Suggest use of @helion.kernel(index_dtype=torch.int64) if index offset is out of bound for int32

helion/_compiler/indexing_strategy.py

@@ -9,6 +9,7 @@
 import sympy
 import torch
 from torch._inductor.utils import triton_type
+from torch._prims_common import compute_required_storage_length
 
 from .. import exc
 from .._compat import get_tensor_descriptor_fn_name
@@ -519,6 +520,27 @@ def compute_shape(
         assert len(input_size) == 0, "invalid subscript"
         return output_size
 
+    @staticmethod
+    def _needs_int64(fake_value: torch.Tensor) -> bool:
+        storage_offset = fake_value.storage_offset()
+        try:
+            required = compute_required_storage_length(
+                fake_value.shape,
+                fake_value.stride(),
+                storage_offset,
+            )
+        except Exception:  # pragma: no cover - defensive fallback
+            return False
+
+        if not isinstance(required, int):
+            return False
+
+        if abs(storage_offset) > torch.iinfo(torch.int32).max:
+            return True
+
+        max_offset = required - 1
+        return max_offset > torch.iinfo(torch.int32).max
+
     @staticmethod
     def create(
         state: CodegenState,
@@ -533,6 +555,8 @@ def create(
         output_size = SubscriptIndexing.compute_shape(fake_value, index)
         env = CompileEnvironment.current()
         dtype = env.triton_index_type()
+        if dtype == "tl.int32" and SubscriptIndexing._needs_int64(fake_value):
+            raise exc.IndexOffsetOutOfRangeForInt32(env.settings.index_dtype)
 
         def _is_size_one(size: int | torch.SymInt) -> bool:
             return env.known_equal(size, 1)

helion/exc.py

@@ -107,6 +107,13 @@ class InvalidIndexingType(BaseError):
     message = "Expected tile/int/None/tensor/etc in tensor[...], got {0!s}."
 
 
+class IndexOffsetOutOfRangeForInt32(BaseError):
+    message = (
+        "Tensor indexing offsets exceed the int32 range, but the kernel index_dtype is {0}. "
+        "Use @helion.kernel(index_dtype=torch.int64) to enable larger offsets."
+    )
+
+
 class DataDependentOutputShapeNotSupported(BaseError):
     message = (
         "{op_desc} is not supported in Helion device loops because it produces "

test/test_indexing.py

@@ -11,6 +11,7 @@
 from helion._testing import RefEagerTestBase
 from helion._testing import TestCase
 from helion._testing import code_and_output
+from helion._testing import skipIfLowVRAM
 from helion._testing import skipIfNormalMode
 from helion._testing import skipIfRefEager
 from helion._testing import skipIfRocm
@@ -241,6 +242,73 @@ def test_block_size_access(x: torch.Tensor) -> torch.Tensor:
         expected = torch.full_like(x, 1, dtype=torch.int32)
         torch.testing.assert_close(result, expected)
 
+    @skipIfLowVRAM("Test allocates ~15GB across multiple CUDA tensors")
+    def test_int32_offset_out_of_range_error(self):
+        repro_config = helion.Config(
+            block_sizes=[32, 32],
+            flatten_loops=[False],
+            indexing="pointer",
+            l2_groupings=[1],
+            loop_orders=[[0, 1]],
+            num_stages=3,
+            num_warps=4,
+            pid_type="flat",
+            range_flattens=[None],
+            range_multi_buffers=[None],
+            range_num_stages=[],
+            range_unroll_factors=[0],
+            range_warp_specializes=[],
+        )
+
+        def make_kernel(*, index_dtype: torch.dtype):
+            kwargs = dict(config=repro_config, static_shapes=False)
+            kwargs["index_dtype"] = index_dtype
+            decorator = helion.kernel(**kwargs)
+
+            @decorator
+            def repro_bf16_add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+                x, y = torch.broadcast_tensors(x, y)
+                out = torch.empty(
+                    x.shape,
+                    dtype=torch.promote_types(x.dtype, y.dtype),
+                    device=x.device,
+                )
+                for tile in hl.tile(out.size()):
+                    out[tile] = x[tile] + y[tile]
+                return out
+
+            return repro_bf16_add
+
+        def run_case(shape, *, index_dtype, expect_int64=False, expect_error=False):
+            kernel = make_kernel(index_dtype=index_dtype)
+            x = torch.randn(*shape, device=DEVICE, dtype=torch.bfloat16)
+            y = torch.randn(*shape, device=DEVICE, dtype=torch.bfloat16)
+            torch.cuda.synchronize()
+            if expect_error:
+                with self.assertRaisesRegex(
+                    helion.exc.IndexOffsetOutOfRangeForInt32,
+                    f"index_dtype is {index_dtype}",
+                ):
+                    code_and_output(kernel, (x, y))
+                torch.cuda.synchronize()
+                return
+
+            code, out = code_and_output(kernel, (x, y))
+            torch.cuda.synchronize()
+            checker = self.assertIn if expect_int64 else self.assertNotIn
+            checker("tl.int64", code)
+            torch.cuda.synchronize()
+            ref_out = torch.add(x, y)
+            torch.cuda.synchronize()
+            torch.testing.assert_close(out, ref_out, rtol=1e-2, atol=1e-2)
+
+        small_shape = (128, 128)
+        large_shape = (51200, 51200)
+
+        run_case(small_shape, index_dtype=torch.int32)
+        run_case(large_shape, index_dtype=torch.int32, expect_error=True)
+        run_case(large_shape, index_dtype=torch.int64, expect_int64=True)
+
     def test_assign_int(self):
         @helion.kernel
         def fn(x: torch.Tensor) -> torch.Tensor: