fix: prevent empty tensor errors by handling missing assistant roles

[vatsalkshah]

• Added logic to use input_buffer for dataset items without an "assistant" role, ensuring non-empty input_ids and target_mask.
• Resolved tensor reshaping errors by preventing the creation of empty tensors.

Summary by CodeRabbit

• Bug Fixes

  • Improved handling of conversation data when responses are incomplete, ensuring more reliable data processing without interruption.

• New Features

  • Added new conversation entries focused on study strategies and time management, providing users with practical advice for effective learning and exam preparation.

• Chores

  • Minor formatting updates in the README and Dockerfile for consistency and clarity.

[coderabbitai]

Walkthrough

The change updates the __getitem__ method in the SFTDataset class. It adds a conditional block that checks if the input_buffer contains data while input_ids is empty. In this scenario, the code logs a warning, encodes the input_buffer into tokens, assigns these tokens to input_ids, and generates a corresponding target_mask filled with zeros. The assertion to compare the lengths of input_ids and target_mask remains unchanged. Additionally, minor formatting changes were made in the Dockerfile, README.md, and new conversation entries were added to demo_data.jsonl.

Changes

File	Summary
dataset.py	Updated SFTDataset.__getitem__: If input_buffer is non-empty while input_ids is empty, logs a warning, encodes input_buffer to tokens, assigns to input_ids, and creates a zero-filled target_mask.
Dockerfile	Modified CMD instruction: Removed and re-added the command to run the application, ending with a newline character.
README.md	Removed newline character before the revision line in the parameters section, ensuring it is adjacent to the preceding line.
data/demo_data.jsonl	Added new conversation entries related to study strategies and time management, maintaining the format of user queries and assistant responses.

Possibly related PRs

• feat: add function call #25: The changes in the main PR are related to the modifications in the __getitem__ method of the SFTDataset class in the retrieved PR, as both involve enhancements to the handling of conversation data within the same method.

Suggested reviewers

• nickcom007

Poem

O! A hop in the code today,
I found an empty path on my way.
With tokens dancing in a clever twist,
I hopped through buffers amidst a mist.
Warning chirps and zero masks in sight,
This rabbit celebrates a change so bright! 🐇✨

---

📜 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 8d9fc22 and 652cfa9.

📒 Files selected for processing (4)

• Dockerfile (1 hunks)
• README.md (1 hunks)
• data/demo_data.jsonl (1 hunks)
• dataset.py (1 hunks)

✅ Files skipped from review due to trivial changes (1)

• Dockerfile

🚧 Files skipped from review as they are similar to previous changes (1)

• dataset.py

🧰 Additional context used

🪛 LanguageTool

README.md

[uncategorized] ~90-~90: Loose punctuation mark.
Context: ...sed for training the model. - revision: The commit hash from the Hugging Face r...

(UNLIKELY_OPENING_PUNCTUATION)

🔇 Additional comments (2)

README.md (1)

90-90: Formatting looks good, no concerns.

The formatting change to remove the extra newline before this line is appropriate and improves readability of the documentation.

🧰 Tools

🪛 LanguageTool

[uncategorized] ~90-~90: Loose punctuation mark.
Context: ...sed for training the model. - revision: The commit hash from the Hugging Face r...

(UNLIKELY_OPENING_PUNCTUATION)

data/demo_data.jsonl (1)

8-9: Added conversation entries provide good test data.

This new conversation entry includes complete user and assistant exchanges about study strategies and time management. The content is well-structured with both user questions and assistant responses, following the same format as other entries in the file.

These additional conversation examples will be useful for testing the dataset handling, especially in the context of the PR's objective to prevent empty tensor errors when handling dataset items.

---

Thank you for using CodeRabbit. We offer it for free to the OSS community and would appreciate your support in helping us grow. If you find it useful, would you consider giving us a shout-out on your favorite social media?

❤️ Share

• X
• Mastodon
• Reddit
• LinkedIn

🪧 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.
  • Generate unit testing code for this file.
  • 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 generate unit testing code for this file.
  • @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 generate unit testing code.
  • @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.

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 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.

[coderabbitai]

Actionable comments posted: 0

🧹 Nitpick comments (1)

dataset.py (1)

82-90: Effective solution for preventing empty tensor errors!

This change appropriately handles dataset items that lack an assistant response by using the accumulated input buffer to generate non-empty tensors. The approach prevents potential errors during tensor reshaping operations in the data collator while preserving all available training data.

A few observations:

• The warning message provides good visibility into potentially problematic data items
• Setting target_mask to all zeros ensures these inputs don't contribute to the loss function (since they have no target outputs)
• This is a graceful fallback that maintains dataset integrity without skipping samples

Consider enhancing the warning message with more context about the specific conversation structure:

-            logger.warning(f"Dataset item {index} has no assistant response. Using input buffer.")
+            logger.warning(f"Dataset item {index} has no assistant response (roles present: {[conv['role'] for conv in conversations]}). Using input buffer.")

📜 Review details

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

📥 Commits

Reviewing files that changed from the base of the PR and between 1e50376 and 8d9fc22.

📒 Files selected for processing (1)

• dataset.py (1 hunks)