Quantized checkpoint support in export and deploy modules

[janekl]

What does this PR do ?

Add support for testing, deploying and running quantized "qnemo" checkpoints in nemo.deploy and nemo.export modules.

Collection: NLP

Changelog

• qnemo support
• bump ammo version to 0.9.4
• update code for trt-llm 0.9.0
• Lambada evaluation setup
• timing of evaluation (with minor improvements)
• documentation

Usage

Building TensorRT-LLM engine and running a basic prompt:

from nemo.export import TensorRTLLM


trt_llm_exporter = TensorRTLLM(model_dir="/path/to/trt_llm_engine_folder")
trt_llm_exporter.export(
    nemo_checkpoint_path="/path/to/qnemo/ckpt.qnemo",
    model_type="llama",
    n_gpus=1,
)
output = trt_llm_exporter.forward(["Hi, how are you?", "I am good, thanks, how about you?"])

Running Lambada test locally:

python tests/export/test_nemo_export.py \
  --model_name local \
  --model_type llama \
  --checkpoint_dir /path/to/qnemo/ckpt.qnemo \
  --trt_llm_model_dir /path/to/trt_llm_engine_folder \
  --min_gpus 1 \
  --run_accuracy \
  --test_deployment True \
  --test_data_path lambada.json

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PR Type:

• New Feature
• Bugfix
• Documentation

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Additional Information

• Related to Open source export and deploy modules #8743

[janekl]

jenkins

[oyilmaz-nvidia]

LGTM. Thanks!