Summary of Changes

Hello @Jintao-Huang, 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 introduces initial support for multimodal models within the Megatron framework. The primary goal is to enable the integration of vision components, specifically for models like Qwen2.5VL, and to ensure that model configurations are correctly handled during the conversion process from HuggingFace formats to Megatron. This lays the groundwork for training and deploying models that can process both text and visual data.

Highlights

• Multimodal Model Integration: Introduces a new Qwen2_5VL_Vit module to support Vision Transformer (ViT) components for multimodal models within the Megatron framework.
• Configuration Handling for Multimodal Models: Enhances the model conversion utility (convert.py) to correctly extract and use text_config for multimodal models when converting HuggingFace configurations to Megatron.
• Improved Argument Passing: Ensures essential model metadata (model_info and model_meta) is passed as extra arguments during Megatron initialization, facilitating proper setup for multimodal architectures.

Code Review

This pull request adds initial support for multimodal models within Megatron. The changes introduce a new Vision Transformer (ViT) model wrapper for Qwen2.5-VL and update the model conversion script to correctly handle multimodal configurations. My review focuses on the new ViT wrapper, where I've identified a couple of critical issues: a missing import that will cause a NameError, and an incorrect implementation of the get_input_embeds method that will lead to a runtime error. I've provided specific suggestions to address these problems. The other changes appear logical for the goal of adding multimodal capabilities.

Code Review

This pull request introduces support for multimodal models within the Megatron framework, with a specific implementation for Qwen2.5-VL. The changes are extensive, touching upon argument handling, model architecture, weight conversion utilities, and the training process to accommodate visual components. Key additions include a Qwen2_5VL_Vit module for the visual tower, modifications to GPTModel to integrate visual embeddings, and updates to the conversion scripts to handle multimodal weights. My review identifies a critical bug in the initialization of the visual processor and a high-severity issue in how positional embedding types are checked, which could lead to incorrect behavior.

Code Review

This PR adds support for multimodal model SFT in Megatron, including new arguments, model classes, and training logic. The changes are extensive and well-structured. However, I've identified a critical issue related to the initialization order of Megatron components. Specifically, patch_megatron_tokenizer is called before Megatron is initialized, which will cause a runtime error. This has a cascading effect, including an uninitialized processor in the new Qwen2_5VL_Vit class. I've also found a minor typo in an error message. Addressing the initialization order is crucial for the new functionality to work.

Code Review

This pull request introduces significant enhancements to support Supervised Fine-Tuning (SFT) for multimodal models within the Megatron framework, covering both full-parameter and LoRA tuning. Key additions include new arguments for controlling component freezing (LLM, ViT, aligner), a new MultimodalGPTModel class to handle multimodal inputs, and the implementation of Multimodal RoPE (mrope). The changes span across model definitions, data processing pipelines, training arguments, and documentation to provide comprehensive support for this new feature. The code is well-structured, and the changes are consistent. I have one suggestion to improve device handling for better portability.

Code Review

This pull request introduces significant new functionality by adding support for Supervised Fine-Tuning (SFT) of multimodal models using Megatron, for both full-parameter and LoRA tuning. The changes are comprehensive, touching upon model definitions, training arguments, data processing, and documentation. Key additions include the MultimodalGPTModel class, which elegantly wraps the existing GPTModel to incorporate visual components, and new arguments like freeze_llm, freeze_vit, and freeze_aligner to control training. The implementation for mRoPE support and updates to context parallelism for multimodal data are also notable. The documentation has been updated with new tutorials and parameter descriptions, which is very helpful. My review found a couple of minor inconsistencies in the new documentation examples, which I've commented on. Overall, this is a well-executed and valuable addition to the framework.

Code Review

This pull request introduces significant new functionality by adding support for Supervised Fine-Tuning (SFT) of multimodal models using Megatron, for both full-parameter and LoRA training. The changes are extensive, touching upon model definitions, argument parsing, data collation, training loops, and weight conversion utilities to accommodate multimodal inputs. New documentation and example scripts are provided to guide users. The implementation includes patches for m-RoPE and updates to handle visual components like ViT and aligners. The code appears well-structured and the changes are consistent across the codebase. I've identified a couple of minor typos in the new documentation files that should be corrected for clarity.