Add hl.rand op with seed arg lowering to tl.rand

helion/language/__init__.py

@@ -21,6 +21,7 @@
 from .matmul_ops import dot as dot
 from .memory_ops import load as load
 from .memory_ops import store as store
+from .random_ops import rand as rand
 from .reduce_ops import reduce as reduce
 from .scan_ops import associative_scan as associative_scan
 from .scan_ops import cumprod as cumprod

helion/language/random_ops.py

@@ -0,0 +1,121 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import torch
+
+from .._compiler.ast_extension import expr_from_string
+from .._compiler.compile_environment import CompileEnvironment
+from ..exc import NotInsideKernel
+from . import _decorators
+from .ref_tile import RefTile
+
+if TYPE_CHECKING:
+    import ast
+
+    from .._compiler.inductor_lowering import CodegenState
+
+__all__ = ["rand"]
+
+
+@_decorators.api(tiles_as_sizes=True)
+def rand(
+    shape: list[object],
+    seed: int,
+    dtype: torch.dtype = torch.float32,
+    device: torch.device | None = None,
+) -> torch.Tensor:
+    """
+    The main propose of ``hl.rand`` is to explicitly pass a seed arg for deterministic
+    randomness in helion kernels, whereas ``torch.rand_like`` doesn't take seed arg
+    (though it can seeded globally)`. ``hl.rand`` lower to ``tl.rand(seed, offset)`` with ``offset``
+    built from a linear range over the allocation and reshaped to the given shape.
+
+    Note:
+        Only use within ``hl.tile()`` loops for creating local tensors.
+        For host allocations, use ``torch.rand()``.
+
+    Args:
+        shape: A list of sizes
+        seed: int seed for the random number generator
+        dtype: currently only float32 supported
+
+    Returns:
+        torch.Tensor: A device tensor of the given shape and dtype filled with random values
+
+    Examples:
+        .. code-block:: python
+
+            @helion.kernel
+            def process_kernel(x: torch.Tensor) -> torch.Tensor:
+                output = torch.zeros_like(x)
+                (m,) = x.shape
+                for (tile_m,) in hl.tile([m]):
+                    output[tile_m] = hl.rand([tile_m], seed=seed)
+                return output
+
+    """
+    raise NotInsideKernel
+
+
+@_decorators.register_fake(rand)
+def _rand_fake(
+    shape: list[int | torch.SymInt],
+    seed: int,
+    dtype: torch.dtype = torch.float32,
+    device: torch.device | None = None,
+) -> torch.Tensor:
+    if not isinstance(shape, (list, tuple)):
+        raise TypeError(f"Expected list[SymInt], got {type(shape).__name__}")
+    env = CompileEnvironment.current()
+    env.add_kernel_tensor_size(shape)
+    return torch.empty(
+        [*shape],
+        dtype=dtype,
+        device=env.device if device is None else device,
+    )
+
+
+@_decorators.codegen(rand)
+def _rand_codegen(state: CodegenState) -> ast.AST:
+    fake_value = state.fake_value
+    assert isinstance(fake_value, torch.Tensor)
+    shape_str = state.device_function.tile_strategy.shape_str(fake_value.size())
+
+    numel = " * ".join(shape_str.strip("[]").split(","))
+    seed_ast = state.ast_arg(1)
+    offs_expr = f"tl.arange(0, {numel}).reshape({shape_str})"
+    expr = f"tl.rand({{seed}}, {offs_expr})"
+
+    return expr_from_string(expr, seed=seed_ast)
+
+
+@_decorators.get_masked_value(rand)
+def _(
+    node: torch.fx.Node,
+) -> float:
+    return 0
+
+
+@_decorators.ref(rand)
+def _(
+    shape: list[int | RefTile],
+    seed: int,
+    dtype: torch.dtype = torch.float32,
+    device: torch.device | None = None,
+) -> torch.Tensor:
+    processed_shape: list[int] = []
+    for s in shape:
+        if isinstance(s, RefTile):
+            processed_shape.append(s.end - s.begin)
+        else:
+            processed_shape.append(int(s))
+    env = CompileEnvironment.current()
+    gen = torch.Generator(device=env.device if device is None else device)
+    gen.manual_seed(seed)
+    return torch.rand(
+        processed_shape,
+        dtype=dtype,
+        generator=gen,
+        device=env.device if device is None else device,
+    )

test/test_rng.py

@@ -348,6 +348,97 @@ def randn_kernel_3d(x: torch.Tensor) -> torch.Tensor:
                 f"Slice {b_idx} std {slice_std} is not well distributed",
             )
 
+    def test_hl_rand_1d(self):
+        @helion.kernel
+        def rand_kernel_tiled_1d(x: torch.Tensor, seed: int) -> torch.Tensor:
+            output = torch.zeros_like(x)
+            (m,) = x.shape
+            for (tile_m,) in hl.tile([m]):
+                output[tile_m] = hl.rand([tile_m], seed=seed)
+            return output
+
+        x_small = torch.ones(128, device=DEVICE)
+        _, output = code_and_output(rand_kernel_tiled_1d, (x_small, 42))
+        _, output2 = code_and_output(rand_kernel_tiled_1d, (x_small, 1337))
+
+        self.assertFalse(
+            torch.allclose(output, output2),
+            "Different seeds should produce different outputs",
+        )
+
+        _, output3 = code_and_output(rand_kernel_tiled_1d, (x_small, 42))
+        self.assertTrue(
+            torch.allclose(output, output3),
+            "Same seed should produce identical outputs",
+        )
+
+        # Check that all values are in [0, 1) range
+        self.assertTrue(torch.all(output >= 0.0), "All values should be >= 0")
+        self.assertTrue(torch.all(output < 1.0), "All values should be < 1")
+
+    def test_hl_rand_2d(self):
+        @helion.kernel
+        def rand_kernel_tiled_2d(x: torch.Tensor, seed: int) -> torch.Tensor:
+            output = torch.zeros_like(x)
+            m, n = x.shape
+            for tile_m, tile_n in hl.tile([m, n]):
+                output[tile_m, tile_n] = hl.rand([tile_m, tile_n], seed=seed)
+            return output
+
+        x_small = torch.ones(128, 128, device=DEVICE)
+        _, output = code_and_output(rand_kernel_tiled_2d, (x_small, 42))
+        _, output2 = code_and_output(rand_kernel_tiled_2d, (x_small, 1337))
+
+        self.assertFalse(
+            torch.allclose(output, output2),
+            "Different seeds should produce different outputs",
+        )
+
+        _, output3 = code_and_output(rand_kernel_tiled_2d, (x_small, 42))
+        self.assertTrue(
+            torch.allclose(output, output3),
+            "Same seed should produce identical outputs",
+        )
+
+        self.assertTrue(torch.all(output >= 0.0), "All values should be >= 0")
+        self.assertTrue(torch.all(output < 1.0), "All values should be < 1")
+
+    def test_hl_rand_3d(self):
+        @helion.kernel
+        def rand_kernel_tiled_3d(x: torch.Tensor, seed: int) -> torch.Tensor:
+            output = torch.zeros_like(x)
+            b, m, n = x.shape
+            for tile_b, tile_m, tile_n in hl.tile([b, m, n]):
+                output[tile_b, tile_m, tile_n] = hl.rand(
+                    [tile_b, tile_m, tile_n], seed=seed
+                )
+            return output
+
+        x_small = torch.ones(16, 32, 64, device=DEVICE)
+        _, output = code_and_output(rand_kernel_tiled_3d, (x_small, 42))
+        _, output2 = code_and_output(rand_kernel_tiled_3d, (x_small, 1337))
+
+        self.assertFalse(
+            torch.allclose(output, output2),
+            "Different seeds should produce different outputs",
+        )
+
+        _, output3 = code_and_output(rand_kernel_tiled_3d, (x_small, 42))
+        self.assertTrue(
+            torch.allclose(output, output3),
+            "Same seed should produce identical outputs",
+        )
+
+        self.assertTrue(torch.all(output >= 0.0), "All values should be >= 0")
+        self.assertTrue(torch.all(output < 1.0), "All values should be < 1")
+
+        # Check distribution properties
+        mean_val = output.mean().item()
+        self.assertTrue(
+            0.4 < mean_val < 0.6,
+            f"Mean {mean_val:.3f} should be around 0.5 for uniform distribution",
+        )
+
 
 if __name__ == "__main__":
     unittest.main()