ColossalAI is an open-source project by hpcaitech, designed to optimize the training and deployment of large AI models through advanced parallelism strategies and memory management. The project is in a robust state with active community engagement and regular updates. However, it faces challenges related to installation and compatibility issues.
YeAnbang
pre-commit-ci[bot]
Hongxin Liu (ver217)
binmakeswell
Tong Li (TongLi3701)
flybird11111
Wenxuan Tan (Edenzzzz)
| Timespan | Opened | Closed | Comments | Labeled | Milestones |
|---|---|---|---|---|---|
| 7 Days | 10 | 0 | 11 | 3 | 1 |
| 30 Days | 10 | 1 | 11 | 3 | 1 |
| 90 Days | 20 | 8 | 23 | 5 | 1 |
| 1 Year | 178 | 102 | 390 | 36 | 1 |
| All Time | 1685 | 1278 | - | - | - |
Like all software activity quantification, these numbers are imperfect but sometimes useful. Comments, Labels, and Milestones refer to those issues opened in the timespan in question.
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| Developer | Avatar | Branches | PRs | Commits | Files | Changes |
|---|---|---|---|---|---|---|
| YeAnbang |  |
1 | 2/2/0 | 3 | 39 | 2290 |
| Hongxin Liu |  |
1 | 8/6/0 | 6 | 38 | 1952 |
| Tong Li |  |
1 | 1/1/1 | 1 | 10 | 1219 |
| Wenxuan Tan |  |
1 | 0/1/0 | 1 | 8 | 440 |
| flybird11111 | |
1 | 2/3/0 | 3 | 8 | 137 |
| binmakeswell |  |
1 | 1/1/0 | 1 | 2 | 2 |
| pre-commit-ci[bot] | |
1 | 0/0/0 | 1 | 1 | 2 |
PRs: created by that dev and opened/merged/closed-unmerged during the period
| Risk | Level (1-5) | Rationale |
|---|---|---|
| Delivery | 4 | The project faces significant delivery risks due to a backlog of unresolved issues and pull requests lacking traceability. The backlog of 178 open issues, with only 102 closed, indicates potential obstacles in achieving project goals. Additionally, the absence of linked issues in several PRs, such as #6210 and #6206, suggests gaps in process adherence that could impede delivery timelines. |
| Velocity | 4 | The project's velocity is at risk due to a slow issue resolution rate and uneven distribution of work among developers. In the last 90 days, only 8 out of 20 newly opened issues were closed, indicating a sluggish pace. Key contributors like YeAnbang and Hongxin Liu are driving progress, but the reliance on a few individuals could slow down velocity if they become unavailable. |
| Dependency | 3 | While there are efforts to maintain dependencies through automated updates (e.g., PR #6179), compatibility issues with PyTorch and CUDA versions (e.g., #5869) pose dependency risks. These challenges could affect the project's ability to integrate new features or maintain stability across different environments. |
| Team | 3 | The team shows strong engagement with key contributors actively participating in development. However, the uneven workload distribution and reliance on specific individuals could lead to burnout or bottlenecks if these contributors are unavailable. The presence of automated tools helps mitigate some risks but does not fully address potential team dynamics issues. |
| Code Quality | 4 | Code quality is at risk due to incomplete documentation and testing in several pull requests (e.g., PR #6181). The lack of linked issues for traceability further exacerbates this risk, potentially leading to unaddressed bugs or inefficiencies. While automated tools are used for code quality checks, human oversight is necessary to ensure comprehensive reviews. |
| Technical Debt | 4 | The accumulation of unresolved issues and pull requests without proper documentation or testing contributes to technical debt. The presence of TODO comments and incomplete feature support in files like colossalai/shardformer/modeling/bert.py highlights areas needing attention to prevent further debt accumulation. |
| Test Coverage | 3 | Test coverage is moderate but could be improved. Some PRs lack thorough tests (e.g., PR #6181), which poses a risk for catching bugs and regressions. While there is some focus on testing through automated processes, more comprehensive test coverage is needed to ensure robustness. |
| Error Handling | 3 | Error handling is addressed through assertions and logging in various files, but it lacks robustness in some areas. For example, colossalai/checkpoint_io/utils.py has utility functions that need better error handling mechanisms to ensure all potential exceptions are logged effectively. This poses a moderate risk if not improved. |
Recent activity in the ColossalAI GitHub repository shows a significant number of issues being reported, with a focus on bugs related to installation, compatibility, and training processes. Notably, many issues involve challenges with specific configurations or environments, such as CUDA versions and multi-GPU setups.
A recurring theme is the difficulty users face when attempting to train large models like LLaMA-2 and ChatGLM2 using ColossalAI's parallelism strategies. Several issues highlight problems with memory management and compatibility with different PyTorch versions. Additionally, there are requests for new features and enhancements, such as support for additional models and improved documentation.
#6209: [BUG]: Failed to install coati in NPU docker environment
#6205: [BUG]: How to install colossal on NPU, see the project has a relevant description, but no relevant tutorial was found
#6204: [BUG]: Using colossalai run results in exception: [Errno 7] Argument list too long: '/bin/bash'
#6202: [BUG]: Error when fine-tuning DeepSeek-R1-Distill-Llama-70B using ColossalAI
#6201: 【Question】Question about initial finetune loss
#4958: [BUG]: Model not compatible with GeminiDDP
#6169: [BUG]: RuntimeError due to dtype mismatch (Float vs BFloat16)
#6160: [BUG]: Gemini saved an additional portion of the weights while using tie_word_embeddings=True
#6157: Update shardformer for transformers=4.46
#6138: [FEATURE]: Lora/QLora in GeminiPlugin and TorchFSDP
#6210: [chat] add distributed impl
#6206: [misc] update torch version
#6181: [checkpointio] support distributed checkpoint io for model saving
#6179: [pre-commit.ci] pre-commit autoupdate
#6173: [fix] launching error on special env variables (#6032)
#6162: [enhance] make input datatype ready for allgather
#6158: add download model from openMind_Hub
#6124: [hotfix] fix parameter shape checking
#6122: [feature] support Gemma2Model for tensor parallel training
#6118: [Colossalai-Ascend] Support llama2-7b, chatglm2-6b finetune and inference on NPU
#6208: [Chat] fix colossalchat bugs
#6199: [doc] DeepSeek V3/R1 news
#6198: [application] add lora sft example data
#6196: [application] Update README
#6195: [release] update version
Overall, while there is active development and maintenance in the ColossalAI project, attention to process improvements such as linking issues and completing checklists could enhance efficiency and traceability in managing pull requests.
Summary
Quantified Reports
Detailed Reportsapplications/ColossalChat/coati/experience_maker/naive.pyImports and Dependencies: The file imports a variety of modules from both internal (coati) and external libraries (torch, transformers). The imports are well-organized, with standard libraries first, followed by third-party libraries, and finally project-specific imports.
Class Definition: The primary class NaiveExperienceMaker extends ExperienceMaker. It is well-documented with a docstring explaining its purpose.
Initialization: The constructor (__init__) is comprehensive, initializing various parameters related to the actor, critic, reward model, tokenizer, and other hyperparameters. It includes assertions to ensure correct configurations based on the use_grpo flag.
Methods:
calculate_advantage: Computes advantage values using Generalized Advantage Estimation (GAE). The method is clear and uses a loop to compute advantages in reverse order.make_experience: This method generates experiences using input tensors. It is complex but well-structured, handling multiple scenarios for token generation and masking. It includes detailed comments explaining key steps.Error Handling: There are assertions and checks to handle invalid configurations or inputs, such as checking the type of stop_token_ids.
Logging: Utilizes a logger for warnings, which is good practice for monitoring runtime behavior.
Performance Considerations: The use of torch.no_grad() and torch.inference_mode() indicates an emphasis on performance by disabling gradient calculations where not needed.
make_experience method into smaller helper functions for better readability and maintainability.applications/ColossalChat/coati/trainer/dpo.pyImports and Dependencies: Similar to the previous file, it has organized imports. Dependencies include both internal modules and external libraries like PyTorch and transformers.
Class Definition: The class DPOTrainer extends SLTrainer. It is well-documented with a comprehensive docstring explaining its arguments.
Initialization: The constructor initializes various components necessary for training, including models, optimizers, schedulers, and configuration parameters. It also sets up logging mechanisms.
Methods:
_before_fit: Prepares the environment before training starts. It handles logging setup with TensorBoard or WandB._train and _eval: Core methods for training and evaluation loops. They are lengthy but logically structured to handle different stages of training._criterion within methods helps in organizing the logic related to loss computation.Error Handling: Assertions are used to ensure correct configurations.
Logging: Extensive use of logging for tracking training progress and metrics.
applications/ColossalChat/coati/trainer/grpo.pyImports and Dependencies: Consistent with other files in terms of structure. Uses both internal modules and external libraries effectively.
Class Definition: The class GRPOTrainer extends OLTrainer. It is well-documented with detailed explanations of its parameters.
Initialization: Comprehensive setup similar to other trainer classes. Initializes models, optimizers, schedulers, buffers, etc., with appropriate configurations.
Methods:
_before_fit, _setup_update_phrase_dataload, _make_experience, _training_step, _learn, _save_checkpoint: These methods cover the lifecycle of training from setup to execution. Each method is focused on a specific aspect of the training process._make_experience aids in separating concerns within the class.Error Handling & Logging: Assertions ensure valid configurations. Logging is used extensively for monitoring progress and debugging.
applications/ColossalChat/examples/training_scripts/lora_finetune.pyScript Setup: This script sets up a command-line interface using argparse for configuring various training parameters. It is structured to initialize distributed training environments using ColossalAI plugins.
Main Functionality (train):
Error Handling & Logging: Uses assertions and conditional checks to validate user inputs. Logs important information about training configuration and progress.
colossalai/checkpoint_io/utils.pyUtility Functions: This file contains numerous utility functions related to checkpoint handling in distributed settings. Functions are generally well-documented with clear explanations of their purpose.
Concurrency & Asynchronous Operations: Utilizes concurrent futures for asynchronous operations, indicating an emphasis on performance optimization during checkpoint saving/loading.
Error Handling & Logging: Includes assertions but lacks extensive error handling mechanisms across all functions. Logging could be improved for better traceability during execution failures.
colossalai/shardformer/modeling/deepseek_v3.pyClass Definitions & Methods:
EpDeepseekV3MoE.Integration with ColossalAI Components: Leverages ColossalAI's parallelism utilities effectively within model definitions.
YeAnbang
pre-commit-ci[bot]
Hongxin Liu (ver217)
binmakeswell
Tong Li (TongLi3701)
flybird11111
Wenxuan Tan (Edenzzzz)