renwei2024/nemo-github-issues huggingface dataset:
Add FSDP for NeMo 2.0
State: closed | Pull request: yes
Labels: name: Run CICD

# 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 
- Made a strategies/ direcotory to hold all strategies (e.g. MegatronStrategy & FSDPStrategy)
- Extracted common logic shared by different strategies into helper functions (utils.py)
- Wrote FSDPStrategy which uses FSDP1 to replace MegatronStrategy if users want to run FSDP

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# GitHub Actions CI

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# Before your PR is "Ready for review"
**Pre checks**:
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## Who can review?

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# Additional Information
* Related to # (issue)


Hey, I noticed that in NeMo 1.X, FSDP support is really not well-integrated across the code base. Many scripts do not work with it because they only implement `NLPDDPStrategy`, checkpoint conversion was a huge undertaking to get working (PR pending), and some feature flags straight-up [don't work](https://github.com/NVIDIA/NeMo/issues/9531) or [had bugs](https://github.com/NVIDIA/NeMo/pull/9335).

I'm super happy that FSDP is supported at all, having it integrated is really nice and for 7B models, performance with default settings was surprisingly higher than Megatron's 4D parallelism. However, I would really wish for and encourage more proper support in NeMo 2.X; it would be amazing if all NeMo workflows – not just training – just worked out of the box. :)

Let me know how to properly help in this endeavor, too, please. I've had the disheartening experience that PRs randomly get ignored, so I don't want to spend time improving the code base if it'll go wasted in the end.
Skip quantization if model is already quantized (QAT resuming)
State: closed | Pull request: yes
Labels: name: stale

name: CI

# What does this PR do ?

When doing QAT for a big model, we often need to resume training. This PR adds a check if model restored from the resume checkpoint is already quantized or not, and if quantized, it skips this step.

**Collection**: N/A

# Changelog 
- Skip quantization if model is already quantized (QAT resuming)

# Usage

- N/A

# GitHub Actions CI

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- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
- [x] Minor improvement

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# Additional Information
* Related to # (issue)


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Update path to Llama model in fine-tuning notebook
State: closed | Pull request: yes
Labels: name: stale

# What does this PR do ?

The path to the model used during the evaluation stage should be updated to reflect the new NGC model registry download location.

**Collection**: LLMs

# Changelog 
- The original path was leftover from the previous code where the model was downloaded from HuggingFace and converted to `.nemo`.

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This PR was closed because it has been inactive for 7 days since being marked as stale.
Update starthere docs text
State: closed | Pull request: yes
Labels: name: cherry-pick

# What does this PR do ?

1-line update to docs

**Collection**: Docs

# Changelog 
- 1-line update to docs


# Usage
N/A

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# Before your PR is "Ready for review"
**Pre checks**:
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- [N/A] Did you write any new necessary tests?
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# Additional Information
* Related to # (issue)
ci: Bump MCore tag
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.

**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 
```

# GitHub Actions CI

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**Pre checks**:
- [ ] 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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Update to Pytorch 24.03
State: closed | Pull request: yes
Labels: name: stale

name: CI

name: Run CICD

Update to Pytorch 24.03

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.
Manually cherry-pick from PR 9679 (PR to main - Support SFT/Eval/PEFT for mcore T5)
State: closed | Pull request: yes
Labels: name: NLP

name: CI

name: Run CICD

# What does this PR do ?

Manually copy changes from: https://github.com/NVIDIA/NeMo/pull/9679/files

**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 
```

# GitHub Actions CI

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# Additional Information
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TorchAudio installation workaround for incorrect `PYTORCH_VERSION` variable
State: closed | Pull request: yes

# 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

**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 
```

# GitHub Actions CI

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To re-run CI remove and add the label again.
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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?
- [ ] Did you add or update any necessary documentation?
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**PR Type**:
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- [x] Bugfix
- [ ] Documentation

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## Who can review?

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# Additional Information
* Related to # (issue)
Log TFLOPs per sec per GPU
State: closed | Pull request: yes
Labels: name: core

name: TTS

name: ASR

name: NLP

name: CI

name: common

name: Multi Modal

name: audio

# What does this PR do ?

Log TFLOPs per sec per GPU (logs to stdout and tensorboard logger)

**Collection**: 'common'

# Changelog 
Supported models- Nemotron, GPT3, Llama2, Llama3, Mixtral
Supported GPUs- H200, H100, A100

** Tested on H100 for supported models
** Llama2 PEFT (LoRA and SFT) support coming soon

# Usage

```python
flops_measurement_callback = FLOPsMeasurementCallback(cfg)
lightning_trainer.callbacks.append(flops_measurement_callback)
```

# GitHub Actions CI

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# Before your PR is "Ready for review"
**Pre checks**:
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# Additional Information
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Add Lita, Vila and Vita TRTLLM export
State: closed | Pull request: yes
Labels: name: Multi Modal

name: Run CICD

# What does this PR do ?

1) modify the neva_model to remove hardcoded 'vision_encoder" name. 
2) add trtllm export support for lita, vila and vita .nemo model.
3) add run example for running lita, vila and vita inference.
4) add triton deployment for these models.

**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 
```

# GitHub Actions CI

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# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
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  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
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- [ ] Bugfix
- [ ] Documentation

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# Additional Information
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Alit/r2.0.0
State: closed | Pull request: yes
Labels: name: cherry-pick

name: stale

name: NLP

name: Run CICD

# 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
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# GitHub Actions CI

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**Pre checks**:
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**PR Type**:
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- [ ] Bugfix
- [ ] Documentation

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## Who can review?

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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.
[NeMo-UX] Make TE and Apex dependencies optional
State: closed | Pull request: yes
Labels: name: NLP

name: CI

name: Multi Modal

name: Run CICD

# 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
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# GitHub Actions CI

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The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
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# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
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  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
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- [ ] Bugfix
- [ ] Documentation

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## Who can review?

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# Additional Information
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add contianer
State: closed | Pull request: yes
Labels: name: documentation

# 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
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
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# GitHub Actions CI

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**Pre checks**:
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  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
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- [ ] Bugfix
- [ ] Documentation

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# Additional Information
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I wanted to train a multitask model, like canary. needed more information on how to build the tokenizer, and data manifest file.
State: closed | Pull request: no
Labels: name: stale

**I wanted to train a multitask model, like canary. needed more information on how to build the tokenizer, and data manifest file.**

**A clear and concise description of what the problem is.**
Due to lack of information on how to build a canary like model, a tutorial, some refrence to build the tokenizer and manifest file, inorder to train such model. 

**Describe alternatives you've considered**
I followed the information available at huggingface model card. but its insufficient. 

@titu1994 @nithinraok @VahidooX  please guide me

@pzelasko  @krishnacpuvvada provide necessary documentation for the same. 

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.
Unable to reproduce cache aware streaming results for Conformer that were there for Fastconformer. 
State: closed | Pull request: no

I trained Conformer-CTC using cache aware method. but unable to reproduce the results that were there with "Fastconformer cache aware streaming". By using Conformer-CTC cache aware method i get partial sub-words, words combined together like ("hello world" ==> herld) , in streaming output.

the same was the case when i deployed it in a RIVA pipeline.

but when i use the "transcribe" function i get proper transcription for the audio file.
@titu1994 @VahidooX
can you please enlighten on this.
When to use and how to use streaming models in .nemo format and in RIVA pipeline.
My question has some relevance with these issues
[https://github.com/https://github.com/NVIDIA/NeMo/discussions/7010]
[https://github.com/https://github.com/NVIDIA/NeMo/discussions/5284]

duplicate of https://github.com/NVIDIA/NeMo/issues/9521
Add option to convert PyTriton response to OpenAI format
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

1. Adds option to convert responses from PyTriton server to OpenAI compatible format. 
2. Also adds `triton_request_timeout` and `openai_format_response` as user defined args which are then written to a json file along with `triton IP` and `triton port` to be accessed by `rest_model_api.py` (Deletes the existing `nemo/deploy/service/config.json` favor of these changes)

**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 
```

# GitHub Actions CI

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The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
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# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
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- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

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# Additional Information
* Related to # (issue)
Fix missing parallelisms
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

CP and EP config values were not being passed to model parallel initialization methods.

**Collection**: [llm]

# 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 
```

# GitHub Actions CI

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# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation

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fixed base branch and target branch

I'd like to add this to main, but instead of manual copy use a function to copy all config attributes that end with _paralle_size. Made https://github.com/NVIDIA/NeMo/compare/main...akoumparouli/copy_parallel_size for this purpose.
Update starthere docs text
State: closed | Pull request: yes

# What does this PR do ?

1-line update to docs

**Collection**: Docs

# Changelog 
- 1-line update to docs


# Usage
N/A

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Remove mask if use fusion mask
State: closed | Pull request: yes
Labels: name: NLP

name: Run CICD

# What does this PR do ?

Set create_attention_mask=False when we have mask in built-in modules like FlashAttention. 

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- Add specific line by line info of high level changes in this PR.

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I don't want to set default to `False` since there are models not using FA or mask fusion.
Set TE flag in legacy -> mcore conversion script
State: closed | Pull request: yes

Same as #9585, but into release branch

#9585 was merged to the main branch two weeks ago so the release branch should already have the commit?

@cuichenx You're right, I'm actually surprised github said we could make this MR. When I look at the `0ec7e90` commit history for that conversion script it actually doesn't even show this commit. So the git history on the conversion script looks okay:

![image](https://github.com/user-attachments/assets/71985909-127c-4e2c-8162-66477dc5d37c)
refactor: notebook branch release
State: closed | Pull request: yes
Labels: name: TTS

name: cherry-pick

name: stale

name: ASR

name: NLP

name: Speaker Tasks

name: Multi Modal

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

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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.
[NeMo-UX] Adding recipes
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

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> Can we rename the directory `models` to `recipes`?
> 
> Otherwise it's confusing why there's a mistral.py under both `collections/llm/gpt/model` and `collections/llm/models`. I don't have any strong preference as long as one of the two directories changes name.

Done
[NeMo-UX] Adding recipies
State: closed | Pull request: yes
Labels: name: core

name: TTS

name: ASR

name: NLP

name: Speaker Tasks

name: CI

name: Multi Modal

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

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# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

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# Add a code snippet demonstrating how to use this 
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**Pre checks**:
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Alit/r2.0.0
State: closed | Pull request: yes
Labels: 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 
- 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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PR that was merged to main: https://github.com/NVIDIA/NeMo/pull/9696/files
Run a sample query for a quantized model conditionally
State: closed | Pull request: yes
Labels: name: NLP

name: CI

name: Run CICD

# What does this PR do ?

Adding a flag to run `_sample_output` in `Quantizer` on demand (defaults to `True`) as this step often hangs for a few minutes at (see [here](https://github.com/NVIDIA/NeMo/actions/runs/9895230553/job/27334855354)):
```
[NeMo I 2024-07-11 17:03:57 quantizer:182] Generating sample output for the model...
Loading extension modelopt_cuda_ext...
```
and this is to disable sample query in Github CI.

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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.

CICD passed: https://github.com/NVIDIA/NeMo/actions/runs/10143611183

can merge once the review comments have been addressed (no need to re-run CI after that)
fix legacy ds padding bug
State: closed | Pull request: yes
Labels: name: NLP

name: Run CICD

# 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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**Pre checks**:
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Allows non-strict load with distributed checkpoints
State: closed | Pull request: yes
Labels: name: cherry-pick

name: NLP

name: Run CICD

# What does this PR do ?

With distributed checkpoints, the mismatches between the runtime model and checkpoint model manifest during `dist_checkpoint.load` (not during `model.load_state_dict` as with regular checkpoints).
This PR adds a flag that allows to adjust load strictness (e.g. ignore unexpected keys).

This PR relies on MCore feature that is not merged yet (merge ETA 5th July): https://gitlab-master.nvidia.com/ADLR/megatron-lm/-/merge_requests/1628 

**Collection**: NLP

# Changelog 
- Add `model.dist_ckpt_load_strictness` flag to control dist ckpt load strictness. The most useful value is `log_all` which warns about all mismatches but performs the checkpoint load for a matching state dict subset.  

# Usage
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