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update docs for 0.20.0rc2 Signed-off-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
55 lines
1.8 KiB
Plaintext
55 lines
1.8 KiB
Plaintext
# PyTorch Backend
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```{note}
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Note:
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This feature is currently experimental, 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 experimental 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._torch.LLM` API with Llama model.
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```{literalinclude} ../../examples/pytorch/quickstart.py
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:language: python
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:linenos:
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```
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## Quantization
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The PyTorch backend supports FP8 and NVFP4 quantization. You can pass quantized models in HF model hub,
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which are generated by [TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer).
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```python
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from tensorrt_llm._torch import LLM
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llm = LLM(model='nvidia/Llama-3.1-8B-Instruct-FP8')
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llm.generate("Hello, my name is")
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```
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Or you can try the following commands to get a quantized model by yourself:
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```bash
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git clone https://github.com/NVIDIA/TensorRT-Model-Optimizer.git
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cd TensorRT-Model-Optimizer/examples/llm_ptq
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scripts/huggingface_example.sh --model <huggingface_model_card> --quant fp8 --export_fmt hf
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```
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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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- [Examples](../../examples/pytorch/README.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 workflow 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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