Update rms_norm.premake to better support AOT compilation

src/ntops/kernels/rms_norm.py

@@ -1,6 +1,6 @@
 import functools
-import math
 
+import ninetoothed
 import ninetoothed.language as ntl
 from ninetoothed import Tensor
 
@@ -20,17 +20,24 @@ def application(input, weight, eps, output, num_normalized_elements):
         output[i] = input[i] / rms * weight[i]
 
 
-def premake(ndim, normalized_shape, dtype=None, block_size=None):
-    dims = tuple(-(dim + 1) for dim in range(len(normalized_shape)))
+def premake(
+    ndim,
+    num_normalized_dims,
+    input_dtype=None,
+    weight_dtype=None,
+    output_dtype=None,
+    block_size=None,
+):
+    dims = tuple(-(dim + 1) for dim in range(num_normalized_dims))
 
     arrangement_ = functools.partial(arrangement, dim=dims, block_size=block_size)
 
     tensors = (
-        Tensor(ndim, other=0, dtype=dtype),
-        Tensor(ndim, dtype=dtype),
-        Tensor(0, dtype=dtype),
-        Tensor(ndim, dtype=dtype),
-        Tensor(0, dtype=dtype, constexpr=True, value=math.prod(normalized_shape)),
+        Tensor(ndim, other=0, dtype=input_dtype),
+        Tensor(ndim, dtype=weight_dtype),
+        Tensor(0, dtype=ninetoothed.float32),
+        Tensor(ndim, dtype=output_dtype),
+        Tensor(0, dtype=ninetoothed.uint64),
     )
 
     return arrangement_, application, tensors

src/ntops/torch.py

@@ -394,7 +394,9 @@ def rms_norm(input, normalized_shape, weight=None, eps=None):
 
     output = torch.empty_like(input)
 
-    kernel = _cached_make(ntops.kernels.rms_norm.premake, input.ndim, normalized_shape)
+    kernel = _cached_make(
+        ntops.kernels.rms_norm.premake, input.ndim, len(normalized_shape)
+    )
 
     kernel(input, weight, eps, output, math.prod(normalized_shape))