[Models] Improve iteration over layers

[lgeiger]

Purpose

Slicing a torch.nn.ModuleList as done in self.layers[self.start_layer:self.end_layer] creates a new torch.nn.ModuleList. This is pretty fast but not free and can't be overlapped with GPU compute as it executes at the start of the Module.forward().

This also shows up in a model profile (this example uses eager mode):

This PR changes the iteration to use itertools.islice which removes this overhead since iteration doesn't create new module lists and can now be overlapped with GPU computation.

This only has a very minor performance impact. In a quick benchmark I'm seeing 0-0.9% faster time to first token and improved throughput though results are noisy. I don't think this hurts readability so I still think it's a worthwhile change.

Test Plan

Covered by existing unittest

Test Result

CI

[github-actions]

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[gemini-code-assist]

Summary of Changes

Hello @lgeiger, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request implements a minor performance optimization by changing how subsets of model layers are iterated over during the forward pass. Instead of creating a new list slice, it uses itertools.islice to provide an iterator over the required range of layers. This aims to reduce CPU overhead associated with list slicing, especially in pipeline parallel setups, potentially leading to small improvements in time to first token and throughput.

Highlights

• Performance Optimization: Replaced list slicing (self.layers[start:end]) with itertools.islice for iterating over model layers in the forward pass across multiple models. This avoids creating a new ModuleList object each time, reducing overhead.
• Pipeline Parallelism: The change is particularly relevant for pipeline parallelism, where iterating over a subset of layers happens frequently. Using islice allows iteration to potentially overlap better with GPU computation.

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[gemini-code-assist]

Code Review

This pull request introduces a well-reasoned optimization by replacing torch.nn.ModuleList slicing with itertools.islice for iterating over layers in various models. The goal is to reduce minor overhead associated with creating new ModuleList objects during each forward pass, potentially offering a small performance improvement.

The changes are consistently applied across numerous model files. The core modification involves:

1. Importing islice from itertools.
2. Replacing expressions like self.layers[self.start_layer:self.end_layer] with islice(self.layers, self.start_layer, self.end_layer).

This change is functionally equivalent for iteration purposes and, as noted in the description, should not negatively impact readability. The provided rationale and links to PyTorch source code support the change's intent and correctness.

Additionally, some minor stylistic improvements (like adding newlines at the end of files and cleaning up docstring whitespace) are included, which are positive.

Overall, the PR is a good example of a targeted micro-optimization that is applied systematically. The existing unit tests should cover the functional equivalence.

[lgeiger]

Not sure who's the best person to ask for a review. Maybe @DarkLight1337 or @ywang96?

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @lgeiger.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[njhill]

I like this change but others may have opinions about negatively impacting code readability.

[DarkLight1337]

I'm fine with this change - islice isn't really a big deal

[lgeiger]

Sounds great! I rebased the PR and resolved all the conflicts.

I didn't change layer iterations like

for i in range(self.start_layer, self.end_layer):
    layer = self.layers[i]

to islice since there are no performance downsides to this but let me know if I should also switch this to islice.

[DarkLight1337]

Feel free to switch them over as well

[lgeiger]

Feel free to switch them over as well

Done in 8175873ed3fcbd11c823b6eae06e51271c9e0d35

[DarkLight1337]

Thanks for the optimization!

[lgeiger]

Rebased to make CI green 💚