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convert_checkpoint.py	convert_checkpoint.py
requirements.txt	requirements.txt

Qwen

This document shows how to build and run a Qwen model in TensorRT-LLM on both single GPU, single node multi-GPU.

Qwen

Overview

The TensorRT-LLM Qwen implementation can be found in model.py. The TensorRT-LLM Qwen example code is located in examples/qwen. There is one main file:

convert_checkpoint.py to build the TensorRT engine(s) needed to run the Qwen model.

In addition, there are two shared files in the parent folder examples for inference and evaluation:

../run.py to run the inference on an input text;
../summarize.py to summarize the articles in the cnn_dailymail dataset.

Support Matrix

Model Name	FP16	FMHA	WO	AWQ	GPTQ	SQ	TP	PP	ST	C++ Runtime	benchmark	IFB	Arch
Qwen-7B(-Chat)	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Ampere+
Qwen-14B(-Chat)	Y	Y	Y	Y*	Y	Y	Y	Y	Y	Y	Y	Y	Ampere+
Qwen-72B(-Chat)	Y	Y	Y	-	Y	Y	Y	Y	Y	Y	Y	Y	Ampere+

*Please note that Qwen-14B-Chat model supports AWQ only with single GPU.

Model Name: the name of the model, the same as the name on HuggingFace
FMHA: Fused MultiHead Attention
WO: Weight Only Quantization (int8 / int4)
AWQ: Activation Aware Weight Quantization (int4)
GPTQ: Generative Pretrained Transformer Quantization (int4)
SQ: Smooth Quantization
TP: Tensor Parallel
PP: Pipeline Parallel
ST: Strongly Typed
IFB: In-flight Batching

*Currently Qwen models does not support dynamic NTK and logn attention. Therefore, accuracy on long sequence input for the 7B and 14B model is not promised.

Usage

The TensorRT-LLM Qwen example code locates at examples/qwen. 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.

Download model weights

Install the dependency packages and setup git-lfs.

# Install dependencies
pip install -r requirements.txt

# Setup git-lfs
git lfs install

Download one or more Qwen models that you would like to build to TensorRT-LLM engines. You may download from the HuggingFace hub:

git clone https://huggingface.co/Qwen/Qwen-7B-Chat   ./tmp/Qwen/7B
git clone https://huggingface.co/Qwen/Qwen-14B-Chat  ./tmp/Qwen/14B
git clone https://huggingface.co/Qwen/Qwen-72B-Chat  ./tmp/Qwen/72B

Or download from the ModelScope hub:

git clone https://www.modelscope.cn/qwen/Qwen-7B-Chat.git   ./tmp/Qwen/7B
git clone https://www.modelscope.cn/qwen/Qwen-14B-Chat.git  ./tmp/Qwen/14B
git clone https://www.modelscope.cn/qwen/Qwen-72B-Chat.git  ./tmp/Qwen/72B

Build TensorRT engine(s)

The convert_checkpoint.py script converts HF weights to TensorRT-LLM checkpoints.

The trtllm-build command builds TensorRT-LLM engines from TensorRT-LLM checkpoints. The number of engine files is also same to the number of GPUs used to run inference.

Normally trtllm-build 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 --workers argument. Please note that currently workers feature only supports single node.

Here're some examples:

# Build a single-GPU float16 engine from HF weights.
# Try --gemm_plugin to prevent accuracy issue.

# Build the Qwen-7B-Chat model using a single GPU and FP16.
python convert_checkpoint.py --model_dir ./tmp/Qwen/7B/ \
                              --output_dir ./tllm_checkpoint_1gpu_fp16 \
                              --dtype float16

trtllm-build --checkpoint_dir ./tllm_checkpoint_1gpu_fp16 \
            --output_dir ./tmp/qwen/7B/trt_engines/fp16/1-gpu \
            --gemm_plugin float16

# Build the Qwen-7B-Chat model using a single GPU and BF16.
python convert_checkpoint.py --model_dir ./tmp/Qwen/7B/ \
                              --output_dir ./tllm_checkpoint_1gpu_bf16 \
                              --dtype bfloat16

trtllm-build --checkpoint_dir ./tllm_checkpoint_1gpu_bf16 \
            --output_dir ./tmp/qwen/7B/trt_engines/bf16/1-gpu \
            --gpt_attention_plugin bfloat16 \
            --gemm_plugin bfloat16

# Build the Qwen-7B-Chat model using a single GPU and apply INT8 weight-only quantization.
python convert_checkpoint.py --model_dir ./tmp/Qwen/7B/ \
                              --output_dir ./tllm_checkpoint_1gpu_fp16_wq \
                              --dtype float16 \
                              --use_weight_only \
                              --weight_only_precision int8

trtllm-build --checkpoint_dir ./tllm_checkpoint_1gpu_fp16_wq \
            --output_dir ./tmp/qwen/7B/trt_engines/weight_only/1-gpu/ \
            --gemm_plugin float16

# Build the Qwen-7B-Chat model using a single GPU and apply INT4 weight-only quantization.
python convert_checkpoint.py --model_dir ./tmp/Qwen/7B/ \
                              --output_dir ./tllm_checkpoint_1gpu_fp16_wq \
                              --dtype float16 \
                              --use_weight_only \
                              --weight_only_precision int4

trtllm-build --checkpoint_dir ./tllm_checkpoint_1gpu_fp16_wq \
            --output_dir ./tmp/qwen/7B/trt_engines/weight_only/1-gpu/ \
            --gemm_plugin float16

# Build Qwen-7B-Chat using 2-way tensor parallelism.
python convert_checkpoint.py --model_dir ./tmp/Qwen/7B/ \
                            --output_dir ./tllm_checkpoint_2gpu_tp2 \
                            --dtype float16 \
                            --tp_size 2

trtllm-build --checkpoint_dir ./tllm_checkpoint_2gpu_tp2 \
            --output_dir ./tmp/qwen/7B/trt_engines/fp16/2-gpu/ \
            --gemm_plugin float16

# Build Qwen-7B-Chat using 2-way tensor parallelism and 2-way pipeline parallelism.
python convert_checkpoint.py --model_dir ./tmp/Qwen/7B/ \
                            --output_dir ./tllm_checkpoint_4gpu_tp2_pp2 \
                            --dtype float16 \
                            --tp_size 2 \
                            --pp_size 2
trtllm-build --checkpoint_dir ./tllm_checkpoint_4gpu_tp2_pp2 \
            --output_dir ./tmp/qwen/7B/trt_engines/fp16/4-gpu/ \
            --gemm_plugin float16

# Build Qwen-14B-Chat using 2-way tensor parallelism.
python convert_checkpoint.py --model_dir ./tmp/Qwen/14B/ \
                            --output_dir ./tllm_checkpoint_2gpu_tp2 \
                            --dtype float16 \
                            --tp_size 2

trtllm-build --checkpoint_dir ./tllm_checkpoint_2gpu_tp2 \
            --output_dir ./tmp/qwen/14B/trt_engines/fp16/2-gpu/ \
            --gemm_plugin float16

# Build Qwen-72B-Chat using 8-way tensor parallelism.
python convert_checkpoint.py --model_dir ./tmp/Qwen/72B/ \
                            --output_dir ./tllm_checkpoint_8gpu_tp8 \
                            --dtype float16 \
                            --tp_size 8

trtllm-build --checkpoint_dir ./tllm_checkpoint_8gpu_tp8 \
            --output_dir ./tmp/qwen/72B/trt_engines/fp16/8-gpu/ \
            --gemm_plugin float16

INT8 KV cache

INT8 KV cache could be enabled to reduce memory footprint. It will bring more performance gains when batch size gets larger.

For INT8 KV cache, convert_checkpoint.py features a --int8_kv_cache option. Setting --int8_kv_cache will calibrate the model, and then export the scaling factors needed for INT8 KV cache inference. Remember to set --strongly_typed when building the engine if you are not using INT8 weight only quantization at the same time.

Example:

python convert_checkpoint.py --model_dir ./tmp/Qwen/7B/   \
                             --output_dir ./tllm_checkpoint_1gpu_fp16_int8kv
                             --dtype float16  \
                             --int8_kv_cache

trtllm-build --checkpoint_dir ./tllm_checkpoint_1gpu_sq \
             --output_dir ./engine_outputs \
             --strongly_typed \
             --gemm_plugin float16

convert_checkpoint.py add new options for the support of INT8 KV cache.

SmoothQuant

The smoothquant supports Qwen models. 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:

python3 convert_checkpoint.py --model_dir ./tmp/Qwen/7B/ --output_dir ./tllm_checkpoint_1gpu_sq --dtype float16 --smoothquant 0.5
trtllm-build --checkpoint_dir ./tllm_checkpoint_1gpu_sq \
             --output_dir ./engine_outputs \
             --gemm_plugin float16

convert_checkpoint.py add new options for the support of INT8 inference of SmoothQuant models.

--smoothquant 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:

# Build model for SmoothQuant in the _per_token_ + _per_channel_ mode
python3 convert_checkpoint.py --model_dir ./tmp/Qwen/7B/ \
                              --output_dir ./tllm_checkpoint_1gpu_sq \
                              --dtype float16 \
                              --smoothquant 0.5 \
                              --per_token \
                              --per_channel

trtllm-build --checkpoint_dir ./tllm_checkpoint_1gpu_sq \
             --output_dir ./engine_outputs \
             --gemm_plugin float16

INT4-GPTQ

You may find the official GPTQ quantized INT4 weights of Qwen-7B-Chat here: Qwen-7B-Chat-Int4. And you need to first install auto-gptq:

pip install auto-gptq

Example of building engine for INT4 GPTQ quantized Qwen model:

python3 convert_checkpoint.py --model_dir ./tmp/Qwen-7B-Chat-Int4 \
                              --output_dir ./tllm_checkpoint_1gpu_gptq \
                              --dtype float16 \
                              --use_weight_only \
                              --weight_only_precision int4_gptq \
                              --per_group \

trtllm-build --checkpoint_dir ./tllm_checkpoint_1gpu_gptq \
                --output_dir ./tmp/Qwen/7B/trt_engines/int4_GPTQ/1-gpu/ \
                --gemm_plugin float16

INT4-AWQ

To run the AWQ Qwen example, the following steps are required:

Weight quantization

NVIDIA AMMO toolkit is used for AWQ weight quantization. Please see examples/quantization/README.md for AMMO installation instructions.

# Quantize Qwen-7B-Chat checkpoint into INT4 AWQ format
python ../quantization/quantize.py --model_dir ./tmp/Qwen/7B/ \
                                   --dtype float16 \
                                   --qformat int4_awq \
                                   --awq_block_size 128 \
                                   --output_dir ./quantized_int4-awq \
                                   --calib_size 32

Build TRT-LLM engine:

trtllm-build --checkpoint_dir ./quantized_int4-awq \
             --output_dir ./tmp/qwen/7B/trt_engines/int4_AWQ/1-gpu/ \
             --gemm_plugin float16

Run

To run a TensorRT-LLM Qwen model using the engines generated by trtllm-build

# With fp16 inference
python3 ../run.py --input_text "你好，请问你叫什么？" \
                  --max_output_len=50 \
                  --tokenizer_dir ./tmp/Qwen/7B/ \
                  --engine_dir=./tmp/Qwen/7B/trt_engines/fp16/1-gpu/

# With bf16 inference
python3 ../run.py --input_text "你好，请问你叫什么？" \
                  --max_output_len=50 \
                  --tokenizer_dir ./tmp/Qwen/7B/ \
                  --engine_dir=./tmp/Qwen/7B/trt_engines/bf16/1-gpu

# With int8 weight only inference
python3 ../run.py --input_text "你好，请问你叫什么？" \
                  --max_output_len=50 \
                  --tokenizer_dir ./tmp/Qwen/7B/ \
                  --engine_dir=./tmp/Qwen/7B/trt_engines/int8_weight_only/1-gpu/

Input [Text 0]: "<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
你好，请问你叫什么？<|im_end|>
<|im_start|>assistant
"
Output [Text 0 Beam 0]: "你好，我是来自阿里云的大规模语言模型，我叫通义千问。<|im_end|>
<|im_start|>
<|im_start|>

"

# With int4 weight only inference
python3 ../run.py --input_text "你好，请问你叫什么？" \
                  --max_output_len=50 \
                  --tokenizer_dir ./tmp/Qwen/7B/ \
                  --engine_dir=./tmp/Qwen/7B/trt_engines/int4_weight_only/1-gpu/

Input [Text 0]: "<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
你好，请问你叫什么？<|im_end|>
<|im_start|>assistant
"
Output [Text 0 Beam 0]: "我叫通义千问，是由阿里云开发的预训练语言模型。<|im_end|>
"

# With INT4 GPTQ quantization
python3 ../run.py --input_text "你好，请问你叫什么？" \
                  --max_output_len=50 \
                  --tokenizer_dir ./tmp/Qwen-7B-Chat-Int4 \
                  --engine_dir=./tmp/Qwen/7B/trt_engines/int4_GPTQ/1-gpu/

Input [Text 0]: "<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
你好，请问你叫什么？<|im_end|>
<|im_start|>assistant
"
Output [Text 0 Beam 0]: "你好，我是通义千问，由阿里云开发。<|im_end|>
"

# With INT4 AWQ quantization
python3 ../run.py --input_text "你好，请问你叫什么？" \
                  --max_output_len=50 \
                  --tokenizer_dir ./tmp/Qwen/7B/ \
                  --engine_dir=./tmp/Qwen/7B/trt_engines/int4_AWQ/1-gpu/

Input [Text 0]: "<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
你好，请问你叫什么？<|im_end|>
<|im_start|>assistant
"
Output [Text 0 Beam 0]: "你好，我是通义千问，由阿里云开发。<|im_end|>
"

# Run 72B model with 8-gpu
mpirun -n 8 --allow-run-as-root \
    python ../run.py --input_text "What is your name?" \
                     --max_output_len=50 \
                     --tokenizer_dir ./tmp/Qwen/72B/ \
                     --engine_dir=./tmp/Qwen/72B/trt_engines/fp16/8-gpu/

Input [Text 0]: "<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
What is your name?<|im_end|>
<|im_start|>assistant
"
Output [Text 0 Beam 0]: "I am QianWen, a large language model created by Alibaba Cloud."

Summarization using the Qwen model

# Run summarization using the Qwen 7B model in FP16.
python ../summarize.py --test_trt_llm \
                       --hf_model_dir ./tmp/Qwen/7B/ \
                       --data_type fp16 \
                       --engine_dir ./tmp/Qwen/7B/trt_engines/fp16/1-gpu/ \
                       --max_input_length 2048 \
                       --output_len 2048

# Run summarization using the Qwen 7B model in BF16.
python ../summarize.py --test_trt_llm \
                       --hf_model_dir ./tmp/Qwen/7B/ \
                       --data_type fp16 \
                       --engine_dir ./tmp/Qwen/7B/trt_engines/bf16/1-gpu/ \
                       --max_input_length 2048 \
                       --output_len 2048

# Run summarization using the Qwen 7B model quantized to INT8.
python ../summarize.py --test_trt_llm \
                       --hf_model_dir  ./tmp/Qwen/7B/ \
                       --data_type fp16 \
                       --engine_dir ./tmp/Qwen/7B/trt_engines/int8_weight_only/1-gpu/ \
                       --max_input_length 2048 \
                       --output_len 2048

# Run summarization using the Qwen 7B model quantized to INT4.
python ../summarize.py --test_trt_llm \
                       --hf_model_dir  ./tmp/Qwen/7B/ \
                       --data_type fp16 \
                       --engine_dir ./tmp/Qwen/7B/trt_engines/int4_weight_only/1-gpu/ \
                       --max_input_length 2048 \
                       --output_len 2048

# Run summarization using the Qwen 7B model in FP16 using two GPUs.
mpirun -n 2 --allow-run-as-root \
    python ../summarize.py --test_trt_llm \
                           --hf_model_dir  ./tmp/Qwen/7B/ \
                           --data_type fp16 \
                           --engine_dir ./tmp/Qwen/7B/trt_engines/fp16/2-gpu/ \
                           --max_input_length 2048 \
                           --output_len 2048

# Run summarization using the Qwen 14B model in FP16 using two GPUs.
mpirun -n 2 --allow-run-as-root \
    python ../summarize.py --test_trt_llm \
                           --hf_model_dir  ./tmp/Qwen/14B/ \
                           --data_type fp16 \
                           --engine_dir ./tmp/Qwen/14B/trt_engines/fp16/2-gpu/ \
                           --max_input_length 2048 \
                           --output_len 2048

Demo output of summarize.py:

python ../summarize.py --test_trt_llm \
                       --hf_model_dir ./tmp/Qwen/7B/ \
                       --data_type fp16 \
                       --engine_dir ./tmp/Qwen/7B/trt_engines/fp16/1-gpu/ \
                       --max_input_length 2048 \
                       --output_len 2048

[11/09/2023-02:21:10] [TRT-LLM] [I] Load tokenizer takes: 0.4043385982513428 sec
Downloading builder script: 100%|███████████████████████████████████████████| 9.27k/9.27k [00:00<00:00, 35.4MB/s]
Downloading and preparing dataset cnn_dailymail/3.0.0 to /root/.cache/huggingface/datasets/ccdv___cnn_dailymail/3
......
[11/09/2023-02:23:33] [TRT-LLM] [I]
 Highlights : ['James Best, who played the sheriff on "The Dukes of Hazzard," died Monday at 88 .\n"Hazzard" ran from 1979 to 1985 and was among the most popular shows on TV .']
[11/09/2023-02:23:33] [TRT-LLM] [I]
 Summary : [['Actor James Best, known for his portrayal of bumbling sheriff Rosco P. Coltrane on TV\'s "The Dukes of Hazzard," has died at 88 after a brief illness. Best\'s career spanned decades in theater and Hollywood, but it was his role in "The Dukes of Hazzard" that made him a household name. The show ran for seven seasons from 1979 to 1985 and became a hit on TV, spawning TV movies, an animated series and video games. Best\'s portrayal of Rosco was beloved by fans for his childlike enthusiasm and goofy catchphrases. He is survived by friends and colleagues who paid tribute to him on social media.']]
[11/09/2023-02:23:33] [TRT-LLM] [I] ---------------------------------------------------------
load rouge ...
Downloading builder script: 5.60kB [00:00, 18.9MB/s]
load rouge done
[11/09/2023-02:24:06] [TRT-LLM] [I] TensorRT-LLM (total latency: 30.13867211341858 sec)
[11/09/2023-02:24:06] [TRT-LLM] [I] TensorRT-LLM beam 0 result
[11/09/2023-02:24:06] [TRT-LLM] [I]   rouge1 : 26.35215119137573
[11/09/2023-02:24:06] [TRT-LLM] [I]   rouge2 : 9.507814774384485
[11/09/2023-02:24:06] [TRT-LLM] [I]   rougeL : 18.171982659482865
[11/09/2023-02:24:06] [TRT-LLM] [I]   rougeLsum : 21.10413175647868

Credits

This Qwen model example exists thanks to Tlntin ([email protected]) and zhaohb ([email protected]).

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README.md

Qwen

Overview

Support Matrix

Usage

Download model weights

Build TensorRT engine(s)

INT8 KV cache

SmoothQuant

INT4-GPTQ

INT4-AWQ

Run

Summarization using the Qwen model

Credits

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README.md

Qwen

Overview

Support Matrix

Usage

Download model weights

Build TensorRT engine(s)

INT8 KV cache

SmoothQuant

INT4-GPTQ

INT4-AWQ

Run

Summarization using the Qwen model

Credits