renwei2024/nemo-github-issues huggingface dataset:
Add example script to run NeMo 2.0 llama pretraining with NeMo-Run
State: closed | Pull request: yes
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I feel like we should hold off on this until we merge the new entrypoint-API in NeMo-Run, see [docs](https://github.com/NVIDIA/NeMo-Run/blob/c284dcc7005ac5a2183dac661173eee46e05df87/examples/entrypoint/README.md). This will simplify this example considerably while adding more flexibility.

> I feel like we should hold off on this until we merge the new entrypoint-API in NeMo-Run, see [docs](https://github.com/NVIDIA/NeMo-Run/blob/c284dcc7005ac5a2183dac661173eee46e05df87/examples/entrypoint/README.md). This will simplify this example considerably while adding more flexibility.

@marcromeyn  But what if we don't want the extra flexibility. The aim of this example is to be able to reproduce exactly what we have been doing. Users can then use CLI or the script itself for personalization. 

Yeah I think we can create a separate CLI folder to host the CLI examples, but would be nice to have both so the users get a clear picture. It will also help show the benefits via a 1:1 comparison.

@ShriyaPalsamudram if the script would only show how to do one thing, so let's say llama3-8b I would agree. But this script offers some form of parameterization using argparse + the `MODEL_SIZE_MAPPING` dict . I believe when we showcase scripts with a form of parameterization we should be showcasing the functionality inside NeMo-Run that is designed for that.
Force `optimizer.param_groups` to match `mcore_optimizer.param_groups`
State: closed | Pull request: yes
Labels: name: core

name: Run CICD

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FYI @gdengk
fix mamba convert/ add test
State: closed | Pull request: yes
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name: Run CICD

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Add WandbPlugin, NsysPlugin and PreemptionPlugin to nemo.lightning.run.plugins
State: closed | Pull request: yes
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can you include some example of how this would be used (at least in PR description)
24.07 vboost numbers
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

24.07 perf numbers for experiments using vboost

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training resume is not working in 24.07 container
State: closed | Pull request: no
Labels: name: bug

**Describe the bug**

In version 24.05, the training continuation process was working smoothly. However, after upgrading to version 24.07, when resuming after a pause, the loss value spikes significantly.

This seems to be a highly critical issue that needs investigation. The problem occurs consistently, regardless of the model size or data, and resuming training is failing 100% of the time.

**Steps/Code to reproduce bug**

1. Perform llama 7b **pretraining** with a mock dataset
2. Save some checkpoints
3. Resume from the saved checkpoint
4. Loss spikes

**Expected behavior**

working normally

**Environment overview (please complete the following information)**

- using 24.07 docker container
- H100

Thank you for the bug report. We're still working on a patch.

Hi,

this issue is fixed in our dev nightly container:
[nvcr.io/nvidian/nemo:nightly](http://nvcr.io/nvidian/nemo:nightly)
Wrap CPU model init with megatron_lazy_init_context
State: closed | Pull request: yes
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Set apply_query_key_layer_scaling=False in Llama3Config
State: closed | Pull request: yes
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Update TRTLLM 0.12
State: closed | Pull request: yes
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Update TRTLLM 0.12

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I know this is still in draft but I did a quick test and llama and nemotron seem working. Got some error with gemma. Looks like there is a API change for gemma. We also need update for the scripts/deploy/nlp/deploy_triton.py.
Enable DDP memory optimization by default
State: closed | Pull request: yes
Labels: name: ASR

name: Run CICD

# What does this PR do ?

Enable DDP memory optimization by default for most ASR setups.

**Collection**: ASR

# Changelog 
- Enable DDP memory optimization by default for most ASR setups.

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Lazy import tokenizers
State: closed | Pull request: yes
Labels: name: NLP

name: common

name: Run CICD

# What does this PR do ?

Had this on my local disk and wanted to push it here as well. 

The goal is to avoid importing all tokenization libraries when the user will only use one.

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Fix train loss broadcasting
State: closed | Pull request: yes
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<SFT> <Long Sequence Length> Remove Attention Mask in SFT Chat Dataset
State: closed | Pull request: yes
Labels: name: NLP

name: Run CICD

# What does this PR do ?

This PR removes the attention mask generation to prevent CPU from running out of memory issue when using SFT chat dataset.

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[NeMo-UX] Turn on mcore performance optimizations
State: closed | Pull request: yes
Labels: name: Run CICD

name: audio

# What does this PR do ?
Turns on various communication/compute overlap optimizations:

- Pipeline parallel overlap, data parallel overlap is well supported and this PR turns it on by default when available

- Tensor parallel overlap is not officially productized in TE, so its not guaranteed to work on all clusters. So it is disabled by default. It can be turned on by setting the `NEMO_ENABLE_TP_COMM_OVERLAP` environment variable. 
Currently TP overlap only supports dense models, so Ive only added a TP overlap config for LLAMA70B, and can add more as more model recipes are added. 

- Wgrad deferral, which overlaps wgrads over the pipeline bubble, has to be tuned per model, so this defaults false.

I tested the full 70b, 8b performance recipes using NeMo-Run as saw no differences to the loss, but a ~10% improvement in performance.

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LGTM! Besides a few docstrings and getter apis. Approving.
FP8 plugin recipes
State: closed | Pull request: yes
Labels: name: Run CICD

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Should wait to merge until after FP8 is verified.

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.
Chchien s2st
State: closed | Pull request: yes
Labels: name: NLP

name: common

name: Multi Modal

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Removing Attention Mask in SFT Chat Dataset
State: closed | Pull request: yes
Labels: name: NLP

# What does this PR do ?

This PR removes the attention mask in SFT chat dataset which revolved the CPU OOM issue when attention mask was turned on for SFT.

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[NeMo UX] Disable softmax fusion for non-te/non-apex test
State: closed | Pull request: yes
Labels: name: CI

name: Run CICD

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[NeMo-UX] Set parallelism correctly when using VPP
State: closed | Pull request: yes
Labels: name: Run CICD

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@marcromeyn I see that the set parallelism call is missing, here's the commit we have been using https://github.com/NVIDIA/NeMo/commit/e6d34effeea37e0def50dbfc3907ee2505165b50 

Is that something you can add to this PR?

@ShriyaPalsamudram It seems a recent commit has changed that implementation, this PR should do the same thing

@marcromeyn great thanks, that addresses my concern. LGTM
Fix llama3 8b long context recipes
State: closed | Pull request: yes

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calculate step time batch end-batch end
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

A new Callback to calculate and log (using nemo logger) step time from batch end to batch end.

**Collection**: [llm]

# Changelog 
```
class DeltaTimingCallback(Callback):
```

# Usage

```python
import nemo_run as run
from nemo.utils.exp_manager import DeltaTimingCallback
from nemo.collections.llm.recipes.llama3_8b import pretrain_recipe

pretrain_recipe.trainer.callbacks.append(run.Config(DeltaTimingCallback))
```

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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.

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.
Updated KL-Div calculation
State: closed | Pull request: yes
Labels: name: NLP

name: common

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# Usage
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# Add a code snippet demonstrating how to use this 
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Log step time
State: closed | Pull request: yes

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Add tests for LazyNeMoIterator and fix case with metadata_only=True and offsets in manifest
State: closed | Pull request: yes
Labels: name: common

name: Run CICD

# What does this PR do ?

Fix https://github.com/NVIDIA/NeMo/issues/10060

**Collection**: ASR

# Changelog 
- Improved test coverage for LazyNeMoIterator and fix for a  case with `metadata_only=True` and offsets in manifest

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tests: Fix L2_Community_vita_Checkpoints_tests_Llama3
State: closed | Pull request: yes
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# Additional Information
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Minor formatting fix
State: closed | Pull request: yes
Labels: name: NLP

# What does this PR do ?

Fix formatting mistake in the ModelOpt Distillation documentation

**Collection**: NLP

# Changelog 
- Indent code block in `docs/source/nlp/distillation.rst`

# Usage

N/A

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ci: Selective triggering
State: closed | Pull request: yes
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name: Run CICD

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