# PyTorch Backend

```{note}
Note:
This feature is currently experimental, and the related API is subjected to change in future versions.
```

To enhance the usability of the system and improve developer efficiency, TensorRT-LLM launches a new experimental backend based on PyTorch.

The PyTorch backend of TensorRT-LLM is available in version 0.17 and later. You can try it via importing `tensorrt_llm._torch`.

## Quick Start

Here is a simple example to show how to use `tensorrt_llm.LLM` API with Llama model.

```{literalinclude} ../../examples/pytorch/quickstart.py
    :language: python
    :linenos:
```

## Quantization

The PyTorch backend supports FP8 and NVFP4 quantization. You can pass quantized models in HF model hub,
which are generated by [TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer).

```python
from tensorrt_llm import LLM
llm = LLM(model='nvidia/Llama-3.1-8B-Instruct-FP8')
llm.generate("Hello, my name is")
```

Or you can try the following commands to get a quantized model by yourself:

```bash
git clone https://github.com/NVIDIA/TensorRT-Model-Optimizer.git
cd TensorRT-Model-Optimizer/examples/llm_ptq
scripts/huggingface_example.sh --model <huggingface_model_card> --quant fp8 --export_fmt hf
```

## Sampling

The PyTorch backend supports most of the sampling features that are supported on the C++ backend, such as temperature, top-k and top-p sampling, stop words, bad words, penalty, context and generation logits, and log probs.

In order to use this feature, it is necessary to enable option `enable_trtllm_sampler` in the `LLM` class, and pass a `SamplingParams` object with the desired options as well. The following example prepares two identical prompts which will give different results due to the sampling parameters chosen:

```python
from tensorrt_llm import LLM
llm = LLM(model='nvidia/Llama-3.1-8B-Instruct-FP8',
          enable_trtllm_sampler=True)
sampling_params = SamplingParams(
        temperature=1.0,
        top_k=8,
        top_p=0.5,
    )
llm.generate(["Hello, my name is",
            "Hello, my name is"], sampling_params)
```

When using speculative decoders such as MTP or Eagle-3, the `enable_trtllm_sampler` option is not yet supported and therefore the subset of sampling options available is more restricted.

## Developer Guide

- [Architecture Overview](./torch/arch_overview.md)
- [Adding a New Model](./torch/adding_new_model.md)
- [Examples](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/pytorch/README.md)

## Key Components

- [Attention](./torch/attention.md)
- [KV Cache Manager](./torch/kv_cache_manager.md)
- [Scheduler](./torch/scheduler.md)

## Known Issues

- 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.
