# Baichuan

This document shows how to build and run a Baichuan models (including `v1_7b`/`v1_13b`/`v2_7b`/`v2_13b`) in TensorRT-LLM on both single GPU and single node multi-GPU.

## Overview

The TensorRT-LLM Baichuan implementation can be found in [tensorrt_llm/models/baichuan/model.py](../../tensorrt_llm/models/baichuan/model.py). The TensorRT-LLM Baichuan example code is located in [`examples/baichuan`](./). There are three main files in that folder::

 * [`build.py`](./build.py) to build the [TensorRT](https://developer.nvidia.com/tensorrt) engine(s) needed to run the Baichuan model,
 * [`run.py`](./run.py) to run the inference on an input text,
 * [`summarize.py`](./summarize.py) to summarize the articles in the [cnn_dailymail](https://huggingface.co/datasets/cnn_dailymail) dataset using the model.

These scripts accept an argument named model_version, whose value should be `v1_7b`/`v1_13b`/`v2_7b`/`v2_13b` and the default value is `v1_13b`.

## Support Matrix
  * FP16
  * BF16
  * INT4 & INT8 Weight-Only
  * INT8 KV CACHE
  * INT8 Smooth Quant

## Usage

The TensorRT-LLM Baichuan example code locates at [examples/baichuan](./). It takes HF weights as input, and builds the corresponding TensorRT engines. The number of TensorRT engines depends on the number of GPUs used to run inference.

### Build TensorRT engine(s)

Need to specify the HF Baichuan checkpoint path. For `v1_13b`, you should use whether [baichuan-inc/Baichuan-13B-Chat](https://huggingface.co/baichuan-inc/Baichuan-13B-Chat) or [baichuan-inc/Baichuan-13B-Base](https://huggingface.co/baichuan-inc/Baichuan-13B-Base). For `v2_13b`, you should use whether [baichuan-inc/Baichuan2-13B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat) or [baichuan-inc/Baichuan2-13B-Base](https://huggingface.co/baichuan-inc/Baichuan2-13B-Base). More Baichuan models could be found on [baichuan-inc](https://huggingface.co/baichuan-inc).

TensorRT-LLM Baichuan builds TensorRT engine(s) from HF checkpoint. If no checkpoint directory is specified, TensorRT-LLM will build engine(s) with dummy weights.

Normally `build.py` only requires single GPU, but if you've already got all the GPUs needed while inferencing, you could enable parallelly building to make the engine building process faster by adding `--parallel_build` argument. Please note that currently `parallel_build` feature only supports single node.

Here're some examples that take `v1_13b` as example:

```bash
# Build a single-GPU float16 engine from HF weights.
# Enable the special TensorRT-LLM GPT Attention plugin (--use_gpt_attention_plugin) to increase runtime performance.
# 7B models should always add --use_gpt_attention_plugin since RoPE is only supported with GPTAttention plugin now.
# Try use_gemm_plugin to prevent accuracy issue.

# Build the Baichuan V1 13B model using a single GPU and FP16.
python build.py --model_version v1_13b \
                --model_dir baichuan-inc/Baichuan-13B-Chat \
                --dtype float16 \
                --use_gemm_plugin float16 \
                --use_gpt_attention_plugin float16 \
                --output_dir ./tmp/baichuan_v1_13b/trt_engines/fp16/1-gpu/

# Build the Baichuan V1 13B model using a single GPU and BF16.
python build.py --model_version v1_13b \
                --model_dir baichuan-inc/Baichuan-13B-Chat \
                --dtype bfloat16 \
                --use_gemm_plugin bfloat16 \
                --use_gpt_attention_plugin bfloat16 \
                --output_dir ./tmp/baichuan_v1_13b/trt_engines/bf16/1-gpu/

# Build the Baichuan V1 13B model using a single GPU and apply INT8 weight-only quantization.
python build.py --model_version v1_13b \
                --model_dir baichuan-inc/Baichuan-13B-Chat \
                --dtype float16 \
                --use_gemm_plugin float16 \
                --use_gpt_attention_plugin float16 \
                --use_weight_only \
                --output_dir ./tmp/baichuan_v1_13b/trt_engines/int8_weight_only/1-gpu/

# Build the Baichuan V1 13B model using a single GPU and apply INT4 weight-only quantization.
python build.py --model_version v1_13b \
                --model_dir baichuan-inc/Baichuan-13B-Chat \
                --dtype float16 \
                --use_gemm_plugin float16 \
                --use_gpt_attention_plugin float16 \
                --use_weight_only \
                --weight_only_precision int4 \
                --output_dir ./tmp/baichuan_v1_13b/trt_engines/int4_weight_only/1-gpu/

# Build Baichuan V1 13B using 2-way tensor parallelism.
python build.py --model_version v1_13b \
                --model_dir baichuan-inc/Baichuan-13B-Chat \
                --dtype float16 \
                --use_gemm_plugin float16 \
                --use_gpt_attention_plugin float16 \
                --output_dir ./tmp/baichuan_v1_13b/trt_engines/fp16/2-gpu/ \
                --world_size 2
```

#### INT8 weight only + INT8 KV cache
For INT8 KV cache, [`hf_baichuan_convert.py`](./hf_baichuan_convert.py) features a
`--calibrate-kv-cache, -kv` option. Setting `-kv` will calibrate the model,
and then export the scaling factors needed for INT8 KV cache inference.


Example:

```bash
python3 hf_baichuan_convert.py -i baichuan-inc/Baichuan-13B-Chat -o ./tmp/baichuan_v1_13b/int8_kv_cache/ --calibrate-kv-cache -t fp16
```

[`build.py`](./build.py) add new options for the support of INT8 KV cache.

`--int8_kv_cache` is the command-line option to enable INT8 KV cache.

In addition, it could be combined with INT8 weight-only quantization, as follows:

Examples of INT8 weight-only quantization + INT8 KV cache

```bash
# Build model with both INT8 weight-only and INT8 KV cache enabled
python build.py --model_version v1_13b \
                --bin_model_dir=./tmp/baichuan_v1_13b/int8_kv_cache/1-gpu/ \
                --dtype float16 \
                --use_gpt_attention_plugin float16 \
                --use_gemm_plugin float16 \
                --output_dir ./tmp/baichuan_v1_13b/trt_engines/int8_kv_cache_weight_only/1-gpu \
                --int8_kv_cache \
                --use_weight_only
```

#### SmoothQuant

The SmoothQuant supports all Baichuan model variants. Unlike the FP16 build where the HF weights are processed and loaded into the TensorRT-LLM directly, the SmoothQuant needs to load INT8 weights which should be pre-processed before building an engine.

Example:
```bash
python3 hf_baichuan_convert.py -i baichuan-inc/Baichuan-13B-Chat -o ./tmp/baichuan_v1_13b/sq0.8/ -sq 0.8 --tensor-parallelism 1 --storage-type fp16
```

[`build.py`](./build.py) add new options for the support of INT8 inference of SmoothQuant models.

`--use_smooth_quant` is the starting point of INT8 inference. By default, it
will run the model in the _per-tensor_ mode.

Then, you can add any combination of `--per-token` and `--per-channel` to get the corresponding behaviors.

Examples of build invocations:

```bash
# Build model for SmoothQuant in the _per_tensor_ mode.
python3 build.py --model_version v1_13b \
                 --bin_model_dir=./tmp/baichuan_v1_13b/sq0.8/1-gpu/ \
                 --use_smooth_quant \
                 --use_gpt_attention_plugin float16 \

# Build model for SmoothQuant in the _per_token_ + _per_channel_ mode
python3 build.py --model_version v1_13b \
                 --bin_model_dir=./tmp/baichuan_v1_13b/sq0.8/1-gpu/ \
                 --use_smooth_quant \
                 --use_gpt_attention_plugin float16 \
                 --per_token \
                 --per_channel
```

Note we use `--bin_model_dir` instead of `--model_dir` and `--meta_ckpt_dir` since SmoothQuant model needs INT8 weights and various scales from the binary files.

### Run

To run a TensorRT-LLM Baichuan model using the engines generated by build.py

```bash
# With fp16 inference
python run.py --model_version v1_13b \
              --max_output_len=50 \
              --tokenizer_dir baichuan-inc/Baichuan-13B-Chat \
              --engine_dir=./tmp/baichuan_v1_13b/trt_engines/fp16/1-gpu/

# With bf16 inference
python run.py --model_version v1_13b \
              --max_output_len=50 \
              --tokenizer_dir baichuan-inc/Baichuan-13B-Chat \
              --engine_dir=./tmp/baichuan_v1_13b/trt_engines/bf16/1-gpu/

# With INT8 weight-only quantization inference
python run.py --model_version v1_13b \
              --max_output_len=50 \
              --tokenizer_dir=baichuan-inc/Baichuan-13B-Chat \
              --engine_dir=./tmp/baichuan_v1_13b/trt_engines/int8_weight_only/1-gpu/

# With INT4 weight-only quantization inference
python run.py --model_version v1_13b \
              --max_output_len=50 \
              --tokenizer_dir=baichuan-inc/Baichuan-13B-Chat \
              --engine_dir=./tmp/baichuan_v1_13b/trt_engines/int8_weight_only/1-gpu/

# With 2-way tensor parallelism inference
mpirun -n 2 --allow-run-as-root \
    python run.py --model_version v1_13b \
                  --max_output_len=50 \
                  --tokenizer_dir=baichuan-inc/Baichuan-13B-Chat \
                  --engine_dir=./tmp/baichuan_v1_13b/trt_engines/fp16/2-gpu/
```

### Summarization using the Baichuan model

```bash
# Run summarization using the Baichuan V1 13B model in FP16.
python summarize.py --model_version v1_13b \
                    --test_trt_llm \
                    --hf_model_location baichuan-inc/Baichuan-13B-Chat \
                    --data_type fp16 \
                    --engine_dir ./tmp/baichuan_v1_13b/trt_engines/fp16/1-gpu/

# Run summarization using the Baichuan V1 13B model quantized to INT8.
python summarize.py --model_version v1_13b \
                    --test_trt_llm \
                    --hf_model_location baichuan-inc/Baichuan-13B-Chat \
                    --data_type fp16 \
                    --engine_dir ./tmp/baichuan_v1_13b/trt_engines/int8_weight_only/1-gpu/

# Run summarization using the Baichuan V1 13B model in FP16 using two GPUs.
mpirun -n 2 --allow-run-as-root \
    python summarize.py --model_version v1_13b \
                        --test_trt_llm \
                        --hf_model_location baichuan-inc/Baichuan-13B-Chat \
                        --data_type fp16 \
                        --engine_dir ./tmp/baichuan_v1_13b/trt_engines/fp16/2-gpu/
```

### Known Issues

 * The implementation of the Baichuan-7B model with INT8 Weight-Only and Tensor
   Parallelism greater than 2 might have accuracy issues. It is under
   investigation.