[RFC] Add support for device for loop indexing

This PR is mostly vibe coded via cursor using gpt5, however heavily modified for correctness and simplicity.

Fixes #598

stack-info: PR: #673, branch: oulgen/stack/99

Author: oulgen

helion/_compiler/device_ir.py

@@ -590,6 +590,38 @@ def visit_For(self, node: ast.For) -> None:
             self._assign(node.target, inner_type.proxy())
             self._body(node.body)
         elif node._loop_type == LoopType.DEVICE:
+            # Try static unrolling when begin/end are compile-time ints
+            begin, end = self._extract_tile_begin_end(node)
+            if isinstance(inner_type, SequenceType):
+                iter_vars = inner_type.unpack()
+                if begin is None:
+                    begin_list = [0] * len(iter_vars)
+                else:
+                    begin_list = begin if isinstance(begin, (list, tuple)) else [begin]
+                end_list = end if isinstance(end, (list, tuple)) else [end]
+                try_static = all(
+                    isinstance(b, int) and isinstance(e, int)
+                    for b, e in zip(begin_list, end_list, strict=True)
+                )
+                count = 1
+                if try_static:
+                    for b, e in zip(begin_list, end_list, strict=True):
+                        count *= max(0, e - b)  # pyright: ignore[reportOperatorIssue]
+                if try_static and count <= 64:
+                    # Assign inner proxy to target and then unroll nested ranges over scalar indices
+                    self._assign(node.target, inner_type.proxy())
+                    self._body(node.body)
+                    return
+            else:
+                # 1D case
+                b0 = 0 if begin is None else begin
+                if isinstance(b0, int) and isinstance(end, int):
+                    trip_count = max(0, end - b0)
+                    if trip_count <= 64:
+                        for iv in range(b0, end):
+                            self._assign(node.target, iv)
+                            self._body(node.body)
+                        return
             rw: ReadWrites = ReadWrites.from_ast(node)
             inputs: LiftTensorArgs = LiftTensorArgs(
                 {
@@ -947,6 +979,11 @@ def visit_Subscript(self, node: ast.Subscript) -> object:
         assert isinstance(value, ExtendedAST)
         type_info = value._type_info
         if isinstance(type_info, SequenceType):
+            index_val = self.visit(node.slice)
+            if isinstance(index_val, int):
+                sequence_val = self.visit(value)
+                assert isinstance(sequence_val, (list, tuple))
+                return sequence_val[index_val]
             if isinstance(node.slice, ast.Constant):
                 return self.visit(value)[self.visit(node.slice)]  # pyright: ignore[reportIndexIssue]
             raise exc.InvalidSequenceSubscription(node.slice)

helion/_compiler/type_propagation.py

@@ -1268,7 +1268,20 @@ def populate_symbol_origins(self, origin: Origin) -> None:
             subtype.populate_symbol_origins(GetItemOrigin(origin, i))
 
     def propagate_getitem(self, key: TypeInfo, origin: Origin) -> TypeInfo:
-        return super().propagate_getitem(key, origin)
+        # Try literal indexing first
+        try:
+            return super().propagate_getitem(key, origin)
+        except exc.TypeInferenceError:
+            # If indexing with a symbolic/grid index on device and the sequence length is known,
+            # conservatively merge all possible element types.
+            if origin.is_device() and isinstance(key, (SymIntType, GridIndexType)):
+                if not self.element_types:
+                    return super().propagate_getitem(key, origin)
+                merged: TypeInfo = self.element_types[0]
+                for candidate in self.element_types[1:]:
+                    merged = merged.merge(candidate)
+                return merged
+            raise
 
     def merge(self, other: TypeInfo, var_name: str | None = None) -> TypeInfo:
         if isinstance(other, SequenceType):
@@ -2161,6 +2174,86 @@ def visit_For(self, node: ast.For) -> TypeInfo:
                     raise exc.NestedGridLoop
 
         self.device_loop_depth += device_loop
+
+        # Try static unrolling for device grid loops when iteration count is known
+        try:
+            if node._loop_type != LoopType.HOST and isinstance(node.iter, ast.Call):
+                call_node = node.iter
+                # Extract begin, end, step; support only 1D grid here
+                begin_val: int | None
+                end_val: int | None
+                step_val: int | None
+
+                if len(call_node.args) == 1:
+                    begin_val = 0
+                    end_type = self.visit(call_node.args[0])
+                    step_type: TypeInfo | None = None
+                else:
+                    begin_type = self.visit(call_node.args[0])
+                    end_type = self.visit(call_node.args[1])
+                    step_type = (
+                        self.visit(call_node.args[2])
+                        if len(call_node.args) >= 3
+                        else None
+                    )
+                    begin_val = (
+                        begin_type.as_literal() if begin_type.is_literal() else None
+                    )  # type: ignore[assignment]
+
+                for kw in call_node.keywords:
+                    if kw.arg == "step" and step_type is None:
+                        step_type = self.visit(kw.value)
+
+                end_val = end_type.as_literal() if end_type.is_literal() else None  # type: ignore[assignment]
+                step_val = (
+                    step_type.as_literal()
+                    if (step_type is not None and step_type.is_literal())
+                    else 1
+                )  # type: ignore[assignment]
+
+                if (
+                    isinstance(begin_val, int)
+                    and isinstance(end_val, int)
+                    and isinstance(step_val, int)
+                ):
+                    # Build concrete iteration values
+                    iter_values = list(range(begin_val, end_val, step_val))
+                    # Small guard to avoid excessive compile-time blowups
+                    if len(iter_values) <= 64:
+                        merged_scope: LocalScope | None = None
+                        for iv in iter_values:
+                            # Emulate _loop_body with loop index bound to a literal
+                            self.push_scope()
+                            self._assign(node.target, LiteralType(self.origin(), iv))
+                            exit_scopes = [self.scope]
+                            for stmt in node.body:
+                                self.visit(stmt)
+                                if isinstance(stmt, (ast.Break, ast.Continue)):
+                                    exit_scopes.append(self.scope.clone())
+                            # Reset loop variable back to its GridIndexType to avoid control-flow merging issues
+                            self._assign(
+                                node.target,
+                                iter_type.propagate_iter(self.origin()),
+                            )
+                            self.pop_scope()
+                            iter_scope = functools.reduce(
+                                lambda x, y: x.merge(y), exit_scopes
+                            )
+                            if merged_scope is None:
+                                merged_scope = iter_scope
+                            else:
+                                merged_scope.merge(iter_scope)
+
+                        if merged_scope is not None:
+                            body = merged_scope
+                            orelse = self._body(node.orelse)
+                            self.scope.merge_if_else(body, orelse)
+                            self.device_loop_depth -= device_loop
+                            return NoType(origin=self.origin())
+        except NotImplementedError:
+            # Fall back to generic handling if we can't statically determine iterations
+            pass
+
         body = self._loop_body(node.body)
         with self.swap_scope(body):
             # second pass for fixed point

test/test_type_propagation.expected

@@ -537,6 +537,74 @@ def root_graph_2():
     _for_loop = helion_language__tracing_ops__for_loop(1, [0], [x_size0], []);  x_size0 = _for_loop = None
     return None
 
+--- assertExpectedJournal(TestTypePropagation.test_for_loop_indexing_in_device_code0)
+from __future__ import annotations
+
+import torch
+import triton
+import triton.language as tl
+from helion.runtime import default_launcher as _default_launcher
+
+@triton.jit
+def _helion_kernel(out, As_item_0, As_item_1, As_item_2, As_item_3, out_size_0, As_item_0_stride_0, As_item_1_stride_0, As_item_2_stride_0, As_item_3_stride_0, out_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 < out_size_0
+    load = tl.load(out + indices_0 * out_stride_0, mask_0, other=0)
+    load_1 = tl.load(As_item_0 + indices_0 * As_item_0_stride_0, mask_0, other=0)
+    v_0 = load + load_1
+    tl.store(out + indices_0 * out_stride_0, v_0, mask_0)
+    load_2 = tl.load(out + indices_0 * out_stride_0, mask_0, other=0)
+    load_3 = tl.load(As_item_1 + indices_0 * As_item_1_stride_0, mask_0, other=0)
+    v_1 = load_2 + load_3
+    tl.store(out + indices_0 * out_stride_0, v_1, mask_0)
+    load_4 = tl.load(out + indices_0 * out_stride_0, mask_0, other=0)
+    load_5 = tl.load(As_item_2 + indices_0 * As_item_2_stride_0, mask_0, other=0)
+    v_2 = load_4 + load_5
+    tl.store(out + indices_0 * out_stride_0, v_2, mask_0)
+    load_6 = tl.load(out + indices_0 * out_stride_0, mask_0, other=0)
+    load_7 = tl.load(As_item_3 + indices_0 * As_item_3_stride_0, mask_0, other=0)
+    v_3 = load_6 + load_7
+    tl.store(out + indices_0 * out_stride_0, v_3, mask_0)
+
+def kernel(As: list[torch.Tensor], *, _launcher=_default_launcher):
+    out = torch.zeros_like(As[0])
+    _BLOCK_SIZE_0 = 16
+    _launcher(_helion_kernel, (triton.cdiv(out.size(0), _BLOCK_SIZE_0),), out, As[0], As[1], As[2], As[3], out.size(0), As[0].stride(0), As[1].stride(0), As[2].stride(0), As[3].stride(0), out.stride(0), _BLOCK_SIZE_0, num_warps=4, num_stages=3)
+    return out
+
+--- assertExpectedJournal(TestTypePropagation.test_for_loop_indexing_in_device_code1)
+from __future__ import annotations
+
+import torch
+import triton
+import triton.language as tl
+from helion.runtime import default_launcher as _default_launcher
+
+@triton.jit
+def _helion_kernel(out, As_item_0, As_item_1, As_item_2, As_item_3, out_size_0, As_item_0_stride_0, As_item_1_stride_0, As_item_2_stride_0, As_item_3_stride_0, out_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 < out_size_0
+    acc = tl.full([_BLOCK_SIZE_0], 0.0, tl.float32)
+    load = tl.load(As_item_0 + indices_0 * As_item_0_stride_0, mask_0, other=0)
+    v_0 = acc + load
+    load_1 = tl.load(As_item_1 + indices_0 * As_item_1_stride_0, mask_0, other=0)
+    v_1 = v_0 + load_1
+    load_2 = tl.load(As_item_2 + indices_0 * As_item_2_stride_0, mask_0, other=0)
+    v_2 = v_1 + load_2
+    load_3 = tl.load(As_item_3 + indices_0 * As_item_3_stride_0, mask_0, other=0)
+    v_3 = v_2 + load_3
+    tl.store(out + indices_0 * out_stride_0, v_3, mask_0)
+
+def kernel(As: list[torch.Tensor], *, _launcher=_default_launcher):
+    out = torch.zeros_like(As[0])
+    _BLOCK_SIZE_0 = 16
+    _launcher(_helion_kernel, (triton.cdiv(out.size(0), _BLOCK_SIZE_0),), out, As[0], As[1], As[2], As[3], out.size(0), As[0].stride(0), As[1].stride(0), As[2].stride(0), As[3].stride(0), out.stride(0), _BLOCK_SIZE_0, num_warps=4, num_stages=3)
+    return out
+
 --- assertExpectedJournal(TestTypePropagation.test_hl_full_usage)
 def hl_full_usage(x: torch.Tensor):
     # Call: TensorType([x_size0, x_size1], torch.int32) SourceOrigin(location=<SourceLocation basic_kernels.py:38>)

test/test_type_propagation.py

@@ -8,8 +8,10 @@
 
 import helion
 from helion import exc
+from helion._testing import DEVICE
 from helion._testing import RefEagerTestDisabled
 from helion._testing import TestCase
+from helion._testing import code_and_output
 from helion._testing import import_path
 import helion.language as hl
 
@@ -132,6 +134,38 @@ def use_unsupported_property(x: torch.Tensor) -> torch.Tensor:
         ):
             type_propagation_report(use_unsupported_property, x)
 
+    def test_for_loop_indexing_in_device_code0(self):
+        @helion.kernel
+        def kernel(As: list[torch.Tensor]) -> torch.Tensor:
+            out = torch.zeros_like(As[0])
+            for tile in hl.tile(out.size()):
+                for i in range(len(As)):
+                    a = As[i]
+                    out[tile] += a[tile]
+            return out
+
+        args = [torch.randn(16, device=DEVICE) for _ in range(4)]
+        code, result = code_and_output(kernel, (args,))
+        torch.testing.assert_close(result, sum(args))
+        self.assertExpectedJournal(code)
+
+    def test_for_loop_indexing_in_device_code1(self):
+        @helion.kernel
+        def kernel(As: list[torch.Tensor]) -> torch.Tensor:
+            out = torch.zeros_like(As[0])
+            for tile in hl.tile(out.size()):
+                acc = hl.zeros(tile)
+                for i in range(len(As)):
+                    a = As[i]
+                    acc = acc + a[tile]
+                out[tile] = acc
+            return out
+
+        args = [torch.randn(16, device=DEVICE) for _ in range(4)]
+        code, result = code_and_output(kernel, (args,))
+        torch.testing.assert_close(result, sum(args))
+        self.assertExpectedJournal(code)
+
 
 if __name__ == "__main__":
     unittest.main()