Slow performance of Categorify operation on Triton Inference Server

[rahuljantwal-8451]

Description

When running an NVTabular workflow with Categorify operations in Triton Inference Server, the performance is significantly slow when dealing with high cardinality data.

Environment

• Merlin Tensorflow Container
• 23.12

Steps to Reproduce

1. Generate a High Cardinality Dataset using generate_dataset.py
2. Process the dataset using NVTabular process_dataset.py
3. Export the NVTabular workflow as a Triton ensemble export_ensemble.py
4. Run the Triton server

tritonserver --model-repository=./ensemble/

Expected Behavior

The Categorify operation should perform efficiently, with category data being cached between requests, resulting in performance similar to that observed in a Jupyter notebook environment.

Actual Behavior

The Categorify operation is slow, with each request taking as long as the first request, suggesting that category data is not being effectively cached between requests.

Results

Below are the result based on benchmarking script - encode.sh

Cardinality	Ensemble Triton	TransformWorkflow Jupyter
50	30 ms	38 ms
5k	30 ms	43 ms
5M	1270 ms	88.8 ms
50M	15833 ms	550 ms