renwei2024/nemo-github-issues huggingface dataset:
perf recipes and Mcore DistOpt params (#10883)
State: closed | Pull request: yes
Labels: name: cherry-pick

name: Run CICD

* 175b gpt3 recipe



* Apply isort and black reformatting



* dist opt params



* 405b dist opt params



* perf recipes and dist opt params



* Apply isort and black reformatting



* MoE dist opt params



* Apply isort and black reformatting



* gpt bias fusion params



* 175b recipe



* Apply isort and black reformatting



* perf params comments



* Apply isort and black reformatting



* MoE perf params comments



* Apply isort and black reformatting



* perf recipes suffix



* specific models fusion params



* Apply isort and black reformatting



---------

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Cherrypick 10883
State: closed | Pull request: yes

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Cherry pick 10883 perf recipes
State: closed | Pull request: yes
Labels: name: core

name: TTS

name: ASR

name: NLP

name: Speaker Tasks

name: CI

name: common

name: Multi Modal

name: audio

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Bump `Dockerfile.ci` (2024-10-21)
State: closed | Pull request: yes
Labels: name: Run CICD

🚀 PR to Bump `Dockerfile.ci`.  

📝 Please remember the following to-do's before merge: 
- [ ] Verify the presubmit CI  

🙏 Please merge this PR only if the CI workflow completed successfully.

[🤖]: Hi @ko3n1g 👋,

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Adding nemo-run recipes for NeMo 2.0 T5 
State: closed | Pull request: yes
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LGTM
Add support and recipes for HF models via AutoModelForCausalLM
State: closed | Pull request: yes
Labels: name: NLP

name: Run CICD

# What does this PR do ?
Can be invoked with `nemo` or via the example

```
nemo llm pretrain --factory 'HfAutoModelForCausalLM(model_name="meta-llama/Llama-3.2-1B", num_gpus_per_node=1)'
```

the example:
```
cd examples/llm/sft/
python3 hf.py --model=meta-llama/Llama-3.2-1B --devices=1 --strategy=ddp
```

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disable dynamo for ddp checker
State: closed | Pull request: yes
Labels: name: CI

name: Run CICD

# What does this PR do ?

CI-passed for DDP test= https://github.com/NVIDIA/NeMo/actions/runs/11409221189/job/31749224039
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Minor fix in gemma 2 ckpt conversion to support Gemma 2B 
State: closed | Pull request: yes
Labels: name: stale

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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# Changelog 
- Change uses "sliding_window" arg in HF ckpt config to inform window_size. This makes it compatible with all sizes: 2B, 9B and 27B
- Gemma 2 9B, and Gemma 2 27B HF ckpts have "sliding_window" and "sliding_window_size" both set to 4096
- Gemma 2 2B config.json in HF ckpt does not have a "sliding_window_size" arg, and only has "sliding_window"


# Usage
* You can potentially add a usage example below

```bash
python3 /opt/NeMo/scripts/checkpoint_converters/convert_gemma_hf_to_nemo.py \
--input_name_or_path /path/to/gemma2/checkpoints/hf/2b \
--output_path /path/to/gemma2-2b.nemo \
--tokenizer_path /path/to/tokenizer.model \
[--cpu]
```

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Please remove overlapping change with https://github.com/NVIDIA/NeMo/pull/10958 or combine the two PRs 

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.
Fix missing runtime_gather_output arg in gemma 2 output layer
State: closed | Pull request: yes
Labels: name: NLP

# What does this PR do ?

* The forward pass in Gemma 2 passes runtime_gather_output
* Leads to unexpected keyword arg error while finetuning gemma 2 2b without it

**Collection**: nlp/models/language_modeling/megatron/gemma2

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Closing this PR, since change is already part of https://github.com/NVIDIA/NeMo/pull/10959
Add more recipes
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

Add finetuning recipes for Baichuan, ChatGLM, Gemma

**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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Fix typo in docstring
State: closed | Pull request: yes

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respect warnings' filters
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?
Before

<img width="1284" alt="Screenshot 2024-10-18 at 4 06 18 PM" src="https://github.com/user-attachments/assets/0750f79b-dc83-4dc1-a76f-436812a18c88">

After
<img width="1284" alt="Screenshot 2024-10-18 at 4 22 44 PM" src="https://github.com/user-attachments/assets/15c160bc-228e-4644-aafe-27aefe2cb622">


**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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# Add a code snippet demonstrating how to use this 
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Also needs https://github.com/NVIDIA/NeMo-Run/pull/80 

[🤖]: Hi @akoumpa 👋,

We wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11421594558) for this PR just finished successfully

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Restore NeMo 2.0 T5 pretraining CICD test
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

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- Add specific line by line info of high level changes in this PR.

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[🤖]: Hi @huvunvidia 👋,

We wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11411783624) for this PR just finished successfully

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add t5 --ci-config option
State: closed | Pull request: yes
Labels: name: CI

name: Run CICD

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closing in favor of 10952.

[🤖]: Hi @akoumpa 👋,

We wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11411494784) for this PR just finished successfully

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switch to NeMo 2.0 recipes
State: closed | Pull request: yes
Labels: name: NLP

name: CI

name: Run CICD

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Approved from CICD perspective 

[🤖]: Hi @dimapihtar 👋,

We wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11556774592) for this PR just finished successfully

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Convert perf plugin env vars to strings
State: closed | Pull request: yes
Labels: name: Run CICD

name: r2.0.0

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[🤖]: Hi @hemildesai 👋,

We wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11410142763) for this PR just finished successfully

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Long context performance doc hot fix
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

Hotfix for table style in long context performance documentation.

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@erhoo82 Hi Sangkug, I guess Github Markdown system doesn't allow the HTML based table styling. This PR fixes the issue. Could you please review this.

LGTM, thanks

Doc only change & 2 approvals : PR good to merge
ci: Fix cherry pick team
State: closed | Pull request: yes
Labels: name: CI

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

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added Lhotse online augmentation tutorial for SE
State: closed | Pull request: yes
Labels: name: Run CICD

name: audio

# What does this PR do ?

Aim of this PR is to push a tutorial of training speech enhancement models with online augmentation to NeMo.

**Collection**:  audio

# Changelog 
- New notebook file with the tutorial added to tutorials/audio/speech_enhancement
- New configuration file for the training "masking" model with online augmentation added to the examples/audio/conf/
- Problem with the RIR online augmentation is fixed in nemo/collections/audio/data/audio_to_audio_lhotse.py
# Usage

All instructions are in the notebook examples/audio/conf/masking_with_online_augmentation.yaml

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Add model_downsampling_factor to NFA cfg
State: closed | Pull request: yes
Labels: name: stale

The `model_downsampling_factor` parameter was missing in the configuration, which caused the `use_buffered_chunked_streaming` option to malfunction. This PR adds the missing parameter and updates the documentation accordingly.

> Thanks for this!
> 
> Actually, the story is that I used to require model_downsample_factor to be in the config, but then I made some logic to calculate it, so that the user doesn't need to worry about it. The logic is here: https://github.com/NVIDIA/NeMo/blob/b6c3c0d5d41d2c695e6a1b23aa573e867b0ec729/tools/nemo_forced_aligner/utils/data_prep.py#L822
> 
> 
> 
> But I forgot to update buffered case/buffered case was merged without realizing this was gone.
> 
> Could you please update the code so that we calculate model_downsample_factor for the buffered case also?

@erastorgueva-nv, in this case, it’s unlikely we can calculate the `model_downsampling_factor` for the bucketed chunked ASR scenario. The reason is that we need to know the number of tokens generated from inference, but to run the inference, we first need the `model_stride_in_sec`, which is computed as the `window_stride` multiplied by the `model_downsampling_factor`. Since the inference requires this value upfront, it seems unavoidable to include `model_downsampling_factor` in the configuration for the buffered case.

Let me know your thoughts!

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.
Enable ckpt features by default, ckpt every 15mins and reduce preempt…
State: closed | Pull request: yes
Labels: name: stale

…ion time to 1min

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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.
Export & deploy updates (part I)
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

Extra changes just for cleanup purposes related to https://github.com/NVIDIA/NeMo/pull/10904, isolated here to facilitate review for main functionalities.

**Collection**: NLP

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[🤖]: Hi @janekl 👋,

We wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11572903981) for this PR just finished successfully

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NeMO dependency issues on HuggingFace Hub (for ASR models)
State: closed | Pull request: no
Labels: name: bug

name: stale

**Describe the bug**

NeMo is not loading a ASR model I wanted to run because of internal dependency issues on HuggingFace Hub.

**Steps/Code to reproduce bug**

Colab Link: [colab link](https://colab.research.google.com/drive/1JqMW-JkyHJ61UOFO9eQhoHkQx6I3cH05?usp=sharing)

```python
!pip install nemo_toolkit['all']

import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.ASRModel.from_pretrained(model_name="nvidia/parakeet-tdt_ctc-110m")
```

**Expected behavior**

It should load up the model. 

**Environment overview (please complete the following information)**

Check colab notebook

**Environment details**

Check colab notebook 

**Additional context**

Check colab notebook

this was solved in `r2.0.0rc1`

please install `!pip install huggingface-hub==0.23.3` 

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.
nemo1 to nemo2 checkpoint convert
State: closed | Pull request: yes
Labels: name: Run CICD

name: r2.0.0

# What does this PR do ?

convert script from nemo1 checkpoint to nemo2 checkpoint

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Akoumparouli/nemo ux fix dir or string artifact
State: closed | Pull request: yes
Labels: name: Run CICD

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[🤖]: Hi @akoumpa 👋,

We wanted to let you know that a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11395010918) 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