text-generation-inference
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llama3-70B-Instruct-AWQ causing CUDA error: an illegal memory access was encountered
System Info
Hello Team,
I am using the following to load the AWQ quantized version on the LLama 3 model on a 4 x A100 GCP m/c. I cannot increase the --max-batch-prefill-tokens since I get the CUDA error: an illegal memory access was encountered. I also observe through nvidia-smi that it does not consume the entire GPU memory but still cause the illegal memory access error.
# Load LLama 3 casperhansen/llama-3-70b-instruct-awq
DOCKER_IMAGE=ghcr.io/huggingface/text-generation-inference:2.0.2
CONTAINER_NAME=eval_llama_3
HF_TOKEN=<my-token>
CUDA_VISIBLE_DEVICES=0,1,2,3
MODEL_ID="casperhansen/llama-3-70b-instruct-awq"
QUANTIZE=awq
VOLUME=~/.cache/huggingface/hub
docker run --rm \
--name ${CONTAINER_NAME} \
--shm-size 4g \
--env HUGGING_FACE_HUB_TOKEN=${HF_TOKEN} \
--env CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES \
-p 8080:80 \
-v ${VOLUME}:/data \
--gpus all \
$DOCKER_IMAGE \
--model-id ${MODEL_ID} \
--num-shard 4 \
--sharded true \
--max-concurrent-requests 3 \
--max-batch-prefill-tokens 24000 \
--max-stop-sequences 20 \
--trust-remote-code \
--quantize ${QUANTIZE}
The GPUs are not even utilized half way though
Wed May 8 09:32:39 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.154.05 Driver Version: 535.154.05 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA A100-SXM4-40GB Off | 00000000:00:04.0 Off | 0 |
| N/A 33C P0 58W / 400W | 13321MiB / 40960MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+----------------------+----------------------+
| 1 NVIDIA A100-SXM4-40GB Off | 00000000:00:05.0 Off | 0 |
| N/A 36C P0 75W / 400W | 13465MiB / 40960MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+----------------------+----------------------+
| 2 NVIDIA A100-SXM4-40GB Off | 00000000:00:06.0 Off | 0 |
| N/A 34C P0 71W / 400W | 13465MiB / 40960MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+----------------------+----------------------+
| 3 NVIDIA A100-SXM4-40GB Off | 00000000:00:07.0 Off | 0 |
| N/A 35C P0 70W / 400W | 13321MiB / 40960MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| 0 N/A N/A 26648 C /opt/conda/bin/python3.10 13312MiB |
| 1 N/A N/A 26649 C /opt/conda/bin/python3.10 13456MiB |
| 2 N/A N/A 26650 C /opt/conda/bin/python3.10 13456MiB |
| 3 N/A N/A 26652 C /opt/conda/bin/python3.10 13312MiB |
+---------------------------------------------------------------------------------------+
Information
- [ ] Docker
- [ ] The CLI directly
Tasks
- [ ] An officially supported command
- [ ] My own modifications
Reproduction
Steps are provided in the problem desription.
Expected behavior
The Model should load without exceptions.