[DSV3] Apply TP on DSV3 #1341
Merged
[DSV3] Apply TP on DSV3 #1341
Conversation
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
facebook-github-bot
added
the
CLA Signed
tianyu-l
approved these changes
Jun 26, 2025
fegin
reviewed
Jun 30, 2025
wwwjn
commented
Jul 1, 2025
|
Note for reviewer: Because this diff is rebased onto #1324, there are bunch of files are being changed. Please focus on changes under |
H-Huang
pushed a commit
to H-Huang/torchtitan
that referenced
this pull request
Jul 8, 2025
## Context Mostly adapted from llama4, change the TP plan based on the difference between deepseek-v3 and llama. Thanks @tianyu-l for the detailed walk through about deepseek-v3 attention model and TP plan! This diff is currently based on pytorch#1324 , and we want to extract the MoE model in DSV3 and llama4 in a shared place. Now we have: 1. FSDP 2. Activation Checkpointing 3. TP 4. CP in progress (hang due to some reason) ## Next Step: 1. Make CP work ## Verification There are minor issue with the numerical verification: With deterministic seed, the loss is not identical. I used `AdamW` optimizer. 1. FSDP degree=4 (blue line) 2. FSDP degree=4, TP degree = 2 (orange line) <img width="1368" alt="Screenshot 2025-07-01 at 5 38 50 PM" src="https://github.com/user-attachments/assets/38d96d75-6868-4482-a603-b9e10c692ed9" /> With `Adam` optimizer, the loss is **exactly the same**: <img width="1368" alt="Screenshot 2025-07-02 at 1 26 32 PM" src="https://github.com/user-attachments/assets/6b501d3c-4841-42b1-95fd-3971b16a5eeb" /> --------- Co-authored-by: Tianyu Liu <lty@fb.com>
wwwjn
added a commit
that referenced
this pull request
Jul 8, 2025
Mostly adapted from llama4, change the TP plan based on the difference between deepseek-v3 and llama. Thanks @tianyu-l for the detailed walk through about deepseek-v3 attention model and TP plan! This diff is currently based on #1324 , and we want to extract the MoE model in DSV3 and llama4 in a shared place. Now we have: 1. FSDP 2. Activation Checkpointing 3. TP 4. CP in progress (hang due to some reason) 1. Make CP work There are minor issue with the numerical verification: With deterministic seed, the loss is not identical. I used `AdamW` optimizer. 1. FSDP degree=4 (blue line) 2. FSDP degree=4, TP degree = 2 (orange line) <img width="1368" alt="Screenshot 2025-07-01 at 5 38 50 PM" src="https://github.com/user-attachments/assets/38d96d75-6868-4482-a603-b9e10c692ed9" /> With `Adam` optimizer, the loss is **exactly the same**: <img width="1368" alt="Screenshot 2025-07-02 at 1 26 32 PM" src="https://github.com/user-attachments/assets/6b501d3c-4841-42b1-95fd-3971b16a5eeb" /> --------- Co-authored-by: Tianyu Liu <lty@fb.com>
wwwjn
added a commit
that referenced
this pull request
Jul 10, 2025
Mostly adapted from llama4, change the TP plan based on the difference between deepseek-v3 and llama. Thanks @tianyu-l for the detailed walk through about deepseek-v3 attention model and TP plan! This diff is currently based on #1324 , and we want to extract the MoE model in DSV3 and llama4 in a shared place. Now we have: 1. FSDP 2. Activation Checkpointing 3. TP 4. CP in progress (hang due to some reason) 1. Make CP work There are minor issue with the numerical verification: With deterministic seed, the loss is not identical. I used `AdamW` optimizer. 1. FSDP degree=4 (blue line) 2. FSDP degree=4, TP degree = 2 (orange line) <img width="1368" alt="Screenshot 2025-07-01 at 5 38 50 PM" src="https://github.com/user-attachments/assets/38d96d75-6868-4482-a603-b9e10c692ed9" /> With `Adam` optimizer, the loss is **exactly the same**: <img width="1368" alt="Screenshot 2025-07-02 at 1 26 32 PM" src="https://github.com/user-attachments/assets/6b501d3c-4841-42b1-95fd-3971b16a5eeb" /> --------- Co-authored-by: Tianyu Liu <lty@fb.com>
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
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.
Context
Mostly adapted from llama4, change the TP plan based on the difference between deepseek-v3 and llama.
Thanks @tianyu-l for the detailed walk through about deepseek-v3 attention model and TP plan! This diff is currently based on #1324 , and we want to extract the MoE model in DSV3 and llama4 in a shared place.
Now we have:
Next Step:
Verification
There are minor issue with the numerical verification: With deterministic seed, the loss is not identical. I used
AdamWoptimizer.With
Adamoptimizer, the loss is exactly the same: