internlm/internlm-chat-20b-4bit

LMDeploy supports LLM model inference of 4-bit weight, with the minimum requirement for NVIDIA graphics cards being sm80, such as A10, A100, Geforce 30/40 series.

Before proceeding with the inference of internlm-chat-20b-4bit, please ensure that lmdeploy is installed.

pip install 'lmdeploy>=0.0.11'

Inference

Please download internlm-chat-20b-4bit model as follows,

git-lfs install
git clone https://huggingface.co/internlm/internlm-chat-20b-4bit

As demonstrated in the command below, first convert the model's layout using turbomind.deploy, and then you can interact with the AI assistant in the terminal

# Convert the model's layout and store it in the default path, ./workspace.
python3 -m lmdeploy.serve.turbomind.deploy \
    --model-name internlm-chat-20b \
    --model-path ./internlm-chat-20b-4bit \
    --model-format awq \
    --group-size 128

# inference
python3 -m lmdeploy.turbomind.chat ./workspace

Serve with gradio

If you wish to interact with the model via web UI, please initiate the gradio server as indicated below:

python3 -m lmdeploy.serve.gradio.app ./workspace --server_name {ip_addr} --server_port {port}

Subsequently, you can open the website http://{ip_addr}:{port} in your browser and interact with the model.

Besides serving with gradio, there are two more serving methods. One is serving with Triton Inference Server (TIS), and the other is an OpenAI-like server named as api_server.

Please refer to the user guide for detailed information if you are interested.

Inference Performance

LMDeploy provides scripts for benchmarking token throughput and request throughput.

token throughput tests the speed of generating new tokens, given a specified number of prompt tokens and completion tokens, while request throughput measures the number of requests processed per minute with real dialogue data.

We conducted benchmarks on internlm-chat-20b-4bit. And token_throughput was measured by setting 256 prompt tokens and generating 512 tokens in response on A100-80G.

Note: The session_len in workspace/triton_models/weights/config.ini is changed to 2056 in our test.

batch	tensor parallel	prompt_tokens	completion_tokens	thr_per_proc(token/s)	rpm (req/min)	mem_per_proc(GB)
1	1	256	512	88.77	-	15.65
16	1	256	512	792.7	220.23	51.46

token throughput

Run the following command,

python benchmark/profile_generation.py \
  --model-path ./workspace \
  --concurrency 1 8 16 --prompt-tokens 256 512 512 1024 --completion-tokens 512 512 1024 1024
  --dst-csv ./token_throughput.csv

You will find the token_throughput metrics in ./token_throughput.csv

batch	prompt_tokens	completion_tokens	thr_per_proc(token/s)	thr_per_node(token/s)	rpm(req/min)	mem_per_proc(GB)	mem_per_gpu(GB)	mem_per_node(GB)
1	256	512	88.77	710.12	-	15.65	15.65	125.21
1	512	512	83.89	671.15	-	15.68	15.68	125.46
1	512	1024	80.19	641.5	-	15.68	15.68	125.46
1	1024	1024	72.34	578.74	-	15.75	15.75	125.96
1	1	2048	80.69	645.55	-	15.62	15.62	124.96
8	256	512	565.21	4521.67	-	32.37	32.37	258.96
8	512	512	489.04	3912.33	-	32.62	32.62	260.96
8	512	1024	467.23	3737.84	-	32.62	32.62	260.96
8	1024	1024	383.4	3067.19	-	33.06	33.06	264.46
8	1	2048	487.74	3901.93	-	32.12	32.12	256.96
16	256	512	792.7	6341.6	-	51.46	51.46	411.71
16	512	512	639.4	5115.17	-	51.93	51.93	415.46
16	512	1024	591.39	4731.09	-	51.93	51.93	415.46
16	1024	1024	449.11	3592.85	-	52.06	52.06	416.46
16	1	2048	620.5	4964.02	-	51	51	407.96

request throughput

LMDeploy uses ShareGPT dataset to test request throughput. Try the next commands, and you will get the rpm (request per minute) metric.

# download the ShareGPT dataset
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json

# 
python profile_throughput.py \
 ShareGPT_V3_unfiltered_cleaned_split.json \
 ./workspace \
 --concurrency 16