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Confusion about Deepspeed Inference

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
20/100
Issue type
Documentation
Clarity
Needs clarification
Activity status
Stale
Tech stack
python

Research direction

The issue names deepspeed.initialize, deepspeed.init_inference, MII, Zero-3, CPU offload, and 70B LLaMA inference. Start by comparing the documented inference paths and their memory and model-parallelism behavior; done would be a documented answer covering the three questions and current best practices.

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Description

Hi, I read the deepspeed docs and have the following confusion:

(1) What's the difference between these methods when in inferencing LLMs?

a. deepspeed.initialize and then write code to generate text

b. deepspeed.init_inference then write code to generate

c. use mii to inference

(2) Which of them are friendly for memory? For example, I want to inference 70b models, which of them support model parallelism that separates model parameters across gpus?

(3) For inference, what's the best practice now for inferencing 70b llama?

a. zero3 + cpu offload (1*a100)

b. zero3 (2*a100)

...

Thank you!

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Python
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