Lightning-AI/pytorch-lightning

Make lazy initialization in plugins more robust

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#7,650 opened on May 21, 2021

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Description

🚀 Feature

We currently set some attributes like sync_batch_norm, num_nodes, devices etc. lazily in the accelerator connector, so the user does not have to provide them at instantiation.

Example:

Trainer(gpus=4, plugins=DDPPlugin(find_unused_parameters=True))  # plugin may require gpus, num_nodes etc.

# AcceleratorConnector does this:
training_type_plugin.num_nodes = ...

This is fragile. Some attributes may have to be recomputed based on the order in which others are set.

Pitch

Provide one single lazy init method that takes all arguments required. The plugin is responsible for making sure dependencies are resolved in one place:

class DDPPlugin(...):

    def lazy_init(self, **kwargs):
         self.num_nodes = kwargs.get("num_nodes")
         self.num_processes = ...

With lazy_init, the _configure_launcher method (#11643) would become obsolete. It can be merged together into lazy_init.

Contributor guide

Research direction
Examine the current lazy initialization in accelerator connector and design a unified lazy init method for plugins that handles all attribute dependencies. Review related PRs like #11643.
Tech stack
pythonpytorch
Domain
backendmachine learning
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
2
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-3 hours
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
PythonPyTorch Lightning
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
75