renwei2024/nemo-github-issues huggingface dataset:
Unify code paths for building TRTLLM engines for nemo & qnemo checkpoints
State: closed | Pull request: yes
Labels: name: stale

# What does this PR do ?

Unify code paths for building TRTLLM engines for nemo & qnemo checkpoints.

**Collection**: NLP

# 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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**PR Type**:
- [x] Refactor
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Fix FSDP gradient calculation with orig params
State: closed | Pull request: yes
Labels: name: NLP

name: Run CICD

As observed by some users in #8487, FSDP causes differences in loss. This is because gradients are not correctly calculated when `fsdp_use_orig_params=True`. Currently, any non-`FlatParameter` is treated as being unsharded, when in reality they may be sharded when `use_orig_params=True`. This leads to a double reduction of sharded gradients. We thus simply adjust the parameters that are reduced manually. This requires use of a private variable in the FSDP module. Alternatively, the value of `self.kwargs['ignored_states']` in `nlp_overrides.py` could be set as an attribute on the model to avoid this private variable access.

Thanks to @ofivite for suggesting that `use_orig_params=True` could be the cause of the issue, which greatly helped with analysis.

# What does this PR do ?

Fix FSDP gradient calculation when `fsdp_use_orig_params=True`.

**Collection**: nlp

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# Additional Information
* Related to #8487 (issue)

Nice, can you fix the DCO?

Done thanks :)
Nvidia GPU is Not accessing in my Docker Container
State: closed | Pull request: no


Error:
```
ERROR: The NVIDIA Driver is present, but CUDA failed to initialize. GPU functionality will not be available.
   [[ System has unsupported display driver / cuda driver combination (error 803) ]]
```

Command:
```
docker run --gpus device=0 --shm-size=2g --net=host --ulimit memlock=-1 --rm -it -v ${PWD}:/workspace -w /workspace -v ${PWD}/results:/results nvcr.io/nvidia/nemo:24.03.01.framework bash
```

![image](https://github.com/NVIDIA/NeMo/assets/116936023/86a68453-4596-47ca-b183-5c138bf4236e)

My Docker version: Docker version 26.1.3, build b72abbb  
My GPU: NVIDIA RTX A6000
Nvidia-smi (non docker)
![image](https://github.com/NVIDIA/NeMo/assets/116936023/9f79917b-1676-4fcc-8214-87cfb7e3bd5c)
Nvidia-smi(docker container)
![image](https://github.com/NVIDIA/NeMo/assets/116936023/c8d4608d-6f02-42a4-9072-55026b0601c9)

---





Similar issue: https://github.com/NVIDIA/NeMo/issues/9268 

My issue got solved instead of pulling nvcr.io/nvidia/nemo:24.03.01.framework pull nvcr.io/nvidia/nemo:dev.framework
HF_TOKEN param for the scripts
State: closed | Pull request: yes
Labels: name: stale

name: NLP

name: Run CICD

# What does this PR do ?

Adds HF TOKEN to the scripts

Hi, I wonder why passing HF-TOKEN via CLI is required instead of passing it via an environment variable? Can you please clarify for future readers? Thank you.

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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Call teardown even when something fails.
State: closed | Pull request: yes
Labels: name: stale

name: Multi Modal

name: Run CICD

# What does this PR do ?

Avoid tons of terminal spam such as:
```
[rank0]:[W528 18:14:19.086678488 ProcessGroupNCCL.cpp:1122] WARNING: process group has NOT been destroyed before it is being destructed. On normal program exit, the application should call destroy_process_group to ensure that any pending NCCL data transfers have finished in this process. In rare cases this process can exit before this point and block the progress of another member of the process group. This constraint has always been present,  but this warning has only been added since PyTorch 2.4
```

when an error occurs, which can obscure the real error.

**Collection**: Stable diffusion

# Changelog 
- stable diffusion: Clean up process groups after an error occurs.

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- - Are any necessary for this?
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Commenting to kill the stale label.

This really just needs a reviewer. Would be great to know if this is the desired solution; I think I'm seeing the same thing in another model and would replicate this approach there, if so.

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conv1d stable version
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
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

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Yuya/add checkpoints section
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

Add NeMo Dev docs to explain NeMo checkpoints, .nemo .ckpt, regular and distributed.

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move pooler under post_process
State: closed | Pull request: yes
Labels: name: NLP

name: CI

name: Run CICD

# What does this PR do ?

Moves pooler layer to post_process in order to avoid saving checkpoint error.

**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 typos in Mixtral NeMo->HF and Starcoder2 NeMo->HF conversion scripts
State: closed | Pull request: yes
Labels: name: bug

name: Run CICD

# What does this PR do ?

There were some typos in these two scripts.

**Collection**: [scripts]

# Changelog 
- scripts/checkpoint_converters/convert_mixtral_nemo_to_hf.py:
  - wrong `nemo_config.num_experts_per_token` parameter
  - `head_num` was undefined
- scripts/checkpoint_converters/convert_mixtral_nemo_to_hf.py:
  - wrong argument `args.out_file`

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# Additional Information
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ci: Properly catch failed tests by introduction of workflow templates
State: closed | Pull request: yes
Labels: name: CI

name: Run CICD

# What does this PR do ?

We want to be notified about failed tests. However, as we send a "cancel" signal to all tests once a single test fails (including itself), no job will ever be marked with failure but with cancel instead. That's just a result due to the race-condition of cancelling workflows by our custom action.

We can only solve that issue by giving our test jobs an output that queries the result code of the main test step.

As we were discussing to refactor tests into a template anyway, I've addressed this refactoring 

**Collection**: [Note which collection this PR will affect]

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- 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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pipeline passed: https://github.com/NVIDIA/NeMo/actions/runs/9382022367
Fixed typos in README.rst
State: closed | Pull request: yes
Labels: name: Run CICD

The following typos have been corrected in **README.rst** file

1. **pytorch** --> **PyTorch**
2. **isntallation** --> **installation**
3. **higly** --> **highly**
4. **transfromer** --> **transformer**


Hi, can you please sign off all your commits? Please see here for instructions https://github.com/NVIDIA/NeMo/pull/9322/checks?check_run_id=25479558364
switch to new runners
State: closed | Pull request: yes
Labels: name: CI

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 
```

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**Pre checks**:
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NameError: name 'ApexGuardDefaults' is not defined
State: closed | Pull request: no
Labels: name: bug

**NameError: name `'ApexGuardDefaults'` is not defined**

I am getting 
```bash
NameError: name 'ApexGuardDefaults' is not defined
```
when import `nemo_nlp` as `from nemo.collections import nlp as nemo_nlp`. 

I installed NeMo after cloning via `./reinstall.sh` command.

```bash
29 except (ImportError, ModuleNotFoundError) as e:
---> 31     TransformerLayer = TransformerLayerSubmodules = ApexGuardDefaults
     32     MLP = MLPSubmodules = ModuleSpec = IdentityOp = ApexGuardDefaults
     33     AttnMaskType = DotProductAttention = TENorm = ApexGuardDefaults

NameError: name 'ApexGuardDefaults' is not defined
```

**Environment overview (please complete the following information)**

 - Environment location:  AWS AMI, V100
 - Method of NeMo install: from source

**Environment details**

If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS: Ubuntu 20.04
- Torch: 2.3.0+cu121
- Python version: Python 3.11.9


is your issue solved ? if solved tell me how i am facing similar issue

I came across this as well. @yonas-g if you found a solution would you mind sharing?

Could be missing an import `pip install megatron_core`
[SD] Fix SD CUDA Graph Failure
State: closed | Pull request: yes
Labels: 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 
```

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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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- [x] Bugfix
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Request for Code and Models
State: closed | Pull request: no
Labels: name: stale

I recently came across the paper titled "**Discrete Audio Representation as an Alternative to Mel-Spectrograms for Speaker and Speech Recognition**" and found it extremely insightful. I am very interested in exploring the methods and results discussed in the paper further.

Would it be possible to share the code and models used in the paper? Having access to these resources would greatly aid in my understanding and allow me to replicate and build upon the research conducted.

Thank you for your time and assistance. I look forward to your response.

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.
Guard cuda memory allocator update
State: closed | Pull request: yes
Labels: name: cherry-pick

name: common

# What does this PR do ?

Adds a try catch for torch cuda memory allocator for Lhotse

**Collection**: [ASR]

# Changelog 
- Adds a guard with try catch to prevent hard crash under some older pytorch versions.

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Guard cuda memory allocator update
State: closed | Pull request: yes
Labels: name: common

# What does this PR do ?

Adds a try catch for torch cuda memory allocator for Lhotse

**Collection**: [ASR]

# Changelog 
- Adds a guard with try catch to prevent hard crash under some older pytorch versions.

# GitHub Actions CI

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**Pre checks**:
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- [ ] Documentation

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# Additional Information
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Install alerting
State: closed | Pull request: yes
Labels: name: CI

name: Run CICD

# What does this PR do ?

Tweets workflow failures out to the world

**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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**Pre checks**:
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  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
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- [ ] Bugfix
- [ ] Documentation

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# Additional Information
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Change modelopt Layer to Use TE attention
State: closed | Pull request: yes
Labels: name: NLP

# 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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Hi, could you also file a MR to enable specifying "a code dataset" that is required to calibrate "code" LLMs like StarCoder2? This should be another option in `get_calib_dataloader` here https://github.com/NVIDIA/NeMo/blob/b0f3138a6be7fab3175deb8935f8492aeb1445bd/examples/nlp/language_modeling/megatron_quantization.py#L72

I think we should import this spec from https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/inference/gpt/model_specs.py, it comes with `core_attention` update pursued here
add code to support hf to nemo lora convertion with tp > 1
State: closed | Pull request: yes
Labels: name: stale

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Fix CUDA Graphs for SD
State: closed | Pull request: yes
Labels: name: Multi Modal

# What does this PR do ?

Fix CUDA Graphs for SD

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# Changelog 
- Add specific line by line info of high level changes in this PR.

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Use closed-formula to round by multiple
State: closed | Pull request: yes
Labels: name: stale

name: NLP

name: Multi Modal

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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[DRAFT] Add lita inference
State: closed | Pull request: yes
Labels: name: core

name: ASR

name: NLP

name: CI

name: common

name: Multi Modal

# What does this PR do ?
Notice this is only a draft.

* Convert LITA ckpt to nemo model
* Add LITA FAST & SLOW tokens
* Add LITA 1.5 img_vid_start_end mode

**Collection**: [multimodal]

# Changelog 
- Added LITA config file
- Added LitaWordEmbeddingMixin class
- Added LITA arguments for inference pipeline
- Verified inference result

# Usage
Please refer to LITA.md for reference.

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@yaoyu-33
Got a question is it normal that the `NeMo/examples/multimodal/multimodal_llm/neva/conf/neva_inference.yaml` seems to do streaming inference. And I see continuous calls/streaming calls to `replace_media_embeddings` with the below command:
```
neva_model_file=/ws/converted_nemo_model/lita-vicuna-v1-3-13b-finetune.nemo
prompt_file=/ws/test/prompt_file.json
output_file=/ws/test/output.json
video_base_path=/ws/test
torchrun --nproc_per_node=1 /ws/NeMo/examples/multimodal/multimodal_llm/neva/neva_evaluation.py \
    --config-path=/opt/NeMo/examples/multimodal/multimodal_llm/neva/conf/ \
    --config-name=neva_inference.yaml \
    tensor_model_parallel_size=1 \
    pipeline_model_parallel_size=1 \
    neva_model_file=$neva_model_file \
    trainer.devices=1 \
    trainer.precision=16 \
    prompt_file=$prompt_file \
    inference.media_base_path=$video_base_path \
    inference.media_type=video \
    output_file=$output_file \
    inference.temperature=0.2 \
    inference.top_k=0 \
    inference.top_p=0.9 \
    inference.greedy=False \
    inference.add_BOS=False \
    inference.all_probs=False \
    inference.repetition_penalty=1.2 \
    inference.insert_media_token=right \
    inference.tokens_to_generate=256 \
    quantization.algorithm=awq \
    quantization.enable=False

```
add code to support hf to nemo lora convertion with tp > 1
State: closed | Pull request: yes
Labels: name: stale

name: NLP

# What does this PR do ?

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Fix typo in HF tutorial
State: closed | Pull request: yes
Labels: name: cherry-pick

# What does this PR do ?

Fix a typo in the tutorial for HF model upload

**Collection**: [Core]

# Changelog 
- Fix missing "_" that causes crash in the last cell.

# Before your PR is "Ready for review"
**Pre checks**:
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- [x] Bugfix
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add support to convert hf to nemo loras with tp > 1
State: closed | Pull request: yes
Labels: name: core

name: stale

name: ASR

name: NLP

name: Multi Modal

# What does this PR do ?

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# Add a code snippet demonstrating how to use this 
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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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Fix typo in HF tutorial
State: closed | Pull request: yes

# What does this PR do ?

Fix a typo in the tutorial for HF model upload

**Collection**: [Core]

# Changelog 
- Fix missing "_" that causes crash in the last cell.

# 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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  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
Fix P-tuning for Llama based models
State: closed | Pull request: yes
Labels: name: cherry-pick

name: Run CICD

# What does this PR do ?

Fixes inference with P-tuning.

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Added the BOS token for Llama, Mistral and Mixtral.
  
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
Speeds up copying of necessary artifact files with SaveRestoreConnector
State: closed | Pull request: yes
Labels: name: core

name: stale

name: NLP

name: Run CICD

# What does this PR do ?

Previously, the SaveRestoreConnector would copy and untar entire
checkpoints just to copy out a tokenizer. For models in the >100GB, this
led to timeouts since only rank=0 did this work, while other ranks moved
on and waited at an all-gather barrier (observed NCCL timeout at 10min).

**Collection**: [Note which collection this PR will affect]

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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.

Closing in favor of https://github.com/NVIDIA/NeMo/pull/9682