perf: use `torch.embedding` for type embedding #4747

caic99 · 2025-05-19T11:21:04Z

The backward step of indexing operation is costly on GPU. Using dedicated torch.embedding mitigates this problem.

Profiling results

Before: 32ms ---

After: 0.5ms

Summary by CodeRabbit

Refactor
- Updated the embedding lookup mechanism in the model, potentially improving how embeddings are retrieved internally. No changes to the user interface or method signatures.

Copilot

Pull Request Overview

This PR optimizes the type embedding lookup in the model’s forward pass by replacing a direct tensor indexing with the dedicated torch.embedding function to improve backward performance.

Swaps manual indexing of the embedding tensor for the torch.embedding functional call in forward.
Achieves significant GPU backward-step speedup (32 ms → 0.5 ms).

coderabbitai · 2025-05-19T11:23:13Z

📝 Walkthrough

Walkthrough

The forward method of the TypeEmbedNet class in network.py was updated to use PyTorch's torch.embedding function for embedding lookups, replacing the previous approach of direct tensor indexing. No method signatures or public APIs were changed.

Changes

File(s)	Change Summary
deepmd/pt/model/network/network.py	Modified `TypeEmbedNet.forward` to use `torch.embedding` for embedding lookup instead of direct tensor indexing.

Sequence Diagram(s)

sequenceDiagram
    participant Caller
    participant TypeEmbedNet
    participant torch

    Caller->>TypeEmbedNet: forward(atype)
    TypeEmbedNet->>TypeEmbedNet: self.embedding(atype.device)
    TypeEmbedNet->>torch: embedding(embedding_tensor, atype)
    torch-->>TypeEmbedNet: embedding_result
    TypeEmbedNet-->>Caller: embedding_result

Note

⚡️ AI Code Reviews for VS Code, Cursor, Windsurf

CodeRabbit now has a plugin for VS Code, Cursor and Windsurf. This brings AI code reviews directly in the code editor. Each commit is reviewed immediately, finding bugs before the PR is raised. Seamless context handoff to your AI code agent ensures that you can easily incorporate review feedback.
Learn more here.

Note

⚡️ Faster reviews with caching

CodeRabbit now supports caching for code and dependencies, helping speed up reviews. This means quicker feedback, reduced wait times, and a smoother review experience overall. Cached data is encrypted and stored securely. This feature will be automatically enabled for all accounts on May 16th. To opt out, configure Review - Disable Cache at either the organization or repository level. If you prefer to disable all data retention across your organization, simply turn off the Data Retention setting under your Organization Settings.
Enjoy the performance boost—your workflow just got faster.

📜 Recent review details

Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between 43e0288 and e9f5058.

📒 Files selected for processing (1)

deepmd/pt/model/network/network.py (1 hunks)

⏰ Context from checks skipped due to timeout of 90000ms (29)

GitHub Check: Test Python (6, 3.12)
GitHub Check: Test Python (6, 3.9)
GitHub Check: Test Python (5, 3.12)
GitHub Check: Test Python (5, 3.9)
GitHub Check: Test Python (4, 3.12)
GitHub Check: Build wheels for cp310-manylinux_aarch64
GitHub Check: Test Python (4, 3.9)
GitHub Check: Test Python (3, 3.12)
GitHub Check: Build wheels for cp311-win_amd64
GitHub Check: Build C++ (clang, clang)
GitHub Check: Test Python (3, 3.9)
GitHub Check: Build wheels for cp311-macosx_arm64
GitHub Check: Build C++ (rocm, rocm)
GitHub Check: Test Python (2, 3.12)
GitHub Check: Build wheels for cp311-macosx_x86_64
GitHub Check: Analyze (python)
GitHub Check: Build C++ (cuda120, cuda)
GitHub Check: Test Python (2, 3.9)
GitHub Check: Build wheels for cp311-manylinux_x86_64
GitHub Check: Test C++ (false)
GitHub Check: Build C++ (cuda, cuda)
GitHub Check: Build C library (2.14, >=2.5.0rc0,<2.15, libdeepmd_c_cu11.tar.gz)
GitHub Check: Test Python (1, 3.12)
GitHub Check: Build wheels for cp311-manylinux_x86_64
GitHub Check: Test C++ (true)
GitHub Check: Analyze (c-cpp)
GitHub Check: Build C library (2.18, libdeepmd_c.tar.gz)
GitHub Check: Build C++ (cpu, cpu)
GitHub Check: Test Python (1, 3.9)

🔇 Additional comments (1)

deepmd/pt/model/network/network.py (1)

289-289: Great optimization for GPU performance!

Replacing the direct indexing operation self.embedding(atype.device)[atype] with torch.embedding(self.embedding(atype.device), atype) is an excellent optimization. This change addresses the performance bottleneck in the backward pass on GPUs as mentioned in the PR description.

The torch.embedding function is specifically optimized for embedding lookups and has a much more efficient implementation of gradients during backpropagation compared to direct indexing. The reported performance improvement from 32ms to 0.5ms in the backward step is very impressive and aligns with expectations for this change.

This modification preserves the same functionality while significantly improving performance, which is exactly what we want from an optimization.

✨ Finishing Touches

📝 Generate Docstrings

Thanks for using CodeRabbit! It's free for OSS, and your support helps us grow. If you like it, consider giving us a shout-out.

❤️ Share

🪧 Tips

Chat

There are 3 ways to chat with CodeRabbit:

Review comments: Directly reply to a review comment made by CodeRabbit. Example:
- I pushed a fix in commit <commit_id>, please review it.
- Explain this complex logic.
- Open a follow-up GitHub issue for this discussion.
Files and specific lines of code (under the "Files changed" tab): Tag @coderabbitai in a new review comment at the desired location with your query. Examples:
- @coderabbitai explain this code block.
- @coderabbitai modularize this function.
PR comments: Tag @coderabbitai in a new PR comment to ask questions about the PR branch. For the best results, please provide a very specific query, as very limited context is provided in this mode. Examples:
- @coderabbitai gather interesting stats about this repository and render them as a table. Additionally, render a pie chart showing the language distribution in the codebase.
- @coderabbitai read src/utils.ts and explain its main purpose.
- @coderabbitai read the files in the src/scheduler package and generate a class diagram using mermaid and a README in the markdown format.
- @coderabbitai help me debug CodeRabbit configuration file.

Support

Need help? Create a ticket on our support page for assistance with any issues or questions.

Note: Be mindful of the bot's finite context window. It's strongly recommended to break down tasks such as reading entire modules into smaller chunks. For a focused discussion, use review comments to chat about specific files and their changes, instead of using the PR comments.

CodeRabbit Commands (Invoked using PR comments)

@coderabbitai pause to pause the reviews on a PR.
@coderabbitai resume to resume the paused reviews.
@coderabbitai review to trigger an incremental review. This is useful when automatic reviews are disabled for the repository.
@coderabbitai full review to do a full review from scratch and review all the files again.
@coderabbitai summary to regenerate the summary of the PR.
@coderabbitai generate docstrings to generate docstrings for this PR.
@coderabbitai generate sequence diagram to generate a sequence diagram of the changes in this PR.
@coderabbitai resolve resolve all the CodeRabbit review comments.
@coderabbitai configuration to show the current CodeRabbit configuration for the repository.
@coderabbitai help to get help.

Other keywords and placeholders

Add @coderabbitai ignore anywhere in the PR description to prevent this PR from being reviewed.
Add @coderabbitai summary to generate the high-level summary at a specific location in the PR description.
Add @coderabbitai anywhere in the PR title to generate the title automatically.

CodeRabbit Configuration File (`.coderabbit.yaml`)

You can programmatically configure CodeRabbit by adding a .coderabbit.yaml file to the root of your repository.
Please see the configuration documentation for more information.
If your editor has YAML language server enabled, you can add the path at the top of this file to enable auto-completion and validation: # yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json

Documentation and Community

Visit our Documentation for detailed information on how to use CodeRabbit.
Join our Discord Community to get help, request features, and share feedback.
Follow us on X/Twitter for updates and announcements.

codecov · 2025-05-19T12:08:58Z

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 84.69%. Comparing base (43e0288) to head (e9f5058).
⚠️ Report is 85 commits behind head on devel.

Additional details and impacted files

@@            Coverage Diff             @@
##            devel    #4747      +/-   ##
==========================================
- Coverage   84.69%   84.69%   -0.01%     
==========================================
  Files         697      697              
  Lines       67474    67473       -1     
  Branches     3540     3540              
==========================================
- Hits        57147    57145       -2     
+ Misses       9197     9196       -1     
- Partials     1130     1132       +2

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

🚀 New features to boost your workflow:

❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.
📦 JS Bundle Analysis: Save yourself from yourself by tracking and limiting bundle sizes in JS merges.

perf: use torch.embedding for type embedding

e9f5058

Copilot AI review requested due to automatic review settings May 19, 2025 11:21

github-actions bot added the Python label May 19, 2025

Copilot AI reviewed May 19, 2025

View reviewed changes

caic99 requested a review from iProzd May 19, 2025 13:20

iProzd approved these changes May 20, 2025

View reviewed changes

iProzd added this pull request to the merge queue May 20, 2025

Merged via the queue into deepmodeling:devel with commit 8176173 May 20, 2025
60 checks passed

caic99 deleted the perf-embedding branch September 9, 2025 07:32

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Uh oh!

perf: use `torch.embedding` for type embedding #4747

perf: use `torch.embedding` for type embedding #4747

Uh oh!

caic99 commented May 19, 2025 •

edited by coderabbitai bot

Loading

Uh oh!

Copilot AI left a comment

Uh oh!

coderabbitai bot commented May 19, 2025

Walkthrough

Changes

Sequence Diagram(s)

Chat

Support

CodeRabbit Commands (Invoked using PR comments)

Other keywords and placeholders

CodeRabbit Configuration File (`.coderabbit.yaml`)

Documentation and Community

Uh oh!

codecov bot commented May 19, 2025 •

edited

Loading

Uh oh!

Uh oh!

Reviewers

Assignees

Labels

Projects

Milestone

Development

Uh oh!

2 participants

perf: use torch.embedding for type embedding #4747

perf: use torch.embedding for type embedding #4747

Uh oh!

Conversation

caic99 commented May 19, 2025 • edited by coderabbitai bot Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

Summary by CodeRabbit

Uh oh!

Copilot AI left a comment

Choose a reason for hiding this comment

Pull Request Overview

Uh oh!

coderabbitai bot commented May 19, 2025

Walkthrough

Changes

Sequence Diagram(s)

Chat

Support

CodeRabbit Commands (Invoked using PR comments)

Other keywords and placeholders

CodeRabbit Configuration File (.coderabbit.yaml)

Documentation and Community

Uh oh!

codecov bot commented May 19, 2025 • edited Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

Codecov Report

Uh oh!

Uh oh!

Reviewers

Assignees

Labels

Projects

Milestone

Development

Uh oh!

2 participants

perf: use `torch.embedding` for type embedding #4747

perf: use `torch.embedding` for type embedding #4747

caic99 commented May 19, 2025 •

edited by coderabbitai bot

Loading

CodeRabbit Configuration File (`.coderabbit.yaml`)

codecov bot commented May 19, 2025 •

edited

Loading