renwei2024/nemo-github-issues huggingface dataset:
fix: T5 encoder_config
State: closed | Pull request: yes
Labels: name: NLP

name: Run CICD

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Fix Docker build. Make Dockerfile consistent with CI
State: closed | Pull request: yes
Labels: name: CI

name: Run CICD

# What does this PR do ?

- Fix Docker container build (currently fails) from `Dockerfile`
- Make `Dockerfile` consistent with `Dockerfile.ci` used on CI
- Fix k2 installation

**Known issues**: `TransformerEngine` custom installation results in failures when no GPUs are present: `from nemo.core import ModelPT` fails with `OSError: libcuda.so.1: cannot open shared object file: No such file or directory`. Removing the custom TransformerEngine installation solves the issue. It is also possible to adjust LD_LIBRARY_PATH, but if done globally, this breaks the container with GPU. So, currently, there is a temporary workaround before imports since `docker build` does not expose GPUs (and should not since we want to compile for all architectures, not only for current GPUs).

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> Looks good but I think the `Dockerfile` was recently renamed on `main` to `Dockerfile.speech`, looks like this change was not pulled into 2.0.0rc1..

@pzelasko, 
Yep, the changes are only in `main`, but git can automatically track the history and correctly modify `Dockerfile.speech` in `main` after cherry-pick.
If we need `Dockerfile` to be renamed also here, please let me know
Update arch check for SD
State: closed | Pull request: yes
Labels: name: Multi Modal

name: Run CICD

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@minitu There's one CI test failing. Does not look relevant to the change introduced here? 

The CI failure doesn't seem relevant to this PR:
```
Error executing job with overrides: ['model.restore_from_path=/home/TestData/nlp/megatron_llama/llama_ci.nemo', 'quantization.calib_dataset=/home/TestData/nlp/test_quantization/test.json', 'quantization.algorithm=int8_sq', 'quantization.num_calib_size=8', 'inference.batch_size=2', 'export.save_path=/home/TestData/nlp/megatron_llama/ci_int8_sq.qnemo']
Traceback (most recent call last):
  File "/workspace/Megatron-LM/megatron/core/dist_checkpointing/strategies/common.py", line 85, in load_sharded_object
    loaded_obj = torch.load(load_path)
  File "/usr/local/lib/python3.10/dist-packages/torch/serialization.py", line 997, in load
    with _open_file_like(f, 'rb') as opened_file:
  File "/usr/local/lib/python3.10/dist-packages/torch/serialization.py", line 444, in _open_file_like
    return _open_file(name_or_buffer, mode)
  File "/usr/local/lib/python3.10/dist-packages/torch/serialization.py", line 425, in __init__
    super().__init__(open(name, mode))
FileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmp4ev5r7lk/model_weights/model.decoder.layers.self_attention.linear_proj._extra_state/shard_0_2.pt'
```
Bump `Dockerfile.ci` (2024-07-18)
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.

Can be closed in favor of #9774
Fix data preprocessing script
State: closed | Pull request: yes
Labels: name: cherry-pick

name: Run CICD

# What does this PR do ?

Fix megatron data preprocessing script (for pretraining data) in the case where input is a *folder* of *uncompressed jsonl* files

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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 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.
Canary Adapters tutorial (#9670)
State: closed | Pull request: yes
Labels: name: core

name: ASR

name: common

name: Run CICD

Cherry-Pick from https://github.com/NVIDIA/NeMo/pull/9670 

---------

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Set default Torch version if YY.MM format is not met
State: closed | Pull request: yes
Labels: name: NLP

name: Run CICD

# What does this PR do ?

Set default nvidia torch version 

**Collection**: [nlp]

# Changelog 
- Update nvidia torch version if YY.MM formatting is not met

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@minitu could you review this PR?

LGTM
Bump `Dockerfile.ci` (2024-07-17)
State: closed | Pull request: yes
Labels: name: stale

name: Run CICD

🚀 PR to Bump `Dockerfile.ci`.  

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

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ci: Remove ko3n1g from reviewers
State: closed | Pull request: yes
Labels: name: CI

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[NeMo UX] Support generating datasets using different train/valid/test distributions
State: closed | Pull request: yes
Labels: name: bug

name: Run CICD

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Fix RNNT alignments test
State: closed | Pull request: yes
Labels: name: ASR

name: CI

name: Run CICD

# What does this PR do ?

Refactor RNN-T alignments tests (failure observed in #9739):
- use fixture to avoid loading the model multiple time
- the test now can run on arbitrary machine (not only on CI)

**Collection**: [ASR]

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ci: Add PAT to create-pullrequest action
State: closed | Pull request: yes
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Fix minor inconsistency in AudioFeatureIterator return type
State: closed | Pull request: yes
Labels: name: stale

name: ASR

# What does this PR do ?

`AudioFeatureIterator` yields `np.ndarray` for the last item while `torch.Tensor` is returned for the rest. This change fixes this minor inconsistency.

**Collection**: ASR

# Changelog 
- Use `torch.zeros` instead of `np.zeros` in one of the cases that causes this inconsistency

# Usage
NA



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# Additional Information
NA


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 mcore commit in Dockerfile
State: closed | Pull request: yes
Labels: name: CI

name: Run CICD

# What does this PR do ?

Currently, the docker build is failing because the Megatron commit pinned in the Dockerfile is too outdated: 
```
13.25   from megatron.core.num_microbatches_calculator import (
13.25 ModuleNotFoundError: No module named 'megatron.core.num_microbatches_calculator'
```
This PR bumps the Megatron version to fix this error. 

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CI passed: https://github.com/NVIDIA/NeMo/actions/runs/9977411918
update branch
State: closed | Pull request: yes
Labels: name: cherry-pick

name: audio

# What does this PR do ?

Updates branch name to r2.0.0rc1

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update branch
State: closed | Pull request: yes
Labels: name: audio

# What does this PR do ?

Updates branch name to r2.0.0rc1

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# Additional Information
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Why did you use dynamic bucketing in Canary training and didnt in stt eu asr training
State: closed | Pull request: no
Labels: name: stale

I see from the paper that you used dynamic bucketing - a way to reducing padding amount, and in the stt-eu config I see that    
```
bucketing_strategy: synced_randomized
bucketing_batch_size: null
 
```
   I assume this null means not doing that. 
    I wonder why?

what is `stt-eu` config you are mentioning about? 

Hi @SatenHarutyunyan, I don't know about `stt-eu` recipe but I can offer some insights. Briefly before we released Canary-1B, we added [lhotse](https://github.com/lhotse-speech/lhotse) as an alternative dataloading backend for NeMo. The original NeMo dataloading implementation only allows you to do "static" bucketing where you have to manually partition your data into buckets and assign a batch size to each. With lhotse dataloading you don't have to do any of that; instead, you set the configuration for the number of buckets and (optionally) the bucket duration bins, and the total audio duration to be seen in a mini-batch. This results in dynamic bucketing and dynamic batch sizes. 

So in short, dynamic bucketing (and other lhotse features) were only available in NeMo shortly before the release of Canary-1B. 

Thank you for the answer, thats very helpful.

I was asking about this recipe. https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_multilingual_fastconformer_hybrid_large_pc_blend_eu

If you use Ihotse for dynamic bucketing, would that be seen in the config.yaml file that yu released?
Does dynamic bucketing help only for training speed or it helps the model training/convergence, better results in simple words?




> If you use Ihotse for dynamic bucketing, would that be seen in the config.yaml file that you released?

Yes, unless the config was minimized before release.

> Does dynamic bucketing help only for training speed or it helps the model training/convergence, better results in simple words?

It helps with speeding up the convergence due to larger batch sizes. You might be interested in our very recent work that allows to tune the batch size to the maximum handled by the GPU for a given model, we've seen solid speedups (same result even 2x faster as measured by wall time on the same number on GPUs).

#9763 

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Fix data preprocessing script
State: closed | Pull request: yes

# What does this PR do ?

Fix megatron data preprocessing script (for pretraining data) in the case where input is a *folder* of *uncompressed jsonl* files

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Fix missing parallelisms
State: closed | Pull request: yes
Labels: name: cherry-pick

name: Run CICD

name: audio

# What does this PR do ?

CP and EP config values were not being passed to model parallel initialization methods.

**Collection**: [llm]

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Restore cicd tests from PR #9677 deletion
State: closed | Pull request: yes
Labels: name: stale

name: CI

name: Run CICD

# What does this PR do ?

PR #9677 accidently removed some tests from previous version of cicd. This PR adds those tests back.

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Revert back updated test
State: closed | Pull request: yes
Labels: name: CI

name: Run CICD

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add a bit more for timeout (#9702)
State: closed | Pull request: yes
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[NeMo-UX] Match nemo 1's default behavior for drop_last and pad_samples_to_global_batch_size
State: closed | Pull request: yes
Labels: name: cherry-pick

name: Run CICD

This PR updates the default behavior of NeMo-UX's dataloader to match NeMo 1's. See https://github.com/NVIDIA/NeMo/blob/d4d03857f432a9248e6d58ecea12b17870e112eb/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py#L1552-L1579.

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[Audio] Metric with Squim objective and MOS
State: closed | Pull request: yes
Labels: name: ASR

name: Run CICD

name: audio

# What does this PR do ?

Add a `Metric` object for calculating Squim MOS.

**Collection**: audio

# Changelog 
- Added a metric wrapper for Squim objective and MOS models
- Added unit tests
- Fixed a bug in `transcribe_utils`

# Usage
For example, in model config:

```python
...
model:
...
  metrics:
    val:
      squim_mos: # Squim MOS
        _target_: nemo.collections.audio.metrics.SquimMOSMetric
        fs: ${model.sample_rate}
      squim_stoi: # Squim STOI
        _target_: nemo.collections.audio.metrics.SquimObjectiveMetric
        metric: stoi
        fs: ${model.sample_rate}
...
```

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TorchAudio installation workaround for incorrect `PYTORCH_VERSION` variable
State: closed | Pull request: yes
Labels: name: cherry-pick

name: Run CICD

# What does this PR do ?

Fixed TorchAudio installation for `nvcr.io/nvidia/pytorch:24.03-py3` container and other environments, where `PYTORCH_VERSION` env variable contains incorrect PyTorch version (real PyTorch version differs).

Should fix https://nvbugs/4738248

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[NeMo-UX] Minor bug fix when TE/Apex not installed
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