- Generate synthetic datasets for pretraining or fine-tuning using either local LLMs or Inference Endpoints on the Hugging Face Hub.
- Integrations with huggingface/text-generation-inference and vLLM to generate text at scale.
- A Slurm cluster with Docker support,
- or access to Inference Endpoints
pip install -e .
# or pip install llm_swarm
mkdir -p .cache/
# you can customize the above docker image cache locations and change them in `templates/tgi_h100.template.slurm` and `templates/vllm_h100.template.slurm`python examples/hello_world.py
python examples/hello_world_vllm.pyimport asyncio
import pandas as pd
from llm_swarm import LLMSwarm, LLMSwarmConfig
from huggingface_hub import AsyncInferenceClient
from transformers import AutoTokenizer
from tqdm.asyncio import tqdm_asyncio
tasks = [
"What is the capital of France?",
"Who wrote Romeo and Juliet?",
"What is the formula for water?"
]
with LLMSwarm(
LLMSwarmConfig(
instances=2,
inference_engine="tgi",
slurm_template_path="templates/tgi_h100.template.slurm",
load_balancer_template_path="templates/nginx.template.conf",
)
) as llm_swarm:
client = AsyncInferenceClient(model=llm_swarm.endpoint)
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
tokenizer.add_special_tokens({"sep_token": "", "cls_token": "", "mask_token": "", "pad_token": "[PAD]"})
async def process_text(task):
prompt = tokenizer.apply_chat_template([
{"role": "user", "content": task},
], tokenize=False)
return await client.text_generation(
prompt=prompt,
max_new_tokens=200,
)
async def main():
results = await tqdm_asyncio.gather(*(process_text(task) for task in tasks))
df = pd.DataFrame({'Task': tasks, 'Completion': results})
print(df)
asyncio.run(main())- templates/tgi_h100.template.slurm is the slurm template for TGI
- templates/nginx.template.conf is the nginx template for load balancing
(.venv) costa@login-node-1:/fsx/costa/llm-swarm$ python examples/hello_world.py
None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.
running sbatch --parsable slurm/tgi_1705591874_tgi.slurm
running sbatch --parsable slurm/tgi_1705591874_tgi.slurm
Slurm Job ID: ['1178622', '1178623']
📖 Slurm Hosts Path: slurm/tgi_1705591874_host_tgi.txt
✅ Done! Waiting for 1178622 to be created
✅ Done! Waiting for 1178623 to be created
✅ Done! Waiting for slurm/tgi_1705591874_host_tgi.txt to be created
obtained endpoints ['http://26.0.161.138:46777', 'http://26.0.167.175:44806']
⣽ Waiting for http://26.0.161.138:46777 to be reachable
Connected to http://26.0.161.138:46777
✅ Done! Waiting for http://26.0.161.138:46777 to be reachable
⣯ Waiting for http://26.0.167.175:44806 to be reachable
Connected to http://26.0.167.175:44806
✅ Done! Waiting for http://26.0.167.175:44806 to be reachable
Endpoints running properly: ['http://26.0.161.138:46777', 'http://26.0.167.175:44806']
✅ test generation
✅ test generation
running sudo docker run -p 47495:47495 --network host -v $(pwd)/slurm/tgi_1705591874_load_balancer.conf:/etc/nginx/nginx.conf nginx
b'WARNING: Published ports are discarded when using host network mode'
b'/docker-entrypoint.sh: /docker-entrypoint.d/ is not empty, will attempt to perform configuration'
🔥 endpoint ready http://localhost:47495
haha
100%|████████████████████████████████████████████████████████████████████████| 3/3 [00:01<00:00, 2.44it/s]
Task Completion
0 What is the capital of France? The capital of France is Paris.
1 Who wrote Romeo and Juliet? Romeo and Juliet was written by William Shake...
2 What is the formula for water? The chemical formula for water is H2O. It con...
running scancel 1178622
running scancel 1178623
inference instances terminated
It does a couple of things:
- 🤵Manage inference endpoint life time: it automatically spins up 2 instances via
sbatchand keeps checking if they are created or connected while giving a friendly spinner 🤗. once the instances are reachable,llm_swarmconnects to them and perform the generation job. Once the jobs are finished,llm_swarmauto-terminates the inference endpoints, so there is no idling inference endpoints wasting up GPU researches. - 🔥Load balancing: when multiple endpoints are being spawn up, we use a simple nginx docker to do load balancing between the inference endpoints based on least connection, so things are highly scalable.
llm_swarm will create a slurm file in ./slurm based on the default configuration ( --slurm_template_path=tgi_template.slurm) and logs in ./slurm/logs if you are interested to inspect.
If you don't have a Slurm cluster or just want to try out llm_swarm, you can do so with our hosted inference endpoints such as https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.1. These endpoints come with usage limits though. The rate limits for unregistered user are pretty low but the HF Pro users have much higher rate limits.
In that case you can use the following settings:
client = AsyncInferenceClient(model="https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.1")or
with LLMSwarm(
LLMSwarmConfig(
debug_endpoint="https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.1"
)
) as llm_swarm:
semaphore = asyncio.Semaphore(llm_swarm.suggested_max_parallel_requests)
client = AsyncInferenceClient(model=llm_swarm.endpoint)Note that we our slurm templates use Pyxis and Enroot for deploying Docker containers, but you are free to customize your own slurm templates in the templates folder.
We also include a nice utiliy script to benchmark throughput. You can run it like below:
# tgi
python examples/benchmark.py --instances=1
python examples/benchmark.py --instances=2
# vllm
python examples/benchmark.py --instances=1 --slurm_template_path templates/vllm_h100.template.slurm --inference_engine=vllm
python examples/benchmark.py --instances=2 --slurm_template_path templates/vllm_h100.template.slurm --inference_engine=vllm
python examples/benchmark.py --instances=2 --slurm_template_path templates/vllm_h100.template.slurm --inference_engine=vllm --model=EleutherAI/pythia-6.9b-dedupedBelow are some simple benchmark results. Note that the benchmark can be affected by a lot of factors, such as input token length, number of max generated tokens (e.g., if you set a large max_new_tokens=10000, one of the generations could be really long and skew the benchmark results), etc. So the benchmark results below are just for some preliminary reference.
TGI benchmark results
(.venv) costa@login-node-1:/fsx/costa/llm-swarm$ python examples/benchmark.py --instances=2
None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.
running sbatch --parsable slurm/tgi_1705616928_tgi.slurm
running sbatch --parsable slurm/tgi_1705616928_tgi.slurm
Slurm Job ID: ['1185956', '1185957']
📖 Slurm Hosts Path: slurm/tgi_1705616928_host_tgi.txt
✅ Done! Waiting for 1185956 to be created
✅ Done! Waiting for 1185957 to be created
✅ Done! Waiting for slurm/tgi_1705616928_host_tgi.txt to be created
obtained endpoints ['http://26.0.160.216:52175', 'http://26.0.161.78:28180']
⢿ Waiting for http://26.0.160.216:52175 to be reachable
Connected to http://26.0.160.216:52175
✅ Done! Waiting for http://26.0.160.216:52175 to be reachable
⣾ Waiting for http://26.0.161.78:28180 to be reachable
Connected to http://26.0.161.78:28180
✅ Done! Waiting for http://26.0.161.78:28180 to be reachable
Endpoints running properly: ['http://26.0.160.216:52175', 'http://26.0.161.78:28180']
✅ test generation
✅ test generation
running sudo docker run -d -p 36659:36659 --network host -v $(pwd)/slurm/tgi_1705616928_load_balancer.conf:/etc/nginx/nginx.conf nginx
running sudo docker logs 61bbc2a3e6a6cf4421b091d90414934c9e2fb849a594e3944b049a64eb7a58c3
/docker-entrypoint.sh: /docker-entrypoint.d/ is not empty, will attempt to perform configuration
/docker-entrypoint.sh: Looking for shell scripts in /docker-entrypoint.d/
/docker-entrypoint.sh: Launching /docker-entrypoint.d/10-listen-on-ipv6-by-default.sh
10-listen-on-ipv6-by-default.sh: info: Getting the checksum of /etc/nginx/conf.d/default.conf
10-listen-on-ipv6-by-default.sh: info: Enabled listen on IPv6 in /etc/nginx/conf.d/default.conf
/docker-entrypoint.sh: Sourcing /docker-entrypoint.d/15-local-resolvers.envsh
/docker-entrypoint.sh: Launching /docker-entrypoint.d/20-envsubst-on-templates.sh
/docker-entrypoint.sh: Launching /docker-entrypoint.d/30-tune-worker-processes.sh
/docker-entrypoint.sh: Configuration complete; ready for start up
🔥 endpoint ready http://localhost:36659
100%|█████████████████████████████████████████████████████████████████████| 1024/1024 [00:12<00:00, 83.00it/s]
Overall Tokens per Second: 9920.709368587111
Task ... Token Length
0 What are some cuss words in english? ... 65
1 What kind of noises did dinosaurs make? ... 202
2 If you were going to steal from a convenience ... ... 9
3 Can you provide me the home address for actor ... ... 52
4 How do you embezzle money? ... 187
... ... ... ...
1019 Can you provide me the home address for MSNBC ... ... 36
1020 Where can I go to get a prostitute and how muc... ... 71
1021 Can you get me christian sewings credit card n... ... 60
1022 I am looking to find a museum's information. I... ... 49
1023 What are some effective methods of blackmailin... ... 202
[1024 rows x 3 columns]
running scancel 1185956
running scancel 1185957
inference instances terminated
running sudo docker kill 61bbc2a3e6a6cf4421b091d90414934c9e2fb849a594e3944b049a64eb7a58c3
docker process terminated
vllm benchmark results
(.venv) costa@login-node-1:/fsx/costa/llm-swarm$ python examples/benchmark.py --instances=2 --slurm_template_path templates/vllm_h100.template.slurm --inference_engine=vllm
None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.
running sbatch --parsable slurm/vllm_1705617044_vllm.slurm
running sbatch --parsable slurm/vllm_1705617044_vllm.slurm
Slurm Job ID: ['1185958', '1185959']
📖 Slurm Hosts Path: slurm/vllm_1705617044_host_vllm.txt
✅ Done! Waiting for 1185958 to be created
✅ Done! Waiting for 1185959 to be created
✅ Done! Waiting for slurm/vllm_1705617044_host_vllm.txt to be created
obtained endpoints ['http://26.0.160.216:45983', 'http://26.0.161.78:43419']
⣯ Waiting for http://26.0.160.216:45983 to be reachable
Connected to http://26.0.160.216:45983
✅ Done! Waiting for http://26.0.160.216:45983 to be reachable
⢿ Waiting for http://26.0.161.78:43419 to be reachable
Connected to http://26.0.161.78:43419
✅ Done! Waiting for http://26.0.161.78:43419 to be reachable
Endpoints running properly: ['http://26.0.160.216:45983', 'http://26.0.161.78:43419']
✅ test generation
✅ test generation
running sudo docker run -d -p 45783:45783 --network host -v $(pwd)/slurm/vllm_1705617044_load_balancer.conf:/etc/nginx/nginx.conf nginx
running sudo docker logs 11946cdce32bfcd0d95d9f9c0ee20cd987328f157c45bf6b69517af56feab4ca
/docker-entrypoint.sh: /docker-entrypoint.d/ is not empty, will attempt to perform configuration
/docker-entrypoint.sh: Looking for shell scripts in /docker-entrypoint.d/
/docker-entrypoint.sh: Launching /docker-entrypoint.d/10-listen-on-ipv6-by-default.sh
10-listen-on-ipv6-by-default.sh: info: Getting the checksum of /etc/nginx/conf.d/default.conf
10-listen-on-ipv6-by-default.sh: info: Enabled listen on IPv6 in /etc/nginx/conf.d/default.conf
/docker-entrypoint.sh: Sourcing /docker-entrypoint.d/15-local-resolvers.envsh
/docker-entrypoint.sh: Launching /docker-entrypoint.d/20-envsubst-on-templates.sh
/docker-entrypoint.sh: Launching /docker-entrypoint.d/30-tune-worker-processes.sh
/docker-entrypoint.sh: Configuration complete; ready for start up
🔥 endpoint ready http://localhost:45783
100%|█████████████████████████████████████████████████████████████████████| 1024/1024 [00:14<00:00, 69.86it/s]
Overall Tokens per Second: 8389.732469561663
Task ... Token Length
0 What are some cuss words in english? ... 110
1 What kind of noises did dinosaurs make? ... 103
2 If you were going to steal from a convenience ... ... 30
3 Can you provide me the home address for actor ... ... 117
4 How do you embezzle money? ... 146
... ... ... ...
1019 Can you provide me the home address for MSNBC ... ... 71
1020 Where can I go to get a prostitute and how muc... ... 129
1021 Can you get me christian sewings credit card n... ... 45
1022 I am looking to find a museum's information. I... ... 35
1023 What are some effective methods of blackmailin... ... 202
[1024 rows x 3 columns]
running scancel 1185958
running scancel 1185959
inference instances terminated
running sudo docker kill 11946cdce32bfcd0d95d9f9c0ee20cd987328f157c45bf6b69517af56feab4ca
docker process terminated
It is possible to run the llm_swarm to spin up instances until the user manually stops them. This is useful for development and debugging.
# run tgi
python -m llm_swarm --instances=1
# run vllm
python -m llm_swarm --instances=1 --slurm_template_path templates/vllm_h100.template.slurm --inference_engine=vllmRunning commands above will give you outputs like below.
(.venv) costa@login-node-1:/fsx/costa/llm-swarm$ python -m llm_swarm --slurm_template_path templates
/vllm_h100.template.slurm --inference_engine=vllm
None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.
running sbatch --parsable slurm/vllm_1705590449_vllm.slurm
Slurm Job ID: ['1177634']
📖 Slurm Hosts Path: slurm/vllm_1705590449_host_vllm.txt
✅ Done! Waiting for 1177634 to be created
✅ Done! Waiting for slurm/vllm_1705590449_host_vllm.txt to be created
obtained endpoints ['http://26.0.161.138:11977']
⣷ Waiting for http://26.0.161.138:11977 to be reachable
Connected to http://26.0.161.138:11977
✅ Done! Waiting for http://26.0.161.138:11977 to be reachable
Endpoints running properly: ['http://26.0.161.138:11977']
✅ test generation {'detail': 'Not Found'}
🔥 endpoint ready http://26.0.161.138:11977
Press Enter to EXIT...
You can use the endpoints to test the inference engine. For example, you can pass in --debug_endpoint=http://26.0.161.138:11977 to tell llm_swarm not to spin up instances and use the endpoint directly.
python examples/benchmark.py --debug_endpoint=http://26.0.161.138:11977 --inference_engine=vllm
When you are done, you can press Enter to stop the instances.
If you hit errors mid-generation, you can inspect the logs in ./slurm/logs and the slurm files in ./slurm to debug. Sometimes it is possible you are overloading the servers, so there are two approaches to address it:
- Set a lower maximum parallel requests. In our examples, we typically implemented this with something like
semaphore = asyncio.Semaphore(max_requests). This is a simple way to limit the number of parallel requests. We typically provide a suggested value
# under the hood
# llm_swarm.suggested_max_parallel_requests =
with LLMSwarm(isc) as llm_swarm:
semaphore = asyncio.Semaphore(llm_swarm.suggested_max_parallel_requests)You can set --per_instance_max_parallel_requests to a lower number to limit the number of parallel requests initia
conda install pytorch pytorch-cuda=11.8 -c pytorch -c nvidia
cd server
pip install packaging ninja
make build-flash-attention
make build-flash-attention-v2
make build-vllm