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h-guo18
3036a9ea9f
## What does this PR do?
**Type of change:** New Feature <!-- Use one of the following: Bug fix,
new feature, new example, new tests, documentation. -->
**Overview:**
- Supported Context Parallel by patching torch ring attention;
- Require following libirary version for stable cp:
- torch2.8.0
- transformers5.0.0
- accelrate1.12.0
- Move to FSDP2
- Removed unused arguments in training script (`--multi_gpu`,
`fsdp_wrap_layer`)
- Bump CI container to `nvcr.io/nvidia/pytorch:25.08-py3`
## Usage
<!-- You can potentially add a usage example below. -->
```bash
./launch_train.sh --model $MODEL \
--output_dir $OUTPUT_DIR \
--data $DATA \
--num_epochs 0.1 \
--train_bs 1 \
--eagle_config eagle_config.json \
--training_seq_len 1024 \
--cp_size 2 #newly added
```
## Testing
- SDPA level correctness: tested TTT attention with/without CP, diff <
1%
```
=== Compare context-parallel (CP) outputs and grads with non-CP ===
Forward output comparison (CP vs Non-CP):
Absolute diff (adiff) cp_out vs out: 0.001953125
Relative diff (rdiff) cp_out vs out: 0.00182342529296875
WQ (query proj) grad comparison (CP vs Non-CP):
Absolute diff (adiff) cp_wq_grad vs wq_grad: 0.0078125
Relative diff (rdiff) cp_wq_grad vs wq_grad: 0.00347900390625
WK (key proj) grad comparison (CP vs Non-CP):
Absolute diff (adiff) cp_wk_grad vs wk_grad: 0.0078125
Relative diff (rdiff) cp_wk_grad vs wk_grad: 0.002471923828125
WV (value proj) grad comparison (CP vs Non-CP):
Absolute diff (adiff) cp_wv_grad vs wv_grad: 0.25
Relative diff (rdiff) cp_wv_grad vs wv_grad: 0.0069580078125
==============================================================
```
- E2E Training Acc
(Llama3.1-8B, Unsynthesized magpie)
<img width="911" height="630" alt="image"
src="https://github.com/user-attachments/assets/1ecacc7f-c720-494c-9c1b-b60e7ced7baa"
/>
- Peak Mem Reserved
(llama3.1-8B, 8xH100, train_length=4k)
| cp_size | max_memory_allocated(MB) |max_memory_reserved (MB) |
|----|--------------------------|--------------------------|
| 1 | 65040.20 |79018.00
| 2 | 50409.17 |73098.00
| 4 | 45120.92 |72052.00
| 8 | 38882.12 |66484.00
- Max Training Length test
(llama3.1-8B, H100)
| cp_size | 6k | 12k | 24k | 48k |
|--------------------|-----|-----|-----|-----|
| 1 | ✅ | OOM | OOM | OOM |
|2 | ✅ | ✅ | OOM | OOM |
| 4 | ✅ | ✅ | ✅ | OOM |
| 8 | ✅ | ✅ | ✅ | ✅ |
## Before your PR is "*Ready for review*"
<!-- If you haven't finished some of the above items you can still open
`Draft` PR. -->
- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: Yes/No <!--- If No, explain
why. -->
- **Did you write any new necessary tests?**: Yes/No
- **Did you add or update any necessary documentation?**: Yes/No
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes/No <!--- Only for new features, API changes, critical bug fixes or
bw breaking changes. -->
## Additional Information
<!-- E.g. related issue. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* Added context parallelism (CP) and data parallelism shard size
configuration parameters to training arguments.
* **Enhancements**
* Improved TTT attention masking support for speculative decoding
workflows.
* Enhanced training launch script with improved parallelism
configuration handling.
* **Chores**
* Updated core dependencies: torch, transformers, accelerate, and wandb.
* Added FSDP configuration file for distributed training setup.
<sub>✏️ Tip: You can customize this high-level summary in your review
settings.</sub>
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: h-guo18 <67671475+h-guo18@users.noreply.github.com>