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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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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 ?
Enables use of megatron timers in NeMo GPT pretraining and megatron_base based models.
Collection: collections/nlp/models/language_modeling
Usage
model.enable_megatron_timers=True
model.megatron_timer_kwargs.log_every_n_steps= 10
model.megatron_timer_kwargs.log_mode=minmax
model.megatron_timer_kwargs.barrier= False
torchrun --nnodes=1 --nproc-per-node=8 \
/gsw/code/NeMo/examples/nlp/language_modeling/megatron_gpt_pretraining.py \
--config-path=/gsw/code/NeMo/examples/nlp/language_modeling/conf \
--config-name=megatron_gpt_config \
trainer.devices=8 \
trainer.num_nodes=1 \
model.enable_megatron_timers=True \
++model.megatron_timer_kwargs.log_option=max \
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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# 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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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 ?
Fix Canary chunked infer on short audios. Previous version works badly on short audios since it pads short audios to the whole max_chunk length (e.g. 40s), while in this fix we allow the last chunk length to be less than the max_chunk | {
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- Updated MegatronNMT properties to detect Float16Module
- Updated the log_softmax function to match the dtypes in megatron_export
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# Changelog
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This PR fixes bugs in inference and sampling when hiddens module and a Perceiver encoder.
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# Changelog
- Add specific line by line info of high level changes in this PR.
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1) Adds support for dict data input type.
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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.
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```python
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Add FP8 support for SD training.
**Collection**: [multimodal/text_to_image/statble_diffusion]
# Changelog
- Add config argument - use_te_fp8 to switch between fp16 and fp8 training
- Add transformer engine fp8 layers to Stable diffusion Unet
- Update a few legacy paths in notebook tutorial
# Usage
```yaml
unet_config:
use_te_fp8=True
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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This PR adds `AudioCodecModel` to documentation.
**Collection**: docs/TTS
# Changelog
- Added info and references to `models`
- Added class to `api`
- Added a block scheme
- Minor fixes in references and docstrings
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n/a
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Add a one line overview of what this PR aims to accomplish.
**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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"Hi, \r\nIf possible, we shall always appreciate any reproducible script with audio to see the error from our end. Hope you set `max_num_speakers` to be 8 however if speaker duration of a speaker is very low then NMESC algorithm tends to suppress those speakers, but how ever you can tweak the `max_rp_threshold` to ... | 2024-02-08T12:09:14 | 2024-03-25T01:44:33 | 2024-03-25T01:44:32 | NONE | {
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I have audio with duration of about 90 minutes and it contains 8 speakers, when I'm running the MSDD I'm getting only 4 speakers.
Some of the speakers have speech ratio pretty low related to the other speakers.
What can I do to get better results?
Using oracle num speakers is not a solution because the speaker confusion rate is very high in that case and not always I'll know the number of speakers.
Thanks | {
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} | # What does this PR do ?
Update Multimodal data preparation tutorial.
**Collection**: Multimodal
# Changelog
- Delete duplicate section
- Populate "TODO" links with actual links from toolkit doc and launcher.
# Jenkins CI
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} | # What does this PR do ?
Fix default beam size of model config
**Collection**: [ASR]
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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Fix default beam size of model config
**Collection**: [ASR]
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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"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."
] | 2024-02-08T00:59:32 | 2024-03-16T01:44:02 | 2024-03-16T01:44:02 | NONE | {
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} | null | null | null | [Inference Performance of LLAMA-2 posted by Nvidia 1](https://docs.nvidia.com/nemo-framework/user-guide/latest/performance/llama.html)
According to the link above, the inference lantecy of LLAMA-2-13B with A100 80GB SXM4 at batch size=1 and tp=1, is less than latency of LLAMA-2-7B under the same condition.
How can we get this performance data? It’s unbelievable and ridiculous. | {
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} | # What does this PR do ?
Allow sequence parallel for lora training. Supports all linear modules as in #7988
**Collection**: NLP
# Changelog
- Set sequence_parallel flags to False in LoRA layers to allow gathering output in LoRA `linear_in`
- For attention_qkv and mlp_fc1, do a manual all_gather (reduce_scatter during backward) along the sequence dimension before each lora module since the previous layer (layernorm) is affected by SP
- For attention_dense and mlp_fc2, do a manually scatter (gather during backward) along the sequence dimension after each lora module since the next layer (layernorm) is affected by SP
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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.
# Additional Information
* Related to # 7988
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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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] 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.
# Additional Information
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} | # What does this PR do ?
Adds support to change vocabulary of multi task models.
Adds support to save the tokenizer directory of all ASR models that implement `ASRBPEMixin`.
**Collection**: [ASR]
# Changelog
- Adds utility method `save_tokenizers()` that extracts all the internal tokenizers of a model into the specified directory.
- Adds support method to change tokenizers of a multi task model.
# Usage
```python
from nemo.collections.asr.models import EncDecMultiTaskModel
asr_model = EncDecMultiTaskModel.from_pretrained("xyz")
# Save tokenizers - this creates a new directory at pwd called `all_tokenizers`
# Then it populates it with all the tokenizer objects stored in the model.
asr_model.save_tokenizers('./all_tokenizers/')
# Change model vocabulary
tokenizer_cfg = OmegaConf.create(asr_model.cfg.tokenizer)
with open_dict(tokenizer_cfg):
tokenizer_cfg.pop('langs', None) # remove old `langs` part of config from aggregate tokenizer
# create new langs config
tokenizer_cfg.langs = OmegaConf.create(dict(
spl_tokens=dict(
dir='all_tokenizers/spl_tokens/',
type='bpe',
),
en=dict(
dir='all_tokenizers/en/',
type='bpe',
),
de=dict(
dir='all_tokenizers/de/',
type='bpe',
),
))
# Change the Multi task models tokenizers and vocabulary
asr_model.change_vocabulary(tokenizer_cfg, new_tokenizer_type='agg')
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
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**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation | {
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This PR fixes issues with using hiddens module at inference time.
**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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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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Fixing hiddens module support.
**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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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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**Collection**: tts, nlp
# Changelog
- TODO
# Usage
```python
# TODO
```
# 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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- [ ] 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
## Who can review?
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"@pzelasko @krishnacpuvvada Revisited tokenizer and think we can meet both needs:\r\n\r\nThere's no reason for the tokenizer to enforce what is being done in the canary prompt format. So let's just generalize task token behavior. We can pass all task tokens through the build method, then s... | 2024-02-07T17:05:05 | 2024-02-24T17:04:07 | 2024-02-24T17:04:07 | COLLABORATOR | {
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} | # What does this PR do ?
- Refactors Canary code for Riva training
**Collection**: [ASR]
# Changelog
- Expands canary coverage for more languages. (Just keep list of iso and bcp tags updated in `language_code.py`
- Introduces bleu metric for in-training monitoring of performance. This replaces the sacrebleu code in validation pass.
- Updates wer metric to be compatible with Multitask decoding
- ToDo: The WER and BLEU metrics can be combined in a single metric but involves moving all WER calls to dictionary outputs. Separate PR.
- ToDo: Have to expand BLEU metric to switch between tokenizers while decoding. Separate PR.
- Edits Canary dataloader for multiple paired translations. Now paired text just needs text input to be a nested dict of `text: {lang1: {text:....spl_tokens:...}, lang2 {text:...spl_tokens....}...`.
- Changes Canary tokenizer to create `spl_tokens` class by default.
- General refactoring of multitask model. Validation and Test steps pass off to ASR model and are more consistent in design with CTC models.
- Changed multitask_decoding in submodules to strip tokens by default. (Avoided extra flag in metrics logic)
# Jenkins CI
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- [Y ] Bugfix
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# Changelog
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} | # What does this PR do ?
Fixing hiddens module support.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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# Jenkins CI
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# Before your PR is "Ready for review"
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"same here",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"can you guide about this issue? @ericharper thank you in advance!"
] | 2024-02-07T11:25:25 | 2024-04-20T09:42:35 | 2024-04-20T09:42:35 | NONE | {
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} | null | null | null | **Describe the bug**
Hi,
I'm trying to finetune Nemotron-3 Model using nvcr.io/nvidia/nemo:23.10 container with this [lora.ipynb notebook](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/lora.ipynb) and this exception occurs:
raise Exception("The provided qkv memory layout is not supported!")
**The full trace is like this:**
```
Traceback (most recent call last):
File "/workspace/nemo/examples/nlp/language_modeling/tuning/megatron_gpt_peft_tuning.py", line 77, in main
trainer.fit(model)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 532, in fit
call._call_and_handle_interrupt(
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py", line 42, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 93, in launch
return function(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 571, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 980, in _run
results = self._run_stage()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 1021, in _run_stage
self._run_sanity_check()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 1050, in _run_sanity_check
val_loop.run()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/utilities.py", line 181, in _decorator
return loop_run(self, *args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/evaluation_loop.py", line 115, in run
self._evaluation_step(batch, batch_idx, dataloader_idx)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/evaluation_loop.py", line 376, in _evaluation_step
output = call._call_strategy_hook(trainer, hook_name, *step_kwargs.values())
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py", line 293, in _call_strategy_hook
output = fn(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/strategies/ddp.py", line 338, in validation_step
return self.model(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/parallel/distributed.py", line 1521, in forward
else self._run_ddp_forward(*inputs, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/parallel/distributed.py", line 1357, in _run_ddp_forward
return self.module(*inputs, **kwargs) # type: ignore[index]
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/overrides/base.py", line 102, in forward
return self._forward_module.validation_step(*inputs, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_gpt_sft_model.py", line 387, in validation_step
return self.inference_step(dataloader_iter, batch_idx, 'validation', dataloader_idx)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_gpt_sft_model.py", line 403, in inference_step
loss = super().validation_step(itertools.chain([batch]), batch_idx)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 963, in validation_step
loss = self.fwd_bwd_step(dataloader_iter, batch_idx, True)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_gpt_sft_model.py", line 347, in fwd_bwd_step
losses_reduced_per_micro_batch = fwd_bwd_function(
File "/usr/local/lib/python3.10/dist-packages/megatron/core/pipeline_parallel/schedules.py", line 327, in forward_backward_no_pipelining
output_tensor = forward_step(
File "/usr/local/lib/python3.10/dist-packages/megatron/core/pipeline_parallel/schedules.py", line 183, in forward_step
output_tensor, loss_func = forward_step_func(data_iterator, model)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 863, in fwd_output_and_loss_func
output_tensor = model(**forward_args)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/megatron/core/models/gpt/gpt_model.py", line 166, in forward
hidden_states = self.decoder(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/megatron/core/transformer/transformer_block.py", line 311, in forward
hidden_states, context = layer(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/megatron/core/transformer/transformer_layer.py", line 153, in forward
attention_output_with_bias = self.self_attention(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/megatron/core/transformer/attention.py", line 263, in forward
core_attn_out = self.core_attention(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/megatron/core/transformer/custom_layers/transformer_engine.py", line 427, in forward
return super().forward(
File "/usr/local/lib/python3.10/dist-packages/transformer_engine/pytorch/attention.py", line 1235, in forward
qkv_layout = _get_qkv_layout(query_layer, key_layer, value_layer,
File "/usr/local/lib/python3.10/dist-packages/transformer_engine/pytorch/attention.py", line 464, in _get_qkv_layout
raise Exception("The provided qkv memory layout is not supported!")
Exception: The provided qkv memory layout is not supported!
```
**and my training config file is this:**
```
name: megatron_gpt_peft_${model.peft.peft_scheme}_tuning
trainer:
devices: 1
accelerator: gpu
num_nodes: 1
precision: '32'
logger: false
enable_checkpointing: false
use_distributed_sampler: false
max_epochs: 3
max_steps: 20000
log_every_n_steps: 10
val_check_interval: 200
gradient_clip_val: 1.0
exp_manager:
explicit_log_dir: null
exp_dir: null
name: ${name}
create_wandb_logger: false
wandb_logger_kwargs:
project: null
name: null
resume_if_exists: true
resume_ignore_no_checkpoint: true
create_checkpoint_callback: true
checkpoint_callback_params:
monitor: validation_${model.data.validation_ds.metric.name}
save_top_k: 1
mode: min
save_nemo_on_train_end: true
filename: ${name}--{${exp_manager.checkpoint_callback_params.monitor}:.3f}-{step}-{consumed_samples}
model_parallel_size: ${model.tensor_model_parallel_size}
always_save_nemo: false
save_best_model: true
create_early_stopping_callback: true
early_stopping_callback_params:
monitor: val_loss
mode: min
min_delta: 0.001
patience: 10
verbose: true
strict: false
model:
seed: 1234
tensor_model_parallel_size: 1
pipeline_model_parallel_size: 1
global_batch_size: 4
micro_batch_size: 1
restore_from_path: /project/models/Nemotron-3-8B-Chat-4k-RLHF.nemo
resume_from_checkpoint: null
save_nemo_on_validation_end: false
sync_batch_comm: false
megatron_amp_O2: false
sequence_parallel: false
activations_checkpoint_granularity: null
activations_checkpoint_method: null
activations_checkpoint_num_layers: null
activations_checkpoint_layers_per_pipeline: null
answer_only_loss: true
gradient_as_bucket_view: false
hidden_dropout: 0.0
attention_dropout: 0.0
ffn_dropout: 0.0
peft:
peft_scheme: ptuning
restore_from_path: null
adapter_tuning:
type: parallel_adapter
adapter_dim: 32
adapter_dropout: 0.0
norm_position: pre
column_init_method: xavier
row_init_method: zero
norm_type: mixedfusedlayernorm
layer_selection: null
weight_tying: false
position_embedding_strategy: null
lora_tuning:
adapter_dim: 32
adapter_dropout: 0.0
column_init_method: xavier
row_init_method: zero
layer_selection: null
weight_tying: false
position_embedding_strategy: null
p_tuning:
virtual_tokens: 15
bottleneck_dim: 1024
embedding_dim: 1024
init_std: 0.023
ia3_tuning:
layer_selection: null
data:
train_ds:
file_names:
- /project/data/therapies.jsonl
global_batch_size: ${model.global_batch_size}
micro_batch_size: ${model.micro_batch_size}
shuffle: true
num_workers: 0
memmap_workers: 2
pin_memory: true
max_seq_length: 2048
min_seq_length: 1
drop_last: true
concat_sampling_probabilities:
- 1.0
label_key: output
add_eos: true
add_sep: false
add_bos: false
truncation_field: input
index_mapping_dir: null
prompt_template: '{input}
{output}'
truncation_method: right
validation_ds:
file_names:
- /project/data/therapies.jsonl
names: null
global_batch_size: ${model.global_batch_size}
micro_batch_size: ${model.micro_batch_size}
shuffle: false
num_workers: 0
memmap_workers: ${model.data.train_ds.memmap_workers}
pin_memory: true
max_seq_length: 2048
min_seq_length: 1
drop_last: false
label_key: ${model.data.train_ds.label_key}
add_eos: ${model.data.train_ds.add_eos}
add_sep: ${model.data.train_ds.add_sep}
add_bos: ${model.data.train_ds.add_bos}
write_predictions_to_file: false
output_file_path_prefix: null
truncation_field: ${model.data.train_ds.truncation_field}
index_mapping_dir: null
prompt_template: ${model.data.train_ds.prompt_template}
tokens_to_generate: 32
truncation_method: right
metric:
name: loss
average: null
num_classes: null
test_ds:
file_names: null
names: null
global_batch_size: ${model.global_batch_size}
micro_batch_size: ${model.micro_batch_size}
shuffle: false
num_workers: 0
memmap_workers: ${model.data.train_ds.memmap_workers}
pin_memory: true
max_seq_length: 2048
min_seq_length: 1
drop_last: false
label_key: ${model.data.train_ds.label_key}
add_eos: ${model.data.train_ds.add_eos}
add_sep: ${model.data.train_ds.add_sep}
add_bos: ${model.data.train_ds.add_bos}
write_predictions_to_file: false
output_file_path_prefix: null
truncation_field: ${model.data.train_ds.truncation_field}
index_mapping_dir: null
prompt_template: ${model.data.train_ds.prompt_template}
tokens_to_generate: 32
truncation_method: right
metric:
name: loss
average: null
num_classes: null
optim:
name: fused_adam
lr: 0.0001
weight_decay: 0.01
betas:
- 0.9
- 0.98
sched:
name: CosineAnnealing
warmup_steps: 50
min_lr: 0.0
constant_steps: 0
monitor: val_loss
reduce_on_plateau: false
```
**Expected behavior**
finetuning the model properly.
**Environment overview (please complete the following information)**
- Environment location: Docker - Nemo:23.10 container
**Additional context**
GPU: A100 - 80GB | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/8358 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8358/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8358/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8358/events | https://github.com/NVIDIA/NeMo/pull/8358 | 2,122,452,676 | PR_kwDOC_bI7s5mOz3E | 8,358 | Add Neva template | {
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"jenkins"
] | 2024-02-07T08:23:59 | 2024-02-21T22:56:22 | 2024-02-21T22:56:21 | COLLABORATOR | {
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"merged_at": "2024-02-21T22:56:21"
} | # What does this PR do ?
Add template for DPO model training 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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adds support to change vocabulary of multi task models.
Adds support to save the tokenizer directory of all ASR models that implement `ASRBPEMixin`.
**Collection**: [ASR]
# Changelog
- Adds utility method `save_tokenizers()` that extracts all the internal tokenizers of a model into the specified directory.
- Adds support method to change tokenizers of a multi task model.
# Usage
```python
from nemo.collections.asr.models import EncDecMultiTaskModel
asr_model = EncDecMultiTaskModel.from_pretrained("xyz")
# Save tokenizers - this creates a new directory at pwd called `all_tokenizers`
# Then it populates it with all the tokenizer objects stored in the model.
asr_model.save_tokenizers('./all_tokenizers/')
# Change model vocabulary
tokenizer_cfg = OmegaConf.create(asr_model.cfg.tokenizer)
with open_dict(tokenizer_cfg):
tokenizer_cfg.pop('langs', None) # remove old `langs` part of config from aggregate tokenizer
# create new langs config
tokenizer_cfg.langs = OmegaConf.create(dict(
spl_tokens=dict(
dir='all_tokenizers/spl_tokens/',
type='bpe',
),
en=dict(
dir='all_tokenizers/en/',
type='bpe',
),
de=dict(
dir='all_tokenizers/de/',
type='bpe',
),
))
# Change the Multi task models tokenizers and vocabulary
asr_model.change_vocabulary(tokenizer_cfg, new_tokenizer_type='agg')
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation | {
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} | # What does this PR do ?
Add Finetuning of Parakeet model with HF Datasets
**Collection**: ASR
# Changelog
- Add tutorial
# 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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"jenkins",
"jenkins",
"jenkins",
"jenkins",
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"jenkins"
] | 2024-02-07T00:41:58 | 2024-02-09T03:44:48 | 2024-02-09T03:44:47 | COLLABORATOR | {
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} | # What does this PR do ?
Add inference script for MultitaskAED models to support long audios, by simple chunking into non-overlapped segments.
**Collection**: [asr]
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"jenkins",
"jenkins",
"@ericharper @ashbhandare what do you think about if we move the mcore logger params to the exp manager section in config?",
"> @ericharper @ashbhandare what do you think about if we move the mcore logger params to the exp manager section in config?\r\n\r\nIt's kind of difficult to do no... | 2024-02-07T00:32:46 | 2024-02-09T06:34:25 | 2024-02-09T06:34:25 | CONTRIBUTOR | {
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} | # What does this PR do ?
Enables use of megatron timers in NeMo GPT pretraining and megatron_base based models.
Collection: collections/nlp/models/language_modeling
Usage
model.enable_megatron_timers=True
model.megatron_timer_kwargs.log_every_n_steps= 10
model.megatron_timer_kwargs.log_mode=minmax
model.megatron_timer_kwargs.barrier= False
torchrun --nnodes=1 --nproc-per-node=8 \
/gsw/code/NeMo/examples/nlp/language_modeling/megatron_gpt_pretraining.py \
--config-path=/gsw/code/NeMo/examples/nlp/language_modeling/conf \
--config-name=megatron_gpt_config \
trainer.devices=8 \
trainer.num_nodes=1 \
model.enable_megatron_timers=True \
++model.megatron_timer_kwargs.log_option=max \
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"jenkins",
"jenkins",
"jenkins",
"jenkins",
"jenkins",
"jenkins",
"jenkins",
"jenkins"
] | 2024-02-06T23:25:51 | 2024-02-16T17:11:18 | 2024-02-16T17:11:18 | MEMBER | {
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} | # What does this PR do ?
Fixes conversion script for NeMo to HF for mistral-7B.
**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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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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# Changelog
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# Changelog
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# Usage
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```python
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```
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# 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.
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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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Fixed a small typo in the GPT model training documentation.
**Collection**: docs
# Changelog
- Fixed spelling mistake on line 38.
# Jenkins CI
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**PR Type**:
- Documentation
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Update huggingface hub
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- [x] Bugfix
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} | # What does this PR do ?
Remove ASR Webapp
**Collection**: [ASR]
# 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?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Remove ASR Webapp
**Collection**: [ASR]
# 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?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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"jenkins"
] | 2024-02-06T18:02:57 | 2024-02-16T00:28:33 | 2024-02-16T00:28:33 | CONTRIBUTOR | {
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} | # What does this PR do ?
Instead of popping max_seqlen and cu_seqlens_argmin from a batch, we just read the dict to preserve the data for later micro-batches when PP > 1.
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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 ?
Fixes the losses of several Megatron models, that were incorrect for `micro_batch_size` > 1
**Collection**: nlp, multimodal
# Changelog
- Fix losses of several Megatron models (`MegatronGPTModel`, `MegatronNevaModel`, `MegatronBertModel`, `MegatronLMEncoderDecoderModel`) that were incorrect for `micro_batch_size` > 1
# What still needs to be done
- [ ] Probably that some new tests are needed. I first want to check which tests fail to identify potential gaps in testing.
- [ ] There is a `# TODO does this also need fixing?` in `MegatronBertModel` as I'm not familiar with the Sequence Order Prediction loss. Would be helpful if someone could double check it, or at least tell me what are the shapes of `sop_logits` and `sentence_order` so I can figure it out.
- [ ] Check whether the handling of `context_parallel_size` > 1 is correct in `MegatronGPTModel`
# Other breaking changes
While fixing this problem I ran into some related issues that forced me to make some design choices leading to "collateral" breaking changes:
1. The validation loss in `MegatronGPTModel` with `validation_drop_last=False` was inconsistent with `drop_last=True` since it was computing a per-token average across the global batch (at least I believe that's what's happening [here](https://github.com/NVIDIA/NeMo/blob/9940ec600/nemo/collections/nlp/models/language_modeling/megatron_gpt_sft_model.py#L483)), while with `drop_last=True` it was using the (incorrect) per-token average across the micro-batch. I changed it so that the behavior should now be a consistent per-sample average across valid samples.
2. The loss could be NaN when all samples in a micro-batch would be fully masked (which typically would only happen with `micro_batch_size=1`). This also led to inconsistency between different micro-batch sizes. I changed it so that there is no NaN anymore, instead fully masked samples have a zero loss. The alternative would be to trigger a NaN as soon as one sample is fully masked, but I decided against it because (i) in order to handle validation with `validation_drop_last=False` we would need to make the loss function depend on this flag, which would make the code more complex (and potentially inconsistent in the presence of fully masked samples in the validation set), and (ii) NaN losses due to fully masked samples is a recurring pain point currently (it forces users to either go through a cumbersome model-dependent data filtering step, or hack the NeMo codebase to work around it).
3. (Follow-up to 2, splitting it for readability) That being said, I think that silently ignoring some samples is also potentially dangerous, so I added a warning when the loss mask is all zeros for some samples. I decided to do it at the dataset level rather than in the model so as to avoid forcing a CPU-GPU synch point on such a check. We could add a flag in the datasets to control whether this is an error or a warning, but I thought I'd get some feedback first.
# 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?
- [ ] 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
## 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
Fixes #8343
Minor remark: I removed some `# TODO: add nemo version here` that seem to date back several years ago and don't make sense to me. | {
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"There are also many odd choices, many torch.nn imports have been removed and arbitrarily using the longer torch.nn.xyz - why ? ",
"> There are also many odd choices, many torch.nn imports have been removed and arbitrarily using the longer torch.nn.xyz - why ?\r\n\r\nThe suggestions come from Flake8 ... | 2024-02-06T15:50:39 | 2024-02-29T01:43:39 | 2024-02-29T01:43:38 | COLLABORATOR | {
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} | # What does this PR do ?
Unused imports cleanup indentified by [Flake8](https://flake8.pycqa.org/en/latest), see https://www.flake8rules.com/rules/F401.html.
**Collection**: [ALL]
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
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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."
] | 2024-02-06T13:04:39 | 2024-03-15T01:44:17 | 2024-03-15T01:44:17 | COLLABORATOR | {
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} | null | null | null | **Describe the bug**
The loss implemented [here](https://github.com/NVIDIA/NeMo/blob/13c1db4ffdaa3b2125ce6e66c93509a39d55b2cf/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py#L1169) is incorrect for `micro_batch_size` > 1 because it computes the average over all tokens in the micro-batch, instead of computing a per-sample loss then averaging it. This leads to inconsistent results when varying `micro_batch_size`.
**Steps/Code to reproduce bug**
Compare results with different values of `micro_batch_size` and notice they are very different.
**Expected behavior**
Varying `micro_batch_size` shouldn't affect results.
**Additional context**
I am creating this issue for visibility (in case someone else runs into the same problem), and will be submitting a PR to fix it. | {
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"There is also an undefined variable `head_num` in this script https://github.com/NVIDIA/NeMo/blob/0bb9e66a6d29b28e8831d1d1dd8a30310173ce46/scripts/nlp_language_modeling/convert_nemo_mixtral_to_hf.py#L124.",
"jenkins",
"bumping, if everything looks ok can we merge? @ericharper @janekl @akoumpa "
] | 2024-02-06T12:49:54 | 2024-02-12T22:06:39 | 2024-02-12T22:06:38 | CONTRIBUTOR | {
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} | # What does this PR do ?
Fixes mistral & mixtral conversion scripts(renamed variable `num_experts` to `num_moe_experts` in mixtral converter).
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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"@biscayan pls follow steps listed at https://github.com/NVIDIA/NeMo/pull/8341/checks?check_run_id=21297714430 to sign off your commit"
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} | # What does this PR do ?
Add fastconformer model for decoder timestamp.
- Fastconformer model shoud have stride in 0.08 secs
- You can use fastconformer model for diarization in offline_diar_with_asr_infer.py
**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
```
# Jenkins CI
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# 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**:
- [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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"Did you discuss with @yidong72 about deleting old retro stuff? Usually we'd want to deprecate at least for a release before deleting.",
"jenkins",
"jenkins",
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"jenkins",
"jenkins",
"jenkins",
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} | # What does this PR do ?
Merging Mcore RETRO from huvu/mcore_retro to r1.23.0
# 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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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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Multimodal r1.23.0 bug fix
**Collection**: [Multimodal, CI]
# 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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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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* Related to # (issue)
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- Add Canary support for decoding with return_hypotheses=True and to return top-k hypotheses
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Blog: https://qwenlm.github.io/blog/qwen1.5/
HF: https://huggingface.co/collections/Qwen/qwen15-65c0a2f577b1ecb76d786524
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} | # What does this PR do ?
Adds bug fixes to support TP, PP =and SP
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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} | # What does this PR do ?
- Fix WER bug in ASR_with_subword_tokenization tutorial
**Collection**: [ASR]
# Changelog
- Added missing parameter to above tutorial
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} | # What does this PR do ?
Instead of popping max_seqlen and cu_seqlens_argmin from a batch, we just read the dict to preserve the data for later micro-batches when PP > 1.
# Jenkins CI
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"I've had the same issue",
"I looked into this a bit more and I think the real problem is that every process is getting mistaken as the master process (i.e. the issue is_global_rank_zero() from [get_rank.py ](https://github.com/NVIDIA/NeMo/blob/main/nemo/utils/get_rank.py)). \r\n\r\nIf I understand things correct... | 2024-02-05T17:34:00 | 2024-04-30T01:44:57 | 2024-04-30T01:44:56 | NONE | {
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} | null | null | null | **Describe the bug**
Nemo version V1.21.0, script ../examples/asr/speech_to_text_finetune.py
ASR model fine tuning script runs fine with a single GPU, but fails for 2 GPUs and 3+ GPUs.
I made small changes to make multiple GPU training working, but want to confirm with the team if there is a better solution and maybe other cases that I should consider.
**Steps/Code to reproduce bug**
python ../examples/asr/speech_to_text_finetune.py
**Expected behavior**
1. when trying to train with 2 GPUs, existing script gives the following error and training crash
File "*/.conda/envs/nemo_j/lib/python3.10/pathlib.py", line 1175, in mkdir self._accessor.mkdir(self, mode) FileExistsError: [Errno 17] File exists: '/model/save/path/~/checkpoints'
This bug is fixed by making change in ".conda/envs/nemo_j/lib/python3.10/site-packages/nemo/core/classes/modelPT.py", line 368
path.parent.mkdir(parents=True) -> path.parent.mkdir(parents=True,exist_ok=True))
2. when trying to train with 3+ GPUs, existing script gives the following error and training crash
First, it gives a similar error to 2 GPU training, but about a different folder.
File "*/.conda/envs/nemo_j/lib/python3.10/pathlib.py", line 1175, in mkdir self._accessor.mkdir(self, mode)
FileExistsError: [Errno 17] File exists: '/model/save/path/~/run_0'
This bug can be fixed similarly, by making a change in "*/.conda/envs/nemo_j/lib/python3.10/site-packages/nemo/utils/exp_manager.py", line 647
new_run_dir.mkdir()-> new_run_dir.mkdir(exist_ok=True)
Secondly, it gives an error about files missing:
FileNotFoundError: [Errno 2] No such file or directory: '/model/save/path/nemo_log_globalrank-0_localrank-0.txt' -> '/model/save/path/run_0/nemo_log_globalrank-0_localrank-0.txt'
It seems trying to move a file named nemo_log_globalrank-0_localrank-0.txt to a subfolder ./run_[0-9], which seems was moved already, so a simple fix was making a change in "*/.conda/envs/nemo_j/lib/python3.10/shutil.py", line 816
os.rename(src, real_dst) -> pass
After these two changes, training with 3 and 4 GPUs scripts runs and WER reduces.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
conda env installed on local Linux machine by the following commands:
conda create --name nemo python==3.10.12
conda activate nemo
conda install -y pytorch=2.1.0 torchvision=0.16.0 torchaudio=2.1.0 pytorch-cuda=11.8 -c pytorch -c nvidia
pip install Cython==3.0.5
pip install nemo-toolkit['all']==1.21.0
**Environment details**
**Additional context**
Training on single node with GPU A100.
My fix seems made the fine tuning working on 1 node with 2+ GPUs, not sure if this fix will generalize to multi-node, multi-GPU training. | {
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"I guess the bug is related to the pytorch lightning (PTL) version upgrade. Since nvcr.io/nvidia/nemo:23.08, PTL is upgraded to 2.0, so there may be some back-compatibility issues for older models trained under PTL>2.0. \r\n\r\nCould you pls try `nvcr.io/nvidia/nemo:23.06` or lower versions of nemo container?",
"... | 2024-02-05T14:56:25 | 2024-02-07T10:23:16 | 2024-02-07T10:23:16 | NONE | {
"total": 0,
"completed": 0,
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} | null | null | null | **Describe the bug**
I'm trying to resume training from last trained checkpoint (vit tts) but i get following error
```
[NeMo W 2024-02-05 13:09:16 experimental:26] `<class 'nemo.collections.tts.models.vits.VitsModel'>` is experimental and not rea
dy for production yet. Use at your own risk.
Creating ClassifyFst grammars. This might take some time...
[NeMo W 2024-02-05 13:09:42 experimental:26] `<class 'nemo.collections.tts.g2p.models.i18n_ipa.IpaG2p'>` is experimental and no
t ready for production yet. Use at your own risk.
[NeMo W 2024-02-05 13:09:45 i18n_ipa:124] apply_to_oov_word=None, This means that some of words will remain unchanged if they a
re not handled by any of the rules in self.parse_one_word(). This may be intended if phonemes and chars are both valid inputs,
otherwise, you may see unexpected deletions in your input.
[NeMo W 2024-02-05 13:09:45 experimental:26] `<class 'nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.IPAToken
izer'>` is experimental and not ready for production yet. Use at your own risk.
[NeMo I 2024-02-05 13:09:45 dataset:228] Loading dataset from /path/to/manifests/train_manifest_text_normed.json.
25087it [00:05, 4743.57it/s]
[NeMo I 2024-02-05 13:09:50 dataset:266] Loaded dataset with 25087 files.
[NeMo I 2024-02-05 13:09:50 dataset:268] Dataset contains 35.62 hours.
[NeMo I 2024-02-05 13:09:50 dataset:376] Pruned 0 files. Final dataset contains 25087 files
[NeMo I 2024-02-05 13:09:50 dataset:378] Pruned 0.00 hours. Final dataset contains 35.62 hours.
[NeMo I 2024-02-05 13:09:51 dataset:228] Loading dataset from /path/to/manifests/val_manifest_text_normed.json.
100it [00:00, 4799.47it/s]
[NeMo I 2024-02-05 13:09:51 dataset:266] Loaded dataset with 100 files.
[NeMo I 2024-02-05 13:09:51 dataset:268] Dataset contains 0.15 hours.
[NeMo I 2024-02-05 13:09:51 dataset:376] Pruned 0 files. Final dataset contains 100 files
[NeMo I 2024-02-05 13:09:51 dataset:378] Pruned 0.00 hours. Final dataset contains 0.15 hours.
[NeMo I 2024-02-05 13:09:51 features:289] PADDING: 1
[NeMo I 2024-02-05 13:09:51 features:297] STFT using exact pad
[NeMo W 2024-02-05 13:09:51 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/torch/nn/utils/weight_norm.py:30: UserWar
ning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/2 Creating ClassifyFst grammars. This might take some time...
25087it [00:05, 4910.99it/s]
100it [00:00, 4593.43it/s] Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/2 ----------------------------------------------------------------------------------------------------
distributed_backend=nccl
All distributed processes registered. Starting with 2 processes
----------------------------------------------------------------------------------------------------
[NeMo W 2024-02-05 13:10:59 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/pytorch_lightning/callbacks/model_checkpo
int.py:617: UserWarning: Checkpoint directory /path/to/vits/resultVitsGermanTTS
/VITS/2024-02-04_10-10-06/checkpoints exists and is not empty.
rank_zero_warn(f"Checkpoint directory {dirpath} exists and is not empty.")
0it [00:00, ?it/s][NeMo I 2024-02-05 13:10:59 dataset:228] Loading dataset from /path/to/manifests/train_manifest_text_normed.json.
25087it [00:01, 23103.62it/s]
25087it [00:01, 23063.96it/s]
[NeMo I 2024-02-05 13:11:00 dataset:266] Loaded dataset with 25087 files.
[NeMo I 2024-02-05 13:11:00 dataset:268] Dataset contains 35.62 hours.
[NeMo I 2024-02-05 13:11:00 dataset:376] Pruned 0 files. Final dataset contains 25087 files
[NeMo I 2024-02-05 13:11:00 dataset:378] Pruned 0.00 hours. Final dataset contains 35.62 hours.
Restoring states from the checkpoint path at resultVitsGermanTTS/VITS/2024-02-04_10-10-06/checkpoints/VITS--loss_gen_all=28.129
7-epoch=37-last.ckpt
LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1]
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]
| Name | Type | Params
------------------------------------------------------------------------
0 | audio_to_melspec_processor | FilterbankFeatures | 0
1 | feat_matching_loss | FeatureMatchingLoss | 0
2 | disc_loss | DiscriminatorLoss | 0
3 | gen_loss | GeneratorLoss | 0
4 | kl_loss | KlLoss | 0
5 | net_g | SynthesizerTrn | 39.7 M
6 | net_d | MultiPeriodDiscriminator | 46.7 M
------------------------------------------------------------------------
86.4 M Trainable params
0 Non-trainable params
86.4 M Total params
345.615 Total estimated model params size (MB)
Restored all states from the checkpoint at resultVitsGermanTTS/VITS/2024-02-04_10-10-06/checkpoints/VITS--loss_gen_all=28.1297-
epoch=37-last.ckpt
0it [00:00, ?it/s][NeMo I 2024-02-05 13:11:14 dataset:228] Loading dataset from /path/to/manifests/train_manifest_text_normed.json.
25087it [00:01, 22758.76it/s]
25087it [00:01, 21809.31it/s]
[NeMo I 2024-02-05 13:11:15 dataset:266] Loaded dataset with 25087 files.
[NeMo I 2024-02-05 13:11:15 dataset:268] Dataset contains 35.62 hours.
[NeMo I 2024-02-05 13:11:15 dataset:376] Pruned 0 files. Final dataset contains 25087 files
[NeMo I 2024-02-05 13:11:15 dataset:378] Pruned 0.00 hours. Final dataset contains 35.62 hours.
Epoch 37: 0%| | 0/375 [00:00<?, ?it/s]
[NeMo W 2024-02-05 13:11:21 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/training_epoch_lo
op.py:151: UserWarning: You're resuming from a checkpoint that ended before the epoch ended. This can cause unreliable results
if further training is done. Consider using an end-of-epoch checkpoint
rank_zero_warn(
Epoch 37: 0%| | 0/375 [00:17<?, ?it/s, v_num=xpkcE
rror executing job with overrides: ['model.sample_rate=22050', 'train_dataset=/path/to/manifests/train_manifest_text_normed.json', 'validation_datasets=/path/to/manifests/val_manifest_text_normed.json', 'phoneme_dict_path=tts_dataset_files/ipa_cmudict-0.7b_nv23
.01.txt', 'heteronyms_path=tts_dataset_files/heteronyms-052722', 'trainer.max_epochs=5000', 'trainer.accelerator=gpu', 'trainer
.check_val_every_n_epoch=2', 'trainer.strategy=ddp_find_unused_parameters_true', 'exp_manager.exp_dir=resultVitsGermanTTS', 'mo
del.text_normalizer.lang=de', '+exp_manager.create_wandb_logger=true', '+exp_manager.wandb_logger_kwargs.name=VITS_DS',
'+exp_manager.wandb_logger_kwargs.project=VITS_GermanTTSCustomDS', '++exp_manager.resume_if_exists=true', '+exp_manager.checkpo
int_callback_params.dirpath=resultVitsGermanTTS/VITS/2024-02-04_10-10-06/checkpoints', 'trainer.devices=2']
Traceback (most recent call last):
File "/workspace/nemo/examples/tts/vits.py", line 29, in main
trainer.fit(model)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 532, in fit
call._call_and_handle_interrupt(
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py", line 42, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 93, in launc
h
return function(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 571, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 980, in _run
results = self._run_stage()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 1023, in _run_stage
self.fit_loop.run()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/fit_loop.py", line 202, in run
self.advance()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/fit_loop.py", line 355, in advance
self.epoch_loop.run(self._data_fetcher)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/training_epoch_loop.py", line 134, in run
self.on_advance_end()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/training_epoch_loop.py", line 249, in on_advance_end
self.val_loop.run()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/utilities.py", line 181, in _decorator
return loop_run(self, *args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/evaluation_loop.py", line 122, in run
return self.on_run_end()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/evaluation_loop.py", line 258, in on_run_end
self._on_evaluation_end()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/evaluation_loop.py", line 303, in _on_evaluation_end
call._call_callback_hooks(trainer, hook_name, *args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py", line 194, in _call_callback_hooks
fn(trainer, trainer.lightning_module, *args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 311, in on_validation_en
d
self._save_topk_checkpoint(trainer, monitor_candidates)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 358, in _save_topk_check
point
raise MisconfigurationException(m)
lightning_fabric.utilities.exceptions.MisconfigurationException: `ModelCheckpoint(monitor='loss_gen_all')` could not find the m
onitored key in the returned metrics: ['epoch', 'step']. HINT: Did you call `log('loss_gen_all', value)` in the `LightningModul
e`?
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
```
**Steps/Code to reproduce bug**
```
WORKING_DIR='/workspace/nemo'
wandb login api-key
cd /path/to/vits
python $WORKING_DIR/examples/tts/vits.py \
--config-path="$WORKING_DIR/examples/tts/conf/" \
--config-name=vits \
model.sample_rate=22050 \ train_dataset="/path/to/manifests/train_manifest_text_normed.json" \
validation_datasets="/path/to/manifests/val_manifest_text_normed.json" \
phoneme_dict_path="tts_dataset_files/ipa_cmudict-0.7b_nv23.01.txt" \
heteronyms_path="tts_dataset_files/heteronyms-052722" \
trainer.max_epochs=5000 \
trainer.accelerator="gpu" \
trainer.check_val_every_n_epoch=2 \
trainer.strategy="ddp_find_unused_parameters_true" \
exp_manager.exp_dir="resultVitsGermanTTS" \
model.text_normalizer.lang="de" \
+exp_manager.create_wandb_logger=true \
+exp_manager.wandb_logger_kwargs.name="VITS_DS" \
+exp_manager.wandb_logger_kwargs.project="VITS_GermanTTSCustomDS" \
++exp_manager.resume_if_exists=true \
+exp_manager.checkpoint_callback_params.save_top_k=20 \
+exp_manager.checkpoint_callback_params.dirpath="resultVitsGermanTTS/VITS/2024-02-04_10-10-06/checkpoints" \
trainer.devices=2
```
**Environment overview**
- Environment location: Docker nvcr.io/nvidia/nemo:23.08
```
docker run --runtime=nvidia -it --rm -v /home/ubuntu/DATA:/mnt/e --shm-size=16g \
-p 8888:8888 -p 6006:6006 --ulimit memlock=-1 --ulimit \
stack=67108864 nvcr.io/nvidia/nemo:23.08
```
**Additional Note**
@stevehuang52 @KunalDhawan
Could you take a look | {
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"@computersrmyfriends Have you been able to get vocoder to create more than 11 seconds of audio? I am unable to figure out how.",
"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 b... | 2024-02-05T11:55:49 | 2024-04-14T01:58:43 | 2024-04-14T01:58:42 | NONE | {
"total": 0,
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def text_to_speech(text, tts_model_name, vocoder_model_name, max_length=100, file_name_prefix=""):
torch.set_grad_enabled(False)
sampling_rates = {
"tts_en_tacotron2": 22050,
"tts_en_fastpitch": 22050,
"tts_en_fastpitch_ipa": 22050,
"tts_en_fastpitch_multispeaker": 44100,
"tts_de_fastpitch_singleSpeaker_thorstenNeutral_2102": 22050,
"tts_de_fastpitch_singleSpeaker_thorstenNeutral_2210": 22050,
"tts_de_fastpitch_multispeaker_5": 44100,
"tts_es_fastpitch_multispeaker": 44100,
"tts_zh_fastpitch_sfspeech": 22050,
"tts_en_waveglow_88m": 22050,
"tts_en_hifigan": 22050,
"tts_en_lj_hifigan_ft_mixertts": 22050,
"tts_en_lj_hifigan_ft_mixerttsx": 22050,
"tts_en_hifitts_hifigan_ft_fastpitch": 44100,
"tts_de_hifigan_singleSpeaker_thorstenNeutral_2102": 22050,
"tts_de_hifigan_singleSpeaker_thorstenNeutral_2210": 22050,
"tts_de_hui_hifigan_ft_fastpitch_multispeaker_5": 44100,
"tts_es_hifigan_ft_fastpitch_multispeaker": 44100,
"tts_zh_hifigan_sfspeech": 22050
}
# Load TTS model
if "tacotron" in tts_model_name.lower():
tts_model = nemo_tts.models.Tacotron2Model.from_pretrained(model_name=tts_model_name)
elif "fastpitch" in tts_model_name.lower():
tts_model = nemo_tts.models.FastPitchModel.from_pretrained(model_name=tts_model_name)
else:
raise ValueError("Unsupported TTS model name")
# Load vocoder model
if "waveglow" in vocoder_model_name.lower():
vocoder = nemo_tts.models.WaveGlowModel.from_pretrained(model_name=vocoder_model_name)
elif "hifigan" in vocoder_model_name.lower():
vocoder = nemo_tts.models.HifiGanModel.from_pretrained(model_name=vocoder_model_name)
else:
raise ValueError("Unsupported vocoder model name")
tts_model.eval()
vocoder.eval()
# Split text into chunks
chunks = split_text(text, max_length)
full_audio = []
for chunk in chunks:
parsed = tts_model.parse(chunk)
spectrogram = tts_model.generate_spectrogram(tokens=parsed)
audio = vocoder.convert_spectrogram_to_audio(spec=spectrogram)
audio_numpy = audio.to().numpy().T
full_audio.extend(audio_numpy)
full_audio_np = np.hstack(full_audio)
file_name = f'samples1/{file_name_prefix}_{tts_model_name}_{vocoder_model_name}.wav'
# Determine the correct sampling rate
tts_samplerate = sampling_rates.get(tts_model_name.split('_')[1], 22050) # Get default based on TTS model type
vocoder_samplerate = sampling_rates.get(vocoder_model_name.split('_')[1], 22050) # Get default based on vocoder model type
samplerate = max(tts_samplerate, vocoder_samplerate) # Choose the higher rate if they differ
sf.write(file_name, full_audio_np, samplerate)
return file_name
# List all TTS and vocoder models
tts_models = [model.pretrained_model_name for model in nemo_tts.models.Tacotron2Model.list_available_models()] + \
[model.pretrained_model_name for model in nemo_tts.models.FastPitchModel.list_available_models()]
vocoder_models = [model.pretrained_model_name for model in nemo_tts.models.WaveGlowModel.list_available_models()] + \
[model.pretrained_model_name for model in nemo_tts.models.HifiGanModel.list_available_models()]
```
Here's what I do for splitting since long texts dont work out afaik,
```
def split_text(text, max_length):
words = text.split()
chunks = []
current_chunk = ""
for word in words:
# Check if adding the word would exceed the max length
if len(current_chunk) + len(word) + 1 > max_length:
# If the chunk is too long and doesn't end with punctuation, add it as is
if not current_chunk[-1] in ".!?":
chunks.append(current_chunk)
current_chunk = word
else:
# If it ends with punctuation, find the nearest punctuation to split
nearest_punct = max(current_chunk.rfind("."), current_chunk.rfind("!"), current_chunk.rfind("?"))
split_at = nearest_punct + 1
chunks.append(current_chunk[:split_at])
current_chunk = current_chunk[split_at:].strip() + " " + word
else:
current_chunk = current_chunk + " " + word if current_chunk else word
if current_chunk:
chunks.append(current_chunk)
return chunks
```
And then, When I use this code to test different model combinations like this,
```
# Generate speech for each combination of TTS and vocoder models
for tts_model_name in tts_models:
for vocoder_model_name in vocoder_models:
print(f"Generating speech with TTS model: {tts_model_name} and vocoder model: {vocoder_model_name}")
text_to_speech(longtext, tts_model_name, vocoder_model_name, max_length=100, file_name_prefix="speech_output")
```
Among the resultant files, many are in slow motion. like these combinations:
_tts_en_tacotron2_tts_de_hui_hifigan_ft_fastpitch_multispeaker.wav
speech_output__tts_en_tacotron2_tts_es_hifigan_ft_fastpitch_multispeaker.wav
speech_output__tts_en_fastpitch_ipa_tts_es_hifigan_ft_fastpitch_multispeaker.wav
speech_output__tts_en_fastpitch_ipa_tts_en_hifitts_hifigan_ft_fastpitch.wav
Multispeaker always results in extreme slow motion low pitch voices. As you can see, I have tried setting the frequencies too based on my limited knowledge.
Any thoughts? | {
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"Both RNNTs and CTCs in general has Pros and Cons. RNNTs converge faster compared to CTCs, due to implicit LM prediction nature of RNNTs can get better WER just with greedy decoding, however RNNTs are ~3x slower in training/inference compared to CTCs, but can be improved with a TDT type decoder + CUDA graphs improv... | 2024-02-05T11:20:18 | 2024-04-06T01:44:08 | 2024-04-06T01:44:07 | NONE | {
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} | null | null | null | Hi! I would like to use an RNN-transducer for a Russian ASR task. I use a config like [conformer_transducer_bpe.yaml](https://github.com/NVIDIA/NeMo/blob/v1.20.0/examples/asr/conf/conformer/conformer_transducer_bpe.yaml), but a few things are unclear to me.
1. Should I use RNN-t instead of CTC + greedy decoding in non-streaming setup? Does RNN-t perform significantly better than CTC + beam search + domain kenlm?
2. I compared validation WERs for CTC and RNN-t setups from catalog.ngc.nvidia.com. There are [STT En Conformer-Transducer Large](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_ctc_large) and [STT En Conformer-Transducer Large](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_transducer_large) for example. I understand that audio-encoders of these models are not equal but they are similar. I conclude that the expected WER improvement from CTC -> RNNT switch is ~10%. My question is about this estimation. I already have ~12WER and ~15WER on my domain data (phone calls and online meeting respectively). According to your experience\intuition, how likely is to get an 1.2-1.5 WER improvement from RNN-t setup comparing to CTC+greedy decoding with the equal audio encoder?
3. I try to use my previously trained CTC-conformer as an frozen audio encoder. I train only a linear adapter, a prediction network and a joint layer. I use my dataset (~600 hours of speech, 300h of phone calls and 300h of online meetings) and parameters from [NeMO base rnnt config](https://github.com/NVIDIA/NeMo/blob/v1.20.0/examples/asr/conf/conformer/conformer_transducer_bpe.yaml) (excluding encoder parameters). It converges but its WER is barely better than WER of my CTC model. Difference is ~0.2 WER. Did you try to train an RNN-t model with encoder frozen? Does RNN-t converges better if encoder is trainable?
4. I heard that CTC head of [hybrid model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#hybrid-transducer-ctc) may perform better than pure CTC model trained on the same data. Have you noticed this effect?
5. Should a prediction network act like a "normal" language model? I mean, if I use default Nemo RNN-t prednet
```yaml
prednet:
pred_hidden: ${model.model_defaults.pred_hidden}
pred_rnn_layers: 1
t_max: null
dropout: 0.2
```
can I use its performance on the next token prediction task as a proxy metric of its impact on performance of the whole RNN-t model? Should I consider different metrics to determine if performance of my RNN-t model is negatively affected by "bad" prednet but not by audio encoder? | {
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"jenkins",
"@jbaczek Could you add the changes in [this NeMo PR](https://github.com/NVIDIA/NeMo/pull/8290/files) to the `AutocastTransformerLayer` here as well? We would need this to comply with the changes to TP knobs in [this TE commit](https://github.com/NVIDIA/TransformerEngine/commit/bdf1afeef0b26455b3b327c7... | 2024-02-05T10:34:45 | 2024-02-26T17:11:15 | 2024-02-23T10:01:57 | COLLABORATOR | {
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} | # What does this PR do ?
Add spec for model built from te.TransformerLayer
**Collection**: nlp
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
Use name of the spec either on config level:
```yaml
[...]
model:
name: megatron_gpt_full_te_layer_autocast
[...]
```
or from CLI
`model.name=megatron_gpt_full_te_layer_autocast`
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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
* To be merged instead of https://github.com/NVIDIA/NeMo/pull/8316/files
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* Related to #8233
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Adds the Attntion Encoder Decoder Multi Task model support into r1.23.0 branch (Canary)
**Collection**: [ASR]
# Changelog
- Adds Multitasking AED models support
# Jenkins CI
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# 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)
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Add check for distributed optimizer which is unsupported for PEFT
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# Changelog
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# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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?
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- [ ] Bugfix
- [ ] Documentation
- [x] UX improvement
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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**: common, 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
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```
# Jenkins CI
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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)
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- [ ] 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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# 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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# Jenkins CI
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# 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.
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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 ?
Multimodal r1.23.0 bug fix
**Collection**: [Multimodal, CI]
# 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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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)
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**PR Type**:
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- [x] 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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* Fixes ASR with Transducers notebook
**Collection**: ASR
# Changelog
- Update path to configs
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation | {
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Instead of popping `max_seqlen` and `cu_seqlens_argmin` from a batch, we just read the dict to preserve the data for later micro-batches when PP > 1.
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] 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 ?
Adding the notebook that allows us to do real-time streaming using a cache-aware fastconformer model (supports single-lookahead and multi-lookahead models).
**Collection**: `tutorials/asr`
# Changelog
- Add cache-aware streaming notebook in `tutorials/asr`
- Add call to `self.setup_streaming_params` inside `set_default_att_context_size` method of `ConformerEncoder`.
# Usage
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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
- [x] 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
* Changes were originally made in https://github.com/NVIDIA/NeMo/pull/8036 but that PR was closed, replaced with this one.
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"merged_at": null
} | # What does this PR do ?
Fix issues in Multimodal in r1.23.0
# Changelog
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} | # What does this PR do ?
fix wrong paper link in post
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- fix decorated import needed for TorchScript, but not compatible with sphinx-autodoc
- use Python 3.10 due to Python 3.10-style type annotations in different places
- add mocks for imports to fix ASR and NLP collections
**Collection**: [Core, Documentation]
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"Looks good, these scripts look much better now. Some minor remarks:\r\n* I often do CTRL+F on some arguments like, for example, `in_file`. For the current style like `parser.add_argument(\"--in-file\", ...)` -- dash vs underscore -- such... | 2024-02-01T17:48:19 | 2024-02-15T18:18:04 | 2024-02-15T18:18:04 | COLLABORATOR | {
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} | # What does this PR do ?
Unify HF converter scripts and arguments
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] 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**:
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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.
# Additional Information
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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.",
"this has not been solved i think\r\n"
] | 2024-02-01T16:24:09 | 2024-03-30T00:24:06 | 2024-03-21T01:44:17 | NONE | {
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```
Traceback (most recent call last):
File "/Users/urospetrovic/ml_projects/thq-transcription-module/export.py", line 44, in <module>
main(args.nemo_file, args.onnx_file, model_type=args.model_type)
File "/Users/urospetrovic/ml_projects/thq-transcription-module/export.py", line 38, in main
model.export(onnx_file, onnx_opset_version=12)
File "/Users/urospetrovic/opt/miniconda3/envs/transcription/lib/python3.10/site-packages/nemo/core/classes/exportable.py", line 113, in export
out, descr, out_example = model._export(
File "/Users/urospetrovic/opt/miniconda3/envs/transcription/lib/python3.10/site-packages/nemo/core/classes/exportable.py", line 177, in _export
input_example = self.input_module.input_example()
File "/Users/urospetrovic/opt/miniconda3/envs/transcription/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1614, in __getattr__
raise AttributeError("'{}' object has no attribute '{}'".format(
AttributeError: 'EncDecDiarLabelModel' object has no attribute 'input_module'. Did you mean: 'output_module'?
```
**Steps/Code to reproduce bug**
I have used this codebase ([convasr_to_single_onnx](https://github.com/NVIDIA/NeMo/blob/v1.0.0b3/scripts/convasr_to_single_onnx.py)), extended by the new type 'diarizer' to be able to handle EncDecDiarLabelModel types.
**Expected behavior**
I was able to successfully convert vad_multilingual_marblenet.nemo to onnx file, but it fails for the diar_msdd_telephonic.nemo one.
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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."
] | 2024-02-01T03:50:21 | 2024-02-24T01:42:43 | 2024-02-24T01:42:43 | CONTRIBUTOR | {
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} | # What does this PR do ?
This PR replaces the CLIP image encoder with the LanguageBind Image encoder (derived from BLIP-2) as implemented in Video-LLaVA
**Collection**: [Note which collection this PR will affect]
# Changelog
- Removes `FrozenCLIPVisionTransfomer` (inherits from `CLIPVisionTransformer`)
- Adds `FrozenLangaugeBindImageEncoder` (inherits from `LanguageBindImageEncoder`)
- Adds `multimodal_encoders/langaugebind/image/image_encoder.py` which implements `LanguageBindImageEncoder` (derived from author's implementation [here](https://github.com/PKU-YuanGroup/LanguageBind))
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
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**Collection**: [Note which collection this PR will affect]
# Changelog
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```python
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https://api.github.com/repos/NVIDIA/NeMo/issues/8298 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8298/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8298/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8298/events | https://github.com/NVIDIA/NeMo/pull/8298 | 2,111,290,932 | PR_kwDOC_bI7s5lonVC | 8,298 | Mcore customization doc | {
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"I think something may have happened when rebasing, now some non doc files are changed. Could you rebase on to r1.23.0?",
"@ericharper I removed the irrelevant changes. Would this work now? "
] | 2024-02-01T01:29:01 | 2024-02-12T17:24:38 | 2024-02-12T17:24:38 | COLLABORATOR | {
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} | # What does this PR do ?
Mcore customization doc
**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
```
# Jenkins CI
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# Before your PR is "Ready for review"
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] 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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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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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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
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} | # What does this PR do ?
Adding the notebook that allows us to do real-time streaming using a cache-aware fastconformer model (supports single-lookahead and multi-lookahead models).
**Collection**: `tutorials/asr`
# Changelog
- Add cache-aware streaming notebook in `tutorials/asr`
- Add call to `self.setup_streaming_params` inside `set_default_att_context_size` method of `ConformerEncoder`.
# Usage
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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
- [x] 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
* Changes were originally made in https://github.com/NVIDIA/NeMo/pull/8036 but that PR was closed, replaced with this one.
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"Hi @gshennvm \r\nFix for incomplete checkpoints was recently merged into main [[PR7952](https://github.com/NVIDIA/NeMo/pull/7952)].\r\nWould it be possible for you to verify if that resolves the problem you noticed?",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label ... | 2024-02-01T00:37:08 | 2024-03-19T01:44:03 | 2024-03-19T01:44:03 | COLLABORATOR | {
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this is not robust because the training job can get preempted in the middle of save, creating a corrupted checkpoint.
Ideally we want nemo to create a temporary file, save to the temporary file and only when done saving should this file be renamed to an actual checkpoint | {
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} | # What does this PR do ?
Updated the configs for AED models
**Collection**: [ASR]
# Changelog
- Config update
# 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?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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"jenkins",
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} | # What does this PR do ?
Add dist checkpoint support for regular (non O2 and non-distributed) optimizers.
This change is required to support dist ckpt for FSDP training
**Collection**: NLP
# Changelog
- Add dist checkpoint support for regular (non O2 and non-distributed) optimizers.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# 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.
# Additional Information
* Related to # (issue)
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"Wait the URL is wrong for the image. Please fix it by uploading to latest release - 1.22 "
] | 2024-01-31T21:44:47 | 2024-02-01T03:01:33 | 2024-02-01T03:01:33 | COLLABORATOR | {
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} | # What does this PR do ?
TDT blog: adding leaderboard screenshot
**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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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)
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**PR Type**:
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- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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"jenkins",
"Merging as is since its urgent. @pzelasko lets pin r1.23 as well asap (send me PR)"
] | 2024-01-31T21:37:46 | 2024-01-31T23:34:14 | 2024-01-31T23:33:47 | COLLABORATOR | {
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} | # What does this PR do ?
Pin lhotse version to 1.19.2. Prevents breaking changes.
**Collection**: All speech collections
# Changelog
- Pin lhotse version to 1.19.2
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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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?
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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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] | 2024-01-31T21:33:14 | 2024-02-15T22:36:23 | 2024-02-15T22:36:23 | CONTRIBUTOR | {
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} | # What does this PR do ?
Adds TP comm overlap knobs to `AutocastTransformerLayer`. Requires https://github.com/NVIDIA/TransformerEngine/pull/649.
**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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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 TDT blog with new RTF numbers collected on different machines.
**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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] 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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update TDT blog with new RTF numbers collected on different machines.
**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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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?
- [ ] 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
- [x] 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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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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} | # What does this PR do ?
Improves "loop labels" greedy decoding implementation, introduced in #7926, with using non-blocking operations (avoiding device-to-host synchronization).
On my local machine, the time for decoding unsorted `test-other` (LibriSpeech) with `stt_en_fastconformer_transducer_large` (2 repeated measurements, in seconds; after warmup the results are better):
bs | precision | RNN-T, loop_labels v1 (main) | RNN-T, loop_labels v2 (this)
-- | -- | -- | --
16 | fp32 | 50s / 50s | 37s / 36s
128 | fp32 | 34s / 33s | 30s / 29s
16 | bf16 | 48s / 46s | 34s / 32s
128 | bf16 | 25s / 24s | 21s / 20s
This algorithm is planned for further improvements with torch.jit.script in #8203
**Collection**: [ASR]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
```shell
python examples/asr/speech_to_text_eval.py \
model_path=<nemo_model.nemo> \
dataset_manifest=<manifest> \
batch_size=128 \
output_filename=<output_mainfest_path> \
rnnt_decoding.greedy.loop_labels=true
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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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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Implementing RETRO's mcore model, dataloader's wrapper, training script.
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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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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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} | # 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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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} | # What does this PR do ?
Add a Chinese open-source LLM Baichuan2 (7B/13B) support.
**Collection**: NLP
# Changelog
- Add Baichuan2 model configuration.
- Add Baichuan2 hf-to-nemo conversion.
- Add Baichuan2 nemo-to-hf conversion.
# Usage
```python
# Model conversion from huggingface to nemo
python ./scripts/nlp_language_modeling/convert_hf_baichuan2_to_nemo.py --in-file '/mount/data/Baichuan2-7B-Base' --out-file '/mount/data/Baichuan2-7B-Base/baichuan2.nemo'
```
```python
# Model conversion from nemo to huggingface
python ./scripts/nlp_language_modeling/convert_nemo_baichuan2_to_hf.py \
--in-file /mount/data/Baichuan2-7B-Base/baichuan2.nemo \
--out-file /mount/data/Baichuan2-7B-Base/pytorch_model.bin \
--hf-in-path /mount/data/Baichuan2-7B-Base \
--hf-out-path /mount/data/Baichuan2-7B-Base-convert
```
```python
# Inference
python ./examples/nlp/language_modeling/megatron_gpt_eval.py \
--config-name=megatron_baichuan2_inference \
gpt_model_file=${BAICHUAN2_NEMO_MODEL} \
trainer.devices=1 \
trainer.num_nodes=1 \
tensor_model_parallel_size=1 pipeline_model_parallel_size=1
```
```python
# Pretrain
python3 -u examples/nlp/language_modeling/megatron_gpt_pretraining.py \
--config-name=megatron_baichuan2_config \
trainer.devices=8 \
trainer.num_nodes=1 \
trainer.val_check_interval=100 \
trainer.precision=bf16 \
trainer.max_steps=1000 \
trainer.max_epochs=1 \
model.megatron_amp_O2=True \
model.sequence_parallel=True \
model.tensor_model_parallel_size=2 \
model.pipeline_model_parallel_size=1 \
model.global_batch_size=16 \
model.micro_batch_size=2
```
```python
# Parameter efficient fine-tuning
python examples/nlp/language_modeling/tuning/megatron_gpt_peft_tuning.py \
trainer.max_steps=200 \
trainer.devices=2 \
trainer.val_check_interval=200 \
exp_manager.exp_dir=exp \
exp_manager.name=test \
model.tensor_model_parallel_size=1 \
model.restore_from_path=${BAICHUAN2_NEMO_MODEL} \
model.global_batch_size=32 \
model.micro_batch_size=4 \
model.data.train_ds.file_names=${TRAIN} \
model.data.validation_ds.file_names=${VALID} \
model.answer_only_loss=True \
model.peft.peft_scheme="lora" \
model.data.train_ds.concat_sampling_probabilities=[1.0] 2>&1|tee /mount/data/test.log
```
```python
# SFT
python ./examples/nlp/language_modeling/tuning/megatron_gpt_sft.py \
trainer.devices=8 \
trainer.val_check_interval=200 \
model.restore_from_path=${BAICHUAN2_NEMO_MODEL} \
model.tensor_model_parallel_size=${TP_SIZE} \
model.pipeline_model_parallel_size=${PP_SIZE} \
model.answer_only_loss=True \
model.global_batch_size=16 \
model.micro_batch_size=2 \
model.data.train_ds.file_names=${TRAIN} \
model.data.train_ds.concat_sampling_probabilities=[1.0] \
model.data.validation_ds.file_names=${VALID} \
model.data.validation_ds.global_batch_size=64 \
model.data.validation_ds.micro_batch_size=8 \
model.megatron_amp_O2=True \
exp_manager.exp_dir=exp \
exp_manager.name=test \
trainer.precision=bf16-mixed 2>&1|tee /mount/data/log/test.log
```
```python
# peft model evaluation
CUDA_VISIBLE_DEVICES='0,1' python ./examples/nlp/language_modeling/tuning/megatron_gpt_peft_eval.py \
trainer.devices=2 \
trainer.num_nodes=1 \
trainer.precision=32 \
model.restore_from_path=${BAICHUAN2_NEMO_MODEL} \
model.peft.restore_from_path=${LORA_MODEL} \
model.data.test_ds.file_names=["path/to/your/dataset.jsonl"] \
model.data.test_ds.global_batch_size=32 \
model.data.test_ds.micro_batch_size=16 \
model.tensor_model_parallel_size=1 \
model.pipeline_model_parallel_size=1 \
inference.greedy=True \
inference.outfile_path=..
```
# Before your PR is "Ready for review"
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"@titu1994 could you help to land it? Prerequisite is mcore=0.5.0 https://github.com/NVIDIA/NeMo/pull/8465"
] | 2024-01-31T11:40:56 | 2024-03-01T16:32:02 | 2024-03-01T16:32:01 | COLLABORATOR | {
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"merged_at": "2024-03-01T16:32:01"
} | # What does this PR do ?
Currently Megatron-LM creates causal attention mask for a local-layer spec model on its own -- see https://github.com/NVIDIA/Megatron-LM/commit/a45805a3ee0645b85b48d14b0a8077fa5b1216b2 -- and hence `attention_mask=None` can be specified in Nemo. In this MR, I'm using this new M-LM functionality when creating `fwd_output_only_func` function.
Broader functional tests for `fwd_output_only_func` will follow in subsequent MRs.
**Collection**: NLP
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [x] 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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"WandB project for tp,pp, sp both nemo legacy bert model and mcore bert model. The curves match https://wandb.ai/shanmugamr/mcore_test?workspace=user-shanmugamr",
"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... | 2024-01-31T00:39:29 | 2024-02-26T01:44:22 | 2024-02-26T01:44:22 | COLLABORATOR | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# 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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