[NVIDIA#6187][feat] add LayerNorm module (NVIDIA#6625)

Signed-off-by: Robin Kobus <19427718+Funatiq@users.noreply.github.com>

Author: Funatiq

tensorrt_llm/_torch/modules/layer_norm.py

@@ -0,0 +1,120 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# 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.
+
+from typing import Optional, Tuple, Union
+
+import torch
+from torch import nn
+
+
+class LayerNorm(nn.Module):
+    """Layer normalization module with configurable weight and bias parameters.
+
+    This implementation provides standard layer normalization with optional
+    learnable parameters and residual connection support.
+
+    Args:
+        hidden_size: The size of the hidden dimension to normalize.
+        eps: Small constant for numerical stability.
+        dtype: Optional data type for parameters.
+        device: Optional device for parameters.
+        has_weights: Whether to include learnable weight parameters.
+        has_bias: Whether to include learnable bias parameters.
+    """
+
+    def __init__(
+        self,
+        *,
+        hidden_size: int,
+        eps: float,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+        has_weights: bool = True,
+        has_bias: bool = True,
+    ):
+        super().__init__()
+        if has_weights:
+            self.weight = nn.Parameter(
+                torch.ones(hidden_size, dtype=dtype, device=device))
+        else:
+            self.register_buffer('weight',
+                                 torch.ones(hidden_size,
+                                            dtype=dtype,
+                                            device=device),
+                                 persistent=False)
+        if has_bias:
+            self.bias = nn.Parameter(
+                torch.zeros(hidden_size, dtype=dtype, device=device))
+        else:
+            self.register_buffer('bias',
+                                 torch.zeros(hidden_size,
+                                             dtype=dtype,
+                                             device=device),
+                                 persistent=False)
+        self.variance_epsilon = eps
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor] = ...,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        """Apply layer normalization to input tensor.
+
+        Args:
+            hidden_states: Input tensor to normalize.
+            residual: Optional residual tensor to add before normalization.
+
+        Returns:
+            Normalized tensor, or tuple of (normalized_tensor, residual) if residual provided.
+        """
+
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        if isinstance(residual, torch.Tensor):
+            hidden_states = hidden_states + residual.to(torch.float32)
+            residual = hidden_states.to(input_dtype)
+
+        hidden_states = nn.functional.layer_norm(
+            hidden_states,
+            hidden_states.shape[-1],
+            weight=self.weight,
+            bias=self.bias,
+            eps=self.variance_epsilon,
+        )
+
+        if residual is ...:
+            return hidden_states
+        else:
+            return hidden_states, residual
+
+    def skip_forward(
+        self,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor] = ...,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        """Skip normalization and return inputs unchanged.
+
+        Args:
+            hidden_states: Input tensor to pass through.
+            residual: Optional residual tensor to pass through.
+
+        Returns:
+            Input tensors unchanged, maintaining same signature as forward.
+        """
+
+        if residual is ...:
+            return hidden_states
+        else:
+            return hidden_states, residual

tensorrt_llm/_torch/modules/rms_norm.py

@@ -1,3 +1,18 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# 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 enum
 from typing import Optional, Tuple, Union
 
@@ -9,13 +24,15 @@
 
 class RMSNorm(nn.Module):
 
-    def __init__(self,
-                 *,
-                 hidden_size: int,
-                 eps: float,
-                 dtype: Optional[torch.dtype] = None,
-                 device: Optional[torch.device] = None,
-                 has_weights: bool = True):
+    def __init__(
+        self,
+        *,
+        hidden_size: int,
+        eps: float,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+        has_weights: bool = True,
+    ):
         super().__init__()
         if has_weights:
             self.weight = nn.Parameter(
@@ -48,6 +65,7 @@ def forward(
             if isinstance(residual, torch.Tensor):
                 hidden_states = hidden_states + residual.to(torch.float32)
                 residual = hidden_states.to(input_dtype)
+
             variance = hidden_states.pow(2).mean(-1, keepdim=True)
             hidden_states = hidden_states * torch.rsqrt(variance +
                                                         self.variance_epsilon)