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Added arpa/ipa flag. Choo
**Collection**: tts
# Changelog
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# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
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replaces `squad_metric_calc.py` with `peft_metric_calc.py` which computes f1 and accuracy and rouge at once.
**Collection**: []
# 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
```
# 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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1. Changed hiddens transformations module from `transformations` to `hiddens`.
2. Added documentations for hiddens module.
**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
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] 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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* Related to # (issue)
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} | null | null | null | **Describe the bug**
A clear and concise description of what the bug is.
**Steps/Code to reproduce bug**
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- 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`
https://github.com/NVIDIA/NeMo/assets/107624041/ef36be57-6419-4574-b618-d0b4c65d7e3d
& `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
Add any other context about the problem here.
Example: GPU model
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} | # What does this PR do ?
Support Llama2 models in mcore.
Support PEFT adapters in mcore.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* `mcore_gpt` needs to be enabled for LLAMA2.
```python
model.mcore_gpt = True
```
# 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
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
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```python
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# Before your PR is "Ready for review"
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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- [ ] Did you add or update any necessary documentation?
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} | # What does this PR do ?
caches ptuning representations in a simple embedding layer.
**Collection**: [NLP]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add conv1d subsampling for ASR
**Collection**: asr
# Changelog
- Add 'striding_conv1d' and 'dw_striding_conv1d' subsampling options. Subsampling is performed using strided conv1d convolutions with kernel size 5 instead of doing reshape + frequency-temporal conv2d + reshape + linear projection.
# Usage
* Simply use 'subsampling: striding_conv1d in conformer yaml config for example.
```
encoder:
_target_: nemo.collections.asr.modules.ConformerEncoder
feat_in: ${model.video_front_end.dim_output}
feat_out: -1 # you may set it if you need different output size other than the default d_model
n_layers: 18
d_model: 512
# Sub-sampling params
subsampling: striding_conv1d # vggnet, striding, stacking or stacking_norm, dw_striding, striding_conv1d_k5, dw_striding_conv1d
subsampling_factor: 8 # must be power of 2 for striding and vggne
...
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# 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 ?
Adding server options to GPT PEFT eval
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
```bash
python /data/NeMo/examples/nlp/language_modeling/tuning/megatron_gpt_peft_eval.py \
model.restore_from_path=/data/megatron_converted_2b_tp1_pp1.nemo \
model.peft.restore_from_path=/data/megatron_gpt_peft_tuning.nemo \
model.data.test_ds.file_names=['/models/dataset/squad/squad_subsamples_formatted_tn/100/1_squad_val.jsonl'] \
model.data.test_ds.global_batch_size=1 \
model.data.test_ds.micro_batch_size=1 \
model.data.test_ds.names=['squad'] \
model.data.test_ds.tokens_to_generate=30 \
server=True
```
```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**:
- [ ] 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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} | * fix ptuning and lora model_parallel_config
* support deprecated models
* update megatron connit sha
---------
# 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
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
replace dummy model_parrallel_config with the real one after initializing base model.
**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
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Add a one line overview of what this PR aims to accomplish.
Adding SHARP interface to Megatron Core
# Changelog
Add SHARP enable argument to parallel state function
# 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**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | # What does this PR do ?
Fix the issues when using megatron_amp_O2 on PEFT models
**Collection**: [Note which collection this PR will affect]
# Changelog
- Transform adapters to fp16/bf16 when the base model uses megatron_amp_O2. When using amp_O2 we should make model parameters in fp16/bf16. Previous PR #7232 handles the precision issue in ColumnParallelLinear and RowParallelLinear in PEFT models, and this PR handles precision casting of the rest of the modules such as layernorm.
- Explicitly set the ptuning inference table to untrainable for FP16/BF16 modules. Previous whitelist only includes the name of the inference table as in a FP32 model. We add its name in FP16/BF16 models to whitelist.
- Explicitly set ptuning inference table to be untrainable in AdapterPTuningModel like in PTuningModel
# 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**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to #7232
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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
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add conv1d subsampling for ASR / lip reading
**Collection**: asr
# Changelog
- Add 'striding_conv1d' and 'dw_striding_conv1d' subsampling options. Subsampling is performed using strided conv1d convolutions with kernel size 5 instead of doing reshape + frequency-temporal conv2d + reshape + linear projection.
# Usage
* Simply use 'subsampling: striding_conv1d in conformer yaml config for example.
```
encoder:
_target_: nemo.collections.asr.modules.ConformerEncoder
feat_in: ${model.video_front_end.dim_output}
feat_out: -1 # you may set it if you need different output size other than the default d_model
n_layers: 18
d_model: 512
# Sub-sampling params
subsampling: striding_conv1d # vggnet, striding, stacking or stacking_norm, dw_striding, striding_conv1d_k5, dw_striding_conv1d
subsampling_factor: 8 # must be power of 2 for striding and vggne
...
```
# 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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This MR integrates dist ckpt with recent improvements of [Apex DistributedOptimizer state dict](https://github.com/NVIDIA/apex/pull/1704).
It makes the [old integration PR](https://github.com/NVIDIA/NeMo/pull/7140) redundant.
**Collection**: NLP
# Changelog
- Add distributed checkpointing integration with DistOpt.
# 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**:
- [ ] 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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} | null | null | null | Hello, I read [this](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_ru_conformer_ctc_large) page and it says that you used `SOVA - RuAudiobooksDevices [260 hours] and RuDevices [75 hours] subsets`.
Could you share the file lists of these subsets? Or code to reproduce these subsets?
Thank you in advance.
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contains a nemo.core change to accept kwargs to pass into hydra instantiate.
currently only support Ptuning and Lora now.
tested on llama2 7b TP SP PP DP | {
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} | # What does this PR do ?
save memory during training by loss mask selection for final output layer + softmax.
**Collection**: [NLP]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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} | # What does this PR do ?
Replaces `super().validation_step` in `inference_step` of `megatron_gpt_sft_model.py` with the `validation_step` from `megatron_gpt_model` but without prefetch as just one batch is passed to the `validation_step` in `megatron_gpt_sft_model` and prefetching causes an error.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# 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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Use generation if user set `write_predictions_to_file` to `True` or `dataset.metric.name!=loss`
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
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```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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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)
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- [ ] 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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Fix the issues when using megatron_amp_O2 on PEFT models
**Collection**: [Note which collection this PR will affect]
# Changelog
- Transform adapters to fp16/bf16 when the base model uses megatron_amp_O2. When using amp_O2 we should make model parameters in fp16/bf16. Previous PR #7232 handles the precision issue in ColumnParallelLinear and RowParallelLinear in PEFT models, and this PR handles precision casting of the rest of the modules such as layernorm.
- Explicitly set the ptuning inference table to untrainable for FP16/BF16 modules. Previous whitelist only includes the name of the inference table as in a FP32 model. We add its name in FP16/BF16 models to whitelist.
- Explicitly set ptuning inference table to be untrainable in AdapterPTuningModel like in PTuningModel
# 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**:
- [ ] 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 #7232
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} | # What does this PR do ?
Move model precision copy to MegatronBaseModel to reduce boilerplate.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Remove `cfg.model.precision` = `cfg.trainer.precision` copy from several examples
- Set precision in `MegatronBaseModel` which all Megatron models inherit from
- Fix some mapping of PTL 2.0 precision to torch datatypes that were exposed by above
# 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**:
- [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
- [X] Refactor
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 ?
It fixes a bug in `end_of_generation_condition()` (#7187) and makes it significantly faster in some cases.
**Collection**: nlp
# Changelog
- Fixed edge case that could make text generation stop earlier than intended
- Speed up text generation when using custom end strings
# Detailed explanation
1. Bugfix
The previous implementation did not verify that `end_string` was encoded
into a single token, which could trigger the end of generation earlier
than intended (see discussion in #7187)
2. Optimization
The previous implementation was scaling linearly with the batch size and
quadratically with the length of the generated sequence, which could
lead to a significant overhead in some situations.
The new implementation is much more efficient in "normal" situations
(where the end of generation is identified by a set of unique tokens),
and raises a warning when it needs to fallback to the inefficient string
matching case.
Note that it does not behave exactly the same as before, because we skip
the string comparison when the end strings all have unique tokens
associated to them. For instance, in the previous implementation, if the
model had generated the string
"Some string.<|endoftext|>"
(where "<|endoftext|>" would really be generated as a string, and not as
a single token), then the previous implementation would have considered
it to be the end of generation (assuming `end_strings` has length > 1),
while the new one would not. The previous behavior was likely a bug
though, since we expect models to generate the special tokens associated
to end strings when they exist (for instance, the standard case
`end_strings=["<|endoftext|>"]` has always been handled by just
comparing the last token to `eod_id`).
3. Minor change
See commit message of ab56968fd for the explanation regarding the warning that was added in `__init__()`
# Tests
Hopefully there are existing tests on CI -- I have tested this myself on my own jobs.
# 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 ##7187
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} | # What does this PR do ?
A few minor fixes for the audio codec training recipe.
Most important, moving the zero_gradient() call back before the loss calculations. Having it after `optim_disc.step()` results in it saving discriminator gradients during the generator `self.manual_backward(loss_gen_all)` calculation which stops the discriminator from learning (adversarial and feature losses quickly become 0).
**Collection**: [TTS]
# Changelog
- Zero gradient before discriminator loss calculation
- Change default mel loss scale from 1.0 to 5.0 as it significantly improves ViSQOL and makes training more consistent.
- Fix name of validation time domain loss
# 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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} | null | null | null | I am trying to install NeMo on my Mac with the following command
```
# Install pynini-2.1.5 using Conda, required for text normalization
conda install -c conda-forge pynini
# Install Cython-0.29.36 / 3.0.0 manually
pip install cython
# Clone the NeMo-1.19.0 repo and install in development mode
git clone https://github.com/NVIDIA/NeMo
cd NeMo
./reinstall.sh
```
It fails with the error,
```
clang -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /Users/li/miniconda3/include -arch arm64 -fPIC -O2 -isystem /Users/li/miniconda3/include -arch arm64 -I/opt/homebrew/opt/llvm/include -I/Users/li/miniconda3/include/python3.9 -c extensions/_pywrapfst.cpp -o build/temp.macosx-11.0-arm64-cpython-39/extensions/_pywrapfst.o -std=c++17 -Wno-register -Wno-deprecated-declarations -Wno-unused-function -Wno-unused-local-typedefs -funsigned-char -stdlib=libc++ -mmacosx-version-min=10.7
extensions/_pywrapfst.cpp:11531:40: error: cannot cast from type 'std::string' (aka 'basic_string<char>') to pointer type 'PyObject *' (aka '_object *')
__pyx_t_2 = __Pyx_PyObject_AsDouble(((PyObject *)__pyx_t_1)); if (unlikely(__pyx_t_2 == ((double)((double)-1)) && PyErr_Occurred())) __PYX_ERR(0, 497, __pyx_L1_error)
^~~~~~~~~~~~~~~~~~~~~
extensions/_pywrapfst.cpp:3198:29: note: expanded from macro '__Pyx_PyObject_AsDouble'
((likely(PyFloat_CheckExact(obj))) ? PyFloat_AS_DOUBLE(obj) :\
^~~
/Users/li/miniconda3/include/python3.9/floatobject.h:24:43: note: expanded from macro 'PyFloat_CheckExact'
#define PyFloat_CheckExact(op) Py_IS_TYPE(op, &PyFloat_Type)
^~
/Users/li/miniconda3/include/python3.9/object.h:130:63: note: expanded from macro 'Py_IS_TYPE'
#define Py_IS_TYPE(ob, type) _Py_IS_TYPE(_PyObject_CAST_CONST(ob), type)
^~
/Users/li/miniconda3/include/python3.9/object.h:113:53: note: expanded from macro '_PyObject_CAST_CONST'
#define _PyObject_CAST_CONST(op) ((const PyObject*)(op))
^~
extensions/_pywrapfst.cpp:1402:43: note: expanded from macro 'likely'
#define likely(x) __builtin_expect(!!(x), 1)
^
extensions/_pywrapfst.cpp:11531:40: error: cannot cast from type 'std::string' (aka 'basic_string<char>') to pointer type 'PyObject *' (aka '_object *')
__pyx_t_2 = __Pyx_PyObject_AsDouble(((PyObject *)__pyx_t_1)); if (unlikely(__pyx_t_2 == ((double)((double)-1)) && PyErr_Occurred())) __PYX_ERR(0, 497, __pyx_L1_error)
^~~~~~~~~~~~~~~~~~~~~
extensions/_pywrapfst.cpp:3198:57: note: expanded from macro '__Pyx_PyObject_AsDouble'
((likely(PyFloat_CheckExact(obj))) ? PyFloat_AS_DOUBLE(obj) :\
^~~
/Users/li/miniconda3/include/python3.9/floatobject.h:51:51: note: expanded from macro 'PyFloat_AS_DOUBLE'
#define PyFloat_AS_DOUBLE(op) (((PyFloatObject *)(op))->ob_fval)
^~
extensions/_pywrapfst.cpp:11531:40: error: cannot cast from type 'std::string' (aka 'basic_string<char>') to pointer type 'PyObject *' (aka '_object *')
__pyx_t_2 = __Pyx_PyObject_AsDouble(((PyObject *)__pyx_t_1)); if (unlikely(__pyx_t_2 == ((double)((double)-1)) && PyErr_Occurred())) __PYX_ERR(0, 497, __pyx_L1_error)
^~~~~~~~~~~~~~~~~~~~~
extensions/_pywrapfst.cpp:3199:27: note: expanded from macro '__Pyx_PyObject_AsDouble'
likely(PyLong_CheckExact(obj)) ?\
^~~
/Users/li/miniconda3/include/python3.9/longobject.h:16:42: note: expanded from macro 'PyLong_CheckExact'
#define PyLong_CheckExact(op) Py_IS_TYPE(op, &PyLong_Type)
^~
/Users/li/miniconda3/include/python3.9/object.h:130:63: note: expanded from macro 'Py_IS_TYPE'
#define Py_IS_TYPE(ob, type) _Py_IS_TYPE(_PyObject_CAST_CONST(ob), type)
^~
/Users/li/miniconda3/include/python3.9/object.h:113:53: note: expanded from macro '_PyObject_CAST_CONST'
#define _PyObject_CAST_CONST(op) ((const PyObject*)(op))
^~
extensions/_pywrapfst.cpp:1402:43: note: expanded from macro 'likely'
#define likely(x) __builtin_expect(!!(x), 1)
^
extensions/_pywrapfst.cpp:11531:40: error: cannot cast from type 'std::string' (aka 'basic_string<char>') to pointer type 'PyObject *' (aka '_object *')
__pyx_t_2 = __Pyx_PyObject_AsDouble(((PyObject *)__pyx_t_1)); if (unlikely(__pyx_t_2 == ((double)((double)-1)) && PyErr_Occurred())) __PYX_ERR(0, 497, __pyx_L1_error)
^~~~~~~~~~~~~~~~~~~~~
extensions/_pywrapfst.cpp:3200:18: note: expanded from macro '__Pyx_PyObject_AsDouble'
PyLong_AsDouble(obj) : __Pyx__PyObject_AsDouble(obj))
^~~
extensions/_pywrapfst.cpp:11531:40: error: cannot cast from type 'std::string' (aka 'basic_string<char>') to pointer type 'PyObject *' (aka '_object *')
__pyx_t_2 = __Pyx_PyObject_AsDouble(((PyObject *)__pyx_t_1)); if (unlikely(__pyx_t_2 == ((double)((double)-1)) && PyErr_Occurred())) __PYX_ERR(0, 497, __pyx_L1_error)
^~~~~~~~~~~~~~~~~~~~~
extensions/_pywrapfst.cpp:3200:50: note: expanded from macro '__Pyx_PyObject_AsDouble'
PyLong_AsDouble(obj) : __Pyx__PyObject_AsDouble(obj))
^~~
5 errors generated.
error: command '/opt/homebrew/opt/llvm/bin/clang' failed with exit code 1
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for pynini
Running setup.py clean for pynini
Building editable for nemo-toolkit (pyproject.toml) ... done
Created wheel for nemo-toolkit: filename=nemo_toolkit-1.21.0rc0-0.editable-py3-none-any.whl size=9136 sha256=314db687677da87457eec52e2b23f442dcaac3ca226eb635123baaa16f8b33be
Stored in directory: /private/var/folders/xn/6n4461lj6bz_dglft4zcx1hh0000gn/T/pip-ephem-wheel-cache-7c06gi35/wheels/82/6f/41/9bf9bf21f571b41675aa88a781d07e1ca16dc9e2121dc1ba3e
Successfully built nemo-toolkit
Failed to build pynini
ERROR: Could not build wheels for pynini, which is required to install pyproject.toml-based projects
```
I am confused because I could be able to install the pynini by `conda install -c conda-forge pynini`. | {
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} | # What does this PR do ?
The motivation is to be able to override the text generation strategy when calling `MegatronGPTModel.generate()`.
It is passed as an explicit argument rather than through `**strategy_args` so as to ensure someone cannot accidentally pass other arguments that would end up being ignored.
It is a keyword-only argument to ensure that if in the future we want to update the signature to `**strategy_args`, we can do it without breaking code.
**Collection**: nlp
# Changelog
- Add the `strategy` argument to `MegatronGPTModel.generate()`
# 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
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Add a finetuning script along with config file for finetuning existing nemo models.
**Collection**: ASR
# Changelog
- Added speech_to_text_finetune.py script
- Added speech_to_text_finetune.yaml config
- Jenkins run
- Doc strings
TODO:
* Update tutorial
# Usage
Eg: without changing tokenizer
```bash
python speech_to_text_finetune.py \
init_from_pretrained_model=stt_en_fastconformer_hybrid_large_pc \
model.tokenizer.update_tokenizer=false
```
or
finetune with a new tokenizer (architecture of base model doesn't change)
```bash
python speech_to_text_finetune.py \
init_from_pretrained_model=stt_en_fastconformer_hybrid_large_pc \
model.tokenizer.update_tokenizer=true \
model.tokenizer.dir=<path_to_tokenizer_dir> \
model.tokenizer.type=<bpe/char>
```
# 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 | {
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} | # What does this PR do ?
Fix resume from checkpoint behavior to have lower priority than experiment dir.
**Collection**: [Note which collection this PR will affect]
# Changelog
- resume from checkpoint behavior
- reorganize `check_resume` for prints
# 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**:
- [ ] 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 comprehensive error messages for cases when input has wrong type
**Collection**: nlp
# Changelog
- Add a couple of error messages
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
Updates the tutorial to support IPA and adds an adapter IPA config.
**Collection**: [Note which collection this PR will affect]
TTS
**PR Type**:
- [ *] New Feature
- [ ] Bugfix
- [ *] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
This PR ensures that the `sample_sequence_batch` function correctly handles the scenario where `all_probs` is set to `True` but `compute_logprob` is `False`.
Earlier the `full_logits` variable was not correctly initialized when the `all_probs=True` but `compute_logprob=False` causing a `RuntimeError` due to an attempt to broadcast a `None` value using `torch.distributed.broadcast`.
**Collection**: NLP
# Changelog
- Updated conditions on lines 704, 754, and 792 in `text_generation_utils.py` to handle cases where `all_probs=True` but `compute_logprob=False`.
This ensures that `full_logits` is correctly initialized, preventing runtime errors during distributed broadcasting.
# Usage
With this fix, users can now set `compute_logprob` to `False` and `all_probs` to `True` in the inference section of the config. This ensures that the `sample_sequence_batch` function correctly handles this specific scenario without encountering the previously observed `RuntimeError`.
For example:
```yaml
inference:
...
all_probs: True # whether to return the log prob for all the tokens in the vocab
...
compute_logprob: False # a flag used to compute logprob of all the input text, a very special case of running inference, default False
```
# 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 ?
MlFlow is required for training and resuming runs from checkpoint. Currently, when the MlFlow logger is enabled in experiment manager it doesn't log model checkpoint as artifacts to MLFlow.
This PR simply adds one line to the `nemo.utils.loggers.MLFlowParams` to allow for logging checkpoints.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add `log_model ` to `nemo.utils.loggers.MLFlowParams`
# Usage
To enable logging checkpoints in MlFlow
```python
exp_manager:
...
create_checkpoint_callback: True
create_mlflow_logger: True
mlflow_logger_kwargs:
...
log_model: True
```
# 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**:
- [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 #7257
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MlFlow is required for training and resuming runs from checkpoint. Currently, when the MlFlow logger is enabled in experiment manager it doesn't log model checkpoint as artifacts to MLFlow
**Describe the solution you'd like**
One of the parameters (`log_model`) must to be passed to `pytorch_lightning.loggers.MLFlowLogger` is not defined in `nemo.utils.loggers.MLFlowParams` dataclass and cannot be set without modifying the code.
**Describe alternatives you've considered**
Doesn't seem to have an alternative
**Additional context**
Add any other context or screenshots about the feature request here.
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} | # What does this PR do ?
Sets a dummy class for type hinting when import is not found.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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Add support for tarred datasets in TTS
**Collection**: tts
# Changelog
- Add specific line by line info of high level changes in this PR.
Created and Iterative TarredTTSDataset class which inherits nemo.collections.asr.data.audio_to_text._TarredAudioToTextDataset.
This class adds takes in tarred dataset and manifests as input and creates batches of data for TTS models.
# Usage
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```python
# Use this config: [examples/tts/conf/fastpitch_align_v1.05_tarred.yaml]
```
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**Pre checks**:
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**PR Type**:
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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
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# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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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?
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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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} | null | null | null | I am trying to reproduce a speaker diarization, using NeuralDiariazer class, in Databricks on Azure and I am facing an issue related to temporal files saving during the voice activity detection. The temporal files were supposed to be saved inside the blob storage, in a folder called "temp_outputs" inside "dbfs", but are not being saved successfully, raising the following error:
`OSError: [Errno 95] Operation not supported`
The problem seems to be in the _run_vad function, during the .frame files writing, in the part of the code below:
https://github.com/NVIDIA/NeMo/blob/f90eea140c1375a921f764f08f0fc998d5a4112b/nemo/collections/asr/models/clustering_diarizer.py#L236-L238
The first .frame file is saved, but the problem starts in the next index of the for loop.
Does not seem to be a problem related to permission, because I am able to save files inside the blob storage, as is shown below:
```python
for f in range (5):
with open(temp_path + f'/{f}.frame', "a", encoding='utf-8') as fout:
for f2 in range (10):
fout.write((f2))
```
The files saved:
**Steps/Code to reproduce bug**
```python
# Initialize NeMo MSDD diarization model
msdd_model = NeuralDiarizer(cfg=create_config(temp_path)).to("cuda")
msdd_model.diarize()
del msdd_model
torch.cuda.empty_cache()
```
"Temp_path" is the temporary path where the temporary files are saved, and where it is located the mono audio that I want to diarize.
create_config is a function where I define all the configurations to instantiate the NeuralDiarizer class, as is shown below.
```python
def create_config(output_dir):
DOMAIN_TYPE = "telephonic" # Can be meeting or telephonic based on domain type of the audio file
CONFIG_FILE_NAME = f"diar_infer_{DOMAIN_TYPE}.yaml"
CONFIG_URL = f"https://raw.githubusercontent.com/NVIDIA/NeMo/main/examples/speaker_tasks/diarization/conf/inference/{CONFIG_FILE_NAME}"
MODEL_CONFIG = os.path.join(output_dir, CONFIG_FILE_NAME)
if not os.path.exists(MODEL_CONFIG):
MODEL_CONFIG = wget.download(CONFIG_URL, output_dir)
config = OmegaConf.load(MODEL_CONFIG)
data_dir = os.path.join(output_dir, "data")
os.makedirs(data_dir, exist_ok=True)
meta = {
"audio_filepath": os.path.join(output_dir, "mono_file.wav"),
"offset": 0,
"duration": None,
"label": "infer",
"text": "-",
"rttm_filepath": None,
"uem_filepath": None,
}
with open(os.path.join(data_dir, "input_manifest.json"), "w") as fp:
json.dump(meta, fp)
fp.write("\n")
pretrained_vad = "vad_multilingual_marblenet"
pretrained_speaker_model = "titanet_large"
config.num_workers = 2 # Workaround for multiprocessing hanging with ipython issue
config.batch_size = 32 # default is 64
config.diarizer.manifest_filepath = os.path.join(data_dir, "input_manifest.json")
config.diarizer.out_dir = (
output_dir # Directory to store intermediate files and prediction outputs
)
config.diarizer.speaker_embeddings.model_path = pretrained_speaker_model
config.diarizer.oracle_vad = (
False # compute VAD provided with model_path to vad config
)
config.diarizer.clustering.parameters.oracle_num_speakers = False
# Here, we use our in-house pretrained NeMo VAD model
config.diarizer.vad.model_path = pretrained_vad
config.diarizer.vad.parameters.onset = 0.8
config.diarizer.vad.parameters.offset = 0.6
config.diarizer.vad.parameters.pad_offset = -0.05
config.diarizer.msdd_model.model_path = (
"diar_msdd_telephonic" # Telephonic speaker diarization model
)
return config
```
**Environment overview (please complete the following information)**
- Environment location: Databricks (Azure)
- Method of NeMo install: pip install
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} | # What does this PR do ?
This PR:
1. Adds the multilingual models (stt_multilingual_fastconformer_hybrid_large_pc, stt_multilingual_fastconformer_hybrid_large_pc_blend_eu) to the pre-trained models catalog and ASR docs
2. Adds the FastConformer Hybrid FR, RU ASR models (stt_ru_fastconformer_hybrid_large_pc, stt_fr_fastconformer_hybrid_large_pc) to the pre-trained models catalog and ASR docs
3. Adds the code-switching models (stt_enes_conformer_transducer_large_codesw, stt_enes_conformer_ctc_large_codesw) to the ASR docs
4. Updates the versions of the FastConformer Hybrid ASR models (EN, ES, IT, DE, PL, BY, HR, UA)
**Collection**: ASR
# Changelog
- Added the FC Hybrid models to nemo/collections/asr/models/hybrid_rnnt_ctc_bpe_models.py
- Updated the ASR docs at NeMo/docs/source/asr/results.rst
- Created new results tables for Multilingual (NeMo/docs/source/asr/data/benchmark_multilingual.csv), Code Switching(NeMo/docs/source/asr/data/benchmark_code_switching.csv)
# 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
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} | # What does this PR do ?
Fix memory leak in utility used by `transcribe_speech.py` when transcribing partial audio segments, by moving logit tensor stored in results to CPU.
**Collection**: [ASR]
# Changelog
- Modify `transcribe_partial_audio` to move logits to CPU when when `logprobs=False`.
# 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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} | null | null | null | In [this logic](https://github.com/NVIDIA/NeMo/blob/c56119623a3ad1a0b2e2767f88b65ddcac0cbd1b/nemo/collections/nlp/parts/nlp_overrides.py#L549), dtype might be `None` and causes errors like:
```
Traceback (most recent call last):
File "/tmp/nvflare/nemo/peft_fedavg_345M/simulate_job/app_site-1/custom/megatron_gpt_peft_tuning.py", line 203, in main
plugins.append(PipelineMixedPrecisionPlugin(precision=plugin_precision, device='cuda', scaler=scaler))
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/parts/nlp_overrides.py", line 525, in __init__
torch.set_autocast_gpu_dtype(dtype)
TypeError: dtype must be a torch.dtype (got NoneType)
```
There should be a informative error message in case dtype is not set correctly. | {
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"merged_at": "2023-08-17T15:32:18"
} | # What does this PR do ?
update language coverage matrix and refs for TN/ITN
**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
```
# 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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https://api.github.com/repos/NVIDIA/NeMo/issues/7246 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7246/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7246/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7246/events | https://github.com/NVIDIA/NeMo/issues/7246 | 1,853,227,793 | I_kwDOC_bI7s5udgMR | 7,246 | Tracking Artifacts as well along with metrics and parameters in Mlflow | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
While we are training ASR model on nemo framework, we are able to track parameters and metrics with minimal inputs in the config.sh. But we are not able to track artifacts i.e. Nothing getting stored here.
**Describe the solution you'd like**
`exp_manager:
...
create_checkpoint_callback: True
create_mlflow_logger: True
mlflow_logger_kwargs:
experiment_name: ${name}
tags:
<Any key:value pairs>
save_dir: './mlruns'
prefix: ''
artifact_location: None
# provide run_id if resuming a previously started run
run_id: Optional[str] = None`
Tracking artifacts would be really helpful along with metrics and parameters. Thanks in advance!
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} | null | null | null | Hi everyone, I am trying to use the titanet_large.onnx for getting the embeddings of audio file. Firstly I follow the official instruction to export the onnx version of the pretrained titanet_large model. And then I follow the solution in issue #1825 but it seems not working.
Here is my code
```python
import numpy as np
import pyaudio as pa
import os, time
import librosa
import IPython.display as ipd
import matplotlib.pyplot as plt
import onnxruntime
import nemo
import nemo.collections.asr as nemo_asr
from nemo.core.classes import IterableDataset
from nemo.core.neural_types import NeuralType, AudioSignal, LengthsType
import torch
from torch.utils.data import DataLoader
from omegaconf import OmegaConf
import copy
# simple data layer to pass audio signal
class AudioDataLayer(IterableDataset):
@property
def output_types(self):
return {
'audio_signal': NeuralType(('B', 'T'), AudioSignal(freq=self._sample_rate)),
'a_sig_length': NeuralType(tuple('B'), LengthsType()),
}
def __init__(self, sample_rate):
super().__init__()
self._sample_rate = sample_rate
self.output = True
def __iter__(self):
return self
def __next__(self):
if not self.output:
raise StopIteration
self.output = False
return torch.as_tensor(self.signal, dtype=torch.float32), \
torch.as_tensor(self.signal_shape, dtype=torch.int64)
def set_signal(self, signal):
self.signal = signal.astype(np.float32) / 32768.
self.signal_shape = self.signal.size
self.output = True
def __len__(self):
return 1
# inference method for audio signal (single instance)
def infer_signal(model, signal):
data_layer.set_signal(signal)
batch = next(iter(data_loader))
audio_signal, audio_signal_len = batch
audio_signal, audio_signal_len = audio_signal.to(model.device), audio_signal_len.to(model.device)
logits = model.forward(input_signal=audio_signal, input_signal_length=audio_signal_len)
return logits
def to_numpy(tensor):
return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
def infer_signal_onnx(model, signal):
data_layer.set_signal(signal)
batch = next(iter(data_loader))
audio_signal, audio_signal_len = batch
audio_signal, audio_signal_len = audio_signal.to(model.device), audio_signal_len.to(model.device)
processed_signal, processed_signal_len = model.preprocessor(
input_signal=audio_signal, length=audio_signal_len,
)
ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(processed_signal), }
ologits = ort_session.run(None, ort_inputs)
alogits = np.asarray(ologits)
logits = torch.from_numpy(alogits[0])
return logits
# sample rate, Hz
SAMPLE_RATE = 16000
model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(model_name='titanet_large')
# Preserve a copy of the full config
cfg = copy.deepcopy(model._cfg)
print(OmegaConf.to_yaml(cfg))
model.preprocessor = model.from_config_dict(cfg.preprocessor)
model.eval()
model = model.to(model.device)
data_layer = AudioDataLayer(sample_rate=cfg.train_ds.sample_rate)
data_loader = DataLoader(data_layer, batch_size=1, collate_fn=data_layer.collate_fn)
model.export('titanet_l.onnx')
ort_session = onnxruntime.InferenceSession('titanet_l.onnx')
wave_file = 'register.wav'
CHANNELS = 1
RATE = 16000
audio, sample_rate = librosa.load(wave_file, sr=RATE)
s1 = time.time()
infer_signal(model, audio)
print(f"Normal took {time.time()-s1} seconds")
s2 = time.time()
infer_signal_onnx(model, audio)
print(f"Normal took {time.time()-s2} seconds")
```
And below is the error log
```bash
Normal took 0.43860721588134766 seconds
Traceback (most recent call last):
File "/Users/ericli/mbox-audio-base/test/use_onnx.py", line 112, in <module>
infer_signal_onnx(model, audio)
File "/Users/ericli/mbox-audio-base/test/use_onnx.py", line 76, in infer_signal_onnx
ologits = ort_session.run(None, ort_inputs)
File "/Users/ericli/miniforge3/envs/audio-base/lib/python3.9/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py", line 213, in run
self._validate_input(list(input_feed.keys()))
File "/Users/ericli/miniforge3/envs/audio-base/lib/python3.9/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py", line 195, in _validate_input
raise ValueError(
ValueError: Required inputs (['length']) are missing from input feed (['audio_signal']).
``` | {
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} | null | null | null | Hi, I'm trying to train a bpe-level conformer and am facing overfitting.
In this regard, I have a few questions and I would be very grateful for the answers to them:
1) Have you encountered overfitting of the BPE conformer and BPE models in general?
2) Have you tried to experiment with char level vs bpe level models? If yes, what are you facing? I mean over/underfitting, stability, approximate increase in metrics.
3) I saw [configs ](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/conformer/conformer_ctc_bpe.yaml) for training conformer from nemo, thanks for posting them, but I couldn't figure out what the global batch size was. [2048](https://github.com/NVIDIA/NeMo/issues/4183#issuecomment-1128444967)?
4) What are some tips for tuning regularization for a conformer?
5) How trainning stability depends on the size of the dictionary?
Before that, I used the char-level conformer, and did not encounter overfitting. I don't have a lot of data, 370 hours for one domain and 130 for another. Therefore, I first train the base model, and then finetune it on domain data. I did some experiments:
1) I finetuned stt_ru_conformer_ctc_large using my domain data and got pretty good quality (better my char level model)
2) I trained my base model, just like I did before with char-level models, and then tried to finetuned it on domain data
The training data configurations are completely the same, except for the weights of models and tokens of tokenizers (the sizes of models and tokenizers are the same).
The length of my audios is from 0.1 to 10 seconds, the size of the bpe dictionary is 128, the average transcription length is 53 characters or 31 tokens.
I use the same (as in nemo models) spec augs, except mask value (mean instead of 0).
I used AdamW, a constant scheduler with lr = 0.0001, global batch size = 512 and faced with overfitting (see plots), then I reduced lr by 10 times and increased global batchs size by 3 times (1536 in total), but this did not solve the problem cardinally. The metrics I get (before the start overfitting) are worse than not only the experiment on finetuning the base model from NeMo, but also worse than the experiments on finetuning my char-level base model.
Plots for char based model:
Plots for bpe based model:
@titu1994
Thank you in advance | {
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} | null | null | null | I have an ARPA 3-gram model trained with sriLM. I know nemo decoders use kenLM models, but I thought it might be compatible with the current decoders as it should also be word level trained. When I run eval_beam_search_ngram_transducer.py and supply kenlm_model with the sriLM, I don't receive any error. However, after performing grid search over a big set parameters.. there is absolutely no improvement, only degraded results. When I run beam_width=1 and beam_alpha=0, I receive the same results as greedy decoding.
```
[NeMo I 2023-08-14 21:14:11 eval_beamsearch_ngram_transducer:384] ==============================Starting the maes decoding===============================
[NeMo I 2023-08-14 21:14:11 eval_beamsearch_ngram_transducer:387] Grid search size: 1
[NeMo I 2023-08-14 21:14:11 eval_beamsearch_ngram_transducer:388] It may take some time...
[NeMo I 2023-08-14 21:14:11 eval_beamsearch_ngram_transducer:389] ==============================================================================================
Loading the LM will be faster if you build a binary file.
Reading /homes/gpirlo/git_repos/nemoasr/examples/asr/train_dir/models/lm_2020_3g_large_200k_cn.3.GramLM
----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
The ARPA file is missing <unk>. Substituting log10 probability -100.
```
Thank you in advance! | {
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} | # What does this PR do ?
Removing deprecated script from the CI.
**Collection**: [NLP]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
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# Before your PR is "Ready for review"
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**PR Type**:
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} | null | null | null | **Describe the bug**
Running `tutorials/tts/FastPitch_Finetuning.ipynb` on Colab get error on `import FastPitchModel`
**Steps/Code to reproduce bug**
```
[/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/modules/common/prompt_encoder.py](https://localhost:8080/#) in TPMLP()
138 def __init__(
139 self,
--> 140 config: ModelParallelConfig,
141 total_virtual_tokens: int,
142 hidden_size: int,
NameError: name 'ModelParallelConfig' is not defined
```
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} | # What does this PR do ?
Temporary fix until pip package is released
**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
```
# 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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} | null | null | null | **Describe the bug**
Hi,
I want to compare the SFT training convergence between bf16 and fp8.
So I am trying to do SFT training in NeMo using fp8 on a H100 GPU.
However, it reports an error during model initialization.
The error log file is in the attachment.
[sft_fp8.log](https://github.com/NVIDIA/NeMo/files/12340953/sft_fp8.log)
It seems that the NeMo fp8 layer names are different from bf16 model.
**Steps/Code to reproduce bug**
```shell
EXE=./nemo/sft/nemo_sft.fp8.py
NEMO_PATH=./GPT-2B-001_bf16_tp1.nemo
FP8=True
python ${EXE} --config-path=${CONFIG_DIR}/conf --config-name=megatron_gpt_sft.yaml \
trainer.num_nodes=1 \
trainer.devices=[${GPU}] \
trainer.max_steps=8 \
exp_manager.create_checkpoint_callback=false \
exp_manager.checkpoint_callback_params.every_n_train_steps=128 \
exp_manager.exp_dir=${EXP_DIR} \
exp_manager.name=nemo-fp8-2b \
exp_manager.create_wandb_logger=${USE_WANDB} \
exp_manager.wandb_logger_kwargs.project=SFT-FP8 \
exp_manager.wandb_logger_kwargs.name=gpt-2b \
model.transformer_engine=${FP8} \
model.fp8=${FP8} \
model.restore_from_path=${NEMO_PATH} \
model.tensor_model_parallel_size=1 \
model.optim.name=fused_adam \
model.optim.lr=1e-5 \
model.data.train_ds.max_seq_length=1024 \
model.data.train_ds.micro_batch_size=2 \
model.data.train_ds.global_batch_size=32 \
model.data.train_ds.file_names=[${DATAFILE}]"
```
**Additional context**
Model: https://huggingface.co/nvidia/GPT-2B-001
Image: nvcr.io/ea-bignlp/nemofw-training:23.07-py3 | {
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} | # What does this PR do ?
* Change middle tokenization logic from tokenization/detokenization to separate prompt template
* Add multiple contexts and truncation fields
* Add truncation augmentation
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
_D.R suggest we document the changes here, e.g._
**Fixes**
- Remove the old logic prompt_template -> tokenize -> compute total tokens-> truncate -> detokenize -> prompt_template -> tokenize
- Add new logic prompt_template -> separate prompt_template -> tokenize -> compute total tokens -> truncate
- In separate prompt_template, we need to carefully adding spaces to the next token
- In separate prompt_template, we need to separate from left to right
-
**New features**
Remove query_key and make context_key have multiple keys by a , for example context_key="context,query"
Make truncation_field can have multiple keys, logic the same as context_key
Add multiple truncation if we have multiple truncation_field . Current logic is to equally truncate each field
Add truncation augmentation (random truncate)
```python
# Add a code snippet demonstrating how to use this
```
# 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
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Add single script for finetuning ASR models.
**Collection**: ASR
# Changelog
- Add script and config yaml file for finetuning
- TODO: Add tests and docs
# Usage
```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
- [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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- Make adapter modules follow the config of base model for precision and megatron_amp_O2
# What does this PR do ?
Make adapter modules follow the config of base model for precision and megatron_amp_O2
**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
```
# 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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} | null | null | null | I have finetuned an english conformer transducer bpe model on romanian and I have a couple of questions after following the tutorials on "Offline ASR" and "ASR confidence estimation":
1. I want to add a kenLM trained model in order to solve some small erros related to missing characters/phonetically transcribed foreign words ("meeting" -> "miting", "business" -> "bisnis"). This is expected because this is how they are pronounced in romanian and the word "meeting" might not appear often in the training set, but I thought that a language model might easily solve this problem.
However, we would also need a timestamp and confidence estimate per word and from what I understood this is possible only for "greedy decoding without LM". Is it actually possible to extract timestamps/confidence for words for beam search+LM? If it is not yet implemented, how complicated would it be and could you suggest a starting point?
2. From what I understood, transducer decoding is more complicated than CTC, so is there any implementation of confidence+timestamp estimation for CTC models with beam search+LM?
Thanks in advance!!
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} | * Add only parameters with requires_grad=True to the optimizer group
# What does this PR do ?
- Add only the parameters with requires_grad=True to the optimizer group and skip frozen parameters. This change can reduce memory usage.
**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
```
# 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 ?
Install mcore commit in the Github Action. Can revert this when the next pip package for mcore is released.
**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
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Remove frozen parameters from the optimizer group of MegatronGPTPEFTModel
# Changelog
- Check the requires_grad attribute of the parameter and only add it to the optimizer group if `requires_grad=True`
# 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.
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} | # What does this PR do ?
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} | null | null | null | **Describe the bug**
There are 2 problems with StatsPoolLayer
1. `biased` argument actually means unbiased (unbiased estimator uses N-1, instead of N)
https://github.com/NVIDIA/NeMo/blob/ab749e4401a3dcdfa5ea969347aaee20b7947c7c/nemo/collections/asr/parts/submodules/tdnn_attention.py#L35-L36
See PyTorch doc: https://pytorch.org/docs/stable/generated/torch.std.html
2. `biased` argument has no effect when `length` is not provided -> discrepancy between when `length` is provided and not provided. Unbiased estimator (PyTorch's default) is always used.
https://github.com/NVIDIA/NeMo/blob/ab749e4401a3dcdfa5ea969347aaee20b7947c7c/nemo/collections/asr/parts/submodules/tdnn_attention.py#L59-L65
**Expected behavior**
I believe `biased` argument should be `unbiased` instead. This argument should be passed to `torch.std()` when `length` is not provided.
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} | null | null | null | **Describe the bug**
I am training a conformer-transducer medium model and on the validation set I reach `val_wer=0.0157-epoch=13.ckpt`, where val_wer is actualy ChER as i use `use_cer=True`. When I try to perform inference on the same validation set using speech_to_text_eval.py (greedy decoding like in training) and that model (or the saved .nemo model in checkpoints/ dir, it's not a conversion from .ckpt to .nemo problem) I get different results: 5.5 WER/ 2.45 ChER. I understood that there might be some differences between the checkpoints values and inference, but 1% is pretty significant
I would like to mention that when using WER as the main metric with a fastconformer transducer large model, I didn't get this weird behavior. The wer from checkpoints matched the wer from the inference script.
I use nemo docker v23.04 and this is my config:
```
# It contains the default values for training a Conformer-Transducer ASR model, large size (~120M) with Transducer loss and sub-word encoding.
# Architecture and training config:
# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective
# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches.
# Here are the recommended configs for different variants of Conformer-Transducer, other parameters are the same as in this config file.
#
# +--------------+---------+---------+----------+------------------+--------------+--------------------------+-----------------+
# | Model | d_model | n_heads | n_layers | conv_kernel_size | weight_decay | pred_hidden/joint_hidden | pred_rnn_layers |
# +==============+=========+========+===========+==================+==============+==========================+=================+
# | Small (14M)| 176 | 4 | 16 | 31 | 0.0 | 320 | 1 |
# +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+
# | Medium (32M)| 256 | 4 | 16 | 31 | 1e-3 | 640 | 1 |
# +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+
# | Large (120M)| 512 | 8 | 17 | 31 | 1e-3 | 640 | 1 |
# +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+
# | XLarge (644M)| 1024 | 8 | 24 | 5 | 1e-3 | 640 | 2 |
# +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+
# Default learning parameters in this config are set for global batch size of 2K while you may use lower values.
# To increase the global batch size with limited number of GPUs, you may use higher accumulate_grad_batches.
# However accumulate_grad_batches is better to be avoided as long as the global batch size is large enough and training is stable.
# You may find more info about Conformer-Transducer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#conformer-transducer
# Pre-trained models of Conformer-Transducer can be found here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/results.html
# The checkpoint of the large model trained on NeMo ASRSET with this recipe can be found here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_transducer_large
# We suggest to use trainer.precision=bf16 for GPUs which support it otherwise trainer.precision=16 is recommended.
# Using bf16 or 16 would make it possible to double the batch size and speedup training/inference. If fp16 is not stable and model diverges after some epochs, you may use fp32.
# Here are the suggested batch size per GPU for each precision and memory sizes:
# +-----------+------------+------------+
# | Precision | GPU Memory | Batch Size |
# +===========+============+============+
# | 32 | 16GB | 8 |
# | | 32GB | 16 |
# | | 80GB | 32 |
# +-----------+------------+------------+
# | 16 or | 16GB | 16 |
# | bf16 | 32GB | 32 |
# | | 80GB | 64 |
# +-----------+------------+------------+
# Note: They are based on the assumption of max_duration of 20. If you have longer or shorter max_duration, then batch sizes may need to get updated accordingly.
name: "Conformer-Transducer-BPE"
init_from_nemo_model: "models/stt_en_conformer_transducer_medium.nemo"
init_strict: false
model:
sample_rate: 16000
compute_eval_loss: true # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag.
log_prediction: true # enables logging sample predictions in the output during training
skip_nan_grad: false
use_cer: true
model_defaults:
enc_hidden: ${model.encoder.d_model}
pred_hidden: 640
joint_hidden: 640
train_ds:
manifest_filepath: "manifests/train.json"
sample_rate: ${model.sample_rate}
batch_size: 8 # you may increase batch_size if your memory allows
shuffle: true
num_workers: 8
pin_memory: true
use_start_end_token: false
trim_silence: false
max_duration: 20
min_duration: 0.1
# tarred datasets
is_tarred: false
tarred_audio_filepaths: null
shuffle_n: 2048
# bucketing params
bucketing_strategy: "synced_randomized"
bucketing_batch_size: null
augmentor:
#white_noise:
# prob: 0.3
# min_level: -90.0
# max_level: -46.0
#shift:
# prob: 0.3
# min_shift_ms: -20.0
# max_shift_ms: 20.0
#gain:
# prob: 0.3
# min_gain_dbfs: -10.0
# max_gain_dbfs: 10.0
speed:
prob: 0.4
sr: 16000
resample_type: "kaiser_best"
min_speed_rate: 0.9
max_speed_rate: 1.1
validation_ds:
manifest_filepath: "manifests/val.json"
sample_rate: ${model.sample_rate}
batch_size: 8
shuffle: false
use_start_end_token: false
num_workers: 8
pin_memory: true
test_ds:
manifest_filepath: null
sample_rate: ${model.sample_rate}
batch_size: 8
shuffle: false
use_start_end_token: false
num_workers: 8
pin_memory: true
# You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py
# recommend to use SPE Unigram tokenizer with vocab size of 1K to 4k when using 4x sub-sampling
tokenizer:
dir: "tokenizers/tokenizer_unigram/tokenizer_spe_unigram_v1024_max_5" # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe)
type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer)
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
sample_rate: ${model.sample_rate}
normalize: "per_feature"
window_size: 0.025
window_stride: 0.01
window: "hann"
features: 80
n_fft: 512
frame_splicing: 1
dither: 0.00001
pad_to: 0
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
freq_masks: 5 # set to zero to disable it
freq_width: 27
time_masks: 10 # set to zero to disable it
time_width: 0.05
encoder:
_target_: nemo.collections.asr.modules.ConformerEncoder
feat_in: ${model.preprocessor.features}
feat_out: -1 # you may set it if you need different output size other than the default d_model
n_layers: 16
d_model: 256
# Sub-sampling parameters
subsampling: striding # vggnet, striding, stacking or stacking_norm, dw_striding
subsampling_factor: 4 # must be power of 2 for striding and vggnet
subsampling_conv_channels: -1 # set to -1 to make it equal to the d_model
causal_downsampling: false
# Reduction parameters: Can be used to add another subsampling layer at a given position.
# Having a 2x reduction will speedup the training and inference speech while keeping similar WER.
# Adding it at the end will give the best WER while adding it at the beginning will give the best speedup.
reduction: null # pooling, striding, or null
reduction_position: null # Encoder block index or -1 for subsampling at the end of encoder
reduction_factor: 1
# Feed forward module's params
ff_expansion_factor: 4
# Multi-headed Attention Module's params
self_attention_model: rel_pos # rel_pos or abs_pos
n_heads: 4 # may need to be lower for smaller d_models
# [left, right] specifies the number of steps to be seen from left and right of each step in self-attention
att_context_size: [-1, -1] # -1 means unlimited context
att_context_style: regular # regular or chunked_limited
xscaling: true # scales up the input embeddings by sqrt(d_model)
untie_biases: true # unties the biases of the TransformerXL layers
pos_emb_max_len: 5000
# Convolution module's params
conv_kernel_size: 31
conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups)
# conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size
# null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0]
conv_context_size: null
### regularization
dropout: 0.1 # The dropout used in most of the Conformer Modules
dropout_pre_encoder: 0.1 # The dropout used before the encoder
dropout_emb: 0.0 # The dropout used for embeddings
dropout_att: 0.1 # The dropout for multi-headed attention modules
# set to non-zero to enable stochastic depth
stochastic_depth_drop_prob: 0.0
stochastic_depth_mode: linear # linear or uniform
stochastic_depth_start_layer: 1
decoder:
_target_: nemo.collections.asr.modules.RNNTDecoder
normalization_mode: null # Currently only null is supported for export.
random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf
blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference.
prednet:
pred_hidden: ${model.model_defaults.pred_hidden}
pred_rnn_layers: 1
t_max: null
dropout: 0.2
joint:
_target_: nemo.collections.asr.modules.RNNTJoint
log_softmax: null # 'null' would set it automatically according to CPU/GPU device
preserve_memory: false # dramatically slows down training, but might preserve some memory
# Fuses the computation of prediction net + joint net + loss + WER calculation
# to be run on sub-batches of size `fused_batch_size`.
# When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size.
# `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss.
# Using small values here will preserve a lot of memory during training, but will make training slower as well.
# An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1.
# However, to preserve memory, this ratio can be 1:8 or even 1:16.
# Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow.
fuse_loss_wer: true
fused_batch_size: 4
jointnet:
joint_hidden: ${model.model_defaults.joint_hidden}
activation: "relu"
dropout: 0.2
decoding:
strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd.
# greedy strategy config
greedy:
max_symbols: 10
# beam strategy config
beam:
beam_size: 2
return_best_hypothesis: False
score_norm: true
tsd_max_sym_exp: 50 # for Time Synchronous Decoding
alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding
loss:
loss_name: "default"
warprnnt_numba_kwargs:
# FastEmit regularization: https://arxiv.org/abs/2010.11148
# You may enable FastEmit to reduce the latency of the model for streaming
fastemit_lambda: 0.0 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start.
clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only.
# Adds Gaussian noise to the gradients of the decoder to avoid overfitting
variational_noise:
start_step: 0
std: 0.0
optim:
name: adamw
lr: 2
# optimizer arguments
betas: [0.9, 0.98]
weight_decay: 1e-3
# scheduler setup
sched:
name: NoamAnnealing
d_model: ${model.encoder.d_model}
# scheduler config override
warmup_steps: 10000
warmup_ratio: null #0.1 or 0.05
min_lr: 1e-5 # inainte era 1e-6, dar aparent nu trebuie sa scada sub 2e-5 ptr a antrena eficient
trainer:
devices: 1 # number of GPUs, -1 would use all available GPUs
num_nodes: 1
max_epochs: 500
max_steps: -1 # computed at runtime if not set
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
accelerator: "gpu"
strategy: ddp
accumulate_grad_batches: 32 # am marit de la 8 (cand aveam 2 gpu-uri) ptr un singur GPU
gradient_clip_val: 0.0
precision: 16 # 16, 32, or bf16
log_every_n_steps: 100 # Interval of logging.
enable_progress_bar: True
resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc.
num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it
check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs
sync_batchnorm: true
enable_checkpointing: False # Provided by exp_manager
logger: false # Provided by exp_manager
benchmark: false # needs to be false for models with variable-length speech input as it slows down training
exp_manager:
exp_dir: null
name: full
create_tensorboard_logger: true
create_checkpoint_callback: true
checkpoint_callback_params:
# in case of multiple validation sets, first one is used
monitor: "val_wer"
mode: "min"
save_top_k: 5
always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints
save_best_model: True
resume_if_exists: True
resume_ignore_no_checkpoint: True
create_wandb_logger: false
wandb_logger_kwargs:
name: null
project: null
```
I would greatly appreaciate any ideas on what may cause this behavior!
BR | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/7223 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7223/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7223/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7223/events | https://github.com/NVIDIA/NeMo/issues/7223 | 1,849,115,567 | I_kwDOC_bI7s5uN0Ov | 7,223 | Out of Date Apex Version | {
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} | null | null | null | **Describe the bug**
The Apex install git version is out of Date.
https://github.com/NVIDIA/NeMo/blob/4833347105b5ec8526ae182cd2e1011b091ea89d/nemo/core/optim/distributed_adam.py#L19-L23
The file tried to load `_coalescing_manager_append_work` but the git version of Apex does not have this.
Code in Apex
```python
# Add args to coalescing manager if using PyTorch <=1.13.1
from torch.distributed.distributed_c10d import _coalescing_manager
if 'device' not in inspect.signature(_coalescing_manager).parameters.keys():
_coalescing_manager_nodevice = _coalescing_manager
@contextlib.contextmanager
def _coalescing_manager(group, device, reqs):
with _coalescing_manager_nodevice(group, reqs):
yield
```
**Steps/Code to reproduce bug**
1. Install apex following instruction
2. try import `MegatronDistributedFusedAdam`
```python
from nemo.core.optim.distributed_adam import MegatronDistributedFusedAdam
```
**Expected behavior**
Warning of no distributed_fused_adam
```
Python 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0]
Type 'copyright', 'credits' or 'license' for more information
IPython 8.14.0 -- An enhanced Interactive Python. Type '?' for help.
In [1]: from nemo.core.optim.distributed_adam import MegatronDistributedFusedAdam
[2023-08-14 14:44:05,627] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[NeMo W 2023-08-14 14:44:13 optimizers:65] Could not import distributed_fused_adam optimizer from Apex
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ in <module>:1 │
│ │
│ /PATH_OF_MY_WORKSPACE/NeMo/nemo/core/optim/distributed_adam.py:19 in <module> │
│ │
│ 16 import itertools │
│ 17 │
│ 18 import torch │
│ ❱ 19 from apex.contrib.optimizers.distributed_fused_adam import ( │
│ 20 │ DistributedFusedAdam, │
│ 21 │ _coalescing_manager, │
│ 22 │ _coalescing_manager_append_work, │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
ImportError: cannot import name '_coalescing_manager_append_work' from 'apex.contrib.optimizers.distributed_fused_adam'
```
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: pip install from source.
**Environment details**
**Additional context**
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} | null | null | null | main_param is not initialized when param.type() not in ['torch.cuda.HalfTensor', 'torch.cuda.BFloat16Tensor']
but it is used in the following line:
https://github.com/NVIDIA/NeMo/blob/f4afdf07ad742bdb2f45135f05d5d7dd8270eb51/nemo/core/optim/optimizer_with_main_params.py#L305 | {
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**Describe the bug**
I am trying to:
1. Run two epochs:
```yaml
trainer:
max_epochs: 2
```
2. *but* stopping the experiment in the middle, then resuming it (due to cluster availability constraints)
Now let's see what happens when stopping after the first epoch, and trying to resume afterwards.
The first experiment runs fine, but resuming doesn't work because the following happens when running the second one:
1. `MegatronGPTModel.setup()` is called at the start, which triggers a call to `build_pretraining_data_loader()` which creates a `MegatronPretrainingSampler` on [this line](https://github.com/NVIDIA/NeMo/blob/ab749e4401a3dcdfa5ea969347aaee20b7947c7c/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py#L993), with `consumed_samples` set to the number of consumed samples during the first run, i.e. `consumed_samples == len(dataset)` (at least with the fix from #7212)
2. As a result, the `MegatronPretrainingSampler` crashes on [this line](https://github.com/NVIDIA/NeMo/blob/ab749e4401a3dcdfa5ea969347aaee20b7947c7c/nemo/collections/nlp/data/language_modeling/megatron/data_samplers.py#L43) because we have already consumed the whole dataset
If you stop before the end of the first epoch (or if you don't have the fix from #7212), the behavior will be a bit different, but it won't work either, at best you'll be able to finish the first epoch but it won't do the second one properly.
**Expected behavior**
The second run should be able to pick up where the first one stopped, and continue until the desired number of epochs have been completed.
**Environment overview**
- Environment location: Docker
- Method of NeMo install: Docker
**Discussion**
I have some potential ideas on how to fix this, but I thought I'd ask here first in case I would be missing something obvious (I find it hard to believe I'd be the first one to try and run more than one epoch on a time-limited cluster...) | {
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} | # What does this PR do ?
Make image centering in NFA README actually work
**Collection**: `tools/nemo_forced_aligner`
# Changelog
- fix align="center" in README
# 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**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ N/A ] Did you write any new necessary tests?
- [ N/A ] Did you add or update any necessary documentation?
- [ N/A ] 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 ?
Update documentation for NFA.
**Collection**: `tools/nemo_forced_aligner`
# Changelog
- Update NeMo README
- Update NFA README - add links to new materials, move most info to new NFA docs page
- Update links to NFA tutorials
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ N/A ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ N/A ] 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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Make sure to `pip install mkdocs-video==1.4.0`, this should make sure the mkdocs build successfully.
**Collection**: Website | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Website] | {
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Adding 2 blog posts: "Introducing NFA" and "How forced alignment works".
**Collection**: Blogs
# Changelog
- add blog posts
- add a new 'Install dependencies' stage containing `pip install mkdocs-video` to the `.github/workflows/gh-docs.yml` file
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N/A
# Before your PR is "Ready for review"
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**PR Type**:
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- [ ] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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**Collection**: [Note which collection this PR will affect]
# Changelog
- set `trainer.val_check_interval=1` for several tests
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# Before your PR is "Ready for review"
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**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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## Who can review?
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# Usage
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# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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} | # What does this PR do ?
Fix `inference.compute_attention_mask=True` if not using FA
**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
```
# 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?
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- [x] 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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} | # What does this PR do ?
It fixes `consumed_samples`, which was off by one batch
**Collection**: nlp
# Changelog
- Fix `consumed_samples`, which was off by one batch
# 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
# Additional Information
This could be an issue for instance when resuming an experiment and using the number of consumed samples to know where to start from (we would end up re-processing a batch that should have been skipped). | {
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} | # What does this PR do ?
Add notebook tutorial for how to use NFA.
**Collection**: `tutorials/tools`
# Changelog
- Add new notebook.
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [N/A] Did you write any new necessary tests?
- [will add links later] Did you add or update any necessary documentation?
- [ N/A ] 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 ?
This change removes random selection when deciding to update the discriminator in the `AudioCodecModel`.
**Collection**: TTS
# Changelog
- Instead of using a random number, update the discriminator `disc_updates_per_period` times for every `disc_update_period` batches
- This fixes an issue when training with multiple GPUs
- Added logging in `audio_codec.py` training script
# Usage
N/A
# 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
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 ?
Currently NFA breaks if there is a 'horizontal ellipses' character (`…`) in the text - specifically the 'restore punctuation' code breaks because it doesn't reconcile the single ellipses character with the 3 periods generated by the tokenizer. A quick for this is to just replace the ellipses with the 3 periods when we read the data (because the text will always end up being 3 periods even if we don't try to restore the punctuation.
**Collection**: `tools/nemo_forced_aligner/`
# Changelog
- add a line which replaces horizontal ellipses character with 3 periods when reading the text in the original manifest.
# Usage
No usage change
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ N/A ] Did you write any new necessary tests?
- [ N/A ] Did you add or update any necessary documentation?
- [ N/A ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Hongbin supported SFT/distOpt for Llama on nemo-mcore
**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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**Pre checks**:
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} | null | null | null | **Describe the bug**
After the first run of the "Installation", when trying to run it again, colab endlessly executes the line "import nemo.collections.asr as nemo_asr".
**Expected behavior**
Import all required modules in a normal period of time
**Environment details**
Free colab (Nvidia Tesla T4)
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Dear Team,
I am training conformer based SSL model where I have configured the parameters
min_duration : 3.2
patch_size: 16
mask_patches: 0.5
num_negatives: 40
sample_from_same_utterance_only: true
sample_from_non_masked: false
codebook_size: 300
num_groups: 2
num_classes: 90000
but I am getting following error
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1129, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 987, in forward
output = self.module(*inputs[0], **kwargs[0])
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1129, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/overrides/base.py", line 82, in forward
output = self.module.training_step(*inputs, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/utils/model_utils.py", line 364, in wrap_training_step
output_dict = wrapped(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/models/ssl_models.py", line 468, in training_step
loss_value, loss_val_dict = self.decoder_loss_step(
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/models/ssl_models.py", line 450, in decoder_loss_step
current_loss_value = current_loss(
File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1129, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/core/classes/common.py", line 963, in __call__
outputs = wrapped(*args, **kwargs)
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/losses/ssl_losses/contrastive.py", line 187, in forward
out_masked_only = out_masked_only.reshape(bs, -1, out_masked_only.shape[-1])
RuntimeError: shape '[16, -1, 128]' is invalid for input of size 113920
I am using Nemo-22.05 images(ngc) and 48, A100-SXM4-40GB GPU i.e 6 nodes for the training. Kindly suggest me what is need to be done ? Thanks in advance | {
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} | # What does this PR do ?
Previously, we check `is_causal` for FA if we have `self.attn_mask_type == AttnMaskType.causal` and `s_q == s_k`. However, we may need causal self-attention for `s_q != s_k` if we have context parallelism proposed by @ksivaman . I change the condition `s_q == s_k` to `inference_mode` in order to prevent using causal when we have `s_q==1` during inference.
**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
```
# 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
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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} | Case where sequence parallel=True, transformer engine=False, and bias=False
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
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# Changelog
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} | The removal of result dir after the test 'Megatron GPT with KERPLE Pretraining and Resume Training TP=2' was commented out by mistake that was leading to the next test using the checkpoint from this test. Hence rolling it back.
# 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
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Move code ported from EnCodec github into encodec_modules and add MIT Meta license to it.
https://github.com/facebookresearch/encodec/blob/main/LICENSE
**Collection**: [TTS]
# Changelog
- Copy/paste code from audio_codec_modules.py and vector_quantization.py into encodec_modules.py
- Add MIT license
# 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)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
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