renwei2024/nemo-github-issues huggingface dataset:
Warning for missing FP8 checkpoint support for vLLM deployment
State: closed | Pull request: yes
Labels: name: Run CICD

name: r2.0.0

# What does this PR do ?

Warning for missing FP8 checkpoint support for vLLM deployment.

**Collection**: NLP

# Changelog 
- Add specific line by line info of high level changes in this PR.

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[🤖]: Hi @janekl 👋,

We wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11370588797) for this PR just finished successfully

So it might be time to merge this PR or get some approvals

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//cc @pablo-garay @ko3n1g
global batch size at different sequence length
State: closed | Pull request: no
Labels: name: stale

Are we supposed to use the same global batch size when increasing sequence_lengh?
Shouldn't the total number of tokens per global batch size remain the same?

This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.

This issue was closed because it has been inactive for 7 days since being marked as stale.
Basic online dynamic FP8 quantization with vLLM
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

Enabling FP8 quantization for vLLM engines. For now only the basic variant based on on-line dynamic scaling is supported. Depending on a use-case it gives up to 20% inference speedup.

Static quantization will follow in a subsequent MR.

**Collection**: NLP

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Using in nemo_export.py
python tests/export/nemo_export.py \
  --model_name local \
  --model_type llama \
  --checkpoint_dir /models/LLAMA3-8B-base-1.nemo \
  --min_tps 1 \
  --run_accuracy True \
  --test_data_path /data/lambada.json \
  --use_vllm True \
  --vllm_export_kwargs '{"quantization": "fp8"}' \
  --debug

# Using in deploy_vllm_triton.py
python scripts/deploy/nlp/deploy_vllm_triton.py \
  --nemo_checkpoint  /models/LLAMA3-8B-base-1.nemo \
  --model_type llama \
  --tensor_parallelism_size 1 \
  --triton_model_name local \
  --quantization fp8
```

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FYI @apanteleev 

[🤖]: Hi @janekl 👋,

We wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11518135457) for this PR just finished successfully

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Update ModelOpt Width Pruning example defaults
State: closed | Pull request: yes
Labels: name: NLP

name: Run CICD

# What does this PR do ?

* Update default pruning parameters based on optimal results
* Disable `sequence_parallel` by default since it does not work with pipeline parallel
* Add an option to prune `num_layers` (depth) as well

**Collection**: [Note which collection this PR will affect]

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- Add specific line by line info of high level changes in this PR.

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```python
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[🤖]: Hi @kevalmorabia97 👋,

We wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11557070090) for this PR just finished successfully

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Perf improvements. Mainly from XAttn mask calculation
State: closed | Pull request: yes

# What does this PR do ?

Improve performance of mask calculations.

**Collection**: VLM

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- Add specific line by line info of high level changes in this PR.

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[Question] Pipeline Parallel for Mamba Megatron
State: closed | Pull request: no

Hi, 

I am wondering if the scripts implemented in Mamba Megatron pipeline parallel will be possibly used inside Nemo? Specifically Mamba Megatron supports converting Mamba both to Tensor Parallel & Pipeline Parallel [at here](https://github.com/NVIDIA/Megatron-LM/blob/main/tools/checkpoint/hybrid_conversion.py). The pipeline parallel code has been using Megatron's [without interleaved implementation](https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/pipeline_parallel/schedules.py#L1274). Will we be able to utilize any of that Megatron implementation for SFT on Mamba in Nemo? 

Thank you! 

@JRD971000
Link Not Found at Mamba Tutorial
State: closed | Pull request: no
Labels: name: stale

Hi, when I am looking at the [Mamba tutorial here ](https://github.com/NVIDIA/NeMo/tree/main/tutorials/llm/mamba#run-fine-tuning), the link for 
```
Follow the steps from [here](https://nemo-framework-tme.gitlab-master-pages.nvidia.com/documentation/user-guide/latest/llms/gemma/dataprep.html) to obtain and preprocess the fine-tuning dataset.
``` 
can't be opened. 

This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.

This issue was closed because it has been inactive for 7 days since being marked as stale.
Packed sequence bug fixes
State: closed | Pull request: yes
Labels: name: NLP

name: CI

name: Run CICD

# What does this PR do ?

1. Save packed dataset in different folders according to the model name, so that different packed datasets don't clash with each other
2. Fix hanging if preparing packed data before a multi-gpu training session
3. Raise mbs>1 error and provide suggestion to user instead of automatically changing the config

**Collection**: [Note which collection this PR will affect]

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It looks like github action uses a container with an older version of TE, so the new checkpoint i added couldn't be loaded. I'll revert to the old checkpoints for now

[🤖]: Hi @cuichenx 👋,

We wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11394622331) for this PR just finished successfully

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Call `ckpt_to_weights_subdir` from `MegatronCheckpointIO`
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

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[🤖]: Hi @ashors1 👋,

We wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11578772531) for this PR just finished successfully

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peft test passed https://github.com/NVIDIA/NeMo/actions/runs/11672168085/job/32500417335 launching full ci now

[🤖]: Hi @ashors1 👋,

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Cherry pick `enable vboost and set LM SM margin (10853)` into `r2.0.0`
State: closed | Pull request: yes
Labels: name: cherry-pick

name: Run CICD

[🤖]: Hi @malay-nagda 👋,<br><br>we've cherry picked #10853 into `r2.0.0` for you! 🚀<br><br>Please review and approve this cherry pick by your convenience\!

Close in favor of #10973
Use a context-manager when opening files
State: closed | Pull request: yes
Labels: name: NLP

name: common

name: Run CICD

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Add a one line overview of what this PR aims to accomplish.

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[🤖]: Hi @akoumpa 👋,

We wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11354229992) for this PR just finished successfully

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[🤖]: Hi @akoumpa 👋,

We wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11374388366) for this PR just finished successfully

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[Cherrypick #10445 and #10898 and #10887] Packed Sequence [NeMo 2]
State: closed | Pull request: yes
Labels: name: cherry-pick

name: NLP

name: Run CICD

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

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waiting to cherrypick #10898 as well
Fixing the UnicodeEncodeError for 'logging'
State: closed | Pull request: yes


# What does this PR do ?

Logging is showing UnicodeEncodeError for Spanish (Non-english character) in [tutorials/asr/Multilang_ASR.ipynb](https://github.com/NVIDIA/NeMo/blob/r2.0.0/tutorials/asr/Multilang_ASR.ipynb). Need to specify the encoding scheme to 'utf-8' to avoid that issue. 

**Collection**: [Note which collection this PR will affect]
NeMo/utils

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Modules fail for Dreambooth example
State: closed | Pull request: no
Labels: name: bug

name: stale

I'm trying to run the dreambooth tutorial, and when executing the dreambooth.py it raises an error related to the libraries, for example:

[NeMo W 2024-10-08 09:12:02 megatron_lm_encoder_decoder_model:78] Megatron num_microbatches_calculator not found, using Apex version.
Traceback (most recent call last):
  File "/opt/NeMo/clip/convert_external_clip_to_nemo.py", line 53, in <module>
    from nemo.collections.multimodal.models.vision_language_foundation.clip.megatron_clip_models import MegatronCLIPModel
  File "/usr/local/lib/python3.10/dist-packages/nemo/collections/multimodal/models/vision_language_foundation/clip/megatron_clip_models.py", line 311, in <module>
    class SiglipMHAPoolingHead(TransformerLayer):
NameError: name 'TransformerLayer' is not defined


I don't think you specify which NeMo image is compatible with this example. Two months ago it was working with the image nvcr.io/nvidia/nemo:24.02, but now it gives the error I put above. 
Please could you tell me which image works with this tutorial? or which versions of the libraries are needed?


Thank you so much. 


Update on the procedure I'm following:
- I use the Nemo framework 24.07 image
- Launch a container with the image (witha  couple of volumes): docker run --runtime=nvidia --gpus all -it --rm \
    -v /mnt/shared_demos/dreambooth/nemo:/opt/NeMo \
    -v /mnt/shared_models/huggingface/cache/hub/models--runwayml--stable-diffusion-v1-5/snapshots/1d0c4ebf6ff58a5caecab40fa1406526bca4b5b9:/opt/models \
    --shm-size=8g -p 8888:8888 \
    --ulimit memlock=-1 --ulimit stack=67108864 \
    nvcr.io/nvidia/nemo:24.05
- I install the main branch from the nemo repository in order to get the nemo toolkit installed: python -m pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[all]
- The one above installs the megatron library in /opt/megatron-lm. However, this is version 0.8; and if I go to the path where it's located, the contents of /megatron/core are not updated, for example, there's no folder called extensions. 

Thus, it installs something that cannot be used for the dreambooth example. 

There's also a section (Megatron-LM) where it is specified to run the following in roder to use it:

- git clone https://github.com/NVIDIA/Megatron-LM.git && \
- cd Megatron-LM && \
- git checkout $mcore_commit && \
- pip install .

This has the versions updated but installs a version of megatron incompatible with dreambooth files (0.10) and image, I believe. 

Could you help me with this issue please? is it related to the branch I'm installing? to the image?


This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.

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Fix peft resume
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

1. Fix assertion error due to "context" folder in base model checkpoint
2. Fix tokenizer loading issue during 3rd restart (First time training: tokenizer is loaded from base model and saved. Second time training: tokenizer is loaded from first checkpoint, but is not saved if the first checkpoint is deleted due to save_top_k=1. Third time training: tokenizer cannot be loaded from second checkpoint)

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- Add specific line by line info of high level changes in this PR.

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**Pre checks**:
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[🤖]: Hi @cuichenx 👋,

We wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11336668220) for this PR just finished successfully

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call __post_init__ after altering config values
State: closed | Pull request: yes
Labels: name: Run CICD

name: r2.0.0

# What does this PR do ?

when we update the model's config using the strategy's values we were not triggering __post_init__
therefore the config could have invalid values


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perf recipes and Mcore DistOpt params
State: closed | Pull request: yes
Labels: name: Run CICD

name: r2.0.0

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Add Hydrarunner to oomptimizer
State: closed | Pull request: no
Labels: name: stale

Oomptimizer requires a standalone config for evaluation. This doesn't take advantage of hydrarunner as used in other ASR workflows. Would make life easier to allow passing hydrarunner changes.


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Cherry pick `Use trainer.local_rank/global_rank (10860)` into `r2.0.0`
State: closed | Pull request: yes
Labels: name: cherry-pick

name: Run CICD

[🤖]: Hi @akoumpa 👋,<br><br>we've cherry picked #10860 into `r2.0.0` for you! 🚀<br><br>Please review and approve this cherry pick by your convenience\!
Update engine build step for TRT-LLM 0.13.0
State: closed | Pull request: yes
Labels: name: Run CICD

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Update the engine build step for quantized ".qnemo" checkpoints for TRT-LLM 0.13.0 update. 

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Adding support for LightningDataModule inside Fabric-API
State: closed | Pull request: yes
Labels: name: core

name: Run CICD

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ckpt convert bug fixes
State: closed | Pull request: yes
Labels: name: NLP

name: Run CICD

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Move collectiob.nlp imports inline for t5
State: closed | Pull request: yes
Labels: name: Run CICD

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dit training diagrams
State: closed | Pull request: yes
Labels: name: stale

name: Run CICD

# What does this PR do ?

Adds diagrams explaining DiT training pipeline.

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@ethanhe42 are there any blockers to merge this PR?

This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.
DiT Training Diagrams
State: closed | Pull request: yes

# What does this PR do ?

Adds diagrams to DiT training docs to explain mixed image-video training and pipeline conditioning.

**Collection**: diffusion

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- added assets folder with diagrams explaining DiT training.

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Bump `Dockerfile.ci` (2024-10-14)
State: closed | Pull request: yes
Labels: name: Run CICD

🚀 PR to Bump `Dockerfile.ci`.  

📝 Please remember the following to-do's before merge: 
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Allow OOMtimizer tokenizer point towards just parent directory
State: closed | Pull request: no
Labels: name: stale

**Is your feature request related to a problem? Please describe.**

oomptimizer requires directly passing the `tokenizer.model` file. This is inconsistent with tokenizer behavior for other ASR modules (we just pass the directory). Probably better to change just to keep consistent.




This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.

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This issue was closed because it has been inactive for 7 days since being marked as stale.
Perf recipes
State: closed | Pull request: yes
Labels: name: stale

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This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.

This PR was closed because it has been inactive for 7 days since being marked as stale.
Bump `Dockerfile.ci` (2024-10-13)
State: closed | Pull request: yes
Labels: name: Run CICD

🚀 PR to Bump `Dockerfile.ci`.  

📝 Please remember the following to-do's before merge: 
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🙏 Please merge this PR only if the CI workflow completed successfully.

[🤖]: Hi @ko3n1g 👋,

We wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11309885258) for this PR just finished successfully

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