[#6187][feat] add LayerNorm module #6625
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📝 WalkthroughWalkthroughA new Changes
Sequence Diagram(s)sequenceDiagram
participant Input
participant LayerNorm
participant Output
Input->>LayerNorm: forward(hidden_states, residual)
alt residual provided
LayerNorm->>LayerNorm: Add residual to hidden_states
end
LayerNorm->>LayerNorm: Compute mean and variance
LayerNorm->>LayerNorm: Normalize and apply weight/bias
LayerNorm->>Output: Return normalized output (and residual if provided)
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Actionable comments posted: 1
🔭 Outside diff range comments (1)
tensorrt_llm/_torch/modules/layer_norm.py (1)
78-78: Add missing docstrings for the LayerNorm class and methods.According to the coding guidelines, interfaces that may be used outside a file should prefer docstrings over comments. The LayerNorm class and its methods should have Google-style docstrings.
Add docstrings for the class and methods:
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__(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. + """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. + """
🧹 Nitpick comments (1)
tensorrt_llm/_torch/modules/layer_norm.py (1)
47-49: Consider implementing Flashinfer support for LayerNorm.While raising NotImplementedError is appropriate for now, consider adding Flashinfer support for LayerNorm in the future to maintain performance parity with RMSNorm.
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**/*.py
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Initialize all externally visible members of a Python class in the constructor.
For interfaces that may be used outside a file, prefer docstrings over comments in Python.
Comments in Python should be reserved for code within a function, or interfaces that are local to a file.
Use Google style docstrings for Python classes and functions, which can be parsed by Sphinx.
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Avoid using reflection in Python when functionality can be easily achieved without reflection.
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**/*.{cpp,h,cu,py}
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🔇 Additional comments (5)
tensorrt_llm/_torch/modules/rms_norm.py (2)
12-20: LGTM! Good formatting improvement.The constructor formatting with proper indentation and trailing commas enhances readability while maintaining the same functionality.
53-53: LGTM! Minor formatting improvement.The additional blank line improves code readability by visually separating the residual handling logic from the normalization computation.
tensorrt_llm/_torch/modules/layer_norm.py (3)
11-40: LGTM! Well-structured constructor with proper parameter handling.The constructor correctly implements configurable weight and bias parameters with appropriate fallbacks to buffers when disabled. The implementation follows the same pattern as RMSNorm for consistency.
57-62: LGTM! Correct LayerNorm implementation.The implementation correctly computes layer normalization using mean and variance along the last dimension, with proper numerical stability through epsilon. The dtype handling and residual support follow established patterns.
69-77: LGTM! Consistent skip_forward implementation.The skip_forward method correctly maintains the same signature and behavior as the RMSNorm implementation, providing consistency across normalization modules.
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## tests/unittest/_torch/test_layer_norm.py
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**/*.py
📄 CodeRabbit Inference Engine (CODING_GUIDELINES.md)
**/*.py: The code developed for TensorRT-LLM should conform to Python 3.8+.
Indent Python code with 4 spaces. Do not use tabs.
Always maintain the namespace when importing in Python, even if only one class or function from a module is used.
Python filenames should use snake_case (e.g., some_file.py).
Python classes should use PascalCase (e.g., class SomeClass).
Python functions and methods should use snake_case (e.g., def my_awesome_function():).
Python local variables should use snake_case. Prefix k for variable names that start with a number (e.g., k_99th_percentile).
Python global variables should use upper snake_case and prefix G (e.g., G_MY_GLOBAL).
Python constants should use upper snake_case (e.g., MY_CONSTANT).
Avoid shadowing variables declared in an outer scope in Python.
Initialize all externally visible members of a Python class in the constructor.
For interfaces that may be used outside a file, prefer docstrings over comments in Python.
Comments in Python should be reserved for code within a function, or interfaces that are local to a file.
Use Google style docstrings for Python classes and functions, which can be parsed by Sphinx.
Attributes and variables in Python can be documented inline; attribute docstrings will be rendered under the docstring for the class.
Avoid using reflection in Python when functionality can be easily achieved without reflection.
When using try-except blocks in Python, limit the except to the smallest set of errors possible.
When using try-except blocks to handle multiple possible variable types in Python, keep the body of the try as small as possible, using the else block to implement the logic.
Files:
tensorrt_llm/_torch/modules/layer_norm.py
**/*.{cpp,h,cu,py}
📄 CodeRabbit Inference Engine (CODING_GUIDELINES.md)
All TensorRT-LLM Open Source Software code should contain an NVIDIA copyright header that includes the current year. This includes .cpp, .h, .cu, .py, and any other source files which are compiled or interpreted.
Files:
tensorrt_llm/_torch/modules/layer_norm.py
🧠 Learnings (6)
📓 Common learnings
Learnt from: moraxu
PR: NVIDIA/TensorRT-LLM#6303
File: tests/integration/test_lists/qa/examples_test_list.txt:494-494
Timestamp: 2025-07-28T17:06:08.621Z
Learning: In TensorRT-LLM testing, it's common to have both CLI flow tests (test_cli_flow.py) and PyTorch API tests (test_llm_api_pytorch.py) for the same model. These serve different purposes: CLI flow tests validate the traditional command-line workflow, while PyTorch API tests validate the newer LLM API backend. Both are legitimate and should coexist.
📚 Learning: applies to **/*.{cpp,h,cu,py} : all tensorrt-llm open source software code should contain an nvidia ...
Learnt from: CR
PR: NVIDIA/TensorRT-LLM#0
File: CODING_GUIDELINES.md:0-0
Timestamp: 2025-08-05T07:49:09.875Z
Learning: Applies to **/*.{cpp,h,cu,py} : All TensorRT-LLM Open Source Software code should contain an NVIDIA copyright header that includes the current year. This includes .cpp, .h, .cu, .py, and any other source files which are compiled or interpreted.
Applied to files:
tensorrt_llm/_torch/modules/layer_norm.py
📚 Learning: applies to **/*.py : the code developed for tensorrt-llm should conform to python 3.8+....
Learnt from: CR
PR: NVIDIA/TensorRT-LLM#0
File: CODING_GUIDELINES.md:0-0
Timestamp: 2025-08-05T07:49:09.875Z
Learning: Applies to **/*.py : The code developed for TensorRT-LLM should conform to Python 3.8+.
Applied to files:
tensorrt_llm/_torch/modules/layer_norm.py
📚 Learning: applies to **/*.{h,hpp} : use a preprocessor guard in header files. the guard name must have prefix ...
Learnt from: CR
PR: NVIDIA/TensorRT-LLM#0
File: CODING_GUIDELINES.md:0-0
Timestamp: 2025-08-05T07:49:09.875Z
Learning: Applies to **/*.{h,hpp} : Use a preprocessor guard in header files. The guard name must have prefix TRTLLM_ followed by the filename, all in caps, and no trailing underscore.
Applied to files:
tensorrt_llm/_torch/modules/layer_norm.py
📚 Learning: in tensorrt-llm testing, it's common to have both cli flow tests (test_cli_flow.py) and pytorch api ...
Learnt from: moraxu
PR: NVIDIA/TensorRT-LLM#6303
File: tests/integration/test_lists/qa/examples_test_list.txt:494-494
Timestamp: 2025-07-28T17:06:08.621Z
Learning: In TensorRT-LLM testing, it's common to have both CLI flow tests (test_cli_flow.py) and PyTorch API tests (test_llm_api_pytorch.py) for the same model. These serve different purposes: CLI flow tests validate the traditional command-line workflow, while PyTorch API tests validate the newer LLM API backend. Both are legitimate and should coexist.
Applied to files:
tensorrt_llm/_torch/modules/layer_norm.py
📚 Learning: in tensorrt_llm/executor/worker.py, the lora adapter cache optimization logic that checks `is_adapte...
Learnt from: amitz-nv
PR: NVIDIA/TensorRT-LLM#5616
File: tensorrt_llm/executor/worker.py:375-384
Timestamp: 2025-07-17T09:01:27.402Z
Learning: In tensorrt_llm/executor/worker.py, the LoRA adapter cache optimization logic that checks `is_adapter_in_cpu_cache()` and conditionally passes None for weights/config has a known race condition issue that cannot be solved with simple error handling or verification checks. This is a known limitation that requires a more comprehensive solution.
Applied to files:
tensorrt_llm/_torch/modules/layer_norm.py
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🔇 Additional comments (4)
tensorrt_llm/_torch/modules/layer_norm.py (4)
1-20: LGTM! Proper copyright header and clean imports.The NVIDIA copyright header is correctly formatted with the current year, and imports follow the namespace convention as required by coding guidelines.
22-35: Excellent class definition and documentation.The class follows PascalCase naming convention and includes a comprehensive Google-style docstring that clearly documents the purpose and all parameters.
37-66: Well-designed constructor with proper parameter handling.The implementation correctly uses keyword-only arguments and handles both learnable parameters and non-learnable buffers appropriately. The use of
persistent=Falsefor buffers when weights/bias are disabled is a good design choice.
100-118: Clean skip_forward implementation.The method correctly maintains the same interface as
forwardwhile bypassing normalization computation. This provides a useful pattern for conditional normalization.
liji-nv
reviewed
Aug 6, 2025
yuxianq
approved these changes
Aug 6, 2025
Funatiq
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/bot run |
…sting parameter formatting Signed-off-by: Robin Kobus <[email protected]>
- Introduce a new LayerNorm class, implementing layer normalization with optional weights and biases. - The forward method handles input tensors and supports residual connections, while the skip_forward method allows bypassing normalization. - The implementation includes checks for Flashinfer availability. Signed-off-by: Robin Kobus <[email protected]>
Signed-off-by: Robin Kobus <[email protected]>
Signed-off-by: Robin Kobus <[email protected]>
…h models Signed-off-by: Robin Kobus <[email protected]>
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| hidden_states = hidden_states + residual.to(torch.float32) | ||
| residual = hidden_states.to(input_dtype) | ||
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| mean = hidden_states.mean(-1, keepdim=True) |
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This is an unfused vanilla implementation. Unfortunately flashinfer only provides RMSNorm, no LayerNorm.
Just curious, why not directly use torch.nn.LayerNorm? I am not sure whether there is an optimized kernel underneath the module though.
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PR_Github #14286 [ run ] completed with state |
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