Upload custom kernels

build/torch-universal/liger_kernels/_ops.py

@@ -1,8 +1,8 @@
 import torch
-ops = torch.ops._liger_kernels_20250507090511
+ops = torch.ops._liger_kernels_20250507091026
 
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_liger_kernels_20250507090511::{op_name}"
+    return f"_liger_kernels_20250507091026::{op_name}"

build/torch-universal/liger_kernels/rms_norm.py

@@ -377,13 +377,12 @@ class LigerRMSNorm(torch.nn.Module):
         in_place (bool, optional): Whether to modify dY in-place to store dX during backward. Defaults to True.
     """
     
-    def __init__(self, hidden_size, eps=1e-6, offset=0.0, casting_mode="llama", in_place=True):
-        super().__init__()
-        self.weight = torch.nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-        self.offset = offset
-        self.casting_mode = casting_mode
-        self.in_place = in_place
+
+    weight: torch.Tensor
+    variance_epsilon: float
+    offset: float = 0
+    casting_mode: str = "llama"
+    in_place: bool = True
     
     def forward(self, hidden_states):
         """

torch-ext/liger_kernels/rms_norm.py

@@ -377,13 +377,12 @@ class LigerRMSNorm(torch.nn.Module):
         in_place (bool, optional): Whether to modify dY in-place to store dX during backward. Defaults to True.
     """
     
-    def __init__(self, hidden_size, eps=1e-6, offset=0.0, casting_mode="llama", in_place=True):
-        super().__init__()
-        self.weight = torch.nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-        self.offset = offset
-        self.casting_mode = casting_mode
-        self.in_place = in_place
+
+    weight: torch.Tensor
+    variance_epsilon: float
+    offset: float = 0
+    casting_mode: str = "llama"
+    in_place: bool = True
     
     def forward(self, hidden_states):
         """