-
Notifications
You must be signed in to change notification settings - Fork 60
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Underloaded Neuron Cores with Llama3 #672
Comments
@dlptv, thank you for your feedback. I see that you added a token in the code snippet you submitted: this is not safe to publicly share hugginface tokens, even with low rights, so please invalidate it as soon as possible. The reason why your cores are not used at full capacity is because you are using a batch size of 1, meaning that requests are processed in sequence instead of being parallelized. Try with a batch_size of 10 and you will see a difference. |
This issue is stale because it has been open 30 days with no activity. Remove stale label or comment or this will be closed in 5 days. |
System Info
Who can help?
@dacorvo
Information
Tasks
examples
folder (such as GLUE/SQuAD, ...)Reproduction (minimal, reproducible, runnable)
I use optimum-cli to export Llama3 model from the official repository with this command:
optimum-cli export neuron --model meta-llama/Meta-Llama-3-8B
--batch_size 1
--sequence_length 8192
--auto_cast_type fp16 `
--num_cores 24
/data/llama3_neuron/
I use the same image for that as I use to run TGI: ghcr.io/huggingface/neuronx-tgi:0.0.23
Then I run the TGI container with this command:
docker run -p 8080:80
--rm
-it
-v $(pwd)/data:/data
--device=/dev/neuron0
--device=/dev/neuron1
--device=/dev/neuron2
--device=/dev/neuron3
--device=/dev/neuron4
--device=/dev/neuron5
--device=/dev/neuron6
--device=/dev/neuron7
--device=/dev/neuron8
--device=/dev/neuron9
--device=/dev/neuron10
--device=/dev/neuron11
--privileged
-e HF_TOKEN=...
-e HF_AUTO_CAST_TYPE="fp16"
-e HF_NUM_CORES=24
ghcr.io/huggingface/neuronx-tgi:latest
--model-id /data/llama3_neuron
--max-batch-size 1
--max-input-length 3164
--max-total-tokens 8192
I run simple load tests with locust, emulating 10 users sending concurrent requests of variable length, and see that all neuron cores are loaded around 60% only and the performance is far from ideal (0.1-0.2 rps):
Expected behavior
Neuron cores are loaded closely to 100%.
The text was updated successfully, but these errors were encountered: