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https://github.com/NVIDIA/TensorRT-LLM.git
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Signed-off-by: nv-guomingz <137257613+nv-guomingz@users.noreply.github.com> Signed-off-by: Wangshanshan <30051912+dominicshanshan@users.noreply.github.com>
45 lines
1.5 KiB
Markdown
45 lines
1.5 KiB
Markdown
# PyTorch Backend
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```{note}
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Note:
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This feature is currently in beta, and the related API is subjected to change in future versions.
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```
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To enhance the usability of the system and improve developer efficiency, TensorRT LLM launches a new backend based on PyTorch.
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The PyTorch backend of TensorRT LLM is available in version 0.17 and later. You can try it via importing `tensorrt_llm._torch`.
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## Quick Start
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Here is a simple example to show how to use `tensorrt_llm.LLM` API with Llama model.
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```{literalinclude} ../../examples/llm-api/quickstart_example.py
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:language: python
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:linenos:
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```
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## Features
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- [Sampling](./torch/features/sampling.md)
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- [Quantization](./torch/features/quantization.md)
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- [Overlap Scheduler](./torch/features/overlap_scheduler.md)
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- [Feature Combination Matrix](./torch/features/feature_combination_matrix.md)
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## Developer Guide
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- [Architecture Overview](./torch/arch_overview.md)
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- [Adding a New Model](./torch/adding_new_model.md)
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## Key Components
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- [Attention](./torch/attention.md)
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- [KV Cache Manager](./torch/kv_cache_manager.md)
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- [Scheduler](./torch/scheduler.md)
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## Known Issues
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- The PyTorch backend on SBSA is incompatible with bare metal environments like Ubuntu 24.04. Please use the [PyTorch NGC Container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch) for optimal support on SBSA platforms.
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## Prototype Features
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- [AutoDeploy: Seamless Model Deployment from PyTorch to TensorRT LLM](./torch/auto_deploy/auto-deploy.md)
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