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} | # What does this PR do ?
Fix LoRA convergence bug, and add a parameter count print out after freezing base model, similar to NeMo 1.
**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)
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**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.
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https://api.github.com/repos/NVIDIA/NeMo/issues/10063 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/10063/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/10063/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/10063/events | https://github.com/NVIDIA/NeMo/pull/10063 | 2,451,844,223 | PR_kwDOC_bI7s53n4Lf | 10,063 | [NeMo-UX] log learning rate before train step | {
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} | Previously, we were logging the learning rate after the train step was completed, which was causing a mismatch between the learning rate and the step count. This updates the logging of the learning rate to fix that.
**Collection**: LLM
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
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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"A few notes:\r\n- You might need to adapt this class to support additional modules: https://github.com/zhehuaichen/NeMo/blob/speechllm-develop/nemo/collections/nlp/parts/nlp_overrides.py#L626\r\n - E.g., adding new modules here: https://github.com/zhehuaichen/NeMo/blob/speechllm-develop/nemo/collections/nlp/parts/nlp_overrides.py#L668\r\n- The above class has several methods related to \"state dict\". Not sure if those are correct for speech LM.\r\n- I excluded the embedding layer of LLM to avoid errors when using it elsewhere. We'd better fix it since the embedding layer can be very large. https://github.com/zhehuaichen/NeMo/commit/44056f2d1f69261c47829181e7d8e4c0c1c077de#diff-e3fcc68c97812462e588465942b917f381808fc65501b32a431b8b311fab6fd3R1691",
"> A few notes:\r\n> \r\n> * You might need to adapt this class to support additional modules: https://github.com/zhehuaichen/NeMo/blob/speechllm-develop/nemo/collections/nlp/parts/nlp_overrides.py#L626\r\n> \r\n> * E.g., adding new modules here: https://github.com/zhehuaichen/NeMo/blob/speechllm-develop/nemo/collections/nlp/parts/nlp_overrides.py#L668\r\n> * The above class has several methods related to \"state dict\". Not sure if those are correct for speech LM.\r\n> * I excluded the embedding layer of LLM to avoid errors when using it elsewhere. We'd better fix it since the embedding layer can be very large. [zhehuaichen@44056f2#diff-e3fcc68c97812462e588465942b917f381808fc65501b32a431b8b311fab6fd3R1691](https://github.com/zhehuaichen/NeMo/commit/44056f2d1f69261c47829181e7d8e4c0c1c077de#diff-e3fcc68c97812462e588465942b917f381808fc65501b32a431b8b311fab6fd3R1691)\r\n\r\nThanks for the note.\r\nSpeechLLM does not add extra nlp modules but only reusing them if that is what you mean. Locally I found the ckpt restore seems working. Will confirm with a cluster run"
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} | # What does this PR do ?
Support FSDP in SALM and BESTOW. Thank @pyf98 for early trying
# Usage
Elaborated and tested here https://docs.google.com/document/d/1fr2UlqThlzqvqL_lpc_HIyrh0yCzL27A-QX1eCI_ojg/edit#heading=h.9o9g0iu9eu69
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} | Previously, several small GPU->CPU copies were used, which caused excess latency linearly proportional to the batch size. For small copies, it is much more efficient to do a single memory copy.
There is still a remaining issue. pack_hypotheses() appears to be copying data from GPU to CPU for the dec_state tensor, one at a time. I would like to remove that, but that is an intrusive change that affects interfaces, so I will not do it for now.
RTFx before this change on librispeech test other: 1766.645297158031
RTFx after this change on librispeech test other: 1795.3508087085368 | {
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"My fix for this was to use `soundfile.info()` (as you did in other parts of the same code) to extract all the meta data needed to create a `Recording` object (after confirming the audio file exists, the above example won't work). I cached the meta data by path string to ease up on file I/O (so it should be called only once per a unique audio file).",
"@neehhaa06 The current code in `nemo/collections/common/data/lhotse/nemo_adapters.py` uses `soundfile` to avoid I/O overhead of loading the whole file. The related comment is here for the `LazyNeMoTarredIterator` class:\r\n```\r\n raw_audio = tar.extractfile(tar_info).read()\r\n # Note: Lhotse has a Recording.from_bytes() utility that we won't use here because\r\n # the profiling indicated significant overhead in torchaudio ffmpeg integration\r\n # that parses full audio instead of just reading the header for WAV files.\r\n # recording = lhotse.Recording.from_bytes(raw_audio, recording_id=tar_info.path)\r\n meta = soundfile.info(BytesIO(raw_audio))\r\n```\r\nThe `meta` variable above returned by `soundfile.info()` is very useful. It includes the sample rate, number of samples, number of channels, etc. I think this is the best thing to use to address this bug report in addition to caching the meta data using the file path string using a dict.",
"Thanks, it looks like there's indeed an issue. Do you mind submitting a PR with your fix?",
"I was asking @FredSRichardson since he mentioned he managed to fix it locally. That said you're also welcome to contribute @neehhaa06 :)",
"I can't contribute directly unfortunately, but I did leave a fairly detailed spec of the change and it's fairly minor. Let me know if you have any questions.",
"Thanks, appreciate it. I added tests and a fix here https://github.com/NVIDIA/NeMo/pull/10198"
] | 2024-08-06T18:04:16 | 2024-08-23T13:56:31 | 2024-08-23T13:56:31 | NONE | {
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These lines of code do not handle a cut's duration correctly when the offset is not zero:
From `nemo/collections/common/data/lhotse/nemo_adapters.py` for the class `LazyNeMoIterator`:
```
def __iter__(self) -> Generator[Cut, None, None]:
for data in self.source:
audio_path = get_full_path(str(data.pop("audio_filepath")), str(self.path))
duration = data.pop("duration")
offset = data.pop("offset", None)
recording = self._create_recording(audio_path, duration, data.pop("sampling_rate", None))
cut = recording.to_cut()
if offset is not None:
cut = cut.truncate(offset=offset, duration=duration, preserve_id=True)
cut.id = f"{cut.id}-{round(offset * 1e2):06d}-{round(duration * 1e2):06d}"
```
The issue is that the function `_create_recording()` in the same file uses the `duration` parameter to compute the number of samples for the whole recording in the returned lhotse `Recording` instance. The duration in the nemo manifest is for the cut starting at the `offset` value within the recording. The duration of the whole recording has to be obtained from somewhere else (possibly the recording itself).
**Steps/Code to reproduce bug**
Run `scripts/speech_recognition/estimate_duration_bins.py -b 30 manifest.json` using a manifest file containing non-zero offsets for example:
```
{"duration": 0.5, "audio_filepath": "/path/to/some/file.wav", "text": "hello world", "offset": 123.4}
{"duration": 1.0, "audio_filepath": "/path/to/some/file.wav", "text": "hello world", "offset": 124.4}
{"duration": 1.0, "audio_filepath": "/path/to/some/file.wav", "text": "hello world", "offset": 125.4}
{"duration": 1.0, "audio_filepath": "/path/to/some/file.wav", "text": "hello world", "offset": 126.4}
{"duration": 1.0, "audio_filepath": "/path/to/some/file.wav", "text": "hello world", "offset": 127.4}
{"duration": 1.0, "audio_filepath": "/path/to/some/file.wav", "text": "hello world", "offset": 128.4}
{"duration": 1.0, "audio_filepath": "/path/to/some/file.wav", "text": "hello world", "offset": 129.4}
{"duration": 1.0, "audio_filepath": "/path/to/some/file.wav", "text": "hello world", "offset": 130.4}
{"duration": 1.0, "audio_filepath": "/path/to/some/file.wav", "text": "hello world", "offset": 131.4}
{"duration": 1.0, "audio_filepath": "/path/to/some/file.wav", "text": "hello world", "offset": 132.4}
{"duration": 5.0, "audio_filepath": "/path/to/some/other-file.wav", "text": "hello world", "offset": 4.0}
{"duration": 4.0, "audio_filepath": "/path/to/some/other-file.wav", "text": "hello world", "offset": 5.0}
{"duration": 4.0, "audio_filepath": "/path/to/some/other-file.wav", "text": "hello world", "offset": 4.0}
```
You'll notice that the `mean` duration in the output is a negative number.
**Expected behavior**
The duration of the whole audio file should be using when creating the `Recording` object in the method `_create_recording()` in `nemo/collections/common/data/lhotse/nemo_adapters.py`. The reported mean duration should not be negative.
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install:
```
git clone https://github.com/NVIDIA/NeMo.git
git checkout r2.0.0rc0
conda install -y -c conda-forge numba==0.57.1
pip install --editable ".[all]"
pip install huggingface-hub==0.23.2 # this fixes a bug with newer versions...
```
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Linux Ubuntu 20.04
- PyTorch version: pytorch-lightning-2.3.3
- Python version: 3.10.12
**Additional context**
Add any other context about the problem here.
Example: GPU model
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} | # What does this PR do ?
Simplified key-names mapping in NeMo to TRT LLM export.
**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
```
# 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**:
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- [ ] 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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"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."
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} | # What does this PR do ?
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"This should be covered by https://github.com/NVIDIA/NeMo/pull/9826, so please include it for your purposes"
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} | # What does this PR do ?
Fix imports and loading of the packed, distributed NeMo checkpoints during conversion to TRT LLM.
**Collection**: nlp
# Changelog
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# Add a code snippet demonstrating how to use this
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} | null | null | null | File "/share//code//NeMo/examples/nlp/language_modeling/megatron_ckpt_to_nemo.py", line 245, in <module>
convert(local_rank, rank, world_size, args)
File "/share//code//NeMo/examples/nlp/language_modeling/megatron_ckpt_to_nemo.py", line 198, in convert
model = MegatronGPTModel.load_from_checkpoint(checkpoint_path, hparams_file=args.hparams_file, trainer=trainer)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/nlp_model.py", line 380, in load_from_checkpoint
model = ptl_load_state(cls, checkpoint, strict=strict, cfg=cfg, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/core/saving.py", line 144, in _load_state
obj = cls(**_cls_kwargs)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 243, in __init__
super().__init__(cfg, trainer=trainer, no_lm_init=True)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_base_model.py", line 118, in __init__
with open_dict(cfg):
File "/usr/lib/python3.10/contextlib.py", line 135, in __enter__
return next(self.gen)
File "/usr/local/lib/python3.10/dist-packages/omegaconf/omegaconf.py", line 983, in open_dict
prev_state = config._get_node_flag("struct")
AttributeError: 'dict' object has no attribute '_get_node_flag'
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} | # What does this PR do ?
* Fixes an issue after introducing dataclass as a mini-batch type.
* Fixes Canary's transcribe() which for certain cases incorrectly used the contents of the "text" field in addition to the prompt (and if it was not available, it still used a default text value).
**Collection**: ASR
# Changelog
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} | # What does this PR do ?
Fix imports and loading of the packed distributed 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
```
# 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.
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} | # What does this PR do ?
This PR adds a ssl-pretraining framework and a flow matching AudioToAudio model
**Collection**: audio
# Changelog
- Add a flow matching generative model in `audio/models/enhancement.py`
- Create `audio/modules/ssl_pretrain_masking` to partially-masked out input in the ssl pre-training stage.
- Create `audio/parts/submodules/transformerunet.py` to implement a Transformer U-Net structure.
- Create `audio/parts/submodules/flow.py` for training/inferencing with the flow matching generative model
- Create `audio/parts/utils/callbacks.py` to add processed audio in the training logger.
# Usage
Model can be first be pre-trained using
```python
python examples/audio/audio_to_audio_train.py --config-name flow_matching_generative_ssl_pretraining.yaml
```
- Please update the `train_ds.manifest_filepath` and `validation_ds.manifest_filepath` with the correct filepaths
After the ssl-pretraining stage is completed, the pretrained model could be finetuned using
```python
python examples/audio/audio_to_audio_train.py --config-name flow_matching_generative_finetuning.yaml
```
- Remember to update the `init_from_nemo_model` with the pretrained checkpoint path in the config file.
Alternatively, one can also just train a flow matching generative from scratch:
```python
python examples/audio/audio_to_audio_train.py --config-name flow_matching_generative.yaml
```
Model can be evaluated using
```python
python examples/audio/audio_to_audio_eval.py \
model_path=${PATH_TO_NEMO_FILE} \
dataset_manifest=${PATH_TO_DATASET_MANIFEST} \
output_dir=${PATH_TO_OUTPUT_DIR} \
input_key=noisy_filepath \
target_key=clean_filepath \
metrics=[sdr,estoi]
```
Sampler setting can be easily overridden, e.g., to change the number of sampling steps as
```python
python examples/audio/audio_to_audio_eval.py ... ++sampler.num_steps=${NUM_STEPS}
```
# 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?
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- [ ] 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?
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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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📝 Please remember the following to-do's before merge:
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"This has been added here: https://github.com/NVIDIA/NeMo/pull/9815\r\n\r\nPlease re-open or create a new issue if needed."
] | 2024-08-05T20:56:46 | 2024-08-21T17:57:54 | 2024-08-21T17:57:54 | NONE | {
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LLAMA3.1 introduces a new ROPE implementation, while NeMo hasn't supported it.
https://github.com/huggingface/transformers/blob/51ab25e2932da15511ced35bcbdfa92d25c4794c/src/transformers/modeling_rope_utils.py#L298
**Describe the solution you'd like**
Support the new ROPE implementation and LLAMA 3.1. | {
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"@ar-nowaczynski Can you check if https://github.com/NVIDIA/NeMo/pull/10125 fixes this issue."
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} | # What does this PR do ?
Fixes basic error in recently introduced #9932 FLOPsMeasurementCallback on_train_end method when `pl_module.logger.experiment` is None.
**Collection**: common
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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"> LGTM, thanks! I like this approach and the possibility of using an explicitly instantiated strategy. Also, learned about `Mapping`, thanks :)\r\n\r\nThanks! I added a test and one fix, should be OK to merge now."
] | 2024-08-05T19:58:59 | 2024-08-19T21:47:52 | 2024-08-19T21:47:51 | COLLABORATOR | {
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} | # What does this PR do ?
This change allows, amongst other things, to specify options for DDP such as `gradient_as_bucket_view=True` which saves a considerable amount of memory for 1B+ 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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- [ ] 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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📝 Please remember the following to-do's before merge:
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} | # What does this PR do ?
This PR recursively finds the leaves of the trainer config and calls `track_io` on the leaves. This handles the case of adding io functionality to plugins which are dictionaries.
**Collection**: llm
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
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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**:
- [ ] 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
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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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* Related to # (issue)
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} | # What does this PR do ?
Use hard-coded chat-template for known models only, need a better strategy.
**Collection**: [Note which collection this PR will affect]
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} | Add support for overlapped gradient and parameter synchronization for GPT SFT model
# What does this PR do ?
Add support for overlapped gradient and parameter synchronization
**Collection**: nlp
# Changelog
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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."
] | 2024-08-05T12:37:40 | 2024-09-12T01:55:27 | 2024-09-12T01:55:27 | CONTRIBUTOR | {
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} | null | null | null | Hello everybody,
I would like to train an Ecapa-TDNN model on Voxceleb2 and Voxceleb2+Cnceleb2 for speaker verification/identification. I don't want it to be SOA (ex: complicated pretraining and then large margin finetuning), but I want it to be moderately performant as fast as possible, as the model performance is not the primary focus.
I can train on an A100 gpu. I was thinking of randomly cropping audios in train-set to 3s and then applying RIR+musan+(0.95-1.05) speed perturbation+ spec. augment on the fly (with 0.3 probability for each one). I would also use the AAM Softmax with s=30, m=0.2 loss. How many epochs would I need to train this model and what is the expected time for training such a model? (on Voxceleb2 for example) Is it possible to obtain good performances with such a simple configuration? (under 1% EER on VoxCeleb1-O for example)
I saw that some people apply offline augmentation to create more speakers, or do 2-stage training with pretraining and then fine-tuning, etc. But I would like to train both models in just a couple of days (as an A100 is a pretty big GPU), and I don't want to achieve peak performances, such as the more complicated pipelines, but decent performances.
Any other tips would be greatly appreciated! I would mostly like to know if I could achieve these performances in a reasonable time and any other tips for fast convergence in as few epochs as possible. :D
Thank you in advance! | {
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"Update:\r\n\r\nApologies. I opened it in the wrong repository. My bad."
] | 2024-08-05T06:46:47 | 2024-08-05T06:55:28 | 2024-08-05T06:55:28 | CONTRIBUTOR | {
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} | null | null | null | Hi!
I have noticed a couple of small changes in the code that can improve its performance:
By refactoring `score_document` as suggested, it will run between 2x and 3x faster, and will reduce by half the memory footprint.
By refactoring '_line_statistics' as suggested, it can run about 21% faster.
I will submit a PR in a few minutes with the changes.
Regards,
Miguel | {
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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."
] | 2024-08-03T15:14:23 | 2024-08-25T01:56:08 | 2024-08-25T01:56:07 | NONE | {
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} | # What does this PR do ?
Disables gradients in `swap_tensors()` within `ema.py` to prevent unintended leaf variable gradient modifications (should they arise). This was found to be necessary in certain projects that use PyTorch Lightning 2+ such as [AlphaFold3-Lightning-Hydra](https://github.com/amorehead/alphafold3-pytorch-lightning-hydra).
**Collection**: [Note which collection this PR will affect]
N/A.
# Changelog
- Disables gradients in `swap_tensors()` within `ema.py` to prevent unintended leaf variable gradient modifications (should they arise).
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"Hi, since you want to run only inference, you could modify the prediction_step in [model code](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/models/ctc_models.py#L572) and run .predict on any model (that uses the feature extractor you are looking for) \nReference: https://lightning.ai/docs/pytorch/stable/deploy/production_basic.html ",
"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."
] | 2024-08-03T04:42:14 | 2024-10-30T01:59:27 | 2024-10-30T01:59:26 | NONE | {
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} | null | null | null | **Issue:**
I want to run the NeMo ASR pipeline to analyze the features using different parameters, datasets, and strategies. However, I do not want to run the conformer training. Instead, I only want to extract the features from the input JSON files and store them.
**Current Understanding:**
From my understanding, the current pipeline flow is as follows:
```
For each batch:
extract features -> apply augmentation (if required) -> train -> validation
```
**Requirement:**
I need to comment out or disable the training & further parts so that the pipeline loops back to the next batch to continue feature extraction. Despite reviewing various parts of the internal code pipeline, I am unable to pinpoint where and how to make these changes.
**Version:**
NeMo version: 1.12.0rc0 (I know it's pretty old, but I have already performed a few experiments using it, so I don't want to change anything.)
Pytorch Lightning: 1.7.4
**Request for Help:**
Could you please guide me on how to disable the training & further portion while ensuring that the feature extraction continues to function as required?
Thank you. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/10035 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/10035/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/10035/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/10035/events | https://github.com/NVIDIA/NeMo/pull/10035 | 2,445,963,352 | PR_kwDOC_bI7s53T5Gi | 10,035 | [lhotse] Support for NeMo tarred manifests with offset field | {
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"@pzelasko I checked, caching `TarInfo`s with a single linear scan lets then extract in O(1):\r\n```python\r\ndef taropen_build(tar_path):\r\n import tarfile\r\n tar = tarfile.open(tar_path)\r\n tarinfo_index = {tarinfo.name : tarinfo for tarinfo in tar.getmembers()}\r\n return lambda member_path, mode: tar.extractfile(tarinfo_index[member_path]) if mode == 'rb' else None\r\n\r\n # instead for uncompressed tars (which makes sense for audio files) can do without caching the TarInfo structure and just cache two ints per string file name, this makes it more amenable to sharing between DataLoader threads and easier to sync in tensors\r\n #import io\r\n #tarinfo_index = {tarinfo.name : (tarinfo.offset_data, tarinfo.size) for tarinfo in tar.getmembers()}\r\n #return lambda member_path, mode: [io.BytesIO(f.seek(e[0]) and f.read(e[1])) for f in [open(tar_path, mode)] for e in [tarinfo_index[member_path]]][0] if mode == 'rb' else None\r\n\r\nif __name__ == '__main__':\r\n taropen = taropen_build('myfile.tar')\r\n print(taropen('mymember.txt', 'rb').read())\r\n```\r\n\r\nSupporting `.tar`s with wav/opus files inside would be very nice, as these small audio files can be in 10s or 100s of thousands which is always a pressure on the file system and adds time to unpacking",
"We already support tars with wav/opus. Linear scan is not feasible for URL data sources (cloud storage, AIStore). "
] | 2024-08-03T02:28:21 | 2024-08-30T19:54:52 | 2024-08-07T15:59:35 | COLLABORATOR | {
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} | # What does this PR do ?
Support for NeMo tarred manifests with offset field in lhotse dataloading.
**Collection**: ASR
# 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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- [ ] 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'm curious if by retraining you might mean continued training? https://docs.nvidia.com/nemo-framework/user-guide/latest/llms/allmodels/continuetraining.html?highlight=continued%2520training#configure-continual-learning\r\n\r\nRegarding your error, the decoder module will be `MegatronGPTModel.model.decoder` I believe. But you can check by inspecting the `MegatronGPTModel` ",
"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."
] | 2024-08-03T01:28:47 | 2024-09-30T02:00:32 | 2024-09-30T02:00:32 | NONE | {
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I am retraining a LLaMA3 model. Due to the limited size of my dataset, I attempted to use `freeze_updates` as referenced in the [NVIDIA NeMo documentation](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/core/neural_modules.html#dynamic-layer-freezing). My configuration is as follows:
```yaml
freeze_updates:
enabled: true # set to false if you want to disable freezing
modules: # list all of the modules you want to have freezing logic for
decoder: 100
```
However, I encountered the following error:
```
AttributeError: 'MegatronGPTModel' object has no attribute 'decoder'
```
I also tried changing decoder to `encoder` or `joint`, but I still faced errors. I would like to ask how to properly configure this setting?
Additionally, within the NeMo framework, is it possible to freeze specific layers, such as only the attention layer? If so, how can I achieve this? Thanks! | {
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} | # What does this PR do ?
Default interval for lr_schedulers was `epoch` which means the lr was never getting updated for nemo.collections.llm examples.
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
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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
- Remove `rank_zero_only=True` flag, unless used as [documented](https://lightning.ai/docs/pytorch/stable/visualize/logging_advanced.html#rank-zero-only)
- Refactor train and val loss logging
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```python
# Add a code snippet demonstrating how to use this
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] | 2024-08-02T19:41:08 | 2024-08-06T15:24:11 | 2024-08-06T15:24:04 | CONTRIBUTOR | {
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# What does this PR do ?
Hi, sorry to bother.
There might be some dataset license issues. So need to remove the dataset downloading and preprocessing script.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
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# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] 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?
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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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"Which version of nemo are you using? The model card: https://huggingface.co/nvidia/nemo-megatron-gpt-5B is using v1.11.0. \r\n\r\nI would recommend using a newer model if possible. This model is quite out of date and likely won't perform as well as more recent models.",
"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."
] | 2024-08-02T19:06:06 | 2024-09-25T18:11:59 | 2024-09-25T18:11:59 | NONE | {
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} | null | null | null | **Describe the bug**
When attempting to launch training using the specified configuration files and code snippet, a key mismatch error occurs between the model and the dictionary. How to proceed?
**Model:**
https://huggingface.co/nvidia/nemo-megatron-gpt-5B/resolve/main/nemo_gpt5B_fp16_tp1.nemo
**Steps/Code to reproduce bug**
1. Load the original configuration file:
```python
ori_conf = OmegaConf.load('models/extracted_model/model_config.yaml')
```
2. Load the new configuration file:
```python
conf = OmegaConf.load('config/megatron_gpt_config.yaml')
```
3. Update the model configuration with keys from the original configuration:
```python
for key in ori_conf.keys():
conf['model'][key] = ori_conf[key]
```
4. Set the global batch size based on the micro batch size:
```python
conf['model']["global_batch_size"] = conf['model']["micro_batch_size"] * conf.get('data_model_parallel_size', 1) * conf.get('gradient_accumulation_steps', 1)
```
5. Update the data prefix:
```python
conf['model']['data']['data_prefix'] = 'data_ready_for_training/validation'
```
6. Update tokenizer configuration:
```python
NEW_TOKENIZER_PATH = "tokenizers/final_merged_arabic_english_gpt2base"
conf['model']['tokenizer']['vocab_file'] = f"{NEW_TOKENIZER_PATH}/vocab.json"
conf['model']['tokenizer']['merge_file'] = f"{NEW_TOKENIZER_PATH}/merges.txt"
conf['model']['vocab_file'] = f"{NEW_TOKENIZER_PATH}/vocab.json"
conf['model']['merges_file'] = f"{NEW_TOKENIZER_PATH}/merges.txt"
```
7. Save the modified configuration:
```python
OmegaConf.save(config=conf, f='config/modifiedmegatron_gpt_config.yaml')
```
8. Launch the training process and observe the key mismatch error.
**Expected behavior**
The training process should launch successfully without any key mismatch errors. The configuration should be correctly applied, and the model should start training using the specified settings.
**Environment overview (please complete the following information)**
- **Environment location:** Bare-metal
- **Method of NeMo install:** pip install
```bash
pip install nemo_toolkit[all]
```
**Environment details**
- **OS version:** Ubuntu 20.04
- **PyTorch version:** 1.11.0
- **Python version:** 3.8.10
**Additional context**
- **GPU model:** NVIDIA A100
The key mismatch error likely arises due to differences between the keys expected by the model and those present in the configuration files. Ensure that all necessary keys are correctly mapped and that there are no discrepancies between the configuration files.
**Log (Sample - not full)**
`Exception has occurred: RuntimeError
Error(s) in loading state_dict for MegatronGPTModel:
Missing key(s) in state_dict: "model.embedding.word_embeddings.weight", "model.embedding.position_embeddings.weight", "model.decoder.layers.0.input_layernorm.weight", "model.decoder.layers.0.input_layernorm.bias", "model.decoder.layers.0.self_attention.linear_proj.weight", "model.decoder.layers.0.self_attention.linear_proj.bias", "model.decoder.layers.0.self_attention.linear_qkv.weight", "model.decoder.layers.0.self_attention.linear_qkv.bias", "model.decoder.layers.0.pre_mlp_layernorm.weight", "model.decoder.layers.0.pre_mlp_layernorm.bias", "model.decoder.layers.0.mlp.linear_fc1.weight", "model.decoder.layers.0.mlp.linear_fc1.bias", "model.decoder.layers.0.mlp.linear_fc2.weight", "model.decoder.layers.0.mlp.linear_fc2.bias", "model.decoder.layers.1.input_layernorm.weight", "model.decoder.layers.1.input_layernorm.bias", "model.decoder.layers.1.self_attention.linear_proj.weight", "model.decoder.layers.1.self_attention.linear_proj.bias", "model.decoder.layers.1.self_attention.linear_qkv.weight",
Unexpected key(s) in state_dict: "model.language_model.embedding.word_embeddings.weight", "model.language_model.embedding.position_embeddings.weight", "model.language_model.encoder.layers.0.input_layernorm.weight", "model.language_model.encoder.layers.0.input_layernorm.bias", "model.language_model.encoder.layers.0.self_attention.query_key_value.weight", "model.language_model.encoder.layers.0.self_attention.query_key_value.bias", "model.language_model.encoder.layers.0.self_attention.query_key_value._extra_state", "model.language_model.encoder.layers.0.self_attention.dense.weight", "model.language_model.encoder.layers.0.self_attention.dense.bias", "model.language_model.encoder.layers.0.self_attention.dense._extra_state", "model.language_model.encoder.layers.0.post_attention_layernorm.weight",
**Config file used**
[megatron_gpt_config.yaml](https://github.com/NVIDIA/NeMo/blob/7cf703c576d7d8d1a55c602353c23ead88b92bb5/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml#L4) | {
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} | # What does this PR do ?
Due to license issue, we need to remove the data downloading and preprocessing script.
**Collection**: [Note which collection this PR will affect]
None of the collection will be affected;
# 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 ?
Update continual pre-training argument override strategy. Input args are going to be merged with existing ones inside the checkpoint.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
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# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] 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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Replacing export functions with the mcore ones.
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} | # What does this PR do ?
Per internal slack conversation, some multi-node trainings suffered from checkpoints being randomly removed between resumed jobs. After disabling `save_top_k` checkpointing option, the training is then running into deadlocks related to unfinished/last checkpoint logic. I traced down the deadlocks to a number of barriers, that when removed, seem to unblock the training.
**Collection**: All
# Changelog
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```python
# Add a code snippet demonstrating how to use this
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# Before your PR is "Ready for review"
**Pre checks**:
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- [x] Bugfix
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## Who can review?
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# Additional Information
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clean up asr finetune script to avoid confusion
**Collection**: [asr]
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1. Adds a feature similar to mcore's --skip-train flag.
2. Adds drop-layers feature support.
3. Adds script which loads the ckpt, trims needed layers, and saves trimmed checkpoint.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
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To 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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- [ ] 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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} | # What does this PR do ?
For instruct models fallback to HF tokenizer.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
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# Before your PR is "Ready for review"
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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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"could you also provide the instruction to run the script?",
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"This PR was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-08-02T03:26:13 | 2024-10-20T23:41:56 | 2024-09-15T02:12:50 | CONTRIBUTOR | {
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} | # What does this PR do ?
This is closed. New PR here: https://github.com/NVIDIA/NeMo/pull/10485
Adds support for upcycling pretrained weights to a MoE model. This follows the paper: SPARSE UPCYCLING: TRAINING MIXTURE-OF-EXPERTS FROM DENSE CHECKPOINTS ([https://arxiv.org/pdf/2212.05055](https://arxiv.org/pdf/2212.05055))
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
```python
python NeMo/examples/nlp/language_modeling/megatron_gpt_upcycling.py \
--config-path=/path/to/MoE/config/ \
--config-name=MoE/config/name.yaml \
+restore_from_path=/path/to/base/model
```
Where you MoE config should contain the target MoE model you want to train. The base model you want to upcycle can also be directly added to this config under the argument `restore_from_path`.
# 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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] | 2024-08-02T02:54:52 | 2024-09-26T01:58:16 | 2024-09-26T01:58:15 | CONTRIBUTOR | {
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} | # What does this PR do ?
Adds support for multiple hard negatives in GPT Embedding model during training
**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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Add a one line overview of what this PR aims to accomplish.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
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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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[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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# 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.
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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".
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- [ ] 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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[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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"I have the same issue...",
"I have the same issue...",
"@XugZhao @Luxur @RadhaGulhane13 The issue is the current Llama 3.1 model on HF (which you fetch when running the conversion script) is built with the new version of the Transformers library, which was released after our `nvcr.io/nvidia/nemo:24.05.llama3.1`, and has some naming differences compared to the version of the Transformer library installed in the `nvcr.io/nvidia/nemo:24.05.llama3.1` container. To resolve the issue you can use the our dev container `nvcr.io/nvidia/nemo:dev`, and run the same command but add `--llama31 True`"
] | 2024-08-01T21:12:17 | 2024-08-24T14:39:55 | 2024-08-24T14:39:55 | NONE | {
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} | null | null | null | **Describe the bug**
I'm trying to convert **LLama3.1-8b huggingface** checkpoint into **nemo** checkpoint using `convert_llama_hf_to_nemo.py` script. It's giving `ValueError: `rope_scaling` must be a dictionary with two fields, `type` and `factor`, got {'factor': 8.0, 'low_freq_factor': 1.0, 'high_freq_factor': 4.0, 'original_max_position_embeddings': 8192, 'rope_type': 'llama3'}`
Find the complete trace below :
```
File "/opt/NeMo/scripts/checkpoint_converters/convert_llama_hf_to_nemo.py", line 327, in <module>
convert(args)
File "/opt/NeMo/scripts/checkpoint_converters/convert_llama_hf_to_nemo.py", line 126, in convert
model = LlamaForCausalLM.from_pretrained(args.input_name_or_path)
File "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py", line 3122, in from_pretrained
config, model_kwargs = cls.config_class.from_pretrained(
File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 609, in from_pretrained
return cls.from_dict(config_dict, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 761, in from_dict
config = cls(**config_dict)
File "/usr/local/lib/python3.10/dist-packages/transformers/models/llama/configuration_llama.py", line 161, in __init__
self._rope_scaling_validation()
File "/usr/local/lib/python3.10/dist-packages/transformers/models/llama/configuration_llama.py", line 181, in _rope_scaling_validation
raise ValueError(
ValueError: `rope_scaling` must be a dictionary with two fields, `type` and `factor`, got {'factor': 8.0, 'low_freq_factor': 1.0, 'high_freq_factor': 4.0, 'original_max_position_embeddings': 8192, 'rope_type': 'llama3'}
```
**Steps/Code to reproduce bug**
```
python <path_to_NeMo>/scripts/checkpoint_converters/convert_llama_hf_to_nemo.py --input_name_or_path Meta-Llama-3.1-8B-Instruct/ --output_path Meta-Llama-3.1-8B-Instruct.nemo
```
**Expected behavior**
Successful conversion of llama3.1-8b hf checkpoints into nemo checkpoint.
**Environment overview (please complete the following information)**
- I'm using `nvcr.io/nvidia/nemo:24.05.llama3.1`
- `doker pull nvcr.io/nvidia/nemo:24.05.llama3.1` & `docker run -it --name nemo nvcr.io/nvidia/nemo:24.05.llama3.1`
**Additional context**
NeMo frameworks currently support only **linear** RoPE scaling for LLaMA models. I'm wondering if there are any ongoing efforts for **dynamic** or **yarn** RoPE scaling to support large context windows with LLaMA 3.1.
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Add a one line overview of what this PR aims to accomplish.
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Adding a function to allow users to get trt-llm checkpoint in safetensor format. | {
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} | # What does this PR do ?
Add the interface to pass the argument for gradient average reduction to MegatronCore
# Changelog
- Add `average_in_collective`
# Usage
- Set by `model.optim.average_in_collective` to enable
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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."
] | 2024-08-01T18:20:31 | 2024-09-19T00:15:06 | 2024-08-30T10:02:36 | CONTRIBUTOR | {
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} | # What does this PR do ?
Adds support for using LoRA adapters on checkpoints exported to vLLM.
**Collection**: NLP
# Changelog
- Moved the LoRA conversion logic from the `convert_nemo_to_canonical.py` script to a reusable module
- Implemented on-load conversion of Nemo format LoRA checkpoints into HF format for vLLM
- Added support for enabling LoRAs on vLLM with automatic max rank detection
- Fixed the logger initialization in the vLLM deployment script
# Usage
```bash
python deploy_vllm_triton.py -nc /path/to/checkpoint.nemo -lc /path/to/lora.nemo -tmn TEST ...
python query.py -mn TEST -p "Prompt text" -lt 0
```
**PR Type**:
- [X] New Feature
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} | # What does this PR do ?
Move pooler into the flag of post processing so that it doesn't get built on all the pp ranks.
Add a one line overview of what this PR aims to accomplish.
**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**:
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- [ ] Documentation
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"This has been resolved, can be closed."
] | 2024-08-01T16:00:54 | 2024-08-23T23:01:35 | 2024-08-23T23:01:35 | COLLABORATOR | {
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When you restart a job, you typically set consumed_samples to a non-zero value. This has the side effect of changing the length returned of the sampler. If the trainer current step is restored to some value N < total, and the trainer uses `len(loader)` to re-compute remaining steps, then when you restart a job the current step is properly restored, but the length of your progress bar (total) shrinks by N so it's then `total - N`. Can you verify that on restart this does not happen? Ideally adding a stop/go test where a trainer job is artificially stopped at some step less than total and resumed, then the total number of steps and all relevant parameters are checked to ensure that resumption worked properly.
This was definitely an issue for us in BioNeMo1 that we "fixed" by changing the length calculation to not be dependent on `consumed_samples`, since it seemed that at the time at least the lightning Trainer used `len(dataloader)` at init time (even when restarting) to determine the length of the progress bar. | {
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"Hi, thank you for your question and investigating the root cause.\r\nThere is a typo in the model card (cc @nithinraok) but this will work:\r\n`asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name=\"nvidia/parakeet-ctc-0.6b\")`\r\nor even this:\r\n`asr_model = nemo_asr.models.ASRModel.from_pretrained(model_name=\"nvidia/parakeet-ctc-0.6b\")`\r\n\r\nYes, there is no parakeet model listed that can be loaded from NGC, but if there is a \"/\" in the model name, we will look for that model on Hugging Face (relevant source code in [here](https://github.com/NVIDIA/NeMo/blob/b88b662c8753e51629fce3245b8ad1a4d126e03f/nemo/core/classes/common.py#L746)).\r\n\r\nApologies for the confusion. We should update the docs with this information, probably [here](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/core/core.html#pretrained). \r\n",
"sorry for the typo, fixed it on the ModelCard"
] | 2024-08-01T15:38:50 | 2024-08-22T21:03:13 | 2024-08-22T21:03:13 | NONE | {
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} | null | null | null | I want to install the https://huggingface.co/nvidia/parakeet-ctc-0.6b model.
I installed nemo with:
`pip install nemo_toolkit['asr']`
According to the Huggingface repo, I use this following command to load the model:
```
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCBPEModel.from_pretrained(model_name="nvidia/parakeet-ctc-0.6b")
```
However, I see this:
```
>nemo_asr.__version__
'1.23.0'
>dir(nemo_asr.models)
['ASRModel',
'AudioToAudioModel',
'ClusteringDiarizer',
'EncDecCTCModel',
'EncDecCTCModelBPE',
'EncDecClassificationModel',
'EncDecDiarLabelModel',
'EncDecFrameClassificationModel',
'EncDecHybridRNNTCTCBPEModel',
'EncDecHybridRNNTCTCModel',
'EncDecK2RnntSeqModel',
'EncDecK2RnntSeqModelBPE',
'EncDecK2SeqModel',
'EncDecK2SeqModelBPE',
'EncDecMultiTaskModel',
'EncDecRNNTBPEModel',
'EncDecRNNTModel',
'EncDecSpeakerLabelModel',
'EncDecTransfModelBPE',
'EncMaskDecAudioToAudioModel',
'NeuralDiarizer',
'SLUIntentSlotBPEModel',
'SpeechEncDecSelfSupervisedModel',
'__builtins__',
...
```
i.e. "EncDecCTCBPEModel" is not in the list!
I assumed there was a renaming, from EncDecCTCBPEModel -> EncDecCTCModelBPE?
Looking in EncDecCTCModelBPE, I see:
```
>nemo_asr.models.EncDecCTCModelBPE.list_available_models()
[PretrainedModelInfo(
pretrained_model_name=stt_en_citrinet_256,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_citrinet_256,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_citrinet_256/versions/1.0.0rc1/files/stt_en_citrinet_256.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_en_citrinet_512,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_citrinet_512,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_citrinet_512/versions/1.0.0rc1/files/stt_en_citrinet_512.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_en_citrinet_1024,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_citrinet_1024,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_citrinet_1024/versions/1.0.0rc1/files/stt_en_citrinet_1024.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_en_citrinet_256_gamma_0_25,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_citrinet_256_gamma_0_25,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_citrinet_256_gamma_0_25/versions/1.0.0/files/stt_en_citrinet_256_gamma_0_25.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_en_citrinet_512_gamma_0_25,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_citrinet_512_gamma_0_25,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_citrinet_512_gamma_0_25/versions/1.0.0/files/stt_en_citrinet_512_gamma_0_25.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_en_citrinet_1024_gamma_0_25,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_citrinet_1024_gamma_0_25,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_citrinet_1024_gamma_0_25/versions/1.0.0/files/stt_en_citrinet_1024_gamma_0_25.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_es_citrinet_512,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_citrinet_512,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_es_citrinet_512/versions/1.0.0/files/stt_es_citrinet_512.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_de_citrinet_1024,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_de_citrinet_1024,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_de_citrinet_1024/versions/1.5.0/files/stt_de_citrinet_1024.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_fr_citrinet_1024_gamma_0_25,
description=For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_fr_citrinet_1024_gamma_0_25,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_fr_citrinet_1024_gamma_0_25/versions/1.5/files/stt_fr_citrinet_1024_gamma_0_25.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_fr_no_hyphen_citrinet_1024_gamma_0_25,
description=For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_fr_citrinet_1024_gamma_0_25,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_fr_citrinet_1024_gamma_0_25/versions/1.5/files/stt_fr_no_hyphen_citrinet_1024_gamma_0_25.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_es_citrinet_1024_gamma_0_25,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_citrinet_1024_gamma_0_25,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_es_citrinet_1024_gamma_0_25/versions/1.8.0/files/stt_es_citrinet_1024_gamma_0_25.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_en_conformer_ctc_small,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_small,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_small/versions/1.6.0/files/stt_en_conformer_ctc_small.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_en_conformer_ctc_medium,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_medium,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_medium/versions/1.6.0/files/stt_en_conformer_ctc_medium.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_en_conformer_ctc_large,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_large,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_large/versions/1.10.0/files/stt_en_conformer_ctc_large.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_en_conformer_ctc_xlarge,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_xlarge,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_xlarge/versions/1.10.0/files/stt_en_conformer_ctc_xlarge.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_en_squeezeformer_ctc_xsmall_ls,
description=For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_squeezeformer_ctc_xsmall_ls,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_squeezeformer_ctc_xsmall_ls/versions/1.13.0/files/stt_en_squeezeformer_ctc_xsmall_ls.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_en_squeezeformer_ctc_small_ls,
description=For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_squeezeformer_ctc_small_ls,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_squeezeformer_ctc_small_ls/versions/1.13.0/files/stt_en_squeezeformer_ctc_small_ls.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_en_squeezeformer_ctc_small_medium_ls,
description=For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_squeezeformer_ctc_small_medium_ls,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_squeezeformer_ctc_small_medium_ls/versions/1.13.0/files/stt_en_squeezeformer_ctc_small_medium_ls.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_en_squeezeformer_ctc_medium_ls,
description=For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_squeezeformer_ctc_medium_ls,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_squeezeformer_ctc_medium_ls/versions/1.13.0/files/stt_en_squeezeformer_ctc_medium_ls.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_en_squeezeformer_ctc_medium_large_ls,
description=For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_squeezeformer_ctc_medium_large_ls,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_squeezeformer_ctc_medium_large_ls/versions/1.13.0/files/stt_en_squeezeformer_ctc_medium_large_ls.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_en_squeezeformer_ctc_large_ls,
description=For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_squeezeformer_ctc_large_ls,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_squeezeformer_ctc_large_ls/versions/1.13.0/files/stt_en_squeezeformer_ctc_large_ls.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_en_conformer_ctc_small_ls,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_small_ls,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_small_ls/versions/1.0.0/files/stt_en_conformer_ctc_small_ls.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_en_conformer_ctc_medium_ls,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_medium_ls,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_medium_ls/versions/1.0.0/files/stt_en_conformer_ctc_medium_ls.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_en_conformer_ctc_large_ls,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_large_ls,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_large_ls/versions/1.0.0/files/stt_en_conformer_ctc_large_ls.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_fr_conformer_ctc_large,
description=For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_fr_conformer_ctc_large,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_fr_conformer_ctc_large/versions/1.5.1/files/stt_fr_conformer_ctc_large.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_fr_no_hyphen_conformer_ctc_large,
description=For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_fr_conformer_ctc_large,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_fr_conformer_ctc_large/versions/1.5.1/files/stt_fr_no_hyphen_conformer_ctc_large.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_de_conformer_ctc_large,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_de_conformer_ctc_large,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_de_conformer_ctc_large/versions/1.5.0/files/stt_de_conformer_ctc_large.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_es_conformer_ctc_large,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_conformer_ctc_large,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_es_conformer_ctc_large/versions/1.8.0/files/stt_es_conformer_ctc_large.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_hi_conformer_ctc_medium,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_hi_conformer_ctc_medium,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_hi_conformer_ctc_medium/versions/1.6.0/files/stt_hi_conformer_ctc_medium.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_mr_conformer_ctc_medium,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_mr_conformer_ctc_medium,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_mr_conformer_ctc_medium/versions/1.6.0/files/stt_mr_conformer_ctc_medium.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_enes_conformer_ctc_large,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_enes_conformer_ctc_large,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_enes_conformer_ctc_large/versions/1.0.0/files/stt_enes_conformer_ctc_large.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_ca_conformer_ctc_large,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ca_conformer_ctc_large,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_ca_conformer_ctc_large/versions/1.11.0/files/stt_ca_conformer_ctc_large.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_rw_conformer_ctc_large,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_rw_conformer_ctc_large,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_rw_conformer_ctc_large/versions/1.11.0/files/stt_rw_conformer_ctc_large.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_enes_conformer_ctc_large_codesw,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_enes_conformer_ctc_large_codesw,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_enes_conformer_ctc_large_codesw/versions/1.0.0/files/stt_enes_conformer_ctc_large_codesw.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_be_conformer_ctc_large,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_be_conformer_ctc_large,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_be_conformer_ctc_large/versions/1.12.0/files/stt_be_conformer_ctc_large.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_hr_conformer_ctc_large,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_hr_conformer_ctc_large,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_hr_conformer_ctc_large/versions/1.11.0/files/stt_hr_conformer_ctc_large.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_it_conformer_ctc_large,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_it_conformer_ctc_large,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_it_conformer_ctc_large/versions/1.13.0/files/stt_it_conformer_ctc_large.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_ru_conformer_ctc_large,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ru_conformer_ctc_large,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_ru_conformer_ctc_large/versions/1.13.0/files/stt_ru_conformer_ctc_large.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_eo_conformer_ctc_large,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_eo_conformer_ctc_large,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_eo_conformer_ctc_large/versions/1.14.0/files/stt_eo_conformer_ctc_large.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_en_fastconformer_ctc_large,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_ctc_large,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_ctc_large/versions/1.0.0/files/stt_en_fastconformer_ctc_large.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_en_fastconformer_ctc_large_ls,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_ctc_large_ls,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_ctc_large_ls/versions/1.0.0/files/stt_en_fastconformer_ctc_large_ls.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_en_fastconformer_ctc_xlarge,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_ctc_xlarge,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_ctc_xlarge/versions/1.20.0/files/stt_en_fastconformer_ctc_xlarge.nemo
),
PretrainedModelInfo(
pretrained_model_name=stt_en_fastconformer_ctc_xxlarge,
description=For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_ctc_xxlarge,
location=https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_fastconformer_ctc_xxlarge/versions/1.20.1/files/stt_en_fastconformer_ctc_xxlarge.nemo
)]
```
There is no parakeet model listed here!
```[tasklist]
### Tasks
```
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https://api.github.com/repos/NVIDIA/NeMo/issues/10004 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/10004/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/10004/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/10004/events | https://github.com/NVIDIA/NeMo/pull/10004 | 2,442,757,362 | PR_kwDOC_bI7s53Ix0v | 10,004 | Re-tarring script | {
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"@zhehuaichen Glad you asked, as it turns out I forgot to push a commit that does it (see https://github.com/NVIDIA/NeMo/pull/10004/commits/8017db4a1d9d5a3db8a9c63a7aaff77061e0a261). \r\n\r\nNow it's all good and ready to merge. "
] | 2024-08-01T15:25:38 | 2024-08-03T13:38:03 | 2024-08-03T13:38:02 | COLLABORATOR | {
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"merged_at": "2024-08-03T13:38:02"
} | # What does this PR do ?
Utility to help people re-build tarred datasets when they want only a small subset of them, based on a filtered manifest.
**Collection**: ASR
# 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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"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-08-01T14:48:54 | 2024-09-09T01:56:55 | 2024-09-09T01:56:54 | NONE | {
"total": 0,
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} | null | null | null | **Describe the bug**
The Docker build command in the README does not build.
**Steps/Code to reproduce bug**
```sh
DOCKER_BUILDKIT=1 docker build -f Dockerfile -t nemo:latest
```
**Expected behavior**
You will get the following error message
```sh
ERROR: "docker buildx build" requires exactly 1 argument.
See 'docker buildx build --help'.
Usage: docker buildx build [OPTIONS] PATH | URL | -
Start a build
````
**Environment overview (please complete the following information)**
- Ubuntu 22.04
- Docker version 27.1.1
**Additional context**
The following command would have built.
```sh
DOCKER_BUILDKIT=1 docker build -f Dockerfile.ci -t nemo:latest .
```
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} | # What does this PR do ?
1. Adds `deploy` method from NeMo 1.0 as a `task` to NeMo 2.0
2. This PR aims at minimum viable product to deploy and expose REST API with NeMo 2.0 and hence only adds `TRTLLM` backend route of deploy module to NeMo 2.0. Also, it only supports the basic arguments for the export method. More optimizations like `paged_kv_cache`, `parallel_embedding`, `lora_plugin`, `multi_block_mode` etc., and other backends (like `in-framework`) will be supported in a subsequent PR.
**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 ?
1. Adds a feature similar to mcore's --skip-train flag.
3. Adds drop-layers feature support.
4. Adds script which loads the ckpt, trims needed layers, and saves trimmed checkpoint.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* Trim the model. Please, note that layer indices start from 1. Llama3 8b model example:
```ubuntu
python -m torch.distributed.launch --nproc_per_node=8 \
NeMo/examples/nlp/language_modeling/megatron_gpt_drop_layers.py \
--path_to_nemo /path/to/model.nemo \
--path_to_save /path/to/save/trimmed_model.nemo \
--tensor_model_parallel_size 8 \
--pipeline_model_parallel_size 1 \
--gpus_per_node 8 \
--drop_layers 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
```
If you want to save trimmed model in zarr ckpt format (deprecated), add this flag to the command:
```ubuntu
--zarr
```
* Validate the model:
```ubuntu
python NeMo/examples/nlp/language_modeling/megatron_gpt_pretraining.py \
--config-path=/path/to/folder/with/model/config \
--config-name=llama3_8b.yaml \
trainer.limit_val_batches=number_of_batches_in_validation_step \
model.restore_from_path=/path/to/trimmed_model.nemo \
model.skip_train=True \
model.data.data_impl=mock \
model.data.data_prefix=[]
```
To use specific dataset instead of mock dataset:
```ubuntu
model.data.data_impl=mmap \
model.data.data_prefix=["path/to/datafile1", "path/to/datafile2"]
```
# 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**:
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- [ ] 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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"@yaoyu-33 could we merge this [fix on sig/clip](https://github.com/NVIDIA/NeMo/pull/9842) pr to main? so that I could rebase and remove the similar fix in this PR. Thanks!"
] | 2024-08-01T00:27:34 | 2024-08-20T19:49:47 | 2024-08-20T19:49:47 | CONTRIBUTOR | {
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} | # What does this PR do ?
Add blend dataset in NeVA for both unpacked and packed sequence.
**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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📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
🙏 Please merge this PR only if the CI workflow completed successfully. | {
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} | # What does this PR do ?
added support for data blending in NeVA.
**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 ?
This introduces a new `MegatronProgressPrinter` class which simply prints the metrics to stdout in a similar style to Megatron. This implementation is more robust as the TQDM progress bar is not well-suited for non-interactive settings.
The train step logging looks like this:
```
Epoch 0, iteration 1/100: | global_step: 0 | reduced_train_loss: 10.93 | train_step_timing in s: 1.352
Epoch 0, iteration 2/100: | global_step: 1 | reduced_train_loss: 9.409 | train_step_timing in s: 1.016
Epoch 0, iteration 3/100: | global_step: 2 | reduced_train_loss: 9.619 | train_step_timing in s: 1.018
Epoch 0, iteration 4/100: | global_step: 3 | reduced_train_loss: 8.796 | train_step_timing in s: 1.014
```
And the validation step logging looks like this:
```
Validation: batch 1 / 32
Validation: batch 2 / 32
Validation: batch 3 / 32
Validation: batch 4 / 32
```
**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.
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Build pooler only in the last pp rank (Dist checkpoint will fail if you build in more than one rank)
**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
```
# 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.
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* Related to # (issue)
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"@yaoyu-33 Could you please review"
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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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} | Use kv_channels to enable cases where head_dim != hidden_size // head_num
for example as is the case with the 2407 modes: https://huggingface.co/mistralai/Mistral-Large-Instruct-2407
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To add fix for r2.0.0rc1.
ORG PR: https://github.com/NVIDIA/NeMo/pull/9973
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Add a code snippet demonstrating how to use this
```
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"> One name change, fix import flags from the CI.\r\n\r\nOK\r\n\r\n> Btw, how are you feeding temperature to the MLP now?\r\n\r\nIt's applied directly in `Categorical` "
] | 2024-07-31T17:23:15 | 2024-08-01T02:03:21 | 2024-08-01T02:03:19 | COLLABORATOR | {
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} | # What does this PR do ?
TL;DR: fix for a bug I accidentally introduced in #9901
Context:
* #9901 changed multi-task beam search decoding module to strip the prompt and EOS away form recognitions; it removed "remove_special_tokens" sprinkled in various places in our codebase (transcribe, wer, bleu, etc.)
* That fix was actually porting a part of my changes in an old closed PR #8833. However, I forgot that we also have to handle the dataset part properly - return GT transcripts with stripped prompts and EOS (which was done there). So I ended up introduced a bug that reference has the prompt, but recognition doesn't in scoring.
* I ported the necessary additional changes from #8833 and adapted them to the current codebase.
* Since I wanted to improve the test coverage of the affected parts, I also pulled the changes from #8885 (another PR I forgot to merge) where I already wrote these tests, and extended them.
**Collection**: ASR
# 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"
**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
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} | # What does this PR do ?
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**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?
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**PR Type**:
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- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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 ?
If a user passes a `save_dir` to a PTL logger, by default we write the logs to `<log_dir> / save_dir`, where `<log_dir>` is determined by NeMoLogger. Previously, `save_dir` was ignored, and the logs would be written to `<log_dir>`.
**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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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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"This PR was closed because it has been inactive for 7 days since being marked as stale."
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PR introduces default beam search and mAES (with Ngram LM fusion) decoding implementations for TDT models.
**Collection**: ASR
# Changelog
- [nemo/collections/asr/parts/submodules/tdt_beam_decoding.py]: Default Beam Search and mAES implementations for TDT.
# Usage
```python
python examples/asr/speech_to_text_eval.py \
model_path="$NEMO_MODEL_PATH" \
dataset_manifest="$DATASET_MANIFEST_PATH" \
rnnt_decoding.strategy=maes \
rnnt_decoding.beam.beam_size=8 \
rnnt_decoding.beam.ngram_lm_model="$KENLM_NGRAM_MODEL_PATH" \
rnnt_decoding.beam.ngram_lm_blank_alpha=5.0 \
rnnt_decoding.beam.durations_beam_size=2
```
# 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?
- [x] Did you add or update any necessary documentation?
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} | # What does this PR do ?
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"Thanks for reporting this. This workload -- [examples/nlp/language_modeling/megatron_gpt_ptq.py](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/megatron_gpt_ptq.py) -- was tested on 1xH100 80GB GPU but I agree that it looks excessive, we'll take a look. For completeness: could you perhaps share the full log for your issue?\r\n\r\nPlease also note that currently this memory requirements would apply only for the calibration step (i.e. the script linked above). For TensorRT-LLM engine the memory consumption is as expected: ca. ~9GB for FP8 Llama3-8B model (you can create engines using [tests/export/nemo_export.py](https://github.com/NVIDIA/NeMo/blob/main/tests/export/nemo_export.py), for example).",
"To successfully calibrate and export the Llama3-8b model on, for example, L4 24GB GPU you can use:\r\n```\r\npython megatron_gpt_ptq.py \\\r\n model.restore_from_path=Llama-8b \\\r\n +model.dist_ckpt_load_on_device=False \\\r\n +model.megatron_amp_O2=true \\\r\n +model.precision=bf16 \\\r\n trainer.precision=bf16 \\\r\n quantization.algorithm=fp8 \\\r\n export.dtype=bf16 \\\r\n inference.batch_size=16\r\n```\r\n\r\nExplanations:\r\n1. The knobs below correctly setup the model for evaluation in `bf16`. Disabling `dist_ckpt_load_on_device` avoids memory spikes on model loading:\r\n```\r\n +model.dist_ckpt_load_on_device=False \\\r\n +model.megatron_amp_O2=true \\\r\n +model.precision=bf16 \\\r\n trainer.precision=bf16 \\\r\n```\r\n2. Parameter `export.dtype=bf16` should be the same as model precision -- either `16` or `bf16` -- to avoid data cast on the export step that may also cause OOM.\r\n3. I had to use slightly lower `inference.batch_size=16`.\r\n\r\nThis should do it. Let me know if you have any other issues/questions."
] | 2024-07-31T14:17:17 | 2024-09-09T08:04:56 | 2024-09-09T08:04:56 | NONE | {
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} | null | null | null | **Describe the bug**
Running FP8 PTQ of Llama3-8b on 1x 4090 (24GB) goes OOM? Is this expected? vLLM FP8 quantization works on the same GPU.
What are the minimum requirements to run this quantization?
I have even tried setting batch size to 1 and it still goes OOM.
**Steps/Code to reproduce bug**
```bash
python scripts/checkpoint_converters/convert_llama_hf_to_nemo.py --input_name_or_path meta-llama/Meta-Llama-3-8B-Instruct --output_path ./llama3_8b_instruct.nemo --precision bf16
python examples/nlp/language_modeling/megatron_gpt_ptq.py model.restore_from_path=llama3_8b_instruct.nemo quantization.algorithm=fp8 export.decoder_type=llama export.save_path=llama3_8b_instruct_fp8 export.inference_tensor_parallel=1 trainer.num_nodes=1 trainer.devices=1
```
**Environment overview (please complete the following information)**
- Environment location: Docker
- Method of NeMo install: source
- If method of install is [Docker], provide `docker pull` & `docker run` commands used:
```bash
docker run --gpus all -it --rm -v ./NeMo:/NeMo --shm-size=8g \-p 8888:8888 -p 6006:6006 --ulimit memlock=-1 --ulimit \stack=67108864 --device=/dev/snd nvcr.io/nvidia/pytorch:23.10-py3
```
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"@yaoyu-33 "
] | 2024-07-31T13:55:31 | 2024-07-31T17:25:00 | 2024-07-31T17:25:00 | CONTRIBUTOR | {
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"merged_at": "2024-07-31T17:25:00"
} | # What does this PR do ?
To add fix for r2.0.0rc1.
ORG PR: https://github.com/NVIDIA/NeMo/pull/9973
**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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} | # 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
# 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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} | Want to enable the following
`
model = llm.MixtralModel(llm.MixtralConfig8x22B())
hf_ckpt_path = "hf://mistralai/Mixtral-8x22B-v0.1"
hf_ckpt_path = 'hf:///mnt/4tb/mixtral_8x22b/mixtral-8x22B-hf/'
ckpt_path = model.import_ckpt(hf_ckpt_path)
`
- pass via hf identifier, ie: `hf_ckpt_path = "hf://mistralai/Mixtral-8x22B-v0.1"`
- pass via local path, ie: `hf_ckpt_path = 'hf:///mnt/4tb/mixtral_8x22b/mixtral-8x22B-hf/"`
This is mainly useful when the user has already downloaded to their local disk a large checkpoint (e.g. > 200B parameters).
# 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 ?
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
# 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"
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- [ ] 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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**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.
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* Related to # (issue)
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**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**:
- [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.
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} | # What does this PR do ?
Fix lita tutorial typo.
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} | Makes the `convert` function of the checkpoint converter an iterator. This allows for reduced memory usage during conversion, in fact less memory usage than the total RAM requirements of a full checkpoint.
If the `--low-mem` CLI option is used, the converter will load only the requested layers from the HF checkpoint and upon conversion of a full layer (e.g. transformer) it will save it to disk. Next, it will load those partially converted checkpoints via mmap, thus only copying to ram layers that are being used.
During the model initialization stage the converter may need the full checkpoint ram, but due to memory mapping it can do with less ram also, thus reducing the ram requirements significantly.
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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."
] | 2024-07-30T21:02:24 | 2024-09-07T01:53:32 | 2024-09-07T01:53:32 | NONE | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
I'd like to be able to get per-dataset metrics during training & eval(testing).
Currently even if I configure the `model.data` with multiple datasets, the val_loss still only shows the total loss not the per-dataset losses. See attached.
Looking at the code, it looks like this is due to NeMo blending the datasets before feeding to the PL trainer (to be confirmed by NeMo engineers). [Code reference](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py).

**Describe the solution you'd like**
I would like that NeMo would have the option to enable per-dataset metrics. So that for each val loop, a set of losses (each corresponding to an input dataset) would be logged.
This is hardly a technical proposal but my intuition is that we could either make it so that one dataloader is created per dataset (this might require not blending the datasets in the first place) and pass a map/list of dataloaders to the PL trainer so that the default behavior of the trainer can be leveraged. Or we could take advantage of the `dataset_id` [field](https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/datasets/blended_dataset.py#L96) from each datum and have some sort of breakdown mechanism in the metrics calculation stage.
**Describe alternatives you've considered**
N/A
**Additional context**
N/A
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"This PR is an replacement of https://github.com/NVIDIA/NeMo/pull/9838 due to signoff issue.",
"Adding decreasing related support in separate PR later ",
"Besides the comment about the in-line imports, LGTM",
"@terrykong This is actually an legacy setting inherited from previous version. They use try except to avoid import error and use \r\n```\r\nif not HAVE_MEGATRON_CORE:\r\n logging.warning(\r\n \"megatron-core was not found. Please see the NeMo README for installation instructions: https://github.com/NVIDIA/NeMo#megatron-gpt.\"\r\n )\r\n```\r\nto log warnings. I am thinking try to avoid this part and log a version warning for not able to use mcore mb calculator like:\r\n```\r\ntry:\r\n from megatron.core.num_microbatches_calculator import get_num_microbatches\r\n\r\nexcept (ImportError, ModuleNotFoundError):\r\n logging.warning(\"megatron-core num_microbatches_calculator not found, using Apex version.\")\r\n from apex.transformer.pipeline_parallel.utils import get_num_microbatches\r\n```"
] | 2024-07-30T18:36:34 | 2024-09-04T18:10:32 | 2024-08-01T22:37:15 | COLLABORATOR | {
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} | # What does this PR do ?
Add Mcore microbatches calculator supports. Added backward compatibility. Merged `init_num_mb_calculator` and `reconfigure_num_mb_calculator`
**Collection**: [Note which collection this PR will affect]
# Changelog
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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.",
"PR is merged: https://github.com/NVIDIA/NeMo/pull/10098"
] | 2024-07-30T17:01:20 | 2024-09-23T11:00:15 | 2024-09-23T11:00:13 | COLLABORATOR | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
Currently when training LLMs, the checkpoints are saved with all of the optimizer states, which generally increases the checkpoint size about 6 times. When the LLM itself is very large (e.g. > 30B parameters) and we are running many training jobs at the same time, this requires having a very large storage on the cluster. In case of SFT experiments where we almost never need to restart training from the middle of the job, saving optimizer states for intermediate checkpoints is really wasteful and it would be great to have some control over this.
**Describe the solution you'd like**
Have a config parameter "save_optimizer_states_for_last_checkpoints" (probably a less verbose name is needed). I'd default it to 2 since we always need a last checkpoint state in case job fails or is preempted and it's good to keep another one in case the last state is corrupted for some reason. Then we can set it to -1 to keep the current behavior of saving all optimizer states, set it to 0 in case we don't want to save any, or anything in between.
The way I'd implement it is by modifying PTL/megatron logic in checkpoint saving and whenever we save a new checkpoint, we would then simply remove the optimizer states from one of the old checkpoints based on the config parameter.
**Describe alternatives you've considered**
A current alternative that we have to use is basically to manually remove the optimizer states when we run out of space, which is of course very inconvenient. Another potential way to solve this is by having a script that runs alongside the training jobs and monitors the training folder and whenever a new checkpoint is saved, it does the cleaning. This way we don't have to modify nemo code, but it does not feel like a good solution.
**Additional context**
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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
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# Add a code snippet demonstrating how to use this
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**PR Type**:
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- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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Change equivalent to https://github.com/NVIDIA/NeMo/pull/9717 but targeted to **r2.0.0rc1** branch.
**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.
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} | # What does this PR do ?
This is @conver334 work to implement a knob to disable global_sample_mapping by default.\
Since @conver334's branch was not on github.com/NVIDIA/NeMo and black/isort had trouble running. As a result I made this PR to unblock this work, but the code is not mine.
**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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"closing in favor of 9968"
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Call `_build_num_microbatches_calculator` instead of `build_num_microbatches_calculator` & pass False to `decrease_batch_size_if_needed`
**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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Adds pydantic-settings to the service | {
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📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
🙏 Please merge this PR only if the CI workflow completed successfully. | {
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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."
] | 2024-07-30T00:00:08 | 2024-08-19T16:39:56 | 2024-08-19T16:39:54 | MEMBER | {
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} | # What does this PR do ?
Import `packaging` instead of `pkg_resources`;
In later versions of `pkg_resources`, `packaging` is removed from the module and in Python 3.12 `pkg_resources` itself is deprecated.
Sol: use the `packaging` package.
Note: this is mostly needed to query TE's version which is a suboptimal way of doing it since you can alter via PYTHONPATH ( & appropriate .so links), so might be better to make a function to check whether __version__ is available and if not fall-back to `packaging` logic.
**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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] | closed | false | null | [] | null | [] | 2024-07-29T22:25:05 | 2024-07-30T07:24:42 | 2024-07-30T07:24:40 | COLLABORATOR | {
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} | # What does this PR do ?
fix mem in refit | {
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