renwei2024/nemo-github-issues huggingface dataset:
Pin transformers
State: closed | Pull request: yes
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Fix document links
State: closed | Pull request: yes

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add large model stable training fix and contrastive loss update for variable seq
State: closed | Pull request: yes
Labels: name: ASR

name: Run CICD

# What does this PR do ?

This PR brings two updates:
* Fix SSL contrastive loss to support loss computation for variable input length rather than computation based on subsampling factor
* Add an option to remove bias from Linear and Conv layers in Conformer layers to support scaling for multi billion parameter training [tested currently for 1B parameter using bf16] 

**Collection**: ASR

# Changelog 
- Updated ssl_loss 
- Added argument for conformerlayer to optionally remove bias, default is False


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ci: Remove duplicated job
State: closed | Pull request: yes
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neva media_type + text generation default fix
State: closed | Pull request: yes
Labels: name: NLP

name: Multi Modal

name: Run CICD

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Add Multimodal Exporter
State: closed | Pull request: yes
Labels: name: Multi Modal

name: Run CICD

# What does this PR do ?

Add video neva TRT export

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CI passed (test that failed flaky & is now fixed): https://github.com/NVIDIA/NeMo/actions/runs/9702892079/job/26779932157
NeVA media_type fix
State: closed | Pull request: yes
Labels: name: core

name: TTS

name: ASR

name: NLP

name: CI

name: common

name: Multi Modal

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if instruct model use HF's tokenizer
State: closed | Pull request: yes
Labels: name: stale

name: NLP

name: Run CICD

# What does this PR do ?
Fixes a bug in mistral converter, affecting mistral-7b-instruct models (only the instruct variants). 
Re-issue: special tokens (e.g. 
) were tokenized instead of being handled correctly.

Add a one line overview of what this PR aims to accomplish.

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Dgalvez/fix greedy batch strategy name r2.0.0rc0
State: closed | Pull request: yes
Labels: name: cherry-pick

name: ASR

name: Run CICD

Same as #9242, but for r2.0.0rc0 branch.

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.
Update nemo.export module for quantized models
State: closed | Pull request: yes
Labels: name: NLP

name: Run CICD

# What does this PR do ?

Fix TRT-LLM engine export issues for quantized "qnemo" models.

This is a cherry-pick mirror of recent changes introduced in the main branch in https://github.com/NVIDIA/NeMo/pull/9218.

**Collection**: [NLP]

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use torch conv1d
State: closed | Pull request: yes
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name: NLP

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[Conversion] How to convert Finetuned T5 checkpoint ended with `.ckpt` to `.nemo` checkpoint with NeMo toolkit?
State: closed | Pull request: no

rt.

This script should work: https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/megatron_ckpt_to_nemo.py

Did the script work? Feel free to re-open if not.
Accept None as an argument to decoder_lengths in GreedyBatchedCTCInfer::forward
State: closed | Pull request: yes
Labels: name: ASR

name: Run CICD

GreedyCTCInfer::forward already allowed for this, so they did not implement the exact same interface. Now, they do.

Also warn about not passing in the decoder_lengths argument. It is likely an error on the user's part not to pass it in explicitly.

# What does this PR do ?

Accept None as an argument to decoder_lengths in GreedyBatchedCTCInfer::forward

**Collection**: ASR

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NeVa token fusion
State: closed | Pull request: yes
Labels: name: NLP

name: Multi Modal

name: Run CICD

# What does this PR do ?

Adding in token fusion via mlp_downsample adapter from VILA paper.

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dist adam transpose fix
State: closed | Pull request: yes
Labels: name: core

name: cherry-pick

name: stale

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.

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Dgalvez/fix greedy batch strategy name
State: closed | Pull request: yes
Labels: name: ASR

name: Run CICD

# What does this PR do ?

Change the name of greedy_batched CTC decoding strategy to greedy_batch.

Fix a spurious warning.


**Collection**: ASR

# Changelog 

1. Lazily warn about using greedy strategy instead of greedy_batch strategy.

Previously, the warning would often run spuriously, since several
existing code paths simply call "change_decoding_strategy()" after
having first initialized a Module, rather than changing the config
before initializing the Module. This can be confusing.

The only problem I can see with this is that using logging inside a
forward() method might interfer with some compiler toolkits like
Torchscript or thunder.compile. Presumably it would be easy to add a
conditional statement to avoid this statement in a compiler context if
necessary.

2. Change "greedy_batched" CTC decoding strategy to "greedy_batch

This will be consistent with the RNN-T decoding strategy named
"greedy_batch".

# Usage
N/A

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pls send PR to `2.0.0rc0` 

Closed in favor of https://github.com/NVIDIA/NeMo/pull/9243, so that this changes incorporated in r2.0 release.
Revert rope fusion defaults
State: closed | Pull request: yes
Labels: name: cherry-pick

name: stale

name: NLP

name: Multi Modal

name: Run CICD

# What does this PR do ?

Revert rope fusion default to False because of Aligner and PTQ test failure

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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.

already merged: https://github.com/NVIDIA/NeMo/pull/9237
fix import
State: closed | Pull request: yes
Labels: name: bug

name: NLP

name: Run CICD

# What does this PR do ?

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# Additional Information
* Related to # (issue)


pipeline passed: https://github.com/NVIDIA/NeMo/actions/runs/9133515299
dist adam transpose fix
State: closed | Pull request: yes
Labels: name: core

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 
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# Usage
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 Speech_Checkpoints_tests was flaky as had 10min timeout instead of recent PR which changed to 20min

CI passed: https://github.com/NVIDIA/NeMo/actions/runs/9133480615
Revert rope fusion defaults
State: closed | Pull request: yes
Labels: name: NLP

name: Multi Modal

name: Run CICD

# What does this PR do ?

Revert rope fusion default to False because of Aligner and PTQ test failure

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
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[DRAFT] Loss Per Token Update in NeVA
State: closed | Pull request: yes
Labels: name: core

name: NLP

name: Multi Modal

# What does this PR do ?

Update NeVA, GPT and optimizer part of the code to enable per token loss in NeVA. This can be turned on by `calculate_per_token_loss`. 

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

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# Additional Information
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Fix ASR_Context_Biasing.ipynb contains FileNotFoundError
State: closed | Pull request: yes
Labels: name: bug

name: cherry-pick

name: ASR

# What does this PR do ?

Fix graphviz installation for ASR_Context_Biasing.ipynb local run

**Collection**: [ASR]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

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# Before your PR is "Ready for review"
**Pre checks**:
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# Additional Information
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Fix ASR_Context_Biasing.ipynb contains FileNotFoundError
State: closed | Pull request: yes
Labels: name: bug

name: ASR

# What does this PR do ?

Fix graphviz installation for ASR_Context_Biasing.ipynb local run

**Collection**: [ASR]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

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# Before your PR is "Ready for review"
**Pre checks**:
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# Additional Information
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Enormous number of `.nemo` checkpoints produced in training
State: closed | Pull request: no
Labels: name: bug

**Describe the bug**

Training FastConformer-Transducer for 76 epochs produced 5 `.ckpt` files, 1 `-last.ckpt` file, and 136 (sic!) `.nemo` files.
I found that the number of saved checkpoints is more or equal to the number of validations runs during training with improvements.

**Steps/Code to reproduce bug**

The easiest way to reproduce the bug (and check the behavior in tests) is to alter the test `test_nemo_checkpoint_always_save_nemo` in `tests/core/test_exp_manager.py` in the following way:
- `"save_top_k:2"`
- check number of checkpoints

In this setup, I expect the number of checkpoints to be either `1` or `2`, but not `3`.
>>> AssertionError: assert 3 == 1

Full test:

```python
def test_nemo_checkpoint_always_save_nemo(self, tmp_path):
        test_trainer = pl.Trainer(accelerator='cpu', enable_checkpointing=False, logger=False, max_epochs=4)
        exp_manager(
            test_trainer,
            {
                "checkpoint_callback_params": {"save_best_model": True, "always_save_nemo": True, "save_top_k": 2},
                "explicit_log_dir": str(tmp_path / "test"),
            },
        )
        model = ExampleModel()
        test_trainer.fit(model)

        assert Path(str(tmp_path / "test" / "checkpoints" / "default.nemo")).exists()
        assert len(list((tmp_path / "test/checkpoints").glob("default*.nemo"))) == 1  # check number of `.nemo` checkpoints

        model = ExampleModel.restore_from(str(tmp_path / "test" / "checkpoints" / "default.nemo"))
        assert float(model(torch.tensor([1.0, 1.0], device=model.device))) == 0.0
```

**Expected behavior**

1 (as previous NeMo versions produced) or `save_top_k` `.nemo` checkpoints

For FastConformer, I expect 1 or 5 checkpoints, since `save_top_k: 5`, `always_save_nemo: True` in the config `examples/asr/conf/fastconformer/fast-conformer_transducer_bpe.yaml`.

**Environment overview (please complete the following information)**

Reproducible in Docker built from the main branch, and also locally on MacOS.

**Environment details**

If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version

**Additional context**

Add any other context about the problem here.
Example: GPU model


@athitten 

@mikolajblaz, maybe the above is related to #9015?


Yes, I checked this is connected (@artbataev thanks for a good repro, it helped a lot!).
In #9015 I introduced a mechanism that makes a .nemo backup of an existing instead of blindly overwriting it.
Maybe we should remove the backup checkpoint after the new one is successfully created?

Lets delete the backups
Add mel codec checkpoints
State: closed | Pull request: yes
Labels: name: TTS

name: Run CICD

# What does this PR do ?

This PR adds pointers to Mel Codec checkpoints.

**Collection**: TTS

# Changelog 
- Added checkpoints to class
- Added checkpoints to docs

# Usage


```python
# automatically load model
from nemo.collections.tts.models import AudioCodecModel
nemo_codec_model = AudioCodecModel.from_pretrained('mel_codec_22khz_medium')

# get discrete tokens from audio
import librosa
import torch
audio, _ = librosa.load(path_to_input_audio, sr=nemo_codec_model.sample_rate)

device = 'cuda' if torch.cuda.is_available() else 'cpu'
audio_tensor = torch.from_numpy(audio).unsqueeze(dim=0).to(device)
audio_len = torch.tensor([audio_tensor[0].shape[0]]).to(device)
nemo_codec_model.freeze()
encoded_tokens, encoded_len = nemo_codec_model.encode(audio=audio_tensor, audio_len=audio_len)

# reconstruct audio from tokens
reconstructed_audio, _ = nemo_codec_model.decode(tokens=encoded_tokens, tokens_len=encoded_len)

# save reconstructed audio
import soundfile as sf
output_audio = reconstructed_audio.cpu().numpy().squeeze()
sf.write(path_to_output_audio, output_audio, nemo_codec_model.sample_rate)
```

# GitHub Actions CI

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# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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# Additional Information
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Expand documentation for data parallelism and distributed optimizer
State: closed | Pull request: yes
Labels: name: documentation

# What does this PR do ?

This expands the documentation for data parallelism and the distributed optimizer

@erhoo82 This may also be a good place to add FSDP documentation.

**Collection**: NLP

# Changelog 
- Expand documentation for data parallelism and the distributed optimizer

# Usage

n/a

# GitHub Actions CI

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# Additional Information
* Matches style of https://github.com/NVIDIA/NeMo/pull/9184

Hello, I assigned myself as a reviewer. I am the tech writer on the NeMo team and provided a review of https://github.com/NVIDIA/NeMo/pull/9184. I will make similar copyedits to the content. Thank you.
Draft: Move NeMo model conversion to TRTLLM builder api 
State: closed | Pull request: yes
Labels: name: stale

name: NLP

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
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# Add a code snippet demonstrating how to use this 
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# Additional Information
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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.