Latency increase when run on multi-GPU

[prd-tuong-nguyen]

System Info

I run your docker image in 2 cases:

• single gpu (--sharded false)
• multi-gpu (--sharded false --num_shard 4) => When I run single-gpu, the total time around 1.5 second and take ~21GG GPU, but when I run on multi-GPU, it take ~2.4second and 19GB/1GPU :( Seem the lower performance when run multi-gpu. Do you meet this problem?

{
  "model_id": "Open-Orca/Mistral-7B-OpenOrca",
  "adapter_id": "",
  "source": "hub",
  "adapter_source": "hub",
  "revision": null,
  "validation_workers": 2,
  "sharded": true,
  "num_shard": 4,
  "quantize": "BitsandbytesNF4",
  "dtype": null,
  "trust_remote_code": false,
  "max_concurrent_requests": 128,
  "max_best_of": 1,
  "max_stop_sequences": 4,
  "max_input_length": 2048,
  "max_total_tokens": 4096,
  "waiting_served_ratio": 1.2,
  "max_batch_prefill_tokens": 4096,
  "max_batch_total_tokens": 100000,
  "max_waiting_tokens": 20,
  "max_active_adapters": 10,
  "adapter_cycle_time_s": 2,
  "hostname": "0.0.0.0",
  "port": 8000,
  "shard_uds_path": "/tmp/lorax-server",
  "master_addr": "localhost",
  "master_port": 29500,
  "huggingface_hub_cache": "/data",
  "weights_cache_override": null,
  "disable_custom_kernels": false,
  "cuda_memory_fraction": 1,
  "json_output": true,
  "otlp_endpoint": null,
  "cors_allow_origin": [],
  "watermark_gamma": null,
  "watermark_delta": null,
  "ngrok": false,
  "ngrok_authtoken": null,
  "ngrok_edge": null,
  "env": false,
  "download_only": false
}

Information

• [X] Docker
• [ ] The CLI directly

Tasks

• [X] An officially supported command
• [ ] My own modifications

Reproduction

Run dokcer with --sharded true --num_shard 4

Expected behavior

Same or better performace when run multi-gpu

[tgaddair]

Hey @prd-tuong-nguyen, what kind of networking do you have between these GPUs? If they're using PCIe, the frequent communication between devices will likely cause performance to degrade. To get good performance on multiple GPUs, you typically need NVLink.

My recommendation to try to use a single GPU if possible, and only use multi-GPU if you have to due to memory constraints (for example, serving a 70b param model with 40GB of VRAM in fp16).

[prd-tuong-nguyen]

@tgaddair Thanks for your reply, I want to take advance of multi-GPU to increase the concurrency. So do you have any solution for this issue? Like if I run each instance on a GPU, what is the best solution to load balancer between them?

[tgaddair]

Hey @prd-tuong-nguyen, in your case I would recommend using data parallelism rather than model parallelism. Specifically, I would run one replica per GPU then put a load balancer in front of them using something like Kubernetes.

As for the best load balancing strategy, if you do not have enough load to keep the GPUs fully utilized, or the number of adapters you're using is relatively low (<25 or so), then I would suggest using a round robin load balancing strategy. That will keep the replicas equally busy, which should help keep latency low.

If, however, you're operating at very high scale, I would suggest using a load balancer with a consistent hashing policy based on the adapter ID, so that you can more efficiently batch together requests for same adapter and maximize throughput.

[prd-tuong-nguyen]

@tgaddair Thank you for your suggestion, I will try it <3

[bi1101]

Hi @prd-tuong-nguyen do you have some performance benchmark on running it with multiple GPUs setup in terms of though put?