Add rms_norm example and test

examples/rms_norm.py

@@ -0,0 +1,61 @@
+from __future__ import annotations
+
+import torch
+
+import helion
+from helion._testing import run_example
+import helion.language as hl
+
+
+@helion.kernel(static_shapes=True)
+def rms_norm(x: torch.Tensor, weight: torch.Tensor, eps: float = 1e-5) -> torch.Tensor:
+    m, n = x.size()
+    assert weight.size(0) == n, f"weight size mismatch {weight.size(0)} != {n}"
+
+    out = torch.empty([m, n], dtype=x.dtype, device=x.device)
+
+    for tile_m in hl.tile(m):
+        x_tile = x[tile_m, :].to(torch.float32)
+
+        # Compute RMS: sqrt(mean(x^2))
+        x_squared = x_tile * x_tile
+        mean_x_squared = torch.mean(x_squared, dim=-1, keepdim=True)
+        rms = torch.rsqrt(mean_x_squared + eps)
+
+        # Apply normalization and weight
+        normalized = x_tile * rms
+        out[tile_m, :] = (normalized * weight[:].to(torch.float32)).to(out.dtype)
+
+    return out
+
+
+def rms_norm_tritonbench(H: int, inp: torch.Tensor) -> torch.Tensor:
+    """Wrapper for tritonbench that matches expected interface."""
+    weight = torch.ones(H, device=inp.device, dtype=inp.dtype)
+    return rms_norm(inp, weight, eps=1e-6)
+
+
+def rms_norm_pytorch(
+    x: torch.Tensor, weight: torch.Tensor, eps: float = 1e-5
+) -> torch.Tensor:
+    input_dtype = x.dtype
+    hidden_states = x.to(torch.float32)
+    variance = hidden_states.pow(2).mean(-1, keepdim=True)
+    hidden_states = hidden_states * torch.rsqrt(variance + eps)
+    return weight * hidden_states.to(input_dtype)
+
+
+def check(m: int, n: int) -> None:
+    x = torch.randn([m, n], device="cuda", dtype=torch.float16)
+    weight = torch.randn([n], device="cuda", dtype=torch.float16)
+    run_example(rms_norm, rms_norm_pytorch, (x, weight, 1e-5))
+
+
+def main() -> None:
+    check(32, 64)
+    check(128, 256)
+    check(1024, 1024)
+
+
+if __name__ == "__main__":
+    main()

test/test_examples.expected

@@ -989,6 +989,54 @@ def _moe_matmul_ogs_make_precompiler(A: torch.Tensor, W: torch.Tensor, expert_to
     from helion.runtime.precompile_shim import make_precompiler
     return make_precompiler(_moe_matmul_ogs_kernel)(expert_token_offsets, expert_token_counts, sorted_to_orig_token_idx, A, W, C, A.stride(0), A.stride(1), C.stride(0), C.stride(1), W.stride(0), W.stride(1), W.stride(2), expert_token_counts.stride(0), expert_token_offsets.stride(0), sorted_to_orig_token_idx.stride(0), max_T_per_expert, N, K, _BLOCK_SIZE_2, _BLOCK_SIZE_1, _BLOCK_SIZE_3, num_warps=4, num_stages=3)
 
+--- assertExpectedJournal(TestExamples.test_rms_norm)
+from __future__ import annotations
+
+import torch
+import triton
+import triton.language as tl
+from torch._inductor.runtime.triton_compat import libdevice
+
+@triton.jit
+def _rms_norm_kernel(x, weight, out, eps, _BLOCK_SIZE_0: tl.constexpr, _RDIM_SIZE_1: 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)
+    indices_1 = tl.arange(0, _RDIM_SIZE_1).to(tl.int32)
+    load = tl.load(x + (indices_0[:, None] * 256 + indices_1[None, :] * 1), None)
+    v_0 = load.to(tl.float32)
+    v_1 = v_0 * v_0
+    mean_x_squared_extra = tl.reshape(tl.sum(v_1, 1), [_BLOCK_SIZE_0, 1])
+    v_2 = 256
+    v_3 = mean_x_squared_extra / v_2.to(tl.float32)
+    v_4 = v_3 + eps
+    v_5 = libdevice.rsqrt(v_4)
+    v_6 = v_0 * v_5
+    load_1 = tl.load(weight + indices_1 * 1, None)
+    v_7 = load_1.to(tl.float32)
+    v_8 = v_7[None, :]
+    v_9 = v_6 * v_8
+    v_10 = v_9.to(tl.float16)
+    tl.store(out + (indices_0[:, None] * 256 + indices_1[None, :] * 1), v_10, None)
+
+def rms_norm(x: torch.Tensor, weight: torch.Tensor, eps: float=1e-05):
+    m, n = x.size()
+    assert weight.size(0) == n, f'weight size mismatch {weight.size(0)} != {n}'
+    out = torch.empty([m, n], dtype=x.dtype, device=x.device)
+    _BLOCK_SIZE_0 = 16
+    _RDIM_SIZE_1 = 256
+    _rms_norm_kernel[triton.cdiv(128, _BLOCK_SIZE_0),](x, weight, out, eps, _BLOCK_SIZE_0, _RDIM_SIZE_1, num_warps=4, num_stages=3)
+    return out
+
+def _rms_norm_make_precompiler(x: torch.Tensor, weight: torch.Tensor, eps: float=1e-05):
+    m, n = x.size()
+    assert weight.size(0) == n, f'weight size mismatch {weight.size(0)} != {n}'
+    out = torch.empty([m, n], dtype=x.dtype, device=x.device)
+    _BLOCK_SIZE_0 = 16
+    _RDIM_SIZE_1 = 256
+    from helion.runtime.precompile_shim import make_precompiler
+    return make_precompiler(_rms_norm_kernel)(x, weight, out, eps, _BLOCK_SIZE_0, _RDIM_SIZE_1, num_warps=4, num_stages=3)
+
 --- assertExpectedJournal(TestExamples.test_softmax)
 from __future__ import annotations
 

test/test_examples.py

@@ -237,6 +237,26 @@ def test_softmax_two_pass_block_ptr(self):
             )
         )
 
+    def test_rms_norm(self):
+        args = (
+            torch.randn([128, 256], device=DEVICE, dtype=torch.float16),
+            torch.randn([256], device=DEVICE, dtype=torch.float16),
+            1e-5,
+        )
+        # Import and use the reference implementation from rms_norm.py
+        mod = import_path(EXAMPLES_DIR / "rms_norm.py")
+        expected = mod.rms_norm_pytorch(*args)
+
+        self.assertExpectedJournal(
+            check_example(
+                "rms_norm",
+                args,
+                expected,
+                block_sizes=[16],
+                indexing="pointer",
+            )
+        )
+
     def test_embedding_pointers(self):
         args = (
             torch.randint(0, 1024, [8, 128], device=DEVICE, dtype=torch.int32),