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} | # 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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} | # 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
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
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.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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?
- [x] 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
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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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
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
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.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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?
- [ ] 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
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"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.c... | 2024-07-15T23:02:47 | 2024-08-29T03:17:58 | 2024-08-29T03:17:57 | COLLABORATOR | {
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"merged_at": "2024-08-29T03:17:57"
} | # 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
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
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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} | # 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
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} | # 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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} | # 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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If you haven't finished some of the above items you can still open "Draft" PR.
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} | # 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
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
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.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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?
- [ ] 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
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # 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
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
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.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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?
- [ ] 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
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # 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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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)
```
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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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The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
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# Before your PR is "Ready for review"
**Pre checks**:
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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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.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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?
- [ ] 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
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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"@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."... | 2024-07-15T07:10:42 | 2024-09-09T01:57:02 | 2024-09-09T01:57:02 | NONE | {
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**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 | {
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"duplicate of https://github.com/NVIDIA/NeMo/issues/9521 "
] | 2024-07-15T07:06:18 | 2024-08-01T13:21:35 | 2024-08-01T13:21:35 | NONE | {
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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] | {
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"I realize that CPU memory leaked in every training step but I don't know why. I tried to change batch_size, num_workker, log_every_n_steps but nothing works. Anyone has an idea? @dimapihtar ",
"Hi, could you try to include these params to your run?\r\n`model.use_cpu_initialization=False`\r\n`model.cpu_offloading... | 2024-07-15T01:41:18 | 2024-09-06T07:25:25 | 2024-08-26T01:52:46 | NONE | {
"total": 0,
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} | null | null | null | **Describe the bug**
Im training Llama3-8b with Nemo framework on 4 A100 80GB GPU. During training, my CPU RAM keeps increasing in every step and it consumed in buff/cache memory. My server has 2TB CPU RAM and it reached 100% with just 20 steps (my epoch has total 900 steps).
Edit: Both my shared and cache memory increase continuously.
Soon my training task will collap.
**Steps/Code to reproduce bug**
Here is my training script (https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/llama2sft.html):
```
torchrun --nproc_per_node=4 \
/opt/NeMo/examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py \
trainer.devices=4 \
trainer.num_nodes=1 \
trainer.val_check_interval=50 \
trainer.max_epochs=1 \
trainer.max_steps=900 \
model.restore_from_path="my_model.nemo" \
model.micro_batch_size=1 \
model.global_batch_size=256 \
model.tensor_model_parallel_size=4 \
model.pipeline_model_parallel_size=1 \
model.megatron_amp_O2=True \
model.sequence_parallel=True \
model.activations_checkpoint_granularity=selective \
model.activations_checkpoint_method=uniform \
model.optim.name=distributed_fused_adam \
model.optim.lr=5e-6 \
model.answer_only_loss=True \
model.peft.peft_scheme=none \
model.data.train_ds.file_names="my_train_data.jsonl" \
model.data.validation_ds.file_names="my_val_data.jsonl" \
model.data.test_ds.file_names="my_test_data.jsonl" \
model.data.train_ds.concat_sampling_probabilities="[1]" \
model.data.train_ds.max_seq_length=8192 \
model.data.validation_ds.max_seq_length=8192 \
model.data.train_ds.micro_batch_size=1 \
model.data.train_ds.global_batch_size=256 \
model.data.validation_ds.micro_batch_size=1 \
model.data.validation_ds.global_batch_size=32 \
model.data.test_ds.micro_batch_size=1 \
model.data.test_ds.global_batch_size=32 \
+model.data.num_workers=4 \
model.data.validation_ds.metric.name=loss \
model.data.test_ds.metric.name=loss \
exp_manager.create_wandb_logger=False \
exp_manager.explicit_log_dir=results_v2 \
exp_manager.resume_if_exists=False \
exp_manager.resume_ignore_no_checkpoint=True \
exp_manager.create_checkpoint_callback=True \
exp_manager.checkpoint_callback_params.monitor=step \
exp_manager.checkpoint_callback_params.mode='max' \
exp_manager.checkpoint_callback_params.save_top_k=4 \
exp_manager.checkpoint_callback_params.save_best_model=True \
exp_manager.checkpoint_callback_params.save_nemo_on_train_end=True
```
**Environment overview**
Environment location: Nemo framework: nvcr.io/nvidia/nemo:24.03.01.framework
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} | # 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
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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. "
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} | # 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
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} | # What does this PR do ?
1-line update to docs
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# Changelog
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# Usage
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"I don't want to set default to `False` since there are models not using FA or mask fusion. "
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} | # What does this PR do ?
Set create_attention_mask=False when we have mask in built-in modules like FlashAttention.
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"#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... | 2024-07-12T16:59:54 | 2024-07-16T22:32:48 | 2024-07-12T17:05:01 | COLLABORATOR | {
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"> Can we rename the directory `models` to `recipes`?\r\n> \r\n> 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.\r\n\r\nDone"
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```python
# Add a code snippet demonstrating how to use this
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# Changelog
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# 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.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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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
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
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.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
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```
[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.
**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
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
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.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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?
- [ ] 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
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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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
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
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.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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?
- [ ] 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
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # 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
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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} | # What does this PR do ?
Changes for the updated version of resiliency features:
- Fault Tolerance
- Straggler Detection
**Collection**: [Note which collection this PR will affect]
# Changelog
- Updated/clarified documentation
- Removed duplicated "straggler detection" section from the documentation
- Updated straggler detection unit test
- Do not pass logger instance to the straggler det. callback
- Exposed `safety_factor` FT param
- Pass `exp_dir` to the FT callback
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} | # What does this PR do ?
Refactor LITA part
**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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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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**PR Type**:
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## Who can review?
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} | # What does this PR do ?
Refactor Notebook `BRANCH = 'main'` to be uniformly the same across all notebooks
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
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* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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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?
- [ ] 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?
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- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
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"Same Error here",
"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."
] | 2024-07-12T10:04:26 | 2024-09-05T01:55:11 | 2024-09-05T01:55:11 | NONE | {
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} | null | null | null | even I use 24.05.01， still stuck in this status
use another sft script which refer by official document is also not work by this error
https://docs.nvidia.com/nemo-framework/user-guide/latest/modelalignment/sft.html#step-2-sft-training
Traceback (most recent call last):
File "/opt/NeMo-Aligner/examples/nlp/gpt/train_gpt_sft.py", line 243, in
main()
File "/opt/NeMo/nemo/core/config/hydra_runner.py", line 129, in wrapper
_run_hydra(
File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 394, in _run_hydra
_run_app(
File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 457, in _run_app
run_and_report(
File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 223, in run_and_report
raise ex
File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 220, in run_and_report
return func()
File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 458, in
lambda: hydra.run(
File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/hydra.py", line 132, in run
_ = ret.return_value
File "/usr/local/lib/python3.10/dist-packages/hydra/core/utils.py", line 260, in return_value
raise self._return_value
File "/usr/local/lib/python3.10/dist-packages/hydra/core/utils.py", line 186, in run_job
ret.return_value = task_function(task_cfg)
File "/opt/NeMo-Aligner/examples/nlp/gpt/train_gpt_sft.py", line 129, in main
ptl_model, updated_cfg = load_from_nemo(
File "/opt/NeMo-Aligner/nemo_aligner/utils/utils.py", line 98, in load_from_nemo
model = cls.restore_from(
File "/opt/NeMo/nemo/collections/nlp/models/nlp_model.py", line 465, in restore_from
return super().restore_from(
File "/opt/NeMo/nemo/core/classes/modelPT.py", line 464, in restore_from
instance = cls._save_restore_connector.restore_from(
File "/opt/NeMo-Aligner/nemo_aligner/utils/utils.py", line 51, in restore_from
return super().restore_from(*args, **kwargs)
File "/opt/NeMo/nemo/collections/nlp/parts/nlp_overrides.py", line 1172, in restore_from
checkpoint = checkpoint_io.load_checkpoint(tmp_model_weights_dir, sharded_state_dict=checkpoint)
File "/opt/NeMo/nemo/utils/callbacks/dist_ckpt_io.py", line 78, in load_checkpoint
return dist_checkpointing.load(
File "/opt/megatron-lm/megatron/core/dist_checkpointing/serialization.py", line 133, in load
validate_sharding_integrity(nested_values(sharded_state_dict))
File "/opt/megatron-lm/megatron/core/dist_checkpointing/serialization.py", line 425, in validate_sharding_integrity
torch.distributed.all_gather_object(all_sharding, sharding)
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/c10d_logger.py", line 75, in wrapper
return func(*args, *kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/distributed_c10d.py", line 2310, in all_gather_object
all_gather(object_size_list, local_size, group=group)
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/c10d_logger.py", line 75, in wrapper
return func(args, kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/distributed_c10d.py", line 2724, in all_gather
work = default_pg.allgather([tensor_list], [tensor])
torch.distributed.DistBackendError: NCCL error in: /opt/pytorch/pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1961, internal error - please report this issue to the NCCL developers, NCCL version 2.19.3
ncclInternalError: Internal check failed.
Last error:
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Duplicate of #9587 for release branch
Also includes transformers version update from #9606
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Add parameters to CLIP model loading for SD training.
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Those internal can look at internal nvbug 4452698. | {
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"That is expected, currently we don't support getting timestamps from tdt/rnnt decoder, so if you are looking for timestamps you could get through CTC, however performance with CTC decoder is a bit degraded compared to tdt decoder but shouldn't be worse. \r\n\r\nI have created this space that gets timestamps from C... | 2024-07-11T15:01:36 | 2024-08-01T15:01:23 | 2024-08-01T15:01:23 | NONE | {
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If I switch the decoder on this model to ctc, it works and also provides timestamps, but i see worse performace in terms of WERs. | {
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} | Due to underlying changes in MLM mcore RETRO, the total number of parameters slightly changed.
We update the `assert num_weights ` accordingly in unit test for RETRO.
# What does this PR do ?
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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
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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"@yaoyu-33 , could you please have another look and let the workflow be run?",
"FWIW, I ran our little test (a `neva_pretrain.py` invocation) with `NEMO_TESTING=1` and things ran fine there.",
"@yaoyu-33 , @ericharper , could you please run the workflow again? Thank you!",
"This PR is stale because it has bee... | 2024-07-11T12:34:07 | 2024-11-30T08:52:30 | 2024-11-28T02:04:04 | NONE | {
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} | The following patch modifies the forward of the NeVa model (the `replace_media_embeddings` method) by introducing the following improvements:
- Elimination of `torch.where` with a single argument. That call introduces device synchronizations when run on GPUs.
- Media index selection manipulation in the construction of `padded_media_indices` can be done in a single vectorized call.
- Reduction in the overall number of kernel launches.
- Elimination of calls to `all`. This also causes device syncs. We can leave it, or run it in debug mode only. It is best to have a dedicated test instead of that assert, imho. | {
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"General comment: NeMo uses google-style docstrings so please use that format.",
"This is already a big PR which in this particular case is OK I feel like since it's a port of functionality that exists in the launcher now. But it seems like it's not rebased correctly and therefore it contains many un-related chan... | 2024-07-11T11:59:34 | 2024-09-10T07:55:53 | 2024-09-10T07:55:48 | COLLABORATOR | {
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} | # 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
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
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.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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?
- [ ] 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
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adds two workflows:
* "NeMo Code freeze"
* "NeMo Code release"
# "NeMo Code freeze"
Started via workflow dispatch. Input is the next version number. Does the following:
* Reads $CURRENT_VERSION from main, check out new branch `r$CURRENT_VERSION` and pushes to origin and opens PR. Comments in that PR serve as platform for documenting changelog
* Bumps `package_info.py` with $NEXT_VERSION and pushes to `main`
# "NeMo Code release"
Started via comment to PR. Creates GH release/tag, builds and pushes the wheel, closes PR
# Additional Information
This automation requires a couple of secrets like Twine credentials or Personal Access Tokens. We an environment `main` and couple these secrets to it such that they are only exposed when running on that protected branch. Secrets are secure as only code-owners permit changes to `main` and on other branches the secrets are not interpolated. | {
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"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.",
"Hi @Kurokabe,\r\nThank you for pointing out. Indeed, T5 inference is not su... | 2024-07-11T10:27:21 | 2024-08-22T20:42:00 | 2024-08-22T01:52:52 | NONE | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
I have been using the MegatronLMEncoderDecoderModel which seems to be the closest implementation for my need (encoder-decoder model), but when generating results (with the `decode` method), the generation becomes slower as it generates more tokens. My guess is that KV-Cache is not used for this model.
After a lot of digging, I found that the MegatronGPTModel uses for inference `set_inference_key_value_memory` and `inference_max_sequence_len`. I have tried to set `set_inference_key_value_memory=True` during the first decoding iteration, then to False, as well as providing only 1 token at a time, similar to `GPTModelTextGenerationStrategy` https://github.com/NVIDIA/NeMo/blob/900ca0b8bac3d038b9854bd498967b0525aaeb35/nemo/collections/nlp/modules/common/text_generation_strategy.py#L312-L327 , but I am faced with many issues, and the usage isn't straightforward.
To notice the generation speed decrease, you can wrap the for loop with tqdm:
```python
from tqdm.auto import tqdm
def decode(
self,
tokens_enc,
enc_mask,
num_tokens_to_generate,
encoder_input=None,
tokenizer=None,
enc_output=None,
enc_output_attn_mask=None,
ignore_ids=[],
bos_id=None, # If bos=None, will use tokenizer.bos_id unless explicitly set to something else.
predicted_tokens_dec=None,
batch_data=None,
sampling_method: str = "greedy-search",
sampling_kwargs: dict = {},
):
...
for i in tqdm(range(num_tokens_to_generate)):
```
**Describe the solution you'd like**
It would be nice if KV-Cache could be used for the MegatronLMEncoderDecoderModel, or provide a snippet of code to use the `decode` method without any slowdown during the generation.
**Describe alternatives you've considered**
`MegatronGPTModel` seems to uses the KV-Cache, but is a decoder only model. Since `MegatronGPTModel` uses internally (to the lower level) a `ParallelTransformer`, like the `MegatronTransformerEncoderModule` and `MegatronTransformerDecoderModule`, a parameter could be specified to `MegatronGPTModel` to enable cross attention inside it to be used like a decoder, with an encoder output specified
```python
def forward(
self,
input_ids,
position_ids,
attention_mask,
enc_output=None, # Add this input
loss_mask=None,
labels=None,
token_type_ids=None,
layer_past=None,
get_key_value=False,
forward_method_parallel_output=None,
encoder_input=None,
set_inference_key_value_memory=False,
inference_max_sequence_len=None,
checkpoint_activations_all_layers=None,
):
lm_output = self.language_model(
input_ids,
position_ids,
attention_mask,
encoder_output=encoder_output, # Add this
layer_past=layer_past,
get_key_value=get_key_value,
encoder_input=encoder_input,
set_inference_key_value_memory=set_inference_key_value_memory,
inference_max_sequence_len=inference_max_sequence_len,
checkpoint_activations_all_layers=checkpoint_activations_all_layers,
)
```
So that GPT model can be used with CrossAttention like [HuggingFace GPT2 model](https://github.com/huggingface/transformers/blob/6c1d0b069de22d7ed8aa83f733c25045eea0585d/src/transformers/models/gpt2/modeling_gpt2.py#L1091), because KV-Cache is implemented in `MegatronGPTModel.generate`
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"I have also run 'pytest test_megamolbart_triton.py' pointing that to my 'fine tuned' .nemo file and it gives me the same.\r\n\r\n======================================================================= short test summary info =======================================================================\r\nERROR test_mega... | 2024-07-11T05:27:52 | 2024-08-19T01:52:43 | 2024-08-19T01:52:43 | NONE | {
"total": 0,
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} | null | null | null | **Describe the bug**
I can successfully run my inference code on the default megamolbart.nemo, but as soon as I run any kind of fine tuning on it then I get the error RuntimeError: Error(s) in loading state_dict for MegaMolBARTModel. I've attached code, config and output for both finetune and infer stages.
My desired outcome is to use the finetuning process to add in some of my own data, then call the inference code to get the SMILES string and the prediction.
**Steps/Code to reproduce bug**
**1. FineTune - code, config and output**
Actually I get the same issue if I take trainer.fit(model) out of the code as well. My Fine Tune Code (company1.6_finetune_donothing.py.txt) just loads the default megamolbart.nemo using finetune_config.yaml.txt (doesn't alter anything if restore_from_path is filled in or not).
The Result (company1.6_finetune_donothing_RESULT.txt) seems fine.
[company1.6_finetune_donothing.py.txt](https://github.com/user-attachments/files/16171757/company1.6_finetune_donothing.py.txt)
[finetune_config.yaml.txt](https://github.com/user-attachments/files/16171759/finetune_config.yaml.txt)
[company1.6_finetune_donothing_RESULT.txt](https://github.com/user-attachments/files/16171760/company1.6_finetune_donothing_RESULT.txt)
**2. Infer - code, config and output**
I then run my infer code (company1.6_infer.py.txt) with the default infer.yaml file (infer.yaml.txt) with just the restore_from_path pointing to the file created in Step 1. NOTE: as can be seen in the 'Expected behavior' section, if I use the default megamolbart.nemo file then it all works as expected.
[company1.6_infer.py.txt](https://github.com/user-attachments/files/16171785/company1.6_infer.py.txt)
[infer.yaml.txt](https://github.com/user-attachments/files/16171786/infer.yaml.txt)
[company1.6_infer_RESULT.txt](https://github.com/user-attachments/files/16171788/company1.6_infer_RESULT.txt)
**TLDR;**
This is my error:
[NeMo I 2024-07-11 01:57:35 regex_tokenizer:254] Loading regex from file = /workspace/bionemo/tokenizers/molecule/megamolbart/vocab/megamolbart.model
[NeMo I 2024-07-11 01:57:35 megatron_base_model:315] Padded vocab_size: 640, original vocab_size: 523, dummy tokens: 117.
[NeMo W 2024-07-11 01:57:35 megatron_lm_encoder_decoder_model:240] Could not find encoder or decoder in config. This is probably because of restoring an old checkpoint. Copying shared model configs to encoder and decoder configs.
[NeMo W 2024-07-11 01:57:35 megatron_lm_encoder_decoder_model:206] bias_gelu_fusion is deprecated. Please use bias_activation_fusion instead.
[NeMo W 2024-07-11 01:57:35 megatron_lm_encoder_decoder_model:206] bias_gelu_fusion is deprecated. Please use bias_activation_fusion instead.
Traceback (most recent call last):
File "/workspace/bionemo/examples/molecule/megamolbart/company1.6_infer.py", line 59, in <module>
inferer = load_model_for_inference(cfg, interactive=True)
File "/workspace/bionemo/bionemo/triton/utils.py", line 238, in load_model_for_inference
model = infer_class(cfg, interactive=interactive, **kwargs)
File "/workspace/bionemo/bionemo/model/molecule/infer.py", line 40, in __init__
super().__init__(
File "/workspace/bionemo/bionemo/model/core/infer.py", line 468, in __init__
super().__init__(
File "/workspace/bionemo/bionemo/model/core/infer.py", line 146, in __init__
self.model = self.load_model(cfg, model=model, restore_path=restore_path, strict=strict_restore_from_path)
File "/workspace/bionemo/bionemo/model/core/infer.py", line 206, in load_model
model = restore_model(
File "/workspace/bionemo/bionemo/model/utils.py", line 363, in restore_model
model = model_cls.restore_from(
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/nlp_model.py", line 465, in restore_from
return super().restore_from(
File "/usr/local/lib/python3.10/dist-packages/nemo/core/classes/modelPT.py", line 442, in restore_from
instance = cls._save_restore_connector.restore_from(
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/parts/nlp_overrides.py", line 751, in restore_from
super().load_instance_with_state_dict(instance, state_dict, strict)
File "/usr/local/lib/python3.10/dist-packages/nemo/core/connectors/save_restore_connector.py", line 203, in load_instance_with_state_dict
instance.load_state_dict(state_dict, strict=strict)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/nlp_model.py", line 447, in load_state_dict
results = super(NLPModel, self).load_state_dict(state_dict, strict=strict)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 2152, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for MegaMolBARTModel:
Missing key(s) in state_dict: "enc_dec_model.encoder_embedding.word_embeddings.weight", "enc_dec_model.encoder_embedding.position_embeddings.weight", "enc_dec_model.decoder_embedding.word_embeddings.weight", "enc_dec_model.decoder_embedding.position_embeddings.weight", "enc_dec_model.enc_dec_model.encoder.model.layers.0.input_layernorm.weight", "enc_dec_model.enc_dec_model.encoder.model.layers.0.input_layernorm.bias", "enc_dec_model.enc_dec_model.encoder.model.layers.0.self_attention.query_key_value.weight", "enc_dec_model.enc_dec_model.encoder.model.layers.0.self_attention.query_key_value.bias", "enc_dec_model.enc_dec_model.encoder.model.layers.0.self_attention.dense.weight",
**Expected behavior**
Run the Inference code using the default downloadable megamolbart.nemo and it works fine as can be seen here:
[company1.6_infer_RESULT_DEFAULT_NEMO.txt](https://github.com/user-attachments/files/16171714/company1.6_infer_RESULT_DEFAULT_NEMO.txt)
aka we can get simple things like Reconstructed SMILES: from the system.
**Environment overview (please complete the following information)**
- Environment location: Azure
- Method of NeMo install: Docker
- If method of install is [Docker], provide `docker pull` & `docker run` commands used:
docker run -it --rm --gpus all -v /home/azureuser/company_info:/workspace/bionemo/company_info nvcr.io/nvidia/clara/bionemo-framework:1.3 bash
also tried
docker run -it --rm --gpus all -v /home/azureuser/company_info:/workspace/bionemo/company_info nvcr.io/nvidia/clara/bionemo-framework:1.6 bash
**Environment details**
NVIDIA docker image is used
**Additional context**
Azure T4 GPU | {
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} | # What does this PR do ?
To cherry pick https://github.com/NVIDIA/NeMo/pull/9578 LITA integration into 2.0.0rc1. That PR was already merged. See details there.
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
NLP and multimodal
# 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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If you haven't finished some of the above items you can still open "Draft" PR.
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} | # What does this PR do ?
The PR adds Megatron loss function and LR scheduler for GPT pretrain.
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
LLM
# Changelog
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"I figured out the test failure issue. It turned out in my unit test I `os.chdir` into a temporary directory and don't `os.chdir` back int... | 2024-07-11T03:13:08 | 2024-07-17T18:42:06 | 2024-07-17T18:42:04 | COLLABORATOR | {
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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
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Fix few issues and docs for neva and clip in r2.0.0rc1
**Collection**: [Note which collection this PR will affect]
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Fix few issues and docs for neva and clip in r2.0.0rc1
**Collection**: [docs, multimodal]
# 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**:
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If you haven't finished some of the above items you can still open "Draft" PR.
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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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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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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Fixes `train.controlnet.controlnet_v1_5_1node_100steps` CI test.
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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.
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} | # What does this PR do ?
Specific LLM/VLM hang when using specific PP settings and with the bias term is set to false hang. By enabling a fused dropout_add_fused we should fix this problem as well as speed up training of LLM/VLM that do not have the bias term
**Collection**: nlp
# Changelog
- Add specific line by line info of high level changes in this PR.
- nemo/collections/nlp/modules/common/megatron/transformer.py - Enabling the bias_dropout_add_fusion to be used for both bias and non bias enabled
- nemo/collections/nlp/modules/common/megatron/fused_bias_dropout_add.py - Check whether the bias term is there and choose relevant fused function
# 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**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [N/A] Did you write any new necessary tests?
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- [N/A] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
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📝 Please remember the following to-do's before merge:
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🚨 Please also keep in mind to _not_ delete the headings of the task commits. They are required by the post-merge automation.
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"We will add support for this in this repo: computing Model Flops Utilization. \r\nOur team has this task in the backlog and will be working on it.",
"@dementrock I'll be adding this support this week. Any particular model(s) that you are looking at? \r\n@ericharper Could you assign this to me?",
"@ericharper c... | 2024-07-10T18:48:02 | 2024-08-23T23:08:59 | 2024-08-23T23:08:59 | NONE | {
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"> Hi! Thank you for your work!\r\n> \r\n> When continue pretra... | 2024-07-10T18:36:34 | 2024-09-04T01:06:46 | 2024-07-12T05:54:25 | COLLABORATOR | {
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} | # What does this PR do ?
Duplicate of #9587 for release branch
Also includes transformers version update from #9606
**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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"@cuichenx , it sounded like @ashors1 wanted you to review and approve this PR? Let me know if someone else is better. While @marcromeyn is out, who should we have review and help us get things we need on the BioNeMo2 side into NeMo2 in general?",
"> @cuichenx , it sounded like @ashors1 wanted you to review and a... | 2024-07-10T17:25:22 | 2024-07-15T16:12:49 | 2024-07-15T16:12:49 | CONTRIBUTOR | {
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} | # What does this PR do ?
Add more flexibility to the default data step inside megatron parallel.
# 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
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
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.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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?
- [ ] 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
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adds a tutorial for Canary Adapters
**Collection**: [ASR]
# Changelog
- Add tutorial for canary adapters
- Adds Neural Module support for preserving freeze and unfreeze partially
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When doing a parallel save, exchange metadata in a whole world instead of a DP group. For yet unknown reason `all_gather_object` crashes with `group != world`
**Collection**: NLP
# Changelog
- Parametrize metadata exchange group for parallel save
- Turn on parallel save by default
# Usage
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* Set `model.dist_ckpt_parallel_save=False` to turn off parallel save
```python
# Add a code snippet demonstrating how to use this
```
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**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
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**Pre checks**:
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} | Signed-off-by: Tu [huutu12312vn@gmail.com](mailto:huutu12312vn@gmail.com)
# What does this PR do ?
Add a Vietnamese language tokenizer for TTS training
**Collection**: [TTS]
# Changelog
- Add VietnameseCharsTokenizer
- Add unit tests for Vietnamese
# Usage
```python
from nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers import VietnameseCharsTokenizer
text = "Xin chào các bạn."
tokenizer = VietnameseCharsTokenizer(
pad_with_space=True,
)
tokens = tokenizer(text)
graphemes = tokenizer.decode(tokens)
graphemes = graphemes.replace('|', '')
print(tokens)
# xin chào các bạn.
print(graphemes)
```
# 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)
- [x] Did you write any new necessary tests?
- [x] 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**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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"Based on DM, this should be already supported in gpt dataset https://github.com/NVIDIA/Megatron-LM/blob/c7a1f82d761577e6ca0338d3521eac82f2aa0904/megatron/core/datasets/gpt_dataset.py#L189\r\nclosing"
] | 2024-07-09T23:44:22 | 2024-07-10T04:01:40 | 2024-07-10T04:01:40 | NONE | {
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} | null | null | null | Right now when performing LLM pretraining, different documents are just naively concatenated without proper masking. This can become problematic for model training with longer context window, as noted in e.g. https://arxiv.org/abs/2402.08268. Llama 3 blog post also mentioned they applied proper masking e.g. see https://ai.meta.com/blog/meta-llama-3/.
Seems like https://github.com/NVIDIA/NeMo/pull/7945 added support for packed sequence with proper masking for SFT. Would it be possible to make pretraining support this too?
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} | # 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
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
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.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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?
- [ ] 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
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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# Usage
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```python
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- [ ] Bugfix
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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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# Before your PR is "Ready for review"
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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"merged_at": "2024-07-10T18:47:57"
} | # 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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# Before your PR is "Ready for review"
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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"@hainan-xv Something is wrong with tests in this branch (not related to TDT). Please, update to main and toggle `Run CICD` label",
"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.",
"@hainan-xv This still an IP?",... | 2024-07-09T16:50:32 | 2024-11-20T19:56:45 | 2024-11-20T19:56:45 | COLLABORATOR | {
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} | # What does this PR do ?
This PR makes TDT models support all-positive durations (previously duration must contain 0).
**Collection**: [ASR]
# Changelog
- the kernal code for computing TDT loss is changed.
- tests are added to test the correctness of new code and also ensures it's back-compatible.
# Usage
N/A
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
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.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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)
- [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**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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"@ericharper @yaoyu-33 @pablo-garay this is good to go? "
] | 2024-07-09T16:21:57 | 2024-07-11T23:19:11 | 2024-07-11T23:19:11 | CONTRIBUTOR | {
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} | * add slurm files to .gitignore
* add differentiable decode to SDXL VAE
* Optionally return predicted noise during the single step sampling process
* also change `get_gamma` as a new function to use inside other functions which may interact with sampling (e.g. draft+)
* debugging sdunet converter script
* Added SD/SDXL conversion script from HF to NeMo
* added 'from_nemo' config for VAE
* tmp commit, please make changes (oci is super slow, cannot even run vim)
* new inference yaml works
* add logging to autoencoder
* !(dont squash) Added enabling support for LinearWrapper for SDLoRA
* added samples_per_batch and fsdp arguments to SDXL inference
* added extra optionally wrapper to FSDP
* remove unncessary comments
* remove unnecessary comments
* Apply isort and black reformatting
---------
# 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
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
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.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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?
- [ ] 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
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
This PR adds additional functionality to SDXL class of models to support Draft+, and:
bug fix for adapter control in LoRA (LinearParallelAdapter)
conversion script for SDXL from huggingface weights to nemo (existing converter script doesnt work)
additional inference config file to load converted hf weights
added FSDP strategy for multimodal models
function to return adapter config, similar to function that returns the adapter itself
# 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
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
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.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# 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?
- [ ] 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
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"I am seeing the same issue, please help! \r\ncc @terrykong ",
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"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-07-09T11:52:08 | 2024-08-23T01:51:58 | 2024-08-23T01:51:58 | NONE | {
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} | null | null | null | **Describe the bug**
Instruct model seems to work as expected, but I am experiencing issues with the Reward model. https://huggingface.co/nvidia/Nemotron-4-340B-Reward/discussions/3
Tested containers: nemo_24_01_framework and nemo_24_03_01_framework
Resource config: #SBATCH --nodes=2 #SBATCH --ntasks-per-node=8 #SBATCH --gpus-per-node=8
Attempted resolution: Repeated downloading of the Nemotron-4-340B-Reward model
Related error: https://github.com/NVIDIA/NeMo/issues/8785
A clear and concise description of what the bug is.
NeMo reports that files are missing from the Nemotron-4-340B-Reward model.
**Steps/Code to reproduce bug**
Run the example code (https://huggingface.co/nvidia/Nemotron-4-340B-Reward) with one of the following containers: nemo_24_01_framework and nemo_24_03_01_framework
**Expected behavior**
A clear and concise description of what you expected to happen.
I expect the job to complete and not produce an error.
**Environment overview (please complete the following information)**
Local H100 DGX cluster, using NGC containers,
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} | # What does this PR do ?
When doing a parallel save, exchange metadata in a whole world instead of a DP group. For yet unknown reason `all_gather_object` crashes with `group != world`
**Collection**: NLP
# Changelog
- Parametrize metadata exchange group for parallel save
- Turn on parallel save by default
# Usage
* By default metadata will be exchanged within the whole world. Set `model.dist_ckpt_parallel_save_within_dp=True` to exchange within DP.
* Set `model.dist_ckpt_parallel_save=False` to turn off parallel save
```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)
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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# Additional Information
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