chore: import upstream snapshot with attribution

2026-07-13 13:36:55 +08:00
commit c8a779b1bb
1887 changed files with 3245738 additions and 0 deletions
@@ -0,0 +1,71 @@
+# GroupNormalizationPlugin [DEPRECATED]
+
+**This plugin is deprecated since TensorRT 10.12 and will be removed in a future release. No alternatives are planned to be provided.**
+
+**Table Of Contents**
+- [Description](#description)
+    * [Non-support for Blackwell and later platforms](#non-support-for-blackwell-and-later-platforms)
+    * [Structure](#structure)
+- [Parameters](#parameters)
+- [Additional Resources](#additional-resources)
+- [License](#license)
+- [Changelog](#changelog)
+- [Known Issues](#known-issues)
+
+## Description
+
+The `GroupNormalizationPlugin` implements Group Normalization, which divides channels into groups and computes normalization statistics within each group. This is particularly useful for vision models where batch sizes may be small.
+
+### Non-support for Blackwell and later platforms
+
+As of TensorRT 10.7, usage of this plugin is not supported on Blackwell or later platforms.
+This plugin can be replaced by TensorRT's native `INormalizationLayer`([C++](https://docs.nvidia.com/deeplearning/tensorrt/api/c_api/classnvinfer1_1_1_i_normalization_layer.html), [Python](https://docs.nvidia.com/deeplearning/tensorrt/operators/docs/Normalization.html)).
+
+
+**Note:** This plugin remains supported on pre-Blackwell platforms.
+
+### Structure
+
+The plugin takes three inputs:
+1. Input tensor with shape `[N, C, H, W]` (batch, channels, height, width), where `C` must be divisible by `num_groups`. (See [Parameters](#parameters) for more details on `num_groups`)
+2. Scale parameters (per-channel, shape `[C]`)
+3. Bias parameters (per-channel, shape `[C]`)
+
+It produces one output with the same dimensions as the input. The normalization is computed as:
+```
+group_mean = mean(input, group)
+group_var = variance(input, group)
+output = gamma (input - group_mean) / sqrt(group_var + epsilon) + beta
+```
+
+Key differences from Instance Normalization:
+- Normalizes across channel groups rather than individual channels
+- More stable for small batch sizes
+- Groups channels to capture cross-channel dependencies
+
+## Parameters
+
+| Parameter    | Type    | Description |
+|--------------|---------|-------------|
+| `epsilon`    | float   | Small value added to variance for numerical stability (default: 1e-5) |
+| `num_groups` | int32   | Number of groups to split channels into; must evenly divide C |
+
+## Additional Resources
+
+- **Original Paper**: [Group Normalization](https://arxiv.org/abs/1803.08494)
+- **ONNX Operator**: [GroupNormalization](https://github.com/onnx/onnx/blob/main/docs/Operators.md#GroupNormalization)
+- **TensorRT Documentation**: [INormalizationLayer](https://docs.nvidia.com/deeplearning/tensorrt/api/c_api/classnvinfer1_1_1_i_normalization_layer.html)
+- [Master README](../README.md) - Back to main documentation
+
+## License
+For terms and conditions for use, reproduction, and distribution, see the [TensorRT Software License Agreement](https://docs.nvidia.com/deeplearning/sdk/tensorrt-sla/index.html).
+
+## Changelog
+- **May 2025**: Add deprecation note.
+
+- **Feb 2025**: Initial release of this README, Deprecation and non-support notice added.
+
+## Known Issues
+- Limited to FP32 precision (native implementation supports mixed precision)
+- No NHWC layout support (native implementation supports multiple layouts)
+- Batch size must be known during network creation