TinyChat 2.0: Efficient and Lightweight Chatbot with AWQ

We introduce TinyChat, a cutting-edge chatbot interface designed for lightweight resource consumption and fast inference speed on GPU platforms. It allows for seamless deployment on consumer-level GPUs such as 3090/4090 and low-power edge devices like the NVIDIA Jetson Orin, empowering users with a responsive conversational experience like never before.

The current release supports:

Llama-3-8B/70B-instruct;
NVILA-3B/8B;
VILA-1.5-3B/8B/13B/40B;
VILA-7B/13B;
LLaVA-7B/13B;
Llama-2-7B/13B-chat;
Vicuna;

Examples

Thanks to AWQ, TinyChat can now deliver more prompt responses through 4-bit inference. The following examples showcase that TinyChat's W4A16 generation is up to 2.7x faster on RTX 4090 and 2.9x faster on Jetson Orin, compared to the FP16 baselines. (Tested with LLaMA-3-8b model.)

TinyChat with LLaMA-3-8b on RTX 4090 (2.7x faster than FP16):

TinyChat with LLaMA-3-8b on Jetson Orin (2.9x faster than FP16):

TinyChat also supports inference with visual language models (e.g., VILA, LLaVA, NVILA). In the following examples, W4A16 quantized models from VILA family are launched with TinyChat.

TinyChat with NVILA-8B on RTX 4090 (single-image inputs):

TinyChat with NVILA-8B on RTX 4090 (multi-image inputs):

TinyChat with video reasoning:

AvVDsFBc6bA.0.mp4

Prompt: What might be the next step according to the video?

Answer: The next step in the video could be to place the shaped dough onto a baking sheet and let it rise before baking.

Online demo: https://vila.mit.edu

Speed Benchmarks

We benchmark TinyChat on NVIDIA RTX 4090 (desktop GPU), Orin (edge GPU), and A100 (server-class GPU).

We use the default implementation from Huggingface for the FP16 baseline. The INT4 implementation applies AWQ and utilizes our fast W4A16 GPU kernel. We also apply additional optimization techniques in the latest release. For example, we fuse all the operations in MHA/GQA/MQA into a single kernel, and fuse positional embedding kernels into the attention kernel. We also pre-allocate key-value caches to avoid the online memory allocation overhead from Huggingface. For W4A16 GEMM, we introduce FP16 accumulation when applicable for higher throughputs.

Decoding Speed

We benchmarked the per-token generation latency for the decoding stage in the following tables.

RTX 4090 Results

Model	FP16 latency (ms)	INT4 latency (ms)	Speedup
LLaMA-3-8B	17.07	6.39	2.69x
LLaMA-2-7B	15.50	5.28	2.94x
LLaMA-2-13B	OOM	9.19	--
Vicuna-7B	15.81	5.33	2.97x
VILA-7B	17.09	5.95	2.87x
VILA-13B	OOM	10.01	--
NVILA-2B	5.26	4.27	1.23x
NVILA-8B	16.12	5.97	2.70x

*: For the decoding speed of language models, we follow the benchmarking setting from exLLaMA (i.e. only 4 context tokens) for the sake of simplicity and fairness. For multi-modal LMs (VILA and NVILA), we benchmark the decoding speed with single image inputs. Specifically, for NVILA, we activate the lite mode during the benchmarking, where each image is correspond to 128 input tokens.

Jetson Orin Results

Model	FP16 latency (ms)	INT4 latency (ms)	Speedup
LLaMA-3-8B	96.00	32.53	2.95x
LLaMA-2-7B	83.95	25.94	3.24x
LLaMA-2-13B	162.33	47.67	3.41x
Vicuna-7B	84.77	26.34	3.22x
VILA-7B	86.95	28.09	3.10x
VILA-13B	OOM	57.14	--
NVILA-2B	24.22	22.25	1.09x
NVILA-8B	86.24	30.48	2.83x

A100 Results

Model	FP16 latency (ms)	INT4 latency (ms)	Speedup
LLaMA-3-8B	12.37	6.29	1.96x
LLaMA-2-7B	10.77	5.71	1.89x
LLaMA-2-13B	19.08	7.90	2.41x
Vicuna-7B	10.54	5.87	1.80x
VILA-7B	13.35	5.92	2.26x
VILA-13B	19.64	8.63	2.28x
NVILA-2B	7.03	5.38	1.31x
NVILA-8B	11.90	5.50	2.16x

Prefilling Speed

In TinyChat 2.0, we also introduce significant prefilling speed optimizations for Large Language Models (LLMs) and Visual Language Models (VLMs). Specifically, with the integration of latest flash attention and FP16 accumulation in GEMM kernels, TinyChat now achieves state-of-the-art prefilling speed on edge devices.

RTX 4090 Results

Time-To-First-Token (TTFT) of Llama-3-8B (Unit: Seconds):

Seq Len	256	512	1024	2048	3072	4096
FP16	0.031	0.055	0.109	0.211	0.336	0.446
TinyChat	0.021	0.033	0.064	0.131	0.200	0.275
Speedup	1.52x	1.68x	1.69x	1.61x	1.68x	1.62x

Time-To-First-Token (TTFT) of Llama-2-7B (Unit: Seconds):

Seq Len	256	512	1024	2048	3072	4096
FP16	0.029	0.058	0.100	0.211	0.329	0.441
TinyChat	0.018	0.031	0.060	0.124	0.193	0.265
Speedup	1.57x	1.83x	1.66x	1.70x	1.70x	1.66x

Jetson Orin Results

Time-To-First-Token (TTFT) of Llama-3-8B (Unit: Seconds):

Seq Len	256	512	1024	2048	3072	4096
FP16	0.206	0.399	0.566	1.519	2.308	3.114
TinyChat	0.166	0.315	0.623	1.248	1.907	2.573
Speedup	1.24x	1.26x	0.91x	1.22x	1.21x	1.21x

Comparison with Other Systems

Time-To-First-Token (TTFT) of 4-bit weight-only quantized Llama3-8B on RTX 4090 across various systems (Unit: Seconds):

Seq Len	256	512	1024	2048	4096
TensorRT-LLM	0.027	0.051	0.100	0.204	0.421
MLC	0.028	0.042	0.081	0.166	0.350
llama.cpp	0.026	0.045	0.086	0.175	0.375
ExLlama v2	0.040	0.051	0.077	0.139	0.294
TinyChat (Legacy)	0.031	0.051	0.101	0.219	0.461
TinyChat 2.0	0.021	0.033	0.065	0.132	0.278

Our approach outperforms all existing projects, achieving state-of-the-art speed.

Context Streaming: Efficient Multi-round Dialogues

In TinyChat 2.0, we introduce chunk-prefilling optimization for multi-round dialogues. For multi-turn inputs, TinyChat will reuse the KV Cache from previous conversations without recomputing them. This optimization eliminates redundant computations and significantly reduce the Time To First Token (TTFT) for subsequent interaction rounds.

RTX 4090 Results

To evaluate Context Streaming, we measure the TTFT in multi-round conversations with a fixed question length of 32 tokens, and varying history lengths from 16 to 1024 tokens. Specifically, in TinyChat 2.0, all history tokens are already prefilled to the existing KV Cache when processing the current query, while baseline systems recompute the history tokens for each query.

Time-To-First-Token (TTFT) of Llama-3-8B (Unit: ms):

History length	16	32	64	128	256	512	1024
FP16	21.49	21.38	23.51	40.82	47.15	75.41	162.27
TinyChat (Legacy)	15.20	14.89	17.61	29.66	44.11	72.50	163.90
TinyChat 2.0	14.30	14.05	14.05	14.43	14.38	14.35	14.49
Speedup	1.54x	1.54x	1.69x	2.84x	3.33x	5.27x	11.45x

Accuracy Evaluation

AWQ also achieves decent performance on the Visual Language Models. We evaluate AWQ on VILA and the lastest NVILA models.

NVILA-8B	AI2D	ChartQA	DocVQA	MMMU_val	SEED	TextVQA	VideoMME
FP16	91.0	84.8	91.7	50.7	76.3	78.1	63.9
AWQ-INT4	90.9	83.3	89.2	49.3	76.2	78.2	62.1

VILA-1.5-3B	VQA-v2	GQA	VizWiz	ScienceQA	TextVQA	POPE	MME	MMBench	MMBench-CN	SEED
FP16	80.4	61.5	53.5	69.0	60.4	85.9	1442.4	63.4	52.7	60.9
AWQ-INT4	80.0	61.1	53.8	67.8	60.4	85.9	1437.3	63.3	51.4	59.8

VILA-1.5-8B	VQA-v2	GQA	VizWiz	ScienceQA	TextVQA	POPE	MME	MMBench	MMBench-CN	SEED
FP16	80.9	61.9	58.7	79.9	66.3	84.4	1577.01	72.3	66.2	64.2
AWQ-INT4	80.3	61.7	59.3	79.0	65.4	82.9	1593.65	71.0	64.9	64.0

VILA-1.5-13B	VQA-v2	GQA	VizWiz	ScienceQA	TextVQA	POPE	MME	MMBench	MMBench-CN	SEED
FP16	82.8	64.3	62.6	80.1	65.0	86.3	1569.55	74.9	66.3	65.1
AWQ-INT4	82.7	64.5	63.3	79.7	64.7	86.7	1531.35	74.7	66.7	65.1

VILA-1.5-40B	VQA-v2	GQA	VizWiz	ScienceQA	TextVQA	POPE	MME	MMBench	MMBench-CN	SEED
FP16	84.3	64.6	62.2	87.2	73.6	87.3	1726.82	82.4	80.2	69.1
AWQ-INT4	84.1	64.4	61.3	86.7	73.2	88.2	1714.79	83.2	79.6	68.9

Usage

Please follow the AWQ installation guidance to install AWQ and its dependencies. If you want to use FlashAttention, start by installing it with: pip install flash-attn --no-build-isolation. However, for some GPUs such as Jetson Orin, there is no pre-built version available. You will need to build it from source. Follow these commands:

git clone https://github.com/Dao-AILab/flash-attention.git
cd flash-attention
sed -i '168 a\    cc_flag.append("-gencode")\n\    cc_flag.append("arch=compute_87,code=sm_87")' setup.py
python setup.py install

This process may take some time as it involves compiling the code. Additionally, please note that these commands are just for Jetson Orin GPUs, whose CUDA compute capability is 87. For other GPUs, you may use nvidia-smi --query-gpu=compute_cap --format=csv to get the compute capability and merely change '87' to that.

Download the pretrained instruction-tuned LLMs:
- For LLaMA-2-chat, please refer to this link;
- For Vicuna, please refer to this link;
- For MPT-chat, please refer to this link;
- For Falcon-instruct, please refer to this link.
Quantize instruction-tuned LLMs with AWQ:

We provide pre-computed AWQ search results for multiple model families, including LLaMA, OPT, Vicuna, VILA, and LLaVA. To get the pre-computed AWQ search results, run:

# git lfs install  # install git lfs if not already
git clone https://huggingface.co/datasets/mit-han-lab/awq-model-zoo awq_cache

You may run a one-line starter below:

./scripts/llama2_demo.sh

Alternatively, you may go through the process step by step. We will demonstrate the quantization process with LLaMA-2. For all other models except Falcon, one only needs to change the model_path and saving locations. For Falcon-7B, we also need to change q_group_size from 128 to 64.

Perform AWQ search and save search results (we already did it for you):

mkdir awq_cache
python -m awq.entry --model_path /PATH/TO/LLAMA2/llama-2-7b-chat \
    --w_bit 4 --q_group_size 128 \
    --run_awq --dump_awq awq_cache/llama-2-7b-chat-w4-g128.pt

Generate real quantized weights (INT4):

mkdir quant_cache
python -m awq.entry --model_path /PATH/TO/LLAMA2/llama-2-7b-chat \
    --w_bit 4 --q_group_size 128 \
    --load_awq awq_cache/llama-2-7b-chat-w4-g128.pt \
    --q_backend real --dump_quant quant_cache/llama-2-7b-chat-w4-g128-awq.pt

Run the TinyChat demo:

cd tinychat
python demo.py --model_type llama \
    --model_path /PATH/TO/LLAMA2/llama-2-7b-chat \
    --q_group_size 128 --load_quant quant_cache/llama-2-7b-chat-w4-g128-awq.pt \ 
    --precision W4A16

Note: if you use Falcon-7B-instruct, please remember to also change q_group_size to 64. You may also run the following command to execute the chatbot in FP16 to compare the speed and quality of language generation:

python demo.py --model_type llama \
    --model_path /PATH/TO/LLAMA2/llama-2-7b-chat \
    --precision W16A16

You can now try using FlashAttention along with chunk prefilling. Use the following two arguments when running demo: --flash --chunk_prefilling.

The above command works well for most cloud and desktop GPUs, since their CPU and GPU memory space are separated. However, for edge GPUs with shared host and device memory, in order to run larger models (e.g. LLaMA-2-70B on 64GB Orin), it is necessary to break down the pretrained checkpoints into small pieces:

python split_ckpt.py --input_path quant_cache/llama-2-7b-chat-w4-g128-awq.pt \
    --output_path quant_cache/llama-2-7b-chat-w4-g128-awq

Then, to run the demo, one can use the following command. The only changes compared with the demo command above are:

We modify the load_quant argument;
We introduce another flag mem_efficient_load.

cd tinychat
python demo.py --model_type llama \
    --model_path /PATH/TO/LLAMA2/llama-2-7b-chat \
    --q_group_size 128 --load_quant quant_cache/llama-2-7b-chat-w4-g128-awq \ 
    --precision W4A16 --mem_efficient_load

(Optional) Run the benchmark script to get TTFT and decoding throughput:

cd tinychat
python benchmark.py --flash \
    --context_length 16 32 64 128 256 512 1024 2048 \
    --model_path /PATH/TO/LLAMA2/llama-2-7b-chat --precision W4A16

To benchmark chunk prefilling, use:

python benchmark.py --chunk_prefilling \
    --model_path /PATH/TO/LLAMA2/llama-2-7b-chat \
    --question_length 32 --context_length 16 32 64 128 256 512 1024 --precision W4A16

Note: The kv caches in the current implementation are pre-allocated. So if you run out of memory, it might be the case that the kv cache is too large. To solve the problem, you may pass in --max_seq_len [a smaller number].

Support Visual Language Models (VILA-1.5, VILA, LLaVA, NVILA)

Our TinyChat also supports visual language models. Follow the instructions below to run VLMs on your own devices!

Step 1-3 are same as the deployment for Language-only models.

Follow the AWQ installation guidance to install AWQ and its dependencies.
Download the pretrained VLMs (VILA).
Quantize the VLMs with AWQ and get the quantized checkpoint in quant_cache. We also provide a sample script for this step.
Run the TinyChat demo for VLMs (with vila15_demo.py for VILA-1.5, vila10_demo.py for VILA and LLaVA):

cd tinychat
python vila15_demo.py \
    --model-path /PATH/TO/VILA/VILA-1.5-13B \
    --quant-path quant_cache/vila-1.5-13b-w4-g128-awq.pt \ 
    --precision W4A16 \
    --image-file /PATH/TO/INPUT/IMAGE \
    --vis-image #Optional

Alternatively, one may also skip the quantization process and directy download the quantized VILA-1.5 checkpoints from here. Take VILA-1.5-13B as an example, after running:

cd tinychat
git clone https://huggingface.co/Efficient-Large-Model/VILA1.5-13b-AWQ

One may run:

python vila15_demo.py \
    --model-path VILA1.5-13b-AWQ \
    --quant-path VILA1.5-13b-AWQ/llm \ 
    --precision W4A16 \
    --image-file /PATH/TO/INPUT/IMAGE \
    --vis-image #Optional

to run the terminal demo directly. You can also use --flash --chunk_prefilling to accelerate. We also support context stage benckmarking for VILA.

python benchmark_context.py --flash --chunk_prefilling     \
    --model_path PATH/TO/Llama-3-VILA1.5-8B     \
    --question_length 32 --context_length 16 32 64 128 256 512 1024 \
    --model_type vila --quant

Note: if you enable --vis-image mode, TinyChat will print input images directly in your terminal. You may need to install termvisage to enable this mode. A terminal emulator is also required.

Note: VILA model family supports multi-image inputs. You can input multiple images in /PATH/TO/INPUT/IMAGE above, each image should be seperated by ,.

TinyChat support NVILA now! We adopt W8A8 SmoothQuant for VisionTower and W4A16 quantization for LLM, achieving 1.3x -3.3x speedup for prefiling satge and nearly 1.5x higher throughput. You can use the commands below to prepare the your model and try four basic tasks of NVILA-video model. To prepared the needed act scale for awq and smoothquant, please run:

python -m awq.entry --model_path PATH/TO/NVILA \
    --smooth_scale --media_path https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-VL/space_woaudio.mp4  \
    --act_scale_path awq_cache/NVILA-VT-smooth-scale.pt --vila-20 \
    --w_bit 4 --q_group_size 128 \
    --run_awq --dump_awq awq_cache/NVILA.pt

Then, please generate real quantized LLM with:

python -m awq.entry --model_path PATH/TO/NVILA/llm \
    --w_bit 4 --q_group_size 128  \
    --load_awq awq_cache/NVILA.pt \
    --q_backend real --dump_quant quant_cache/NVILA-w4-g128-awq.pt --vila-20

Next, try chatting with it using the command below to experience shorter Time To First Token (TTFT) and higher decoding throughput.

python nvila_demo.py --model-path EPATH/TO/NVILA       \
    --quant_path PATH/TO/NVILA-w4-g128-v2.pt      \
    --media PATH/TO/MEDIA    \
    --act_scale_path PATH/TO/NVILA-smooth-scale.pt \
    --quant_llm --chunk --model_type nvila

Team

TinyChat is developed by the following wonderful team:

Shang Yang: Project Lead, TinyChat v1 and v2 Lead;
Haotian Tang: Project Lead, TinyChat v1 Lead, v2 Mentor;
Yuming Lou: TinyChat v2 Lead;
Junxian Guo: TinyChat v2 Contributor;
Song Han: Project Advisor.

Credits also go to AWQ algorithm leads: Ji Lin and Jiaming Tang.

Reference

TinyChat is inspired by the following open-source projects: FasterTransformer, FlashAttention, vLLM, FastChat, llama_cu_awq, LLaVA, termvisage.

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README.md

README.md

TinyChat 2.0: Efficient and Lightweight Chatbot with AWQ

Contents

Examples

Speed Benchmarks

Decoding Speed

RTX 4090 Results

Jetson Orin Results

A100 Results

Prefilling Speed

RTX 4090 Results

Jetson Orin Results

Comparison with Other Systems

Context Streaming: Efficient Multi-round Dialogues

RTX 4090 Results

Accuracy Evaluation

Usage

Support Visual Language Models (VILA-1.5, VILA, LLaVA, NVILA)

Team

Reference

Files

README.md

Latest commit

History

README.md

File metadata and controls

TinyChat 2.0: Efficient and Lightweight Chatbot with AWQ

Contents

Examples

Speed Benchmarks

Decoding Speed

RTX 4090 Results

Jetson Orin Results

A100 Results

Prefilling Speed

RTX 4090 Results

Jetson Orin Results

Comparison with Other Systems

Context Streaming: Efficient Multi-round Dialogues

RTX 4090 Results

Accuracy Evaluation

Usage

Support Visual Language Models (VILA-1.5, VILA, LLaVA, NVILA)

Team

Reference