[zero] Suggests a minor change to confusing variable names in the ZeRO optimizer.#3173
Merged
ver217 merged 8 commits intohpcaitech:mainfrom Apr 27, 2023
Merged
[zero] Suggests a minor change to confusing variable names in the ZeRO optimizer.#3173ver217 merged 8 commits intohpcaitech:mainfrom
ver217 merged 8 commits intohpcaitech:mainfrom
Conversation
Contributor
|
Hi @yhna940 Thanks for your contribution. But the way you named is little confusing. For each |
Contributor
|
The code coverage for the changed files is 85%. Click me to view the complete report |
Contributor
Author
Thank you for your feedback. I understand the concern about the naming conventions and appreciate your suggestion. However, I would like to propose an alternative term, I hope this explanation clarifies my reasoning for suggesting the term To summarize my suggestions:
|
Contributor
Author
|
Hi @1SAA Based on our previous discussions, I have renamed the variables that were causing confusion related to Mixed Precision. Could you please review them once again? Thank you! |
Member
|
Hi @yhna940 Thanks for your contribution, but there are some conflicts in this PR. Could you please solve them first? Thanks. |
Contributor
Author
Hello @binmakeswell , the conflict has been resolved. thank you |
Contributor
|
Hi @yhna940 I am willing to merge your pr once tests in CI are passed. |
Contributor
Author
Hi @1SAA Github Action CI LogsLocal Test Log |
Contributor
|
Hi @yhna940 It seems like there exists a memory leak in our tests. I think this problem is not caused by your code. I will fix this error soon. |
1SAA
approved these changes
Apr 10, 2023
Contributor
|
@yhna940 Can you sync our main branch updates first? We've already fixed the CI test. So after this, we can merge your this PR. Thanks. |
Contributor
Author
@ver217 I have synced the main branch updates to this PR. Thanks for letting me know that the CI test issue has been resolved. |
Contributor
|
The code coverage for the changed files is 86%. Click me to view the complete report |
hyunwoongko
pushed a commit
to EleutherAI/oslo
that referenced
this pull request
May 25, 2023
…O optimizer (#183) ## Title - [zero] Suggests a minor change to confusing variable names in the ZeRO optimizer ## Description It seems that the variable names related to the mixed precision parameter group do not comprehensively cover its characteristics, so I suggest a few changes. These changes are very trivial, but hopefully they will alleviate some of the confusion for beginners like me. Currently, the entire parameter group is named `fp16_param_groups`, and the parts managed by the gpu at the current rank are described as `fp32_flat_param_groups_of_current_rank`. This state perfectly represents the characteristics when the master weight is a half-tensor or the dtype specified in the `__init__`method is fp16. In other cases, however, its characteristics do not correspond to the variable it. I would like to propose an alternative term, `working_param` and `master_param`. The term is more closely related to the concept of the mixed-precision training context. Using `working_param` and `master_weight` would create a clear distinction between the two types of parameters and help avoid confusion. To summarize my suggestions: - `fp16` -> `working` - `fp32` -> `master` ## Linked Issues - N/A ## Reference - hpcaitech/ColossalAI#3173
dyanos
pushed a commit
to EleutherAI/oslo
that referenced
this pull request
Jun 8, 2023
…O optimizer (#183) ## Title - [zero] Suggests a minor change to confusing variable names in the ZeRO optimizer ## Description It seems that the variable names related to the mixed precision parameter group do not comprehensively cover its characteristics, so I suggest a few changes. These changes are very trivial, but hopefully they will alleviate some of the confusion for beginners like me. Currently, the entire parameter group is named `fp16_param_groups`, and the parts managed by the gpu at the current rank are described as `fp32_flat_param_groups_of_current_rank`. This state perfectly represents the characteristics when the master weight is a half-tensor or the dtype specified in the `__init__`method is fp16. In other cases, however, its characteristics do not correspond to the variable it. I would like to propose an alternative term, `working_param` and `master_param`. The term is more closely related to the concept of the mixed-precision training context. Using `working_param` and `master_weight` would create a clear distinction between the two types of parameters and help avoid confusion. To summarize my suggestions: - `fp16` -> `working` - `fp32` -> `master` ## Linked Issues - N/A ## Reference - hpcaitech/ColossalAI#3173
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
4 participants
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
📌 Checklist before creating the PR
[doc/gemini/tensor/...]: A concise description🚨 Issue number
N/A
📝 What does this PR do?
It seems that the variable names related to the mixed precision parameter group do not comprehensively cover its characteristics, so I suggest a few changes. These changes are very trivial, but hopefully they will alleviate some of the confusion for beginners like me.
Currently, the entire parameter group is named
fp16_param_groups, and the parts managed by the gpu at the current rank are described asfp32_flat_param_groups_of_current_rank. This state perfectly represents the characteristics when the master weight is a half-tensor or the dtype specified in the__init__method is fp16. In other cases, however, its characteristics do not correspond to the variable it. So I want it to be renamed according to the sharding state, not the data type, according to thefsdpconvention ofpytorch. (with names like flatten_sharded_optim_state_dict and full_optim_state_dict).This is a related but more trivial issue, but it seems that the
param_storemethods don't even need to specify fp16.Thank you :)
💥 Checklist before requesting a review
⭐️ Do you enjoy contributing to Colossal-AI?
Tell us more if you don't enjoy contributing to Colossal-AI.