renwei2024/nemo-github-issues huggingface dataset:
Cherry pick `Fix TRTLLM parallel_embedding (10975)` into `r2.0.0`
State: closed | Pull request: yes
Labels: name: cherry-pick

name: Run CICD

[🤖]: Hi @meatybobby 👋,<br><br>we've cherry picked #10975 into `r2.0.0` for you! 🚀<br><br>Please review and approve this cherry pick by your convenience\!
Fix export of configuration parameters to Weights and Biases
State: closed | Pull request: yes
Labels: name: core

name: Run CICD

# What does this PR do ?

This PR corrects an error in ModelPT.py that only partially converts the _hparams_initial from an OmegaConf object to a dictionary before exporting to the various loggers. 

This was particularly an issue with search functionality based on metadata in Weights and Biases due to the cfg object being a string representation of the OmegaConf object.

**Collection**: Core

# Changelog 
- Adds an additional check and conversion of the OmegaConf self._cfg to ensure that Logger.log_hyperparameters from Pytorch Lightning is guaranteed to find a JSON compatible structure at L1650 of core/classes/ModelPT.py

## Reference Images

### Prior to fix

cfg is recorded as a str(OmegaConf.DictConfig) in Weights and Biases 

![Screenshot 2024-10-22 at 11 21 02](https://github.com/user-attachments/assets/740ae152-8f53-4789-aea5-4884361c83e4)


### After fix

cfg is correctly searchable in Weights and Biases

![Screenshot 2024-10-22 at 11 21 17](https://github.com/user-attachments/assets/505d5871-d4cf-4e58-9009-f1198a697374)


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# Additional Information
* Related to # (issue) LLM-5007


@ericharper @titu1994 can you trigger the Nemo_CICD_Test, cicd-test-container-setup, and gpu-test actions?
Akoumparouli/mixtral recipe fix r2.0.0
State: closed | Pull request: yes
Labels: name: core

name: ASR

name: Run CICD

name: r2.0.0

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Cherry pick `minor fix (10990)` into `r2.0.0`
State: closed | Pull request: yes
Labels: name: cherry-pick

name: Run CICD

[🤖]: Hi @JRD971000 👋,<br><br>we've cherry picked #10990 into `r2.0.0` for you! 🚀<br><br>Please review and approve this cherry pick by your convenience\!
Cherry pick `Reflect CLI change nemorun -> nemo (10443)` into `r2.0.0`
State: closed | Pull request: yes
Labels: name: cherry-pick

name: Run CICD

[🤖]: Hi @marcromeyn 👋,<br><br>we've cherry picked #10443 into `r2.0.0` for you! 🚀<br><br>Please review and approve this cherry pick by your convenience\!
add lora recipt for 405b
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minor fix
State: closed | Pull request: yes
Labels: name: documentation

name: r2.0.0

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Documentation only change. No need to run CI
NeuralDiarizer with the telephonic config mix speakers at the very beginning of shorter audio files (less than 2 minutes duration)
State: closed | Pull request: no
Labels: name: bug

name: stale

**Describe the bug**

I am using NeuralDiarizer with the default diar_infer_telephonic.yaml settings (nemo version 1.21.0). I am using it to diarize the real-life phone call recordings.
I have experienced the same issue for almost any shorter audio file (less than 2 minutes duration) I have diarized: the first couple of utterances, pronounced by two different speakers, are merged into the same one, and labeled such that it was spoken by a single speaker. 
After the initial glitch, diarizer continues to work with the very precise predictions, so this issue is really only about the very first couple of sentences.

Any recommendation how to improve its precision for that particular problem? 

**Steps/Code to reproduce bug**

I am using NeuralDiarizer with the default diar_infer_telephonic.yaml settings file, with this addition:
```python
meta = {
        "audio_filepath": os.path.join(output_dir, "mono_file.wav"),
        "offset": 0,
        "duration": None,
        "label": "infer",
        "text": "-",
        "rttm_filepath": None,
        "uem_filepath": None,
    }
    with open(os.path.join(data_dir, "input_manifest.json"), "w") as fp:
        json.dump(meta, fp)
        fp.write("\n")

    pretrained_vad = "vad_multilingual_marblenet"
    pretrained_speaker_model = "titanet_large"
    config.num_workers = 0
    config.diarizer.manifest_filepath = os.path.join(data_dir, "input_manifest.json")
    config.diarizer.out_dir = (
        output_dir  # Directory to store intermediate files and prediction outputs
    )

    config.diarizer.speaker_embeddings.model_path = pretrained_speaker_model
    config.diarizer.oracle_vad = (
        False  # compute VAD provided with model_path to vad config
    )
    config.diarizer.clustering.parameters.oracle_num_speakers = False

    config.diarizer.clustering.parameters.enhanced_count_thres = 80
    config.diarizer.clustering.parameters.max_speaker_num = 2

    # Here, we use our in-house pretrained NeMo VAD model
    config.diarizer.vad.model_path = pretrained_vad
    config.diarizer.vad.parameters.onset = 0.8
    config.diarizer.vad.parameters.offset = 0.6
    config.diarizer.vad.parameters.pad_offset = -0.05
    config.diarizer.msdd_model.model_path = (
        "diar_msdd_telephonic"  # Telephonic speaker diarization model
    )
``` 


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.
ci: Update tests
State: closed | Pull request: yes
Labels: name: CI

name: Run CICD

name: r2.0.0

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ci: Send team alerts on specific keywords
State: closed | Pull request: yes
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name: Run CICD

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gpt3 175b cli
State: closed | Pull request: yes
Labels: name: Run CICD

name: r2.0.0

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PTQ via NeMo-Run CLI
State: closed | Pull request: yes
Labels: name: Run CICD

name: skip-docs

# What does this PR do ?

Adding support for PTQ in nemo CLI.

Prerequisite: https://github.com/NVIDIA/NeMo/pull/10642.

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage

```bash
nemo llm ptq nemo_checkpoint=/models/Llama-3-70b \
  calib_tp=8 \
  export_config.inference_tensor_parallel=2 \
  export_config.path=/models/Llama-3-70b-FP8
```

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llm.generate fixes
State: closed | Pull request: yes
Labels: name: Run CICD

name: r2.0.0

# What does this PR do ?

Apply the following fixes to llm.generate

- load from context subdir instead of ckpt dir
- disabling initializing optimizer
- add logic to find exact McoreGPTModel
- require user to provide their own trainer for llm.generate

Usage:
```
    strategy = nl.MegatronStrategy(tensor_model_parallel_size=tp, init_model_parallel=False)
    trainer = nl.Trainer(devices=tp, strategy=strategy)
    print(
        llm.generate(
            output_path,
            trainer=trainer,
            prompts=["Deep learning is"],
            inference_params=CommonInferenceParams(num_tokens_to_generate=20),
        )
    )
```
Example output:
```
[InferenceRequest(request_id='0', prompt='Deep learning is', inference_parameters=CommonInferenceParams(temperature=1.0, top_k=0, top_p=0.0, return_log_probs=False, num_tokens_to_generate=20), prompt_tokens=[34564, 6975, 374, 128001, 128001, 128001, 128001, 128001, 128001, 128001, 128001, 128001, 128001, 128001, 128001, 128001, 128001, 128001, 128001, 128001, 128001, 128001, 128001], arrival_time=1729579214.2338436, status=<Status.COMPLETED: 4>, encoder_prompt=None, generated_text=' the topic of hot interest nowadays. Thanks to its unique representatives including computer vision, natural language program etc', generated_tokens=tensor([  279,  8712,   315,  4106,  2802, 46877,    13, 11361,   311,  1202,
         5016, 24005,  2737,  6500, 11376,    11,  5933,  4221,  2068,  5099],
       device='cuda:0'), generated_log_probs=None, generated_length=20)]
```


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LoRA support for HF::AutoModelForCausalLM
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?
https://github.com/NVIDIA/NeMo/pull/10962 + lora

Running the example:
```
$ python3 NeMo/examples/llm/peft/hf.py --model /mnt/4tb/nemo_lite/phi3-mini-4k/

...
      | Name  | Type            | Params | Mode
    --------------------------------------------------
    0 | model | Phi3ForCausalLM | 3.9 B  | train
    --------------------------------------------------
    50.3 M    Trainable params
    3.8 B     Non-trainable params
    3.9 B     Total params
    15,485.645Total estimated model params size (MB)
    679       Modules in train mode
    0         Modules in eval mode
Epoch 0:   0%|▏               126/43800 [00:18<1:45:46,  6.88it/s, v_num=4-36, lr=1e-5, train_log_step=1.400]
```

TODO + add nemorun recipe example output



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@akoumpa : Any chance you have the script to convert phi3-mini-4k from hf to .nemo or megatron format ? Its ok even if it is a test script. I'll use it as a starting point
Update cicd-main.yml tests to use \tmp
State: closed | Pull request: yes
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name: CI

name: Run CICD

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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.
[Fix] Fixed sampler override and audio_key in prepare_audio_data
State: closed | Pull request: yes
Labels: name: ASR

name: Run CICD

name: audio

# What does this PR do ?

This PR is fixing two issues

1) Currently, `process_audio.py` tries to override `sampler` even if `cfg.sampler` is an empty dict (so nothing to override)
2) Currently, `transcribe_speech.py` is crashing if using `audio_key != "audio_filepath"`

**Collection**: audio, ASR

# Changelog 
1) Skip sampler override if `cfg.sampler` is an empty dict
2) In `transcribe_utils`, assign the resolved audio path to `item["audio_filepath"]`

# Usage

N/A

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I tested the changes on my local branch, didn't face any problems
Bump `Dockerfile.ci` (2024-10-22)
State: closed | Pull request: yes
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🚀 PR to Bump `Dockerfile.ci`.  

📝 Please remember the following to-do's before merge: 
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fix peft resume (#10887)
State: closed | Pull request: yes
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cherrypick for https://github.com/NVIDIA/NeMo/pull/10887

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https://github.com/NVIDIA/NeMo/pull/10893
cherry-pick (#10936) in r2.0.0
State: closed | Pull request: yes
Labels: name: cherry-pick

name: Run CICD

* Add __repr__ to Artifact



* nemo.lightning.io.artifact: represent strings as fdl.Config to avoid path adjustment during restoration



* t5 test minification



* Apply isort and black reformatting



---------

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Megatron Multilingual En Any 500M
State: closed | Pull request: no

Hi!

I'm using the [Megatron Multilingual En Any 500M model](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/megatronnmt_en_any_500m) with [this example script](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/machine_translation/nmt_transformer_infer_megatron.py).

I got the script to work in a [Nemo Docker container](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/machine_translation/nmt_transformer_infer_megatron.py) in the sense that the script runs without error. However, the output file only contains empty lines indicating that the translation doesn't work properly.
The model seems to be loaded fine and there is no hint in the logs.

Any idea what is going wrong? Is this model still supported?

Thanks!




Hi @rogerwelo . Can you specify which Nemo container you are using?

I've been using this one (`nvcr.io/nvidia/nemo:23.08`) for inference with this model. If you are using a different container, can you try it out?

Shuoyang

I ended up using Riva and followed the steps described [here](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/quick-start-guide.html#local-deployment-using-quick-start-scripts). This worked fine.
Fix TRTLLM parallel_embedding
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name: r2.0.0

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Qwen2 Recipe
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Perf Env Plugin Cherry-pick
State: closed | Pull request: yes
Labels: name: cherry-pick

name: Run CICD

Cherry picks https://github.com/NVIDIA/NeMo/commit/b77c74302ab81ba4e9ee21ccf800f852b6fcad96 and https://github.com/NVIDIA/NeMo/commit/d467cd88d20fc716c8de8732bd1a0891527e8a26
Update T5 tokenizer (adding additional tokens to tokenizer config)
State: closed | Pull request: yes
Labels: name: NLP

name: common

name: Run CICD

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Update T5 tokenizer: adding additional tokens to tokenizer config, so these additional special tokens will be saved inside the checkpoint, which can be loaded and used without manually adding additional tokens.

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SC1/SC2 Recipe
State: closed | Pull request: yes
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[🤖]: Hi @suiyoubi 👋,

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Nemo2 Recipe for SC1/SC2
State: closed | Pull request: yes
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