renwei2024/nemo-github-issues huggingface dataset:
Converting HF model to Nemo gets an error
State: closed | Pull request: no
Labels: name: bug

name: stale

**Describe the bug**
I am trying to convert the [OPT-350m](https://huggingface.co/facebook/opt-350m) hugging face model to Nemo format. How can I map the following keys?
```
- model.decoder.project_out.weight
- model.decoder.project_in.weight
```
for other weight names, I can successfully map to Nemo format.

A clear and concise description of what the bug is.

**Steps/Code to reproduce bug**

Please list *minimal* steps or code snippet for us to be able to reproduce the bug.

A  helpful guide on on how to craft a minimal bug report  http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports. 


**Expected behavior**

A clear and concise description of what you expected to happen.

**Environment overview (please complete the following information)**

 - Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)] Docker
 - Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install. nemo:24.03.01.framework
 - If method of install is [Docker], provide `docker pull` & `docker run` commands used

**Environment details**

If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version : Ubuntu 20.04
- PyTorch version : nemo:24.03.01.framework
- Python version : nemo:24.03.01.framework

**Additional context**

Add any other context about the problem here.
Example: GPU model : H100


Following up regarding issue. Please let me know if any more information is needed.

According to this [HF implementation](https://github.com/huggingface/transformers/blob/v4.44.2/src/transformers/models/opt/modeling_opt.py):
* `project_out` should be the final output layer, so it should be mapped to `model.output_layer.weight`
* `project_in` seems to be a linear layer projecting from embedding dim down to hidden size. Not sure if NeMo supports it.

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.

@qijiaxing 
isn't the 'lm_head.weight' key should be mapped to model.output_layer.weight?
To give more understanding, I mapped lm_head.weight to model.output_layer.weight, which seems to be working fine. 
now I am left with mapping
HF keys : ['model.decoder.project_out.weight', 'model.decoder.project_in.weight'] to
Nemo Keys.

Surprising it complains ['model.decoder.final_layernorm.weight', 'model.decoder.final_layernorm.bias'] from Nemo side, even though I made sure it doesn't include final layer norm explicitly.

Please let me know if needed any more explaination.

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.
Adding T5 to NeMo 2.0
State: closed | Pull request: yes
Labels: name: CI

name: common

name: Run CICD

# What does this PR do ?

Adding support for T5 in NeMo 2.0. Main files working on:
+ T5 specific:
    - megatron_t5_pretraining.py 
    - pre_training.py (using T5Dataset from mcore)
    - t5.py
+ Others:
    - auto_tokenizer.py: adding inv_vocab() to tokenizer to be compatible with mcore's T5Dataset 
    - lr_scheduler.py: adding missing warmup support

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> There are some unwanted changed in `cicd-main.yml`. Can you please revert these?

Done.

Does the commit support the encoder and decoder are put into different devices? Could the encoder and decoder be model parallelized with their own parallism config?
Fix Llama3 and Mixtral Long Context Recipe
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

Add Nemoron4 15b and 22b model 8k configs and Long Context Recipes for 16k and 64k along with it.
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Add Llama31 Config
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.

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[NeMo-UX] Handle absolute logger directories in nemo_logger
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

This fixes a bug with nemo_logger that arises when the trainer's `logger.save_dir` is absolute, which happens by default. The fix is to convert the absolute path to a relative one before overwriting the logger directory.

**Collection**: LLM

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# Additional Information
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Add Qwen2 to Nemo 2
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.

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[NeMo-UX[ Move nemotron imports inline
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

This PR moves the imports for nemotron of transformers & tokenizer inline. This is needed for pip-installs where we don't want to throw an import error when someone doesn't have transformers installed. 

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# Additional Information
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24.07 perf numbers
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 
- 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
* Related to # (issue)
Akoumparouli/nemo ux llama sft and peft examples
State: closed | Pull request: yes

# 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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```python
# Add a code snippet demonstrating how to use this 
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# Additional Information
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Revert torchrun fix for model import
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

torchrun fix was broken; when making the model-import trainer, it was using the env-vars.

Reverting this as it's breaking other things.

**Collection**: [Note which collection this PR will affect]

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# Additional Information
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ASR models with transcription steering prompt (canary, parakeet)
State: closed | Pull request: no
Labels: name: feature

name: stale

name: ASR

**Is your feature request related to a problem? Please describe.**

is there a way for the canary and parakeet models (https://huggingface.co/nvidia/canary-1b) to steer transcription like whisper in order to have correct name spellings for example or adapt a certain style?

goal is to have better, more accurate subtitles

**Describe the solution you'd like**

pass string to the inference methods for these models to have more accurate resulting transcriptions. for example to fix spelling of names or titles

**additional context**

see whisper prompting here https://cookbook.openai.com/examples/whisper_prompting_guide

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.

Thanks for the request. We don't offer exact functionality now. However you could use [this script ](https://github.com/NVIDIA/NeMo/blob/main/scripts/asr_context_biasing/eval_greedy_decoding_with_context_biasing.py)for CTC or hybrid RNNT-CTC models to boost such words while transcribing.
`speech_to_text_aed_chunked_infer.py`  throws `TypeError: 'TranscriptionConfig' object is not iterable`
State: closed | Pull request: no
Labels: name: bug

name: stale

**Describe the bug**

canary model chunked infer with `speech_to_text_aed_chunked_infer.py` throws this error:
`TypeError: 'TranscriptionConfig' object is not iterable`

**Steps/Code to reproduce bug**

run this `main` function: https://github.com/NVIDIA/NeMo/blob/7cc99e95fa753f46dffffc47a19e3c1fa375159c/examples/asr/asr_chunked_inference/aed/speech_to_text_aed_chunked_infer.py#L118 

throws at this line
https://github.com/NVIDIA/NeMo/blob/7cc99e95fa753f46dffffc47a19e3c1fa375159c/examples/asr/asr_chunked_inference/aed/speech_to_text_aed_chunked_infer.py#L122


**misc**
might be legacy code?


might be that it will also throw here: https://github.com/NVIDIA/NeMo/blob/7cc99e95fa753f46dffffc47a19e3c1fa375159c/examples/asr/asr_chunked_inference/rnnt/speech_to_text_buffered_infer_rnnt.py#L149

@stevehuang52 pls have a look at this. 

Thanks for pointing out the issue, it will be fixed in https://github.com/NVIDIA/NeMo/pull/10581

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.
salm export trtllm
State: closed | Pull request: yes
Labels: name: Multi Modal

name: Run CICD

# What does this PR do ?

Add SALM export

**Collection**: [NA]  (nemo multimodal export)

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage

Run export:
Please refer to `https://github.com/Slyne/NeMo/blob/salm_trtllm_llm/examples/multimodal/speech_llm/export/README.md`

Run deploy:
```bash 
python3 NeMo/scripts/deploy/multimodal/deploy_triton.py \
        --modality="audio" \
        --visual_checkpoint=NeMo/examples/multimodal/speech_llm/export/output/speechllm_fc_llama2_7b_perception \
        --llm_checkpoint=NeMo/examples/multimodal/speech_llm/export/speechllm_fc_llama2_7b_llm_merged.nemo \
        --llm_model_type="llama" \
        --model_type="salm" \
        --triton_model_name="salm" \
        --max_input_len=4096 \
        --max_output_len=256 \
        --max_multimodal_len=3072 \
        --triton_model_repository=/tmp/trt_model_dir/
```

Run query:
```
python3 NeMo/scripts/deploy/multimodal/query.py \
        --model_name="salm" \
        --model_type="salm" \
        --input_text="Q: what's the transcription of the audio? A:" \
        --input_media=/ws/data/test-clean-processed/7729-102255-0009.wav
```

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@meatybobby
Log Gradient Norms
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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**Pre checks**:
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[NeMo-UX] Don't create attention mask for GPTs
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?
For flash and fused attention kernels, the attention mask is implicitly generated by the kernel, so no need to generate the mask from the dataloader. This saves memory, especially for causal masks of long sequence lengths.

Flash attention and fused attention are well supported and should cover all the models supported by NeMo, and are already turned on by default, so turning this on by default as well. 

This also removes a HtoD at the start of the first layer, since the attention mask is on CPU for GPTDataset. This gives a ~5% speedup on the first pipeline parallel stage:
![image](https://github.com/user-attachments/assets/fd239482-deb9-4d3c-a8e5-b9796e4ce581)



Add a one line overview of what this PR aims to accomplish.

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# Additional Information
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[NeMo-UX] checkpointing improvements
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?
- saves weights and artifacts to separate directories
- provides the option to skip saving optimizer states on train end

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Lower bound transformers to support Nemotron
State: closed | Pull request: yes
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# What does this PR do ?

transformers support Nemotron start 4.44.0

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Param and Grad Debug Logger
State: closed | Pull request: yes
Labels: 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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Add option to assert some value for attributes instead of just logging

TODO: add some tests on a dummy model with asserts on precision

Don't we need to enable the megatron_gpt_pretraining test in CI?

@hemildesai I thought it is already being used in the L2 tests: https://github.com/NVIDIA/NeMo/blob/maanug/param-debugger/.github/workflows/cicd-main.yml#L5142

> @hemildesai I thought it is already being used in the L2 tests: https://github.com/NVIDIA/NeMo/blob/maanug/param-debugger/.github/workflows/cicd-main.yml#L5142

Ah I see. Sorry my bad.

[🤖]: Hi @maanug-nv 👋,

I just wanted to let you know that, you know, a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11077400886) for this PR just finished successfully ✨

So it might be time to merge this PR or like to get some approvals 🚀

But I'm just a 🤖 so I'll leave it you what to do next.

Have a great day! 

//cc @ko3n1g

[🤖]: Hi @maanug-nv 👋,

We wanted to let you know that a [CICD pipeline](https://github.com/$REPOSITORY/actions/runs/$RUN_ID) for this PR just finished successfully

So it might be time to merge this PR or get some approvals

I'm just a bot so I'll leave it you what to do next.

//cc @pablo-garay @ko3n1g
Move trt imports in nemo.collections.llm inside respective functions
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

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# Additional Information
* Related to # (issue)
mcore distributed optimizer interface
State: closed | Pull request: yes
Labels: name: core

name: NLP

name: Run CICD

# What does this PR do ?

The interface for megatron core distributed optimizer

# Changelog 
- Support native fp8; fp8-param optimizer
- Support the overlap of param allgather with optimizer.step
- Support the alignment of param AllGather and grad ReduceScatter

# Usage
- Set `fp8_params` to enable fp8-param optimizer
- Set `optim.overlap_param_gather_with_optimizer_step` to enable the overlap of param AllGather with optim.step
- Set `optim align_grad_reduce` and `optim. align_param_gather` to align the data-parallel collectives across pipeline-parallel ranks

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# Additional Information
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refactor: Deprecate `async_grad_allreduce`
State: closed | Pull request: yes
Labels: name: NLP

name: Run CICD

# What does this PR do ?

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# Additional Information
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McoreDistributedOptimizer wrapper
State: closed | Pull request: yes
Labels: name: core

name: stale

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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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.

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.
Add Starcoder to Nemo 2
State: closed | Pull request: yes
Labels: name: Run CICD

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# Additional Information
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Sanity checks for unfinished checkpoints removal
State: closed | Pull request: yes
Labels: name: core

name: Run CICD

# What does this PR do ?

The following scenario was recently observed:
- Last checkpoint writing was interrupted due to an irrelevant issue
- Hence last checkpoint was marked as "unfinished" (and there was no other last checkpoint)
- Hence the following training silently skipped the last checkpoint and started from scratch
- As a result, training was not making expected progress despite many training runs

This PR adds some sanity checks to ensure that when the last/end checkpoint is unfinished, it wont get silently skipped and deleted. Instead, user is supposed to remove them manually. 

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@jbieniusiewi could you also add the same check for 2.0. The unfinished checkpoint filtering is [here](https://github.com/NVIDIA/NeMo/blob/main/nemo/lightning/resume.py#L112-L113) in 2.0.

Thank you for the review @maanug-nv. 
I've added checks for NeMo 2.0. One more approval is needed to merge it, so I re-requested a review.

~~-@ashors1 Do we have/plan to have tests for AutoResume?~~ Looks like they are in `tests/lightning/test_nemo_logger.py`. @jbieniusiewi final request, can we include the same test you added for exp manager to NeMoLogger/AutoResume as well?

@maanug-nv Sure, I've added tests to ``tests/lightning/test_nemo_logger.py``.