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Description Describe the bug
When we start serve_reward_model.py and run annotation, the server goes down during processing. It will crash on specific samples. These samples have a long context.
error.log
What we did
We built the source, but the issue has not been solved.
We also tried nvidia/Llama2-13B-SteerLM-RM, but ran into the same issue.
It runs without an issue on nvcr.io/nvidia/nemo:24.05.01 (critic speedup #219 is the main difference.).
The estimated processing time has also increased from 2 hours (nvcr.io/nvidia/nemo:24.05.01) to 7 hours (nvcr.io/nvidia/nemo:24.07).
Steps/Code to reproduce bug
export HYDRA_FULL_ERROR=1
export MODEL="/workspace/models/Llama3-70B-SteerLM-RM"
python /opt/NeMo-Aligner/examples/nlp/data/steerlm/preprocess_openassistant_data.py --output_directory=data/oasst
python /opt/NeMo-Aligner/examples/nlp/gpt/serve_reward_model.py \
rm_model_file=${MODEL} \
trainer.num_nodes=1 \
trainer.devices=8 \
++model.tensor_model_parallel_size=8 \
++model.pipeline_model_parallel_size=1 \
inference.inference_micro_batch_size=2 \
inference.port=1424
python /opt/NeMo-Aligner/examples/nlp/data/steerlm/attribute_annotate.py \
--input-file=data/oasst/train.jsonl \
--output-file=data/oasst/train_labeled.jsonl \
--port=1424
Before run attribute_annotate.py, you should apply #350
Expected behavior
The process is completed without the server going down.
Environment overview (please complete the following information)
DGX-C A100 * 8
nvcr.io/nvidia/nemo:24.07
Environment details
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
OS version
PyTorch version
Python version
Additional context
Add any other context about the problem here.
Example: GPU model
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Describe the bug
When we start
serve_reward_model.pyand run annotation, the server goes down during processing. It will crash on specific samples. These samples have a long context.error.log
What we did
nvidia/Llama2-13B-SteerLM-RM, but ran into the same issue.nvcr.io/nvidia/nemo:24.05.01(critic speedup #219 is the main difference.).nvcr.io/nvidia/nemo:24.05.01) to 7 hours (nvcr.io/nvidia/nemo:24.07).Steps/Code to reproduce bug
Before run
attribute_annotate.py, you should apply #350Expected behavior
The process is completed without the server going down.
Environment overview (please complete the following information)
nvcr.io/nvidia/nemo:24.07Environment details
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
Additional context
Add any other context about the problem here.
Example: GPU model