensor_descriptor', unroll_loops=False)
+helion.Config(block_sizes=[64, 16, 128], loop_orders=[[0, 1]], l2_groupings=[4], num_warps=32, num_stages=2, indexing='tensor_descriptor', unroll_loops=False)
+helion.Config(block_sizes=[16, 16, 16], loop_orders=[[0, 1]], l2_groupings=[16], num_warps=16, num_stages=3, indexing='block_ptr', unroll_loops=False)
+helion.Config(block_sizes=[16, 32, 32], loop_orders=[[0, 1]], l2_groupings=[4], num_warps=4, num_stages=7, indexing='block_ptr', unroll_loops=True)""",
         )
 
     @patch.object(_compat, "_supports_tensor_descriptor", lambda: True)
@@ -187,6 +187,34 @@ def test_differential_evolution_search(self):
         fn = bound_kernel.compile_config(best)
         torch.testing.assert_close(fn(*args), args[0] @ args[1], rtol=1e-2, atol=1e-1)
 
+    def test_loop_unroll(self):
+        @helion.kernel()
+        def fn(x: torch.Tensor) -> torch.Tensor:
+            out = torch.zeros_like(x)
+            for tile in hl.tile(x.size()):
+                out[tile] = x[tile]
+                for i in range(1, 4):
+                    out[tile] += i
+            return out
+
+        args = (torch.randn(4, device=DEVICE),)
+        spec = fn.bind(args).config_spec
+        configs = ConfigGeneration(spec).random_population(10)
+        self.assertExpectedInline(
+            "\n".join(map(repr, configs)),
+            """\
+helion.Config(block_sizes=[4], num_warps=4, num_stages=3, indexing='pointer', unroll_loops=False)
+helion.Config(block_sizes=[2], num_warps=32, num_stages=5, indexing='block_ptr', unroll_loops=True)
+helion.Config(block_sizes=[1], num_warps=4, num_stages=4, indexing='block_ptr', unroll_loops=False)
+helion.Config(block_sizes=[4], num_warps=2, num_stages=8, indexing='block_ptr', unroll_loops=True)
+helion.Config(block_sizes=[1], num_warps=2, num_stages=3, indexing='block_ptr', unroll_loops=True)
+helion.Config(block_sizes=[4], num_warps=8, num_stages=5, indexing='pointer', unroll_loops=False)
+helion.Config(block_sizes=[4], num_warps=2, num_stages=5, indexing='block_ptr', unroll_loops=False)
+helion.Config(block_sizes=[1], num_warps=1, num_stages=4, indexing='block_ptr', unroll_loops=True)
+helion.Config(block_sizes=[2], num_warps=32, num_stages=7, indexing='pointer', unroll_loops=True)
+helion.Config(block_sizes=[2], num_warps=2, num_stages=2, indexing='pointer', unroll_loops=True)""",
+        )
+
     def test_use_default_config(self):
         @helion.kernel(use_default_config=True)
         def add(a, b):

test/test_loops.py

@@ -1499,6 +1499,103 @@ def _fn_make_precompiler(x: torch.Tensor):
     return make_precompiler(_fn_kernel)(x, out, x.size(0), out.stride(0), x.stride(0), _BLOCK_SIZE_0, num_warps=4, num_stages=3)""",
         )
 
+    def test_loop_unroll3(self):
+        @helion.kernel()
+        def fn(x: torch.Tensor) -> torch.Tensor:
+            out = torch.zeros_like(x)
+            for tile in hl.tile(x.size()):
+                out[tile] = x[tile]
+                for i in range(1, 4):
+                    out[tile] += i
+            return out
+
+        x = torch.randn(4, device=DEVICE)
+        code, output = code_and_output(fn, (x,), block_sizes=[4], unroll_loops=True)
+        torch.testing.assert_close(output, x + 6)
+        self.assertExpectedInline(
+            code,
+            """\
+from __future__ import annotations
+
+import torch
+import triton
+import triton.language as tl
+
+@triton.jit
+def _fn_kernel(x, out, x_size_0, out_stride_0, x_stride_0, _BLOCK_SIZE_0: tl.constexpr):
+    pid_0 = tl.program_id(0)
+    offset_0 = pid_0 * _BLOCK_SIZE_0
+    indices_0 = (offset_0 + tl.arange(0, _BLOCK_SIZE_0)).to(tl.int32)
+    mask_0 = indices_0 < x_size_0
+    load = tl.load(x + indices_0 * x_stride_0, mask_0, other=0)
+    tl.store(out + indices_0 * out_stride_0, load, mask_0)
+    offset_1 = 1
+    load_1 = tl.load(out + indices_0 * out_stride_0, mask_0, other=0)
+    v_0 = offset_1.to(tl.float32)
+    v_1 = load_1 + v_0
+    tl.store(out + indices_0 * out_stride_0, v_1, mask_0)
+    offset_1 = 2
+    load_1 = tl.load(out + indices_0 * out_stride_0, mask_0, other=0)
+    v_0 = offset_1.to(tl.float32)
+    v_1 = load_1 + v_0
+    tl.store(out + indices_0 * out_stride_0, v_1, mask_0)
+    offset_1 = 3
+    load_1 = tl.load(out + indices_0 * out_stride_0, mask_0, other=0)
+    v_0 = offset_1.to(tl.float32)
+    v_1 = load_1 + v_0
+    tl.store(out + indices_0 * out_stride_0, v_1, mask_0)
+
+def fn(x: torch.Tensor):
+    out = torch.zeros_like(x)
+    _BLOCK_SIZE_0 = 4
+    _fn_kernel[triton.cdiv(x.size(0), _BLOCK_SIZE_0),](x, out, x.size(0), out.stride(0), x.stride(0), _BLOCK_SIZE_0, num_warps=4, num_stages=3)
+    return out
+
+def _fn_make_precompiler(x: torch.Tensor):
+    out = torch.zeros_like(x)
+    _BLOCK_SIZE_0 = 4
+    from helion.runtime.precompile_shim import make_precompiler
+    return make_precompiler(_fn_kernel)(x, out, x.size(0), out.stride(0), x.stride(0), _BLOCK_SIZE_0, num_warps=4, num_stages=3)""",
+        )
+
+        code, output = code_and_output(fn, (x,), block_sizes=[4], unroll_loops=False)
+        torch.testing.assert_close(output, x + 6)
+        self.assertExpectedInline(
+            code,
+            """\
+from __future__ import annotations
+
+import torch
+import triton
+import triton.language as tl
+
+@triton.jit
+def _fn_kernel(x, out, x_size_0, out_stride_0, x_stride_0, _BLOCK_SIZE_0: tl.constexpr):
+    pid_0 = tl.program_id(0)
+    offset_0 = pid_0 * _BLOCK_SIZE_0
+    indices_0 = (offset_0 + tl.arange(0, _BLOCK_SIZE_0)).to(tl.int32)
+    mask_0 = indices_0 < x_size_0
+    load = tl.load(x + indices_0 * x_stride_0, mask_0, other=0)
+    tl.store(out + indices_0 * out_stride_0, load, mask_0)
+    for offset_1 in range(1, 4, 1):
+        load_1 = tl.load(out + indices_0 * out_stride_0, mask_0, other=0)
+        v_0 = offset_1.to(tl.float32)
+        v_1 = load_1 + v_0
+        tl.store(out + indices_0 * out_stride_0, v_1, mask_0)
+
+def fn(x: torch.Tensor):
+    out = torch.zeros_like(x)
+    _BLOCK_SIZE_0 = 4
+    _fn_kernel[triton.cdiv(x.size(0), _BLOCK_SIZE_0),](x, out, x.size(0), out.stride(0), x.stride(0), _BLOCK_SIZE_0, num_warps=4, num_stages=3)
+    return out
+
+def _fn_make_precompiler(x: torch.Tensor):
+    out = torch.zeros_like(x)
+    _BLOCK_SIZE_0 = 4
+    from helion.runtime.precompile_shim import make_precompiler
+    return make_precompiler(_fn_kernel)(x, out, x.size(0), out.stride(0), x.stride(0), _BLOCK_SIZE_0, num_warps=4, num_stages=3)""",
+        )
+
 
 if __name__ == "__main__":
     unittest.main()