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vllm_br/model_executor/layers/layernorm.py

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+################################################################################
+# Copyright(c)2020-2025 Shanghai Biren Technology Co., Ltd. All rights reserved.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+################################################################################
+import os
+from typing import Optional, Tuple, Union
+
+import torch
+import torch_br
+from fastcore.basics import patch_to
+from torch import Tensor, nn
+
+from vllm.model_executor.layers.layernorm import RMSNorm
+
+
+@patch_to(RMSNorm)
+def forward_oot(
+    self,
+    x: torch.Tensor,
+    residual: Optional[torch.Tensor] = None,
+) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+    if self.weight.data.dtype == torch.bfloat16:
+        self.weight.data = self.weight.data.to(torch.float32)
+
+    if residual is not None:
+        y_supa, add_out_supa = torch_br.supa_add_rmsnorm_infer(  # type: ignore
+            x, residual, self.weight.data, self.variance_epsilon)
+        return y_supa, add_out_supa
+    else:
+        if len(x.shape) == 2:
+            x = x.unsqueeze(0)
+        if len(x.shape) == 4:
+            x = x.squeeze(0)
+
+        x = torch_br.supa_rmsnorm_infer(
+            x,
+            self.weight.data,
+            self.variance_epsilon  # type: ignore
+        )
+        return x
+
+
+@patch_to(RMSNorm)
+def enabled(cls) -> bool:
+    return True
+
+
+@patch_to(nn.LayerNorm)
+def forward(self, input: Tensor) -> Tensor:
+    if os.environ.get("USE_BR_FUSED_LAYERNORM",
+                      'False').lower() not in {'false', '0', ''}:
+        return torch_br.fused_layernorm(input, self.weight, self.bias,
+                                        self.eps)
+    else:
+        return nn.functional.layer_norm(input, self.normalized_shape,
+                                        self.weight, self.bias, self.eps)