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https://api.github.com/repos/NVIDIA/NeMo/issues/3925 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3925/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3925/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3925/events | https://github.com/NVIDIA/NeMo/pull/3925 | 1,192,164,223 | PR_kwDOC_bI7s41mw8I | 3,925 | Stateless timer fix for PTL 1.6 | {
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Fixes a stateless timer restore issue because PTL 1.6 changed their Timer API
**Collection**: all
# Changelog
- Override `save_state_dict()` and `load_state_dict()` to not load time information.
# Usage
N/A
# 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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ypeError Traceback (most recent call last)
[<ipython-input-6-f04173251243>](https://localhost:8080/#) in <module>()
---> 19 fastpitch = FastPitchModel.from_pretrained("tts_en_fastpitch")
8 frames
[/usr/local/lib/python3.7/dist-packages/yaml/__init__.py](https://localhost:8080/#) in dump(data, stream, Dumper, **kwds)
198 Emit YAML parsing events into a stream.
199 If stream is None, return the produced string instead.
--> 200 """
201 getvalue = None
202 if stream is None:
TypeError: dump_all() got an unexpected keyword argument 'sort_keys' | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3923 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3923/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3923/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3923/events | https://github.com/NVIDIA/NeMo/issues/3923 | 1,190,130,798 | I_kwDOC_bI7s5G7_Ru | 3,923 | Speech transcription with background music | {
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I've been testing the NeMo transcription engine for some time now and I came across a question. In the case of voice with background music, how would NeMo behave?
Would I need to train with audios with background music? Or would that be a problem?
An example, a person narrating some text and an audio in the background. I would like to have the result of what the person is saying. | {
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} | null | null | null | **Describe the bug**
NeMo uses the `json.dumps` function to save dictionaries to JSON files. `json.dumps` is problematic when there are non-English characters involved. For example, I work with the Slovenian language and `json.dumps` will printout `\u0161` instead of `č` at all locations. To resolve this you need to use the `ensure_ascii=False` option for `json.dumps`. I saw that at a couple of location NeMo already uses this but at the majority locations it does not.
**Steps/Code to reproduce bug**
Run `examples/asr/asr_chunked_inference/rnnt/speech_to_text_buffered_infer_rnnt.py` or a similar transcription script with a model for a language that has non-English characters and inspect the transcript.
**Expected behavior**
Non-English characters should be printed properly in outputs and not as a unicode number.
**Environment overview (please complete the following information)**
NeMo container 22.01.
**Proposed solution**
Add `ensure_ascii=False` to all `json.dumps` calls in NeMo. If you are interested in this, I can prepare a PR.
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"merged_at": "2022-04-01T03:01:54"
} | Signed-off-by: Dima Rekesh <[email protected]>
# What does this PR do ?
bugfix for the cleanup flag
Add a one line overview of what this PR aims to accomplish.
bugfix for the cleanup flag
**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**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | Signed-off-by: Vahid <[email protected]>
# What does this PR do ?
Added stt_en_conformer_transducer_large_ls to NGC. A Conformer-Transducer-Large just trained on LS.
**Collection**: ASR
# Changelog
- Added a new model stt_en_conformer_transducer_large_ls to NGC (A Conformer-Transducer-Large just trained on LS)
**PR Type**:
- [x ] New Feature
- [ ] Bugfix
- [ ] Documentation
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# What does this PR do ?
Fix bugs in MegatronBert export to support models trained on NeMo < 1.5
Refactor code for NLP models forward functions based on Bert
Fix a bug in Intent Slot classification on path join
Modify export.py to showcase exporting legacy MegatronBert NLP models
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**:
- [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 ?
Makes server URL generic for NMT GRPC and not specific to being on localhost
**Collection**: Tools
# Changelog
- Accept server URL directly instead of just a localhost port in `client.py`
# Usage
```
python tools/nmt_grpc_service/client.py --source_language en --target_language de --text "This is a sentence." --server_url 1.1.1.1:50052
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fix bugs in MegatronBert export to support models trained on NeMo < 1.5
Refactor code for NLP models forward functions based on Bert
Fix a bug in Intent Slot classification on path join
Modify export.py to showcase exporting legacy MegatronBert NLP models
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# 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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} | null | null | null | **Describe the bug**
When running the tutorial notebook for Tacotron2 training in Google Colab. A Hydra issue is raised:
`Traceback (most recent call last):
File "[tacotron2.py](http://tacotron2.py/)", line 44, in <module>
main() # noqa pylint: disable=no-value-for-parameter
File "/usr/local/lib/python3.7/dist-packages/nemo/core/config/hydra_runner.py", line 103, in wrapper
strict=None,
TypeError: _run_hydra() got an unexpected keyword argument 'strict'`
**Steps/Code to reproduce bug**
Running each cell of the Tacotron2 tutorial notebook in a new Colab session. NOTE: Lines in the first cell were uncommented as per the instructions.
**Expected behavior**
Training to begin without error on the example dataset provided.
**Environment overview (please complete the following information)**
- Environment location: Collab
- Method of NeMo install: As per instructions within notebook. Please specify exact commands you used to install.
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} | null | null | null | Maybe I misclick some button, so t post message again and give more detail
Describe the bug
In website [https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Relation_Extraction-BioMegatron.ipynb，](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Relation_Extraction-BioMegatron.ipynb%EF%BC%8C) I run the code step by step, and i get the 404 client error on the last line of chapter "[Setting up a NeMo Experiment](https://github.com/NVIDIA/NeMo/issues/url)"
Steps/Code to reproduce bug
model = nemo_nlp.models.TextClassificationModel(cfg=config.model, trainer=trainer) in chapter "Setting up a NeMo Experiment"
Environment overview (please complete the following information)
Method of NeMo install: Google Colaboratory,
Environment details
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
python 3.7.13
pytorch 1.10.0
cuda 11.1
Error Message:
When I execute the following code:
trainer.fit(model)
I get the following error message:
Traceback (most recent call last):
File "/home/bob/anaconda3/envs/for_relation_extraction/lib/python3.8/site-packages/transformers/configuration_utils.py", line 594, in _get_config_dict
resolved_config_file = cached_path(
File "/home/bob/anaconda3/envs/for_relation_extraction/lib/python3.8/site-packages/transformers/file_utils.py", line 1921, in cached_path
output_path = get_from_cache(
File "/home/bob/anaconda3/envs/for_relation_extraction/lib/python3.8/site-packages/transformers/file_utils.py", line 2125, in get_from_cache
_raise_for_status(r)
File "/home/bob/anaconda3/envs/for_relation_extraction/lib/python3.8/site-packages/transformers/file_utils.py", line 2046, in _raise_for_status
raise RepositoryNotFoundError(f"404 Client Error: Repository Not Found for url: {request.url}")
transformers.file_utils.RepositoryNotFoundError: 404 Client Error: Repository Not Found for url: https://huggingface.co/megatron-bert-cased/resolve/main/config.json
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/bob/anaconda3/envs/for_relation_extraction/lib/python3.8/site-packages/nemo/collections/nlp/modules/common/huggingface/huggingface_utils.py", line 112, in get_huggingface_lm_model
automodel = AutoModel.from_pretrained(pretrained_model_name)
File "/home/bob/anaconda3/envs/for_relation_extraction/lib/python3.8/site-packages/transformers/models/auto/auto_factory.py", line 424, in from_pretrained
config, kwargs = AutoConfig.from_pretrained(
File "/home/bob/anaconda3/envs/for_relation_extraction/lib/python3.8/site-packages/transformers/models/auto/configuration_auto.py", line 637, in from_pretrained
config_dict, _ = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
File "/home/bob/anaconda3/envs/for_relation_extraction/lib/python3.8/site-packages/transformers/configuration_utils.py", line 546, in get_config_dict
config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
File "/home/bob/anaconda3/envs/for_relation_extraction/lib/python3.8/site-packages/transformers/configuration_utils.py", line 606, in _get_config_dict
raise EnvironmentError(
OSError: megatron-bert-cased is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models'
If this is a private repository, make sure to pass a token having permission to this repo with `use_auth_token` or log in with `huggingface-cli login` and pass `use_auth_token=True`.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/bob/Desktop/PythonWork/Relation_Extraction/app.py", line 176, in <module>
model = nemo_nlp.models.TextClassificationModel(cfg=config.model, trainer=trainer)
File "/home/bob/anaconda3/envs/for_relation_extraction/lib/python3.8/site-packages/nemo/collections/nlp/models/text_classification/text_classification_model.py", line 60, in __init__
self.bert_model = get_lm_model(
File "/home/bob/anaconda3/envs/for_relation_extraction/lib/python3.8/site-packages/nemo/collections/nlp/modules/common/lm_utils.py", line 107, in get_lm_model
model = get_huggingface_lm_model(
File "/home/bob/anaconda3/envs/for_relation_extraction/lib/python3.8/site-packages/nemo/collections/nlp/modules/common/huggingface/huggingface_utils.py", line 114, in get_huggingface_lm_model
raise ValueError(f"{pretrained_model_name} is not supported by HuggingFace. {e}")
ValueError: megatron-bert-cased is not supported by HuggingFace. megatron-bert-cased is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models'
If this is a private repository, make sure to pass a token having permission to this repo with `use_auth_token` or log in with `huggingface-cli login` and pass `use_auth_token=True`.
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} | Signed-off-by: Yang Zhang <[email protected]>
adds check for input threshhold for wfst normalization | {
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} | Bumps [pytorch-lightning](https://github.com/PyTorchLightning/pytorch-lightning) from 1.5.10 to 1.6.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/PyTorchLightning/pytorch-lightning/releases">pytorch-lightning's releases</a>.</em></p>
<blockquote>
<h2>PyTorch Lightning 1.6: Support Intel's Habana Accelerator, New efficient DDP strategy (Bagua), Manual Fault-tolerance, Stability and Reliability.</h2>
<p>The core team is excited to announce the PyTorch Lightning 1.6 release ⚡</p>
<ul>
<li><a href="https://github.com/PyTorchLightning/pytorch-lightning/blob/HEAD/#highlights">Highlights</a></li>
<li><a href="https://github.com/PyTorchLightning/pytorch-lightning/blob/HEAD/#bc-changes">Backward Incompatible Changes</a></li>
<li><a href="https://github.com/PyTorchLightning/pytorch-lightning/blob/HEAD/#changelog">Full Changelog</a></li>
<li><a href="https://github.com/PyTorchLightning/pytorch-lightning/blob/HEAD/#contributors">Contributors</a></li>
</ul>
<p><!-- raw HTML omitted --><!-- raw HTML omitted --></p>
<h2>Highlights</h2>
<p>PyTorch Lightning 1.6 is the work of 99 contributors who have worked on features, bug-fixes, and documentation for a total of over 750 commits since 1.5. This is our most active release yet. Here are some highlights:</p>
<h3>Introducing Intel's Habana Accelerator</h3>
<p>Lightning 1.6 now supports the Habana® framework, which includes Gaudi® AI training processors. Their heterogeneous architecture includes a cluster of fully programmable Tensor Processing Cores (TPC) along with its associated development tools and libraries and a configurable Matrix Math engine.</p>
<p>You can leverage the <a href="https://pytorch-lightning.readthedocs.io/en/stable/accelerators/hpu.html">Habana</a> hardware to accelerate your Deep Learning training workloads simply by passing:</p>
<pre lang="python"><code>trainer = pl.Trainer(accelerator="hpu")
<h1>single Gaudi training</h1>
<p>trainer = pl.Trainer(accelerator="hpu", devices=1)</p>
<h1>distributed training with 8 Gaudi</h1>
<p>trainer = pl.Trainer(accelerator="hpu", devices=8)
</code></pre></p>
<h3>The Bagua Strategy</h3>
<p>The <a href="https://pytorch-lightning.readthedocs.io/en/stable/accelerators/gpu.html#bagua">Bagua Strategy</a> is a deep learning acceleration framework that supports multiple, advanced distributed training algorithms with state-of-the-art system relaxation techniques. Enabling <a href="https://github.com/BaguaSys/bagua">Bagua</a>, which can be considerably faster than vanilla PyTorch DDP, is as simple as:</p>
<pre lang="python"><code>trainer = pl.Trainer(strategy="bagua")
<h1>or to choose a custom algorithm</h1>
<p>trainer = pl.Trainer(strategy=BaguaStrategy(algorithm="gradient_allreduce") # default
</code></pre></p>
<h3>Towards stable Accelerator, Strategy, and Plugin APIs</h3>
<p>The <code>Accelerator</code>, <code>Strategy</code>, and <code>Plugin</code> APIs are a core part of PyTorch Lightning. They're where all the distributed boilerplate lives, and we're constantly working to improve both them and the overall PyTorch Lightning platform experience.</p>
<p>In this release, we've made some large changes to achieve that goal. Not to worry, though! The only users affected by these changes are those who use custom implementations of Accelerator and Strategy (<code>TrainingTypePlugin</code>) as well as certain Plugins. In particular, we want to highlight the following changes:</p>
<ul>
<li>All <code>TrainingTypePlugin</code>s have been renamed to <code>Strategy</code> (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/11120">#11120</a>). Strategy is a more appropriate name because it encompasses more than simply training communcation. This change is now aligned with the changes we implemented in 1.5, which introduced the new <a href="https://github.com/PyTorchLightning/pytorch-lightning/releases/tag/1.5.0#strategy-and-devices"><code>strategy</code> and <code>devices</code> flags to the Trainer</a>.</li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a href="https://github.com/PyTorchLightning/pytorch-lightning/blob/master/CHANGELOG.md">pytorch-lightning's changelog</a>.</em></p>
<blockquote>
<h2>[1.6.0] - 2022-03-29</h2>
<h3>Added</h3>
<ul>
<li>Allow logging to an existing run ID in MLflow with <code>MLFlowLogger</code> (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/12290">#12290</a>)</li>
<li>Enable gradient accumulation using Horovod's <code>backward_passes_per_step</code> (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/11911">#11911</a>)</li>
<li>Add new <code>DETAIL</code> log level to provide useful logs for improving monitoring and debugging of batch jobs (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/11008">#11008</a>)</li>
<li>Added a flag <code>SLURMEnvironment(auto_requeue=True|False)</code> to control whether Lightning handles the requeuing (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/10601">#10601</a>)</li>
<li>Fault Tolerant Manual
<ul>
<li>Add <code>_Stateful</code> protocol to detect if classes are stateful (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/10646">#10646</a>)</li>
<li>Add <code>_FaultTolerantMode</code> enum used to track different supported fault tolerant modes (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/10645">#10645</a>)</li>
<li>Add a <code>_rotate_worker_indices</code> utility to reload the state according the latest worker (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/10647">#10647</a>)</li>
<li>Add stateful workers (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/10674">#10674</a>)</li>
<li>Add an utility to collect the states across processes (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/10639">#10639</a>)</li>
<li>Add logic to reload the states across data loading components (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/10699">#10699</a>)</li>
<li>Cleanup some fault tolerant utilities (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/10703">#10703</a>)</li>
<li>Enable Fault Tolerant Manual Training (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/10707">#10707</a>)</li>
<li>Broadcast the <code>_terminate_gracefully</code> to all processes and add support for DDP (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/10638">#10638</a>)</li>
</ul>
</li>
<li>Added support for re-instantiation of custom (subclasses of) <code>DataLoaders</code> returned in the <code>*_dataloader()</code> methods, i.e., automatic replacement of samplers now works with custom types of <code>DataLoader</code> (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/10680">#10680</a>)</li>
<li>Added a function to validate if fault tolerant training is supported. (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/10465">#10465</a>)</li>
<li>Added a private callback to manage the creation and deletion of fault-tolerance checkpoints (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/11862">#11862</a>)</li>
<li>Show a better error message when a custom <code>DataLoader</code> implementation is not well implemented and we need to reconstruct it (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/10719">#10719</a>)</li>
<li>Show a better error message when frozen dataclass is used as a batch (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/10927">#10927</a>)</li>
<li>Save the <code>Loop</code>'s state by default in the checkpoint (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/10784">#10784</a>)</li>
<li>Added <code>Loop.replace</code> to easily switch one loop for another (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/10324">#10324</a>)</li>
<li>Added support for <code>--lr_scheduler=ReduceLROnPlateau</code> to the <code>LightningCLI</code> (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/10860">#10860</a>)</li>
<li>Added <code>LightningCLI.configure_optimizers</code> to override the <code>configure_optimizers</code> return value (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/10860">#10860</a>)</li>
<li>Added <code>LightningCLI(auto_registry)</code> flag to register all subclasses of the registerable components automatically (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/12108">#12108</a>)</li>
<li>Added a warning that shows when <code>max_epochs</code> in the <code>Trainer</code> is not set (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/10700">#10700</a>)</li>
<li>Added support for returning a single Callback from <code>LightningModule.configure_callbacks</code> without wrapping it into a list (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/11060">#11060</a>)</li>
<li>Added <code>console_kwargs</code> for <code>RichProgressBar</code> to initialize inner Console (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/10875">#10875</a>)</li>
<li>Added support for shorthand notation to instantiate loggers with the <code>LightningCLI</code> (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/11533">#11533</a>)</li>
<li>Added a <code>LOGGER_REGISTRY</code> instance to register custom loggers to the <code>LightningCLI</code> (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/11533">#11533</a>)</li>
<li>Added info message when the <code>Trainer</code> arguments <code>limit_*_batches</code>, <code>overfit_batches</code>, or <code>val_check_interval</code> are set to <code>1</code> or <code>1.0</code> (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/11950">#11950</a>)</li>
<li>Added a <code>PrecisionPlugin.teardown</code> method (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/10990">#10990</a>)</li>
<li>Added <code>LightningModule.lr_scheduler_step</code> (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/10249">#10249</a>)</li>
<li>Added support for no pre-fetching to <code>DataFetcher</code> (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/11606">#11606</a>)</li>
<li>Added support for optimizer step progress tracking with manual optimization (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/11848">#11848</a>)</li>
<li>Return the output of the <code>optimizer.step</code>. This can be useful for <code>LightningLite</code> users, manual optimization users, or users overriding <code>LightningModule.optimizer_step</code> (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/11711">#11711</a>)</li>
<li>Teardown the active loop and strategy on exception (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/11620">#11620</a>)</li>
<li>Added a <code>MisconfigurationException</code> if user provided <code>opt_idx</code> in scheduler config doesn't match with actual optimizer index of its respective optimizer (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/11247">#11247</a>)</li>
<li>Added a <code>loggers</code> property to <code>Trainer</code> which returns a list of loggers provided by the user (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/11683">#11683</a>)</li>
<li>Added a <code>loggers</code> property to <code>LightningModule</code> which retrieves the <code>loggers</code> property from <code>Trainer</code> (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/11683">#11683</a>)</li>
<li>Added support for DDP when using a <code>CombinedLoader</code> for the training data (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/11648">#11648</a>)</li>
<li>Added a warning when using <code>DistributedSampler</code> during validation/testing (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/11479">#11479</a>)</li>
<li>Added support for <code>Bagua</code> training strategy (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/11146">#11146</a>)</li>
<li>Added support for manually returning a <code>poptorch.DataLoader</code> in a <code>*_dataloader</code> hook (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/12116">#12116</a>)</li>
<li>Added <code>rank_zero</code> module to centralize utilities (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/11747">#11747</a>)</li>
<li>Added a <code>_Stateful</code> support for <code>LightningDataModule</code> (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/11637">#11637</a>)</li>
<li>Added <code>_Stateful</code> support for <code>PrecisionPlugin</code> (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/pull/11638">#11638</a>)</li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/PyTorchLightning/pytorch-lightning/commit/44e3edbf7a3fffd3069e2bc24c6ee143de79944b"><code>44e3edb</code></a> Cleanup CHANGELOG (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/issues/12507">#12507</a>)</li>
<li><a href="https://github.com/PyTorchLightning/pytorch-lightning/commit/e3893b9eca33496f4af0b5581fe222594a236900"><code>e3893b9</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/issues/12509">#12509</a> from RobertLaurella/patch-1</li>
<li><a href="https://github.com/PyTorchLightning/pytorch-lightning/commit/041da417db26a7d9de723f25866e5d8e38fe2039"><code>041da41</code></a> Remove TPU Availability check from parse devices (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/issues/12326">#12326</a>)</li>
<li><a href="https://github.com/PyTorchLightning/pytorch-lightning/commit/4fe0076a1ad05efd2385ab6e6827ecfbf7520bb9"><code>4fe0076</code></a> Prepare for the 1.6.0 release</li>
<li><a href="https://github.com/PyTorchLightning/pytorch-lightning/commit/17215ed91b7fafed0594304a752f2f8e3590e998"><code>17215ed</code></a> Fix titles capitalization in docs</li>
<li><a href="https://github.com/PyTorchLightning/pytorch-lightning/commit/a775804b4027a0038997f228e85184ea18ba4c3d"><code>a775804</code></a> Update Plugins doc (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/issues/12440">#12440</a>)</li>
<li><a href="https://github.com/PyTorchLightning/pytorch-lightning/commit/71e25f39d9a3e2cf3870d522c88705ea18dcdae8"><code>71e25f3</code></a> Update CI in README.md (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/issues/12495">#12495</a>)</li>
<li><a href="https://github.com/PyTorchLightning/pytorch-lightning/commit/c6cb6341e7e319191a83aa52bbbd4448ba0a372f"><code>c6cb634</code></a> Add usage of Jupyter magic command for loggers (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/issues/12333">#12333</a>)</li>
<li><a href="https://github.com/PyTorchLightning/pytorch-lightning/commit/42169a23a06197b68460d2629b650ed8228fcd18"><code>42169a2</code></a> Add typing to <code>LightningModule.trainer</code> (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/issues/12345">#12345</a>)</li>
<li><a href="https://github.com/PyTorchLightning/pytorch-lightning/commit/2de6a9bbbff44398f868a1594ed5a42bc726d872"><code>2de6a9b</code></a> Fix warning message formatting in save_hyperparameters (<a href="https://github-redirect.dependabot.com/PyTorchLightning/pytorch-lightning/issues/12498">#12498</a>)</li>
<li>Additional commits viewable in <a href="https://github.com/PyTorchLightning/pytorch-lightning/compare/1.5.10...1.6.0">compare view</a></li>
</ul>
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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Fix DALI tests on 22.03 container
**Collection**: [ASR]
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | null | null | null | **Describe the bug**
when running the second cell of [Inference_DurationPitchControl.ipynb](https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/tts/Inference_DurationPitchControl.ipynb#scrollTo=Om8Q6noETUxt) in colab, I get
```
[NeMo E 2022-03-30 19:30:51 vocabs:325] Torch distributed needs to be initialized before you initialized <nemo.collections.common.data.vocabs.Phonemes object at 0x7ff436821290>. This class is prone to data access race conditions. Now downloading corpora from global rank 0. If other ranks pass this before rank 0, errors might result.
[NeMo E 2022-03-30 19:30:53 vocabs:325] Torch distributed needs to be initialized before you initialized <nemo.collections.common.data.vocabs.Phonemes object at 0x7ff42eb93410>. This class is prone to data access race conditions. Now downloading corpora from global rank 0. If other ranks pass this before rank 0, errors might result.
[NeMo E 2022-03-30 19:30:55 common:480] Model instantiation failed!
Target class: nemo.collections.tts.models.fastpitch.FastPitchModel
Error(s): dump_all() got an unexpected keyword argument 'sort_keys'
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/nemo/core/classes/common.py", line 459, in from_config_dict
instance = imported_cls(cfg=config, trainer=trainer)
File "/usr/local/lib/python3.7/dist-packages/nemo/collections/tts/models/fastpitch.py", line 83, in __init__
super().__init__(cfg=cfg, trainer=trainer)
File "/usr/local/lib/python3.7/dist-packages/nemo/core/classes/modelPT.py", line 149, in __init__
f"If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method "
File "/usr/local/lib/python3.7/dist-packages/omegaconf/omegaconf.py", line 774, in to_yaml
Dumper=get_omega_conf_dumper(),
File "/usr/local/lib/python3.7/dist-packages/yaml/__init__.py", line 200, in dump
"""
TypeError: dump_all() got an unexpected keyword argument 'sort_keys'
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
[<ipython-input-10-27feb807c5d3>](https://localhost:8080/#) in <module>()
17
18 # Load the models from NGC
---> 19 fastpitch = FastPitchModel.from_pretrained("tts_en_fastpitch").eval().cuda()
20 hifigan = HifiGanModel.from_pretrained("tts_hifigan").eval().cuda()
21 sr = 22050
8 frames
[/usr/local/lib/python3.7/dist-packages/yaml/__init__.py](https://localhost:8080/#) in dump(data, stream, Dumper, **kwds)
198 Emit YAML parsing events into a stream.
199 If stream is None, return the produced string instead.
--> 200 """
201 getvalue = None
202 if stream is None:
TypeError: dump_all() got an unexpected keyword argument 'sort_keys'
```
**Steps/Code to reproduce bug**
1. Open [Inference_DurationPitchControl.ipynb](https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/tts/Inference_DurationPitchControl.ipynb#scrollTo=Om8Q6noETUxt) in Colab
2. Change runtime type to "GPU"
3. Run cell 1
4. Run cell 2
**Expected behavior**
Cell should run without errors or warnings
**Environment overview (please complete the following information)**
- Environment location: Colab
- Method of NeMo install: `!python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]`
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https://api.github.com/repos/NVIDIA/NeMo/issues/3908 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3908/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3908/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3908/events | https://github.com/NVIDIA/NeMo/pull/3908 | 1,185,979,572 | PR_kwDOC_bI7s41SfC0 | 3,908 | Add a missing line to ASR_with_NeMo.ipynb | {
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"merged_at": "2022-03-31T06:28:27"
} | # What does this PR do ?
Add a missing line to inference step. The WER is different with and without the line.
**Collection**: ASR
# Changelog
- Add a missing line to inference step in ASR_with_NeMo.ipynb
- Deleted lines were handled automatically by PyCharm.
# 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?
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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
- [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 ?
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**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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- [ ] 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)
- [x] ci tests are passing
- [x] SH tests are passing
- [ ] with LM matches baseline numbers | {
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Error message during the docker build:
```
=> [internal] load build context 0.7s
=> => transferring context: 34.81MB 0.6s
=> [nemo-src 1/1] COPY . . 0.1s
=> [nemo-deps 2/14] RUN apt-get update && apt-get install -y libsndfile1 sox libfreetype6 pyth 59.1s
=> [nemo-deps 3/14] RUN pip uninstall -y sacrebleu torchtext 0.9s
=> [nemo-deps 4/14] WORKDIR /tmp/torchaudio_build 0.0s
=> ERROR [nemo-deps 5/14] RUN git clone --depth 1 --branch release/0.11 https://github.com/pytorch/audio.git & 12.4s
------
> [nemo-deps 5/14] RUN git clone --depth 1 --branch release/0.11 https://github.com/pytorch/audio.git && cd audio && git submodule update --init --recursive && BUILD_SOX=1 python setup.py install && cd .. && rm -r audio:
#10 0.442 Cloning into 'audio'...
#10 1.963 Submodule 'kaldi' (https://github.com/kaldi-asr/kaldi) registered for path 'third_party/kaldi/submodule'
#10 1.964 Submodule 'third_party/kenlm/submodule' (https://github.com/kpu/kenlm) registered for path 'third_party/kenlm/submodule'
#10 1.975 Cloning into '/tmp/torchaudio_build/audio/third_party/kaldi/submodule'...
#10 9.030 Cloning into '/tmp/torchaudio_build/audio/third_party/kenlm/submodule'...
#10 10.54 Submodule path 'third_party/kaldi/submodule': checked out '3eea37dd09b55064e6362216f7e9a60641f29f09'
#10 10.66 Submodule path 'third_party/kenlm/submodule': checked out '5cea457db26950a73d638425c183b368c06ed7c6'
#10 12.13 -- Git branch: release/0.11
#10 12.13 -- Git SHA: 820b383b3b21fc06e91631a5b1e6ea1557836216
#10 12.13 -- Git tag: v0.11.0
#10 12.13 -- PyTorch dependency: torch==1.11.0a0+17540c5
#10 12.13 -- Building version 0.11.0+820b383
#10 12.13 --- Initializing submodules
#10 12.13 --- Initialized submodule
#10 12.13 --- Fetching zlib-1.2.11.tar.gz
#10 12.13 Traceback (most recent call last):
#10 12.13 File "setup.py", line 181, in <module>
#10 12.13 _main()
#10 12.13 File "setup.py", line 144, in _main
#10 12.13 _fetch_third_party_libraries()
#10 12.13 File "setup.py", line 128, in _fetch_third_party_libraries
#10 12.13 _fetch_archives(_parse_sources())
#10 12.13 File "setup.py", line 122, in _fetch_archives
#10 12.13 torch.hub.download_url_to_file(url, dest, progress=False)
#10 12.13 File "/opt/conda/lib/python3.8/site-packages/torch/hub.py", line 457, in download_url_to_file
#10 12.13 u = urlopen(req)
#10 12.13 File "/opt/conda/lib/python3.8/urllib/request.py", line 222, in urlopen
#10 12.13 return opener.open(url, data, timeout)
#10 12.13 File "/opt/conda/lib/python3.8/urllib/request.py", line 531, in open
#10 12.13 response = meth(req, response)
#10 12.13 File "/opt/conda/lib/python3.8/urllib/request.py", line 640, in http_response
#10 12.13 response = self.parent.error(
#10 12.13 File "/opt/conda/lib/python3.8/urllib/request.py", line 569, in error
#10 12.13 return self._call_chain(*args)
#10 12.13 File "/opt/conda/lib/python3.8/urllib/request.py", line 502, in _call_chain
#10 12.13 result = func(*args)
#10 12.13 File "/opt/conda/lib/python3.8/urllib/request.py", line 649, in http_error_default
#10 12.13 raise HTTPError(req.full_url, code, msg, hdrs, fp)
#10 12.13 urllib.error.HTTPError: HTTP Error 404: Not Found
------
executor failed running [/bin/sh -c git clone --depth 1 --branch release/0.11 https://github.com/pytorch/audio.git && cd audio && git submodule update --init --recursive && BUILD_SOX=1 python setup.py install && cd .. && rm -r audio]: exit code: 1
```
**Steps/Code to reproduce bug**
Run the command:
```
DOCKER_BUILDKIT=1 docker build -f Dockerfile -t nemo:latest .
```
**Expected behavior**
Docker image build without errors
**Environment overview (please complete the following information)**
Environment is on a Ubuntu 20.04 workstation with docker installed.
**Additional context**
GPU: A6000, CUDA 11.4
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} | # What does this PR do ?
Adds some block configs to group together PRs under each domain when building the changelog.
**Collection**: [docs]
# Usage
After building the changelog, each domain will have a dangling `</details>` tag in the raw markdown, move that to the end of the domains PR list to group them together.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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} | # What does this PR do ?
Bounds PTL since pypi PTL 1.6 is out.
**Collection**: All
# Changelog
- Change PTL requirements file.
# Usage
N/A
# 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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} | Signed-off-by: Yang Zhang <[email protected]>
fixes citations for text norm, deletes MegatronBertModule from nlp api since it does not exist and created doc build error | {
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} | # What does this PR do ?
Comments out GPT CI tests for resuming training temporarily due to hanging.
**Collection**: NLP
# Changelog
- Comments out GPT CI resume tests
# Usage
N/A
# 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 ?
Updates NeMo CI to the PyTorch 22.03 container
**Collection**: All
# Changelog
- Changes the Jenkinsfile with the path to the new container.
# Usage
N/A
# 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 | **Describe the bug**
```python
from nemo.collections.nlp.data.language_modeling.megatron.dataset_utils import compile_helper
compile_helper()
```
Throws the following error:
```
/usr/bin/python3: No module named pybind11
g++ -O3 -Wall -shared -std=c++11 -fPIC -fdiagnostics-color helpers.cpp -o helpers.cpython-38-x86_64-linux-gnu.so
helpers.cpp:25:10: fatal error: pybind11/pybind11.h: No such file or directory
25 | #include <pybind11/pybind11.h>
| ^~~~~~~~~~~~~~~~~~~~~
compilation terminated.
make: *** [Makefile:23: helpers.cpython-38-x86_64-linux-gnu.so] Error 1
[NeMo E 2022-03-29 09:34:40 dataset_utils:77] Making C++ dataset helpers module failed, exiting.
```
**Expected behavior**
Should not throw an error.
**Environment overview (please complete the following information)**
- Environment location: metal
- Method of NeMo install: from source
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Ubuntu 20.04.2
- PyTorch version: 1.10.0+cu113
- Python version: 3.8
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} | …ce generation code
Signed-off-by: Jure Demsar <[email protected]>
# What does this PR do ?
Diarization with ASR JSON output now also includes sentences. This is a continuation of https://github.com/NVIDIA/NeMo/pull/3791, which I accidentally closed when cleaning up the repo.
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
Diarization with ASR JSON output now also includes sentences.
# Usage
You can use the `examples/speaker_tasks/diarization/offline_diarization.py` script and check the output JSON file (`.../pred_rttms/uniq_name.json`).
# 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? **No.**
- [x] Did you add or update any necessary documentation? **No.**
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)? **No.**
- [x] Reviewer: Does the PR have correct import guards for all optional libraries? **N/A.**
**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?
@titu1994, @redoctopus, @jbalam-nv, or @okuchaiev
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 #3708 (https://github.com/NVIDIA/NeMo/issues/3708)
When implementing I used an approach that changes the original codebase as little as possible. In other words, I tried to be non-invasive. After reviewing the existing code for sentences (`string_out`) preparation in `make_json_output()` of `/nemo/collections/asr/parts/utils/diarization_utils.py` I feel like the whole process could be done much more efficiently and elegantly. Currently, the sentence preparation is appending words to a string where the beginning of the string are the time stamps. When the ending time stamp needs to be corrected the code then uses string splitting to extract and replace/correct the end time value. This is quite clunky and inefficient. It would be much more elegant if time stamps and the sentences were stored and built within a dictionary and then printed out at the end.
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this is an untested code, which is
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In website https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Relation_Extraction-BioMegatron.ipynb， I run the code step by step, and i get the 404 client error on the last line of chapter "Setting up a NeMo Experiment"
**Steps/Code to reproduce bug**
model = nemo_nlp.models.TextClassificationModel(cfg=config.model, trainer=trainer) in chapter "[Setting up a NeMo Experiment](url)"
**Environment overview (please complete the following information)**
- Method of NeMo install: Google Colaboratory,
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- python 3.7.13
- pytorch 1.10.0
- cuda 11.1
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} | # What does this PR do ?
Add MegatronBert support for more NLP models;
Add support for finetuning a downstream task which used MegatronBert as pretrained model
Fix RIVA and ONNX export issues of Bert and MegatronBert (This is coming from https://github.com/NVIDIA/NeMo/pull/3883 PR)
The change in order of arguments won't break usage of anyone unless they deliberately turned off parameters check and passed arguments in positional fashion. ONNX export is one of the places that does that internally in PyTorch and that's why we need to make that order consistent across HF and Nemo BERT models and NLPModel that uses those modules as input_module.
NLPModel __init__() refactoring ( https://github.com/NVIDIA/NeMo/pull/3887 )
This is done to reduce code duplication and make it manageable. Each class has their own minor config differences and registers own artifacts, and those subtle inconsistencies break assumptions downstream (e.g. in Riva)
**Collection**: [Note which collection this PR will affect]
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**:
- [ 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)
- [x ] 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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} | Signed-off-by: Dima Rekesh <[email protected]>
# What does this PR do ?
improvements to et_commonvoice_data.py: logging, handling of overwrites, tgz cleanup
**Collection**: ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
```python
# python get_commonvoice_data.py --num_workers=4 --language es --log --files_to_process test.tsv dev.tsv
```
# 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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} | Signed-off-by: ekmb <[email protected]>
# 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
- [ X] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
This PR will have fixes needed for PTL 1.6.0. The PR will first install PTL 1.6.0rc0 via the Jenkinsfile. Then when PTL 1.6.0 is on PYPI, PTL will be installed via .reinstall.sh. Do not merge this PR until PTL 1.6.0 is on PYPI.
**Collection**: Core
# 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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} | # What does this PR do ?
1. It implements a processor `JaMeCabProcessor` to train and evaluate models where Japanese is segmented with MeCab
2. Fixes a bug in one-many translation BLEU score eval. Old code would not index into self.tgt_language to select the appropriate language.
**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.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adding cache-aware streaming Conformer training and inference with look-ahead support. It is achieved by training a model with limited effective right context and then perform the streaming with activation caching support. Limiting the right context would reduce the accuracy in compare to the an offline model but it gives better accuracy and significantly higher throughput by dropping duplicates in the computations which happens in buffered-based streaming.Large right context decreases the WER while increasing the latency.
It supports the three following modes:
1-fully causal model with zero look-ahead with zero latency
2-regular look-ahead
3-chunk-aware look-ahead with small duplication in computations.
It supports both Conformer-CTC and Conformer-Transducer and they can get trained with regular scripts but the configs files in the following folder:
NeMo/examples/asr/conf/conformer/streaming/
A model trained in streaming mode can get evaluated with the following script:
NeMo/examples/asr/conf/conformer/streaming/speech_to_text_streaming_infer.py
This script would simulate the streaming inference for a single audio or a manifest of audio files. Streaming can be done in multi-streaming mode (batched inference) for the manifest file to speed up the streaming. It can also compare the results with offline evaluation and report the differences in both the WER and models' outputs.
The accuracy of the model in both the offline evaluation and streaming is going to be exactly the same. In offline mode, the whole audio is passed through the model while in streaming audio is passed chunk by chunk.
# Changelog
- Added frame-wise streaming Conformer models with look-ahead support and caching mechanism for streaming inference.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
**PR Type**:
- [x ] New Feature
- [ ] Bugfix
- [ ] Documentation
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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 ?
This PR adds RNN-based encoder to NeMo. It enables us to have LSTM-Transducer (RNN-T) and LSTM-CTC models.
# Changelog
- Added RNN encoder for ASR models (LSTM-T and LSTM-CTC models)
- Added stacking downsampling
- Added support for proj_size to Transducer decoders
- Added skip_nan_grad support for ASR models which skips the gradients when there is a nan or inf in the gradients
# Usage
```python
# Add a code snippet demonstrating how to use this
```
**PR Type**:
- [x ] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do?
Populate the GPT/BERT `list_available_models` with uploaded models. So, user can see all the models available by
```python
MegatronBertModel.list_available_models()
```
User can easily load the model by
```python
MegatronBertModel.from_pretrained('megatron_bert_345m_cased', trainer)
```
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} | # What does this PR do ?
Add various functionalities to dialogue domain for NeMo
**Collection**: NLP
# Changelog
1. Support Zero Shot Intent Recognition
2. Further refactored Dialogue module
3. Implement Dialogue GPT Generation Model
4. Support MS Marco Data Processor
5. Implement Dialogue S2S Generation Model (HF fully supported, Megatron training supported, inference pending integration of common generation API)
6. Support System Response Generation using user utterance and system slots based on SGD dataset
7. Support Design Data Processor
8. Implement HF BART based classifier into zero shot intent model
9. Implement Dialogue Nearest Neighbour Model
10. Refactor Dialogue SGD Data Processor to make interface with models cleaner
11. Update Nearest Neighbour Model and ZeroShotIntentModel to support SGD dataset and ZeroShot Datasets
12. Support Mellon QA Data Processor
13. Add Documentation and Tutorial
See details in [NVIDIA only dev log](https://docs.google.com/document/d/1IUerv_PLPLgzm5rLEgvHITz_RmK4cKdJKJCV7BXTJ9c/edit?usp=sharing)
# 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?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | Signed-off-by: Boris Fomitchev <[email protected]>
# What does this PR do ?
Fixing remaining issues with exported order of inputs of NLP models:
* Except Punct&Capital, exported order of inputs of BERT networks was wrong
* There were discrepancies between Bert and Roberta/CamamBert forward() argument orders in nlp/modules/common/huggingface
Also cleaned up unused input_types()/output_types() overloads
**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 | **Describe the bug**
I'm trying to convert a CitriNet model I trained with NeMo v1.7.0 to the Riva format using the `nemo2riva` CLI tool.
Everytime I run:
```sh
nemo2riva --out my-model.riva my-model.nemo
```
I get the following error:
```sh
ERROR: Failed to restore model from NeMo file : my-model.nemo. Please make sure you have the latest NeMo package installed with [all] dependencies.
Traceback (most recent call last):
File "/home/allabana/.virtualenvs/nm-debug/bin/nemo2riva", line 8, in <module>
sys.exit(nemo2riva())
File "/home/allabana/.virtualenvs/nm-debug/lib/python3.8/site-packages/nemo2riva/cli/nemo2riva.py", line 50, in nemo2riva
Nemo2Riva(args)
File "/home/allabana/.virtualenvs/nm-debug/lib/python3.8/site-packages/nemo2riva/convert.py", line 42, in Nemo2Riva
raise e
File "/home/allabana/.virtualenvs/nm-debug/lib/python3.8/site-packages/nemo2riva/convert.py", line 35, in Nemo2Riva
model = ModelPT.restore_from(restore_path=nemo_in)
File "/home/allabana/.virtualenvs/nm-debug/lib/python3.8/site-packages/nemo/core/classes/modelPT.py", line 270, in restore_from
instance = cls._save_restore_connector.restore_from(
File "/home/allabana/.virtualenvs/nm-debug/lib/python3.8/site-packages/nemo/core/connectors/save_restore_connector.py", line 136, in restore_from
instance = calling_cls.from_config_dict(config=conf)
File "/home/allabana/.virtualenvs/nm-debug/lib/python3.8/site-packages/nemo/core/classes/common.py", line 473, in from_config_dict
instance = cls(cfg=config)
TypeError: Can't instantiate abstract class ModelPT with abstract methods list_available_models, setup_training_data, setup_validation_data
```
I've tried every combination of `nemo2riva` and `nemo_toolkit` from 1.7.0-1.10.0 and 1.3.0-1.7.0 respectively.
I would install using:
```sh
# X = [7, 8, 9, 10]
pip install nemo2riva-1.X.0b0-py3-none-any.whl
# X = [3, 4, 5, 6, 7]
pip install 'nemo_toolkit[all]==1.X.0'
```
**Steps/Code to reproduce bug**
Use any nemo model, including the pretrained citrinet models and try to convert it
```sh
ngc registry resource download-version "nvidia/riva/riva_quickstart:1.10.0-beta"
cd riva_quickstart_v1.10.0-beta
pip install nemo2riva-1.10.0b0-py3-none-any.whl
nemo2riva --out my-model.riva my-model.nemo
```
**Expected behavior**
`nemo2riva` converts the model succesfully.
**Environment overview (please complete the following information)**
- Environment location: Docker and Bare metal
- Method of NeMo install: `pip install 'nemo_toolkit[all]==1.7.0'`
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
Using Docker image `nvcr.io/nvidia/riva/riva-speech:1.10.0-beta-servicemaker`
**Environment details**
Docker and local
Otherwise, please provide:
- OS version: Ubuntu 20.04
- PyTorch version: 1.10.0
- Python version: 3.8
**Additional context**
GPU model: RTX 3090
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} | null | null | null | Are there plans to add python 3.9 as one of the supported python versions?
The current documentation says that only up to 3.8 is supported but there are some suggestions to use 3.9 in other issues, and I haven't found any discussion of updating the supported versions to include it.
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} | # What does this PR do ?
Fixing a Jenkins Test error from the previous commit
**Collection**: NLP and Jenkinsfile
# Changelog
- Renamed folder in Jenkinsfile in line 1026
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [x] 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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} | null | null | null | I tried offline Diarization with ASR using the following models
1. stt_en_quartznet15x5
2. stt_en_conformer_ctc_large
3. stt_en_citrinet_1024
But running the sample code for diarization with asr works for the first and third models. The second model conformer ctc large is giving blank output with the following message:
->Only EncDecCTCModelBPE models trained with per_feature normalization are supported currently.
I tried with the example notebook of the NeMo repository as well.
<img width="978" alt="Screenshot 2022-03-23 at 11 37 01 PM" src="https://user-images.githubusercontent.com/10113553/159766811-04288452-46a5-4531-a9a2-f162a49fd21b.png">
[[Ref source link]
](https://github.com/NVIDIA/NeMo/tree/main/examples/speaker_tasks/diarization)
<img width="702" alt="Screenshot 2022-03-23 at 11 33 45 PM" src="https://user-images.githubusercontent.com/10113553/159766263-b5e3ab3b-dcac-4e5f-8204-f71d3577e0b0.png">
Please suggest a solution if I am missing something or is it not supported yet by the NeMo stack yet.
Thanks,
Rafi
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} | # What does this PR do ?
Refactor to combine p-tuning and prompt tuning code as well as integrate it with the new evaluation code/api.
Prompt tuning and p-tuning are now jointly called 'prompt learning' within NeMo.
**Collection**: BigNLP
# Changelog
- Added MegatronGPTPromptLearningModel class
- Added GPTPromptLearningDataset class, merging p-tuning and prompt-tuning dataset classes
- Removed p-tuning dataset and model classes
- Removed prompt tuning code from the MegatronGPTModel, GPTModel, and TransformerLanguageModel classes
- Edited inference/generation code to accommodate p-tuning/prompt-tuning
# Usage
```python
python megatron_gpt_prompt_learning.py --config-name=megatron_gpt_prompt_learning_config.yaml
python megatron_gpt_eval.py --config-name=megatron_gpt_inference.yaml
```
# 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? (**Will come in a different PR**)
- [ ] 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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} | # What does this PR do ?
Fixing a Jenkins Test error from the previous commit
**Collection**: NLP and Jenkinsfile
# Changelog
- Renamed folder in Jenkinsfile in line 1014
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [x] 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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] | closed | false | null | [] | null | null | 2022-03-23T13:57:13 | 2022-03-23T19:43:57 | 2022-03-23T19:43:56 | NONE | {
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} | null | null | null | **Describe the bug**
When using nemo container `nvcr.io/nvidia/nemo:1.4.0` on a kubernetes environment, there seems to be increase ephemeral storage usage. I had mounted the experiment directory and data dir as volumes for the pod running the training on, was just wondering if there are other directories that are expected to grow for eg , storage for base model and intermediate models (?) , model dependencies like vocabularies etc (?) . Really not sure any help would be appreciated.
**Steps/Code to reproduce bug**
On a strict k8s cluster which enforces ephemeral storage limits, I was trying to run the token classification task.
**Expected behavior**
Hopefully if the directory that is exepcted to grow can be converted into a volume.
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: Running `nvcr.io/nvidia/nemo:1.4.0` as a Pod with 10Gi ephemeral storage
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} | Signed-off-by: Carol Anderson <[email protected]>
# What does this PR do ?
Addresses the problem raised in #2769, where models cannot be reloaded if the data directory specified in the config is missing, such as when a model is reloaded on a different machine from the one on which it was trained.
**Collection**: NLP
# Changelog
- Added a line to the IntentSlotClassificationModel and MultiLabelIntentSlotClassificationModel `__init__` methods to check whether `_is_model_being_restored()` is False before setting it to True.
- Also fixed minor errors in the Zero Shot Intent Recognition model code: changed the display name of the tutorial notebook and corrected an incorrect type hint in the inference dataset class.
# 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)
- [x] 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 #2769
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"url": "https://api.github.com/repos/NVIDIA/NeMo/pulls/3873",
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"merged_at": null
} | # What does this PR do ?
When adding pipeline parallelism implementation we had to disable async allreduce in the case of tensor parallelism only. This causes a perf regression when training small models. With this PR we are able to add back async grad allreduce for O2 training.
A subsequent PR will implement async grad allreduce for O1 training.
**Collection**: NLP
# Changelog
- Update `training_step` with async handler from O2 recipe
# 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**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | I wanted to convert my TTS system with TensorRT, HifiGAN models weren't problematic at all, but I've been unable to get the TensorRT engine from Fastpitch models.
I've exported both the HifiGAN and FastPitch models to ONNX, thanks to _Exportable.export()_ method, but the FastPitch ones fail to even get parsed by the ONNX parser from TensorRT.
That's the error I get:
```
[03/22/2022-09:23:02] [TRT] [E] [shuffleNode.cpp::symbolicExecute::387] Error Code 4: Internal Error (Reshape_1126: IShuffleLayer applied to shape tensor must have 0 or 1 reshape dimensions: dimensions were [-1,2])
[03/22/2022-09:23:02] [TRT] [E] parsers/onnx/ModelImporter.cpp:780: While parsing node number 1137 [Pad -> "1682"]:
[03/22/2022-09:23:02] [TRT] [E] parsers/onnx/ModelImporter.cpp:781: --- Begin node ---
[03/22/2022-09:23:02] [TRT] [E] parsers/onnx/ModelImporter.cpp:782: input: "1655"
input: "1680"
input: "1681"
output: "1682"
name: "Pad_1137"
op_type: "Pad"
attribute {
name: "mode"
s: "constant"
type: STRING
}
[03/22/2022-09:23:02] [TRT] [E] parsers/onnx/ModelImporter.cpp:783: --- End node ---
[03/22/2022-09:23:02] [TRT] [E] parsers/onnx/ModelImporter.cpp:785: ERROR: parsers/onnx/ModelImporter.cpp:179 In function parseGraph:
[6] Invalid Node - Pad_1137
[shuffleNode.cpp::symbolicExecute::387] Error Code 4: Internal Error (Reshape_1126: IShuffleLayer applied to shape tensor must have 0 or 1 reshape dimensions: dimensions were [-1,2])
In node 1137 (parseGraph): INVALID_NODE: Invalid Node - Pad_1137
[shuffleNode.cpp::symbolicExecute::387] Error Code 4: Internal Error (Reshape_1126: IShuffleLayer applied to shape tensor must have 0 or 1 reshape dimensions: dimensions were [-1,2])
```
So it clearly seems like the TensorRT can't handle that particular padding.
I've tried using different ONNX Opset versions (11, 12, 13), tried converting the models from INT64 to INT32 myself, since I suspected the problem might lie in the fact that TensorRT doesn't natively support INT64 and it was attempting to cast the model down to INT32 itself, but nothing seems to work.
The machine I'm working on has **CUDA 11.4** which I can't change, so I'm using **TensorRT v.8.2.2.1**.
Has anyone been successful in using TensorRT with FastPitch models or are they just not compatible? So far it seems to be a problem specific to FastPitch only.
If someone succeeded, was there a need to change the model - e.g. the paddings? Or if it worked out of the box, what's NeMo/ONNX opset/TensorRT versions combination to use? | {
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} | null | null | null | Hi there,
I use [export.py](https://github.com/NVIDIA/NeMo/blob/6dd4263fecd03a39688dfd99866ac8d5842b65e6/scripts/export.py) to export titanet to onnx, and succeed(get titanet-l.onnx). Then I use tensorRT OnnxParser to parse this onnx, and get error like:
```
[03/22/2022-09:41:57] [TRT] [E] ModelImporter.cpp:774: --- Begin node ---
[03/22/2022-09:41:57] [TRT] [E] ModelImporter.cpp:775: input: "608"
output: "609"
name: "If_380"
op_type: "If"
attribute {
name: "then_branch"
g {
node {
output: "610"
name: "Constant_381"
op_type: "Constant"
attribute {
name: "value"
t {
dims: 1
data_type: 7
raw_data: "\377\377\377\377\377\377\377\377"
}
type: TENSOR
}
doc_string: ""
}
node {
input: "601"
input: "610"
output: "611"
name: "Squeeze_382"
op_type: "Squeeze"
doc_string: ""
}
name: "torch-jit-export1"
output {
name: "611"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_param: "Squeeze611_dim_0"
}
dim {
dim_param: "Squeeze611_dim_1"
}
}
}
}
}
}
type: GRAPH
}
attribute {
name: "else_branch"
g {
node {
input: "601"
output: "612"
name: "Identity_383"
op_type: "Identity"
doc_string: ""
}
name: "torch-jit-export2"
output {
name: "612"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_param: "Identity612_dim_0"
}
dim {
dim_param: "Identity612_dim_1"
}
dim {
dim_param: "Identity612_dim_2"
}
}
}
}
}
}
type: GRAPH
}
doc_string: "/root/miniconda3/envs/myconda/lib/python3.8/site-packages/nemo/collections/asr/modules/conv_asr.py(844): forward\n/root/miniconda3/envs/myconda/lib/python3.8/site-packages/torch/nn/modules/module.py(1090): _slow_forward\n/root/miniconda3/envs/myconda/lib/python3.8/site-packages/torch/nn/modules/module.py(1102): _call_impl\n/root/miniconda3/envs/myconda/lib/python3.8/site-packages/nemo/collections/asr/models/label_models.py(235): forward_for_export\n/root/miniconda3/envs/myconda/lib/python3.8/site-packages/torch/nn/modules/module.py(1090): _slow_forward\n/root/miniconda3/envs/myconda/lib/python3.8/site-packages/torch/nn/modules/module.py(1102): _call_impl\n/root/miniconda3/envs/myconda/lib/python3.8/site-packages/torch/jit/_trace.py(118): wrapper\n/root/miniconda3/envs/myconda/lib/python3.8/site-packages/torch/jit/_trace.py(127): forward\n/root/miniconda3/envs/myconda/lib/python3.8/site-packages/torch/nn/modules/module.py(1102): _call_impl\n/root/miniconda3/envs/myconda/lib/python3.8/site-packages/torch/jit/_trace.py(1166): _get_trace_graph\n/root/miniconda3/envs/myconda/lib/python3.8/site-packages/torch/onnx/utils.py(388): _trace_and_get_graph_from_model\n/root/miniconda3/envs/myconda/lib/python3.8/site-packages/torch/onnx/utils.py(437): _create_jit_graph\n/root/miniconda3/envs/myconda/lib/python3.8/site-packages/torch/onnx/utils.py(493): _model_to_graph\n/root/miniconda3/envs/myconda/lib/python3.8/site-packages/torch/onnx/utils.py(724): _export\n/root/miniconda3/envs/myconda/lib/python3.8/site-packages/torch/onnx/utils.py(107): export\n/root/miniconda3/envs/myconda/lib/python3.8/site-packages/torch/onnx/__init__.py(316): export\n/root/miniconda3/envs/myconda/lib/python3.8/site-packages/nemo/core/classes/exportable.py(142): export\n/home/ccl/sv/test/export.py(137): nemo_export\n/home/ccl/sv/test/export.py(162): <module>\n/root/ccl/.pycharm_helpers/pydev/_pydev_imps/_pydev_execfile.py(18): execfile\n/root/ccl/.pycharm_helpers/pydev/pydevd.py(1147): run\n/root/ccl/.pycharm_helpers/pydev/pydevd.py(1752): main\n/root/ccl/.pycharm_helpers/pydev/pydevd.py(1758): <module>\n"
[03/22/2022-09:41:57] [TRT] [E] ModelImporter.cpp:776: --- End node ---
[03/22/2022-09:41:57] [TRT] [E] ModelImporter.cpp:779: ERROR: ModelImporter.cpp:179 In function parseGraph:
[6] Invalid Node - If_380
If_380_OutputLayer: IIfConditionalOutputLayer inputs must have the same shape.
```
Here is my parse code:
```
emb_onnx_path = 'titanet-l.onnx'
logger = trt.Logger(trt.Logger.WARNING)
network_flags = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(logger) as builder:
with builder.create_network(flags=network_flags) as network, trt.OnnxParser(
network, logger
) as parser, builder.create_builder_config() as builder_config:
parser.parse_from_file(emb_onnx_path)
```
I don't know whether it's a bug or just usage error. Could you please have a look?
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[data.zip](https://github.com/NVIDIA/NeMo/files/8322010/data.zip)
**Describe the bug**
I am trying to decode hindi speech file with recently released STT Hi Conformer-CTC Medium.
Once loaded AM and LM I run script scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py as described in tutorial.
After passing greedy decoging phase, script starts the beam search decoding but fails to load KenLM binary.
**Steps/Code to reproduce bug**
1. Download the latest NeMo docker container with using command:
docker pull nvcr.io/nvidia/nemo:22.01
2. Run docker container using command:
docker run --runtime=nvidia -it --rm -v --shm-size=16g --ulimit memlock=-1 --ulimit stack=67108864 \
--mount=type=bind,source=/mnt/asr/korenevsky,target=/mnt/asr/korenevsky nvcr.io/nvidia/nemo:22.01
3. Download stt_hi_conformer_ctc_medium.nemo and 5-gram-kenlm.bin from https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_hi_conformer_ctc_medium and put them into the folder /mnt/asr/korenevsky/test_nemo_hindi/ which is mouned to docker container.
4. Put wav-file to recognize and corresponding json manifest into /mnt/asr/korenevsky/test_nemo_hindi/data (see attached zip-file)
5. Run decoding script using following command
`python /workspace/nemo/scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py \
--nemo_model_file /mnt/asr/korenevsky/test_nemo_hindi/stt_hi_conformer_ctc_medium.nemo \
--input_manifest /mnt/asr/korenevsky/test_nemo_hindi/data/manifest.json \
--kenlm_model_file /mnt/asr/korenevsky/test_nemo_hindi/5-gram-kenlm.bin \
--acoustic_batch_size 32 \
--beam_width 128 \
--beam_alpha 1.5 \
--beam_beta 2.0 \
--preds_output_folder /mnt/asr/korenevsky/test_nemo_hindi/data/predictions \
--decoding_mode beamsearch_ngram
`
This causes the following messages:
Traceback (most recent call last):
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/modules/beam_search_decoder.py", line 64, in __init__
from ctc_decoders import Scorer, ctc_beam_search_decoder_batch
ModuleNotFoundError: No module named 'ctc_decoders'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/workspace/nemo/scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py", line 357, in <module>
main()
File "/workspace/nemo/scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py", line 341, in main
beam_search_eval(
File "/workspace/nemo/scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py", line 71, in beam_search_eval
beam_search_lm = nemo_asr.modules.BeamSearchDecoderWithLM(
File "/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/modules/beam_search_decoder.py", line 66, in __init__
raise ModuleNotFoundError(
ModuleNotFoundError: BeamSearchDecoderWithLM requires the installation of ctc_decoders from scripts/asr_language_modeling/ngram_lm/install_beamsearch_decoder
s.sh
6. Install Beamsearch decoder using following commands:
export BOOST_ROOT=/mnt/asr/korenevsky/boost (otherwise CMake error occures)
cd /workspace/nemo/scripts/asr_language_modeling/ngram_lm
bash ./install_beamsearch_decoders.sh
7. Then run step 5 again. The following messages appear:
[NeMo I 2022-03-22 06:40:39 eval_beamsearch_ngram:325] ==============================Starting the beam search decoding===============================
[NeMo I 2022-03-22 06:40:39 eval_beamsearch_ngram:326] Grid search size: 1
[NeMo I 2022-03-22 06:40:39 eval_beamsearch_ngram:327] It may take some time...
[NeMo I 2022-03-22 06:40:39 eval_beamsearch_ngram:328] ==============================================================================================
terminate called after throwing an instance of 'lm::FormatLoadException'
what(): kenlm/lm/vocab.cc:52 in void lm::ngram::{anonymous}::ReadWords(int, lm::EnumerateVocab*, lm::WordIndex, uint64_t) threw FormatLoadException because `expected_count != index'.
The binary file has the wrong number of words at the end. This could be caused by a truncated binary file.
./test_beam_1file.sh: line 10: 1454 Aborted (core dumped) python /workspace/nemo/scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py --nemo_model_file /mnt/asr/korenevsky/test_nemo_hindi/stt_hi_conformer_ctc_medium.nemo --input_manifest /mnt/asr/korenevsky/test_nemo_hindi/data/manifest.json --kenlm_model_file /mnt/asr/korenevsky/test_nemo_hindi/5-gram-kenlm.bin --acoustic_batch_size 32 --beam_width 128 --beam_alpha 1.5 --beam_beta 2.0 --preds_output_folder /mnt/asr/korenevsky/test_nemo_hindi/data/predictions --decoding_mode beamsearch_ngram
But KenLM 5-gram-kenlm.bin can be loaded successfully with KenLM python interface, so it doesn't seem to be truncated.
**Expected behavior**
Successful beam search decoding
**Environment overview (please complete the following information)**
docker pull nvcr.io/nvidia/nemo:22.01
docker run --runtime=nvidia -it --rm -v --shm-size=16g --ulimit memlock=-1 --ulimit stack=67108864 \
--mount=type=bind,source=/mnt/asr/korenevsky,target=/mnt/asr/korenevsky nvcr.io/nvidia/nemo:22.01
**Environment details**
setting environment variable BOOST_ROOT to point Boost build
**Additional context**
GPU A100 was used
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} | # What does this PR do ?
Make nvfuser work at bias+dropout+add fusion at GPT inference
# Changelog
Apply _torch.enable_grad_ to the fusion at training pass
# 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
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} | # What does this PR do ?
Index mapping files are created during nemo megatron training. This PR makes the directory that they are saved in configurable.
**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
```bash
# add to your model config the directory to save the index mappings
model.data.index_mapping_dir=/raid/data/gpt_pile/index_mappings
```
# 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**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/3866 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3866/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3866/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3866/events | https://github.com/NVIDIA/NeMo/pull/3866 | 1,175,781,324 | PR_kwDOC_bI7s40wrPe | 3,866 | Update webdataset version | {
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} | # What does this PR do ?
Attempts to update webdataset version to use better seeding that was introduced in more recent versions.
**Collection**: NLP
# Changelog
- Change webdataset version requirements file
- Change NMT tarredataset to use webdataset pipeline
# Usage
N/A
# 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/3865 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3865/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3865/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3865/events | https://github.com/NVIDIA/NeMo/issues/3865 | 1,175,715,544 | I_kwDOC_bI7s5GE_7Y | 3,865 | I'm getting syntax error trying loading nlp (webdataset import) | {
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} | null | null | null | from nemo.collections import nlp as nemo_nlp gives me an error
```
File "/usr/local/lib/python3.7/dist-packages/webdataset/cache.py", line 43
while data := stream.read(chunk_size):
^
SyntaxError: invalid syntax
```
that looks to originate in webdataset import.
The webdataset version 0.2.4
and the last version of Nemo.
Anyone can help? | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3864 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3864/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3864/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3864/events | https://github.com/NVIDIA/NeMo/issues/3864 | 1,175,247,268 | I_kwDOC_bI7s5GDNmk | 3,864 | [Question] Loading trainer configs for resume training of ASR models | {
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} | null | null | null | Hi,
I know that when one want to fine-tune an ASR model can easily load pre-trained models' wights by using [this link hints and commands](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#fine-tuning-configurations). Now, my questions is that, how I can load the trainer configs for **resume training**? Assume, I had a long training and for some reasons, the running server shut down before the training procedure has completed. So, I want to load the last checkpoint as well as the trainer last configs (_i.e._, last learning rate value, optimizer state, etc.).
Can you please guide me how I can do this (loading the last state of trainer configs)?
Best | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3863 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3863/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3863/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3863/events | https://github.com/NVIDIA/NeMo/issues/3863 | 1,174,998,900 | I_kwDOC_bI7s5GCQ90 | 3,863 | Pre-trained HuggingFace decoder fails with NotImplementedError | {
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} | null | null | null | Hi 👋 I'm trying to load pre-trained HuggingFace model ([mBART](https://huggingface.co/facebook/mbart-large-cc25)) for machine translation as described in [this doc](https://github.com/NVIDIA/NeMo/blob/2ce45369f7ab6cd20c376d1ed393160f5e54be0c/docs/source/nlp/machine_translation.rst).
I'm using [this script](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/machine_translation/enc_dec_nmt.py) as a base. However, I was only able to load pre-trained encoder but not decoder. I see there are corresponding fields in this [huggingface config file](https://github.com/NVIDIA/NeMo/blob/stable/examples/nlp/machine_translation/conf/huggingface.yaml):
```
decoder:
library: nemo
model_name: null
pretrained: false
```
But when I try to load pre-trained decoder and pass corresponding parameters it fails with `NotImplementedError`:
```
Traceback (most recent call last):
File "enc_dec_nmt.py", line 125, in main
mt_model = MTEncDecModel(cfg.model, trainer=trainer)
File "/home/user/anaconda3/envs/oMeN/lib/python3.8/site-packages/nemo/collections/nlp/models/machine_translation/mt_enc_dec_model.py", line 193, in __init__
embedding=self.decoder.embedding,
File "/home/user/anaconda3/envs/oMeN/lib/python3.8/site-packages/nemo/collections/nlp/modules/common/decoder_module.py", line 51, in embedding
raise NotImplementedError
NotImplementedError
```
Parameters:
```
python3 enc_dec_nmt.py \
--config-path=conf \
--config-name=huggingface \
model.encoder.pretrained=true \
model.encoder.model_name=facebook/mbart-large-cc25 \
model.decoder.library=huggingface \
model.decoder.hidden_size=1024 \
model.decoder.inner_size=4096 \
model.decoder.model_name=facebook/mbart-large-cc25 \
model.decoder.pretrained=true \
model.decoder_tokenizer.tokenizer_model=facebook/mbart-large-cc25 \
model.decoder_tokenizer.library=huggingface \
model.train_ds.src_file_name=/nas/vkriger/bart_nemo/train.correct.short.txt \
model.train_ds.tgt_file_name=/nas/vkriger/bart_nemo/train.noisy.short.txt \
model.validation_ds.src_file_name=/nas/vkriger/bart_nemo/val.correct.short.txt \
model.validation_ds.tgt_file_name=/nas/vkriger/bart_nemo/val.noisy.short.txt \
model.test_ds.src_file_name=/nas/vkriger/bart_nemo/val.correct.short.txt \
model.test_ds.tgt_file_name=/nas/vkriger/bart_nemo/val.noisy.short.txt \
model.encoder_tokenizer.tokenizer_model=facebook/mbart-large-cc25 \
trainer.devices=2
```
I've tried to implement omitting fields and methods by hand in [HuggingFaceDecoderModule](https://github.com/NVIDIA/NeMo/blob/c4ec081c2a6d718f8917155d55a006b5f6fcbb27/nemo/collections/nlp/modules/common/huggingface/huggingface_decoder.py#L25) (these are `self._embedding`, `self._decoder`, `self._max_sequence_length` and `forward` method similarly to what's in [this file](https://github.com/NVIDIA/NeMo/blob/c4ec081c2a6d718f8917155d55a006b5f6fcbb27/nemo/collections/nlp/modules/common/transformer/transformer.py#L164)) and the `NotImplementedError` was gone but I am not sure if I did it the right way. As far as I see in the logs the decoder remains plain `TransformerDecoder` but not `MBartDecoder`. I attached the modified `HuggingFaceDecoderModule` file below.
Is it possible to load pre-trained HuggingFace decoder at the moment? If no, are there any workarounds? Lastly, can I use my modified `HuggingFaceDecoderModule` file as a workaround?
**Additional files**
Modified HuggingFaceDecoderModule: https://github.com/zzomg/for_issues/blob/main/huggingface_decoder.py
Config file: https://github.com/zzomg/for_issues/blob/main/huggingface.yaml
Test files: [train.correct.short.txt](https://github.com/zzomg/for_issues/blob/main/train.correct.short.txt) [train.noisy.short.txt](https://github.com/zzomg/for_issues/blob/main/train.noisy.short.txt) [val.correct.short.txt](https://github.com/zzomg/for_issues/blob/main/val.correct.short.txt) [val.noisy.short.txt](https://github.com/zzomg/for_issues/blob/main/val.noisy.short.txt)
Thank you!
**Environment details**
NeMo ver.: 1.7.0rc0
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} | # What does this PR do ?
Adds support to train Neural Machine Translation models with Megatron Transformer models that support tensor parallelism.
**Collection**: NLP
# Changelog
- Add a `MegatronNMT` class that inherits from `MegatronLMEncoderDecoderModel` and overrides appropriate functions and calls classmethods from `MTEncDecModel`
- Change appropriate methods in `MTEncDecModel` to classmethods so they can be called in `MegatronNMTModel`.
- Add a config file and training script.
# Usage
```python
examples/nlp/machine_translation/megatron_nmt_training.py -cn aayn_base_megatron model.tensor_model_parallel_size=2
```
# 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 ?
It's ports ADLR implementation of RADTTS to NeMo.
It affects TTS collection. Specifically, TTS/Torch.
# Changelog
- Added modules, model, loss functions, and helper functions for RADTTS
# Usage
Their is two stage training which requires two config files that are found under /examples/tts/conf/
- for each stage of the training different learning rates are used
- first stage learning rate (le4, 1e4, 5e5, 1e6(with fp= 32)) , config file: rad-tts_dec
- second stage learning rate (1e3, 1e4, 1e5), config file: rad-tts_feature_pred
```python
# Add a code snippet demonstrating how to use this
Once the config files are filled out. Run examples/tts/radtts_train.py
# 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)
- [ x] 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.
- onnx exportability is still not added
- not tested with multi-speaker TTS training
## 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)
Adding P_voiced and voiced_mask to data.py will conflict with the othre TTS repos. Let's discuss about them. | null | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3859 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3859/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3859/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3859/events | https://github.com/NVIDIA/NeMo/pull/3859 | 1,174,104,937 | PR_kwDOC_bI7s40rpLp | 3,859 | WIP: Megatron support | {
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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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} | Let's review the proposed `generate` API for models that can generate text.
Note, it is for discussion. **DON'T** approve and merge it. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3857 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3857/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3857/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3857/events | https://github.com/NVIDIA/NeMo/pull/3857 | 1,174,097,747 | PR_kwDOC_bI7s40rnp1 | 3,857 | RADTTS-NeMo Porting | {
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} | # What does this PR do ?
It's ports ADLR implementation of RADTTS to NeMo.
It affects TTS collection. Specifically, TTS/Torch.
# Changelog
- Added modules, model, loss functions, and helper functions for RADTTS
# Usage
Their is two stage training which requires two config files that are found under /examples/tts/conf/
- for each stage of the training different learning rates are used
- first stage learning rate (le4, 1e4, 5e5, 1e6(with fp= 32)) , config file: rad-tts_dec
- second stage learning rate (1e3, 1e4, 1e5), config file: rad-tts_feature_pred
```python
# Add a code snippet demonstrating how to use this
Once the config files are filled out. Run examples/tts/radtts_train.py
# 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)
- [ x] 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.
- onnx exportability is still not added
- not tested with multi-speaker TTS training
## 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)
Adding P_voiced and voiced_mask to data.py will conflict with the othre TTS repos. Let's discuss about them. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3856 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3856/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3856/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3856/events | https://github.com/NVIDIA/NeMo/pull/3856 | 1,174,030,808 | PR_kwDOC_bI7s40rbcG | 3,856 | Added Hindi and Marathi Models in Nemo pretrained ASR_CTC_BPE models … | {
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} | [] | closed | false | null | [] | null | null | 2022-03-18T21:10:36 | 2022-04-26T06:33:19 | 2022-03-18T22:28:04 | CONTRIBUTOR | {
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"url": "https://api.github.com/repos/NVIDIA/NeMo/pulls/3856",
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"merged_at": "2022-03-18T22:28:04"
} | Signed-off-by: Megh Makwana [[email protected]](mailto:[email protected])
# What does this PR do ?
Add new pretrained Hindi and Marathi ASR models (Conformer CTC Medium) to list_available_models and to documentation
Collection: [Note which collection this PR will affect]
ASR
# Changelog
Add new pretrained Hindi and Marathi ASR models (Conformer CTC Medium) to the method list_available_models in EncDecCTCModelBPE
# Usage
Import pretrained Hindi and Marathi Conformer-CTC-Medium Models
```python
asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="stt_hi_conformer_ctc_medium")
asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="stt_mr_conformer_ctc_medium")
```
# 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/3855 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3855/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3855/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3855/events | https://github.com/NVIDIA/NeMo/issues/3855 | 1,173,640,919 | I_kwDOC_bI7s5F9FbX | 3,855 | Can't setup the environment to run ASR with Transducers tutorial | {
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] | closed | false | null | [] | null | null | 2022-03-18T14:15:13 | 2022-03-19T09:11:19 | 2022-03-19T09:11:19 | NONE | {
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} | null | null | null | Hi guys,
I am trying to run the [ASR_with_Transducers](https://github.com/NVIDIA/NeMo/blob/stable/tutorials/asr/ASR_with_Transducers.ipynb) tutorials but it has a version incompatible issue.
At first, I tried to setup the Conda env by following the installation guide and this [instruction](https://github.com/NVIDIA/NeMo/issues/3804#issuecomment-1061569531), the numba already installed from Conda but got this issue:
```
[NeMo W 2022-03-18 22:52:28 nemo_logging:349] /home/tako/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/trainer/configuration_validator.py:291: LightningDeprecationWarning: Base `Callback.on_train_batch_start` hook signature has changed in v1.5. The `dataloader_idx` argument will be removed in v1.7.
rank_zero_deprecation(
[NeMo W 2022-03-18 22:52:28 nemo_logging:349] /home/tako/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/trainer/configuration_validator.py:291: LightningDeprecationWarning: Base `Callback.on_train_batch_end` hook signature has changed in v1.5. The `dataloader_idx` argument will be removed in v1.7.
rank_zero_deprecation(
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2]
[NeMo W 2022-03-18 22:52:33 modelPT:496] The lightning trainer received accelerator: <pytorch_lightning.accelerators.gpu.GPUAccelerator object at 0x7fce77a780d0>. We recommend to use 'ddp' instead.
[NeMo I 2022-03-18 22:52:33 modelPT:587] Optimizer config = Novograd (
Parameter Group 0
amsgrad: False
betas: [0.9, 0.0]
eps: 1e-08
grad_averaging: False
lr: 0.05
weight_decay: 0.001
)
[NeMo I 2022-03-18 22:52:33 lr_scheduler:833] Scheduler "<nemo.core.optim.lr_scheduler.CosineAnnealing object at 0x7fce52dbf280>"
will be used during training (effective maximum steps = 3000) -
Parameters :
(warmup_steps: null
warmup_ratio: null
min_lr: 1.0e-06
last_epoch: -1
max_steps: 3000
)
| Name | Type | Params
------------------------------------------------------------------------
0 | preprocessor | AudioToMelSpectrogramPreprocessor | 0
1 | encoder | ConvASREncoder | 2.3 M
2 | decoder | RNNTDecoder | 35.4 K
3 | joint | RNNTJoint | 47.3 K
4 | loss | RNNTLoss | 0
5 | spec_augmentation | SpectrogramAugmentation | 0
6 | wer | RNNTBPEWER | 0
------------------------------------------------------------------------
2.3 M Trainable params
0 Non-trainable params
2.3 M Total params
9.380 Total estimated model params size (MB)
[NeMo I 2022-03-18 22:52:36 rnnt_wer_bpe:231]
[NeMo I 2022-03-18 22:52:36 rnnt_wer_bpe:232] reference :rubout g m e f three nine
[NeMo I 2022-03-18 22:52:36 rnnt_wer_bpe:233] predicted :uuuubuujjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj
[NeMo I 2022-03-18 22:52:39 rnnt_wer_bpe:231]
[NeMo I 2022-03-18 22:52:39 rnnt_wer_bpe:232] reference :m e l v i n
[NeMo I 2022-03-18 22:52:39 rnnt_wer_bpe:233] predicted :uuuubuuuujjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj
Epoch 0: 0%
0/60 [00:00<?, ?it/s]
---------------------------------------------------------------------------
CudaAPIError Traceback (most recent call last)
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/numba/cuda/cudadrv/driver.py:2188, in Linker.add_ptx(self, ptx, name)
2187 try:
-> 2188 driver.cuLinkAddData(self.handle, enums.CU_JIT_INPUT_PTX,
2189 ptxbuf, len(ptx), namebuf, 0, None, None)
2190 except CudaAPIError as e:
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/numba/cuda/cudadrv/driver.py:303, in Driver._wrap_api_call.<locals>.safe_cuda_api_call(*args)
302 retcode = libfn(*args)
--> 303 self._check_error(fname, retcode)
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/numba/cuda/cudadrv/driver.py:343, in Driver._check_error(self, fname, retcode)
342 raise CudaDriverError("CUDA initialized before forking")
--> 343 raise CudaAPIError(retcode, msg)
CudaAPIError: [222] Call to cuLinkAddData results in UNKNOWN_CUDA_ERROR
During handling of the above exception, another exception occurred:
LinkerError Traceback (most recent call last)
Input In [32], in <cell line: 2>()
1 # Train the model
----> 2 trainer.fit(model)
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:740, in Trainer.fit(self, model, train_dataloaders, val_dataloaders, datamodule, train_dataloader, ckpt_path)
735 rank_zero_deprecation(
736 "`trainer.fit(train_dataloader)` is deprecated in v1.4 and will be removed in v1.6."
737 " Use `trainer.fit(train_dataloaders)` instead. HINT: added 's'"
738 )
739 train_dataloaders = train_dataloader
--> 740 self._call_and_handle_interrupt(
741 self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path
742 )
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:685, in Trainer._call_and_handle_interrupt(self, trainer_fn, *args, **kwargs)
675 r"""
676 Error handling, intended to be used only for main trainer function entry points (fit, validate, test, predict)
677 as all errors should funnel through them
(...)
682 **kwargs: keyword arguments to be passed to `trainer_fn`
683 """
684 try:
--> 685 return trainer_fn(*args, **kwargs)
686 # TODO: treat KeyboardInterrupt as BaseException (delete the code below) in v1.7
687 except KeyboardInterrupt as exception:
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:777, in Trainer._fit_impl(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
775 # TODO: ckpt_path only in v1.7
776 ckpt_path = ckpt_path or self.resume_from_checkpoint
--> 777 self._run(model, ckpt_path=ckpt_path)
779 assert self.state.stopped
780 self.training = False
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1199, in Trainer._run(self, model, ckpt_path)
1196 self.checkpoint_connector.resume_end()
1198 # dispatch `start_training` or `start_evaluating` or `start_predicting`
-> 1199 self._dispatch()
1201 # plugin will finalized fitting (e.g. ddp_spawn will load trained model)
1202 self._post_dispatch()
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1279, in Trainer._dispatch(self)
1277 self.training_type_plugin.start_predicting(self)
1278 else:
-> 1279 self.training_type_plugin.start_training(self)
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py:202, in TrainingTypePlugin.start_training(self, trainer)
200 def start_training(self, trainer: "pl.Trainer") -> None:
201 # double dispatch to initiate the training loop
--> 202 self._results = trainer.run_stage()
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1289, in Trainer.run_stage(self)
1287 if self.predicting:
1288 return self._run_predict()
-> 1289 return self._run_train()
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1319, in Trainer._run_train(self)
1317 self.fit_loop.trainer = self
1318 with torch.autograd.set_detect_anomaly(self._detect_anomaly):
-> 1319 self.fit_loop.run()
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/loops/base.py:145, in Loop.run(self, *args, **kwargs)
143 try:
144 self.on_advance_start(*args, **kwargs)
--> 145 self.advance(*args, **kwargs)
146 self.on_advance_end()
147 self.restarting = False
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/loops/fit_loop.py:234, in FitLoop.advance(self)
231 data_fetcher = self.trainer._data_connector.get_profiled_dataloader(dataloader)
233 with self.trainer.profiler.profile("run_training_epoch"):
--> 234 self.epoch_loop.run(data_fetcher)
236 # the global step is manually decreased here due to backwards compatibility with existing loggers
237 # as they expect that the same step is used when logging epoch end metrics even when the batch loop has
238 # finished. this means the attribute does not exactly track the number of optimizer steps applied.
239 # TODO(@carmocca): deprecate and rename so users don't get confused
240 self.global_step -= 1
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/loops/base.py:145, in Loop.run(self, *args, **kwargs)
143 try:
144 self.on_advance_start(*args, **kwargs)
--> 145 self.advance(*args, **kwargs)
146 self.on_advance_end()
147 self.restarting = False
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py:193, in TrainingEpochLoop.advance(self, *args, **kwargs)
190 self.batch_progress.increment_started()
192 with self.trainer.profiler.profile("run_training_batch"):
--> 193 batch_output = self.batch_loop.run(batch, batch_idx)
195 self.batch_progress.increment_processed()
197 # update non-plateau LR schedulers
198 # update epoch-interval ones only when we are at the end of training epoch
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/loops/base.py:145, in Loop.run(self, *args, **kwargs)
143 try:
144 self.on_advance_start(*args, **kwargs)
--> 145 self.advance(*args, **kwargs)
146 self.on_advance_end()
147 self.restarting = False
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py:88, in TrainingBatchLoop.advance(self, batch, batch_idx)
86 if self.trainer.lightning_module.automatic_optimization:
87 optimizers = _get_active_optimizers(self.trainer.optimizers, self.trainer.optimizer_frequencies, batch_idx)
---> 88 outputs = self.optimizer_loop.run(split_batch, optimizers, batch_idx)
89 else:
90 outputs = self.manual_loop.run(split_batch, batch_idx)
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/loops/base.py:145, in Loop.run(self, *args, **kwargs)
143 try:
144 self.on_advance_start(*args, **kwargs)
--> 145 self.advance(*args, **kwargs)
146 self.on_advance_end()
147 self.restarting = False
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py:215, in OptimizerLoop.advance(self, batch, *args, **kwargs)
214 def advance(self, batch: Any, *args: Any, **kwargs: Any) -> None: # type: ignore[override]
--> 215 result = self._run_optimization(
216 batch,
217 self._batch_idx,
218 self._optimizers[self.optim_progress.optimizer_position],
219 self.optimizer_idx,
220 )
221 if result.loss is not None:
222 # automatic optimization assumes a loss needs to be returned for extras to be considered as the batch
223 # would be skipped otherwise
224 self._outputs[self.optimizer_idx] = result.asdict()
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py:266, in OptimizerLoop._run_optimization(self, split_batch, batch_idx, optimizer, opt_idx)
259 closure()
261 # ------------------------------
262 # BACKWARD PASS
263 # ------------------------------
264 # gradient update with accumulated gradients
265 else:
--> 266 self._optimizer_step(optimizer, opt_idx, batch_idx, closure)
268 result = closure.consume_result()
270 if result.loss is not None:
271 # if no result, user decided to skip optimization
272 # otherwise update running loss + reset accumulated loss
273 # TODO: find proper way to handle updating running loss
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py:378, in OptimizerLoop._optimizer_step(self, optimizer, opt_idx, batch_idx, train_step_and_backward_closure)
375 self.optim_progress.optimizer.step.increment_ready()
377 # model hook
--> 378 lightning_module.optimizer_step(
379 self.trainer.current_epoch,
380 batch_idx,
381 optimizer,
382 opt_idx,
383 train_step_and_backward_closure,
384 on_tpu=(self.trainer._device_type == DeviceType.TPU and _TPU_AVAILABLE),
385 using_native_amp=(self.trainer.amp_backend is not None and self.trainer.amp_backend == AMPType.NATIVE),
386 using_lbfgs=is_lbfgs,
387 )
389 self.optim_progress.optimizer.step.increment_completed()
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/core/lightning.py:1652, in LightningModule.optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, optimizer_closure, on_tpu, using_native_amp, using_lbfgs)
1571 def optimizer_step(
1572 self,
1573 epoch: int,
(...)
1580 using_lbfgs: bool = False,
1581 ) -> None:
1582 r"""
1583 Override this method to adjust the default way the
1584 :class:`~pytorch_lightning.trainer.trainer.Trainer` calls each optimizer.
(...)
1650
1651 """
-> 1652 optimizer.step(closure=optimizer_closure)
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/core/optimizer.py:164, in LightningOptimizer.step(self, closure, **kwargs)
162 assert trainer is not None
163 with trainer.profiler.profile(profiler_action):
--> 164 trainer.accelerator.optimizer_step(self._optimizer, self._optimizer_idx, closure, **kwargs)
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/accelerators/accelerator.py:339, in Accelerator.optimizer_step(self, optimizer, opt_idx, closure, model, **kwargs)
329 """performs the actual optimizer step.
330
331 Args:
(...)
336 **kwargs: Any extra arguments to ``optimizer.step``
337 """
338 model = model or self.lightning_module
--> 339 self.precision_plugin.optimizer_step(model, optimizer, opt_idx, closure, **kwargs)
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py:163, in PrecisionPlugin.optimizer_step(self, model, optimizer, optimizer_idx, closure, **kwargs)
161 if isinstance(model, pl.LightningModule):
162 closure = partial(self._wrap_closure, model, optimizer, optimizer_idx, closure)
--> 163 optimizer.step(closure=closure, **kwargs)
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/torch/optim/lr_scheduler.py:65, in _LRScheduler.__init__.<locals>.with_counter.<locals>.wrapper(*args, **kwargs)
63 instance._step_count += 1
64 wrapped = func.__get__(instance, cls)
---> 65 return wrapped(*args, **kwargs)
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/torch/optim/optimizer.py:88, in Optimizer._hook_for_profile.<locals>.profile_hook_step.<locals>.wrapper(*args, **kwargs)
86 profile_name = "Optimizer.step#{}.step".format(obj.__class__.__name__)
87 with torch.autograd.profiler.record_function(profile_name):
---> 88 return func(*args, **kwargs)
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/nemo/core/optim/novograd.py:83, in Novograd.step(self, closure)
81 loss = None
82 if closure is not None:
---> 83 loss = closure()
85 for group in self.param_groups:
86 for p in group["params"]:
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py:148, in PrecisionPlugin._wrap_closure(self, model, optimizer, optimizer_idx, closure)
135 def _wrap_closure(
136 self,
137 model: "pl.LightningModule",
(...)
140 closure: Callable[[], Any],
141 ) -> Any:
142 """This double-closure allows makes sure the ``closure`` is executed before the
143 ``on_before_optimizer_step`` hook is called.
144
145 The closure (generally) runs ``backward`` so this allows inspecting gradients in this hook. This structure is
146 consistent with the ``PrecisionPlugin`` subclasses that cannot pass ``optimizer.step(closure)`` directly.
147 """
--> 148 closure_result = closure()
149 self._after_closure(model, optimizer, optimizer_idx)
150 return closure_result
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py:160, in Closure.__call__(self, *args, **kwargs)
159 def __call__(self, *args: Any, **kwargs: Any) -> Optional[Tensor]:
--> 160 self._result = self.closure(*args, **kwargs)
161 return self._result.loss
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py:142, in Closure.closure(self, *args, **kwargs)
140 def closure(self, *args: Any, **kwargs: Any) -> ClosureResult:
141 with self._profiler.profile("training_step_and_backward"):
--> 142 step_output = self._step_fn()
144 if step_output.closure_loss is None:
145 self.warning_cache.warn(
146 "`training_step` returned `None`. If this was on purpose, ignore this warning..."
147 )
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py:435, in OptimizerLoop._training_step(self, split_batch, batch_idx, opt_idx)
433 lightning_module._current_fx_name = "training_step"
434 with self.trainer.profiler.profile("training_step"):
--> 435 training_step_output = self.trainer.accelerator.training_step(step_kwargs)
436 self.trainer.training_type_plugin.post_training_step()
438 del step_kwargs
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/accelerators/accelerator.py:219, in Accelerator.training_step(self, step_kwargs)
214 """The actual training step.
215
216 See :meth:`~pytorch_lightning.core.lightning.LightningModule.training_step` for more details
217 """
218 with self.precision_plugin.train_step_context():
--> 219 return self.training_type_plugin.training_step(*step_kwargs.values())
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py:213, in TrainingTypePlugin.training_step(self, *args, **kwargs)
212 def training_step(self, *args, **kwargs):
--> 213 return self.model.training_step(*args, **kwargs)
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/nemo/utils/model_utils.py:355, in wrap_training_step(wrapped, instance, args, kwargs)
353 @wrapt.decorator
354 def wrap_training_step(wrapped, instance: 'pl.LightningModule', args, kwargs):
--> 355 output_dict = wrapped(*args, **kwargs)
357 if isinstance(output_dict, dict) and output_dict is not None and 'log' in output_dict:
358 log_dict = output_dict.pop('log')
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/nemo/collections/asr/models/rnnt_models.py:711, in EncDecRNNTModel.training_step(self, batch, batch_nb)
708 compute_wer = False
710 # Fused joint step
--> 711 loss_value, wer, _, _ = self.joint(
712 encoder_outputs=encoded,
713 decoder_outputs=decoder,
714 encoder_lengths=encoded_len,
715 transcripts=transcript,
716 transcript_lengths=transcript_len,
717 compute_wer=compute_wer,
718 )
720 tensorboard_logs = {'train_loss': loss_value, 'learning_rate': self._optimizer.param_groups[0]['lr']}
722 if compute_wer:
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/torch/nn/modules/module.py:1110, in Module._call_impl(self, *input, **kwargs)
1106 # If we don't have any hooks, we want to skip the rest of the logic in
1107 # this function, and just call forward.
1108 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1109 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110 return forward_call(*input, **kwargs)
1111 # Do not call functions when jit is used
1112 full_backward_hooks, non_full_backward_hooks = [], []
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/nemo/core/classes/common.py:822, in typecheck.__call__(self, wrapped, instance, args, kwargs)
819 instance._validate_input_types(input_types=input_types, ignore_collections=self.ignore_collections, **kwargs)
821 # Call the method - this can be forward, or any other callable method
--> 822 outputs = wrapped(*args, **kwargs)
824 instance._attach_and_validate_output_types(
825 output_types=output_types, ignore_collections=self.ignore_collections, out_objects=outputs
826 )
828 return outputs
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/nemo/collections/asr/modules/rnnt.py:885, in RNNTJoint.forward(self, encoder_outputs, decoder_outputs, encoder_lengths, transcripts, transcript_lengths, compute_wer)
882 self.loss.reduction = None
884 # compute and preserve loss
--> 885 loss_batch = self.loss(
886 log_probs=sub_joint,
887 targets=sub_transcripts,
888 input_lengths=sub_enc_lens,
889 target_lengths=sub_transcript_lens,
890 )
891 losses.append(loss_batch)
893 # reset loss reduction type
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/torch/nn/modules/module.py:1110, in Module._call_impl(self, *input, **kwargs)
1106 # If we don't have any hooks, we want to skip the rest of the logic in
1107 # this function, and just call forward.
1108 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1109 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110 return forward_call(*input, **kwargs)
1111 # Do not call functions when jit is used
1112 full_backward_hooks, non_full_backward_hooks = [], []
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/nemo/core/classes/common.py:822, in typecheck.__call__(self, wrapped, instance, args, kwargs)
819 instance._validate_input_types(input_types=input_types, ignore_collections=self.ignore_collections, **kwargs)
821 # Call the method - this can be forward, or any other callable method
--> 822 outputs = wrapped(*args, **kwargs)
824 instance._attach_and_validate_output_types(
825 output_types=output_types, ignore_collections=self.ignore_collections, out_objects=outputs
826 )
828 return outputs
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/nemo/collections/asr/losses/rnnt.py:288, in RNNTLoss.forward(self, log_probs, targets, input_lengths, target_lengths)
285 self._loss.reduction = self.reduction
287 # Compute RNNT loss
--> 288 loss = self._loss(acts=log_probs, labels=targets, act_lens=input_lengths, label_lens=target_lengths)
290 # Loss reduction can be dynamic, so reset it after call
291 if self.reduction != 'mean_batch':
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/torch/nn/modules/module.py:1110, in Module._call_impl(self, *input, **kwargs)
1106 # If we don't have any hooks, we want to skip the rest of the logic in
1107 # this function, and just call forward.
1108 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1109 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110 return forward_call(*input, **kwargs)
1111 # Do not call functions when jit is used
1112 full_backward_hooks, non_full_backward_hooks = [], []
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/rnnt_pytorch.py:166, in RNNTLossNumba.forward(self, acts, labels, act_lens, label_lens)
162 # NOTE manually done log_softmax for CPU version,
163 # log_softmax is computed within GPU version.
164 acts = torch.nn.functional.log_softmax(acts, -1)
--> 166 return self.loss(
167 acts, labels, act_lens, label_lens, self.blank, self.reduction, self.fastemit_lambda, self.clamp
168 )
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/rnnt_pytorch.py:62, in _RNNTNumba.forward(ctx, acts, labels, act_lens, label_lens, blank, reduction, fastemit_lambda, clamp)
59 minibatch_size = acts.size(0)
60 costs = torch.zeros(minibatch_size, device=acts.device, dtype=acts.dtype)
---> 62 loss_func(
63 acts,
64 labels=labels,
65 input_lengths=act_lens,
66 label_lengths=label_lens,
67 costs=costs,
68 grads=grads,
69 blank_label=blank,
70 fastemit_lambda=fastemit_lambda,
71 clamp=clamp,
72 num_threads=0,
73 )
75 if reduction in ['sum', 'mean']:
76 costs = costs.sum().unsqueeze_(-1)
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/rnnt.py:223, in rnnt_loss_gpu(acts, labels, input_lengths, label_lengths, costs, grads, blank_label, fastemit_lambda, clamp, num_threads)
219 else:
220 ### FLATTEN GRAD TENSOR ###
221 grads, grads_shape = rnnt_helper.flatten_tensor(grads)
--> 223 status = wrapper.cost_and_grad(
224 acts=acts.data,
225 grads=grads.data,
226 costs=costs.data,
227 pad_labels=labels.data,
228 label_lengths=label_lengths.data,
229 input_lengths=input_lengths.data,
230 )
232 if status != global_constants.RNNTStatus.RNNT_STATUS_SUCCESS:
233 raise RuntimeError("Could not calculate forward scores")
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt.py:249, in GPURNNT.cost_and_grad(self, acts, grads, costs, pad_labels, label_lengths, input_lengths)
239 if (
240 acts is None
241 or grads is None
(...)
245 or input_lengths is None
246 ):
247 return global_constants.RNNTStatus.RNNT_STATUS_INVALID_VALUE
--> 249 return self.compute_cost_and_score(acts, grads, costs, pad_labels, label_lengths, input_lengths)
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt.py:158, in GPURNNT.compute_cost_and_score(self, acts, grads, costs, labels, label_lengths, input_lengths)
155 used_offset, (denom, alphas, betas, llForward, llBackward) = self._prepare_workspace()
157 ######## START EXECUTION ########
--> 158 self.log_softmax(acts, denom)
160 # Compute alphas
161 gpu_rnnt_kernel.compute_alphas_kernel[self.minibatch_, self.maxU_, self.stream_, 0](
162 acts,
163 denom,
(...)
173 self.blank_,
174 )
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt.py:104, in GPURNNT.log_softmax(self, acts, denom)
91 """
92 Computes the log softmax denominator of the input activation tensor
93 and stores the result in denom.
(...)
101 This kernel inplace updates the `denom` tensor
102 """
103 # // trans_acts + pred_acts -> log_softmax denominator
--> 104 reduce.reduce_max(
105 acts,
106 denom,
107 rows=self.alphabet_size_,
108 cols=self.minibatch_ * self.maxT_ * self.maxU_,
109 minus=False,
110 stream=self.stream_,
111 )
113 reduce.reduce_exp(
114 acts,
115 denom,
(...)
119 stream=self.stream_,
120 )
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/reduce.py:353, in reduce_max(acts, denom, rows, cols, minus, stream)
332 def reduce_max(acts: torch.Tensor, denom, rows: int, cols: int, minus: bool, stream):
333 """
334 Helper method to call the Warp Reduction Kernel to perform `max` reduction.
335
(...)
351 stream: CUDA Stream.
352 """
--> 353 return ReduceHelper(
354 I_opid=I_Op.IDENTITY.value,
355 R_opid=R_Op.MAXIMUM.value,
356 acts=acts,
357 output=denom,
358 num_rows=rows,
359 num_cols=cols,
360 minus=minus,
361 stream=stream,
362 )
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/nemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/reduce.py:294, in ReduceHelper(I_opid, R_opid, acts, output, num_rows, num_cols, minus, stream)
292 grid_size = num_cols
293 # call kernel
--> 294 _reduce_rows[grid_size, CTA_REDUCE_SIZE, stream, 0](I_opid, R_opid, acts, output, num_rows)
296 return True
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/numba/cuda/compiler.py:868, in _KernelConfiguration.__call__(self, *args)
867 def __call__(self, *args):
--> 868 return self.dispatcher.call(args, self.griddim, self.blockdim,
869 self.stream, self.sharedmem)
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/numba/cuda/compiler.py:1001, in Dispatcher.call(self, args, griddim, blockdim, stream, sharedmem)
999 kernel = next(iter(self.overloads.values()))
1000 else:
-> 1001 kernel = _dispatcher.Dispatcher._cuda_call(self, *args)
1003 kernel.launch(args, griddim, blockdim, stream, sharedmem)
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/numba/cuda/compiler.py:1009, in Dispatcher._compile_for_args(self, *args, **kws)
1007 assert not kws
1008 argtypes = [self.typeof_pyval(a) for a in args]
-> 1009 return self.compile(tuple(argtypes))
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/numba/cuda/compiler.py:1115, in Dispatcher.compile(self, sig)
1112 self._insert(c_sig, kernel, cuda=True)
1113 self.overloads[argtypes] = kernel
-> 1115 kernel.bind()
1116 self.sigs.append(sig)
1117 return kernel
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/numba/cuda/compiler.py:626, in _Kernel.bind(self)
622 def bind(self):
623 """
624 Force binding to current CUDA context
625 """
--> 626 self._codelibrary.get_cufunc()
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/numba/cuda/codegen.py:201, in CUDACodeLibrary.get_cufunc(self)
198 if cufunc:
199 return cufunc
--> 201 cubin = self.get_cubin(cc=device.compute_capability)
202 module = ctx.create_module_image(cubin)
204 # Load
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/numba/cuda/codegen.py:174, in CUDACodeLibrary.get_cubin(self, cc)
172 ptxes = self._get_ptxes(cc=cc)
173 for ptx in ptxes:
--> 174 linker.add_ptx(ptx.encode())
175 for path in self._linking_files:
176 linker.add_file_guess_ext(path)
File ~/anaconda3/envs/nemo_stt/lib/python3.9/site-packages/numba/cuda/cudadrv/driver.py:2191, in Linker.add_ptx(self, ptx, name)
2188 driver.cuLinkAddData(self.handle, enums.CU_JIT_INPUT_PTX,
2189 ptxbuf, len(ptx), namebuf, 0, None, None)
2190 except CudaAPIError as e:
-> 2191 raise LinkerError("%s\n%s" % (e, self.error_log))
LinkerError: [222] Call to cuLinkAddData results in UNKNOWN_CUDA_ERROR
ptxas application ptx input, line 9; fatal : Unsupported .version 7.3; current version is '7.2'
```
GPU infos:
```
Fri Mar 18 23:12:27 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.80 Driver Version: 460.80 CUDA Version: 11.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 GeForce RTX 3090 Off | 00000000:3B:00.0 Off | N/A |
| 54% 25C P8 25W / 350W | 0MiB / 24268MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 1 GeForce RTX 3090 Off | 00000000:5E:00.0 Off | N/A |
| 58% 25C P8 23W / 350W | 0MiB / 24268MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 2 GeForce RTX 3090 Off | 00000000:AF:00.0 Off | N/A |
| 0% 29C P8 27W / 350W | 0MiB / 24268MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
```
nvcc --version:
'''
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Sun_Mar_21_19:15:46_PDT_2021
Cuda compilation tools, release 11.3, V11.3.58
Build cuda_11.3.r11.3/compiler.29745058_0
'''
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} | null | null | null | I noticed that when training a `TransformerLMModel` using a `TarredSentenceDataset` the number of optimisation steps is incorrect in the following case:
* Training for a maximum number of epochs (not maximum number of steps)
* Using more than 1 GPU worker
* Using `shard_strategy: scatter`
In the above case, the dataset length should be `self.metadata['num_batches'] / num_workers` below.
https://github.com/NVIDIA/NeMo/blob/fe30166a3122aefe651052e2bbdd950a842e239b/nemo/collections/nlp/data/language_modeling/sentence_dataset.py#L273
If the dataset length is not divided by the number of workers (as is now), PyTorch Lightning has a too large number of steps/epoch and no validation is performed at the end of one epoch. (PTL does not divide the total dataset length by the number of workers for `IterableDataset`s).
However, when the dataset length is calculated as `self.metadata['num_batches'] / num_workers` then there is a problem when calculating the total number of optimisation steps. When the number of optimisation steps is calculated, the dataset length is divided by the number of workers, resulting in a too low number of optimisation steps in this case.
https://github.com/NVIDIA/NeMo/blob/fe30166a3122aefe651052e2bbdd950a842e239b/nemo/core/optim/lr_scheduler.py#L857
To calculate the correct total number of optimisation steps, it should not be divided by the number of workers when we have a dataset of type `IterableDataset`, use `shard_strategy: scatter`, more than one GPU worker, and train for a maximum number of epochs.
I noticed this problem only with `TarredSentenceDataset` but I think it is also the same for other `Tarred*Dataset`s. | {
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} | # What does this PR do ?
It's ports ADLR implementation of RADTTS to NeMo.
It affects TTS collection. Specifically, TTS/Torch.
# Changelog
- Added modules, model, loss functions, and helper functions for RADTTS
# Usage
Their is two stage training which requires two config files that are found under /examples/tts/conf/
- for each stage of the training different learning rates are used
- first stage learning rate (le4, 1e4, 5e5, 1e6(with fp= 32)) , config file: rad-tts_dec
- second stage learning rate (1e3, 1e4, 1e5), config file: rad-tts_feature_pred
```python
# Add a code snippet demonstrating how to use this
Once the config files are filled out. Run examples/tts/radtts_train.py
# 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)
- [ x] 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.
- onnx exportability is still not added
- not tested with multi-speaker TTS training
## 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)
Adding P_voiced and Voiced_mask might create conflicts with the other TTS repos so let's discuss about it. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3852 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3852/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3852/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3852/events | https://github.com/NVIDIA/NeMo/pull/3852 | 1,173,264,940 | PR_kwDOC_bI7s40pDJ9 | 3,852 | RADTTS-NeMo Porting | {
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} | # What does this PR do ?
It's ports ADLR implementation of RADTTS to NeMo.
It affects TTS collection. Specifically, TTS/Torch.
# Changelog
- Added modules, model, loss functions, and helper functions for RADTTS
# Usage
Their is two stage training which requires two config files that are found under /examples/tts/conf/
- for each stage of the training different learning rates are used
- first stage learning rate (le4, 1e4, 5e5, 1e6(with fp= 32)) , config file: rad-tts_dec
- second stage learning rate (1e3, 1e4, 1e5), config file: rad-tts_feature_pred
```python
# Add a code snippet demonstrating how to use this
Once the config files are filled out. Run examples/tts/radtts_train.py
# 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)
- [ x] 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.
- onnx exportability is still not added
- not tested with multi-speaker TTS training
## 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)
Adding P_voiced and voiced_mask to data.py will conflict with the othre TTS repos. Let's discuss about them. | {
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} | # What does this PR do ?
It's ports ADLR implementation of RADTTS to NeMo.
It affects TTS collection. Specifically, TTS/Torch.
# Changelog
- Added modules, model, loss functions, and helper functions for RADTTS
# Usage
Their are two stages of training which requires two config files that are found under /examples/tts/conf/
- for each stage of the training different learning rates are used
- first stage learning rate (le4, 1e4, 5e5, 1e6(with fp= 32)) , config file: rad-tts_dec
- second stage learning rate (1e3, 1e4, 1e5), config file: rad-tts_feature_pred
```python
# Add a code snippet demonstrating how to use this
Once the config files are filled out. Run examples/tts/radtts_train.py
# 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)
- [ x] 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.
- onnx exportability is still not added
- not tested with multi-speaker TTS training
## 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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Thanks for the great job you are doing.
When trying to use NeuralModuleFactory, I have the following error:
ModuleNotFoundError: No module named 'nemo.core.NeuralModuleFactory'
I am stick and need your help on how to solve this problem.
Best regards | {
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https://github.com/NVIDIA/NeMo/blob/v1.0.2/tutorials/asr/01_ASR_with_NeMo.ipynb
I only replaced the data set to speech commands from "an4" as it seems the link for an4 is not working.
In the section "Using NeMo for Automatic Speech Recognition", the very first cell where I import nemo:
NeMo's "core" package
import nemo
NeMo's ASR collection - this collections contains complete ASR models and
building blocks (modules) for ASR
import nemo.collections.asr as nemo_asr
I get the following output:
[NeMo W 2022-03-17 16:03:28 optimizers:46] Apex was not found. Using the lamb optimizer will error out.
File "/usr/local/lib/python3.7/dist-packages/webdataset/cache.py", line 43
while data := stream.read(chunk_size):
^
SyntaxError: invalid syntax
Could anyone please point at a fix for this? | {
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} | # What does this PR do ?
Add megatron t5 xnli eval only script - it takes in finetuned checkpoint and output test results on test dataset.
**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**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# 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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} | Signed-off-by: smajumdar <[email protected]>
# What does this PR do ?
Corrects links for asr tutorials
**Collection**: [ASR]
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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} | # What does this PR do ?
Disables NVFUSER and gets global batch size from config in build dataset
**Collection**: NLP
# Changelog
- Comment out call that enables nvfuser in ngc container
- Get global batch size from config
# 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
- [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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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Changes the container for NeMo CI testing to support T5 PP CI tests. It uses the Pytorch 22.02 container with Apex built from a particular commit.
**Collection**: all
# Changelog
N/A
# Usage
N/A
# 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 ?
Improvements to documentation and code checks.
**Collection**: [Core,ASR]
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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} | # What does this PR do ?
Added quick notes to the ends of several of the tutorial notebooks (asr, nlp, and tts) on deploying with Riva.
**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 ?
This PR adds a regex tokenizer (currently required by MegaMolBART).
**Collection**: `nemo.collections.common.tokenizers`
# Changelog
- `nemo/collections/common/tokenizers/regex_tokenizer.py`
- `tests/collections/nlp/test_regex_tokenizer.py`
# Usage
In order to build a regexp tokenizer model use the following command.
The script will create:
* `.vocab` file - with learned vocabulary
* `.model` file - with provided regex
To build vocabulary from text files:
```
python -- scripts/nlp_language_modeling/build_regex_tokenizer.py \
--regex '\[[^\]]+]|Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p|\(|\)|\.|=|#|-|\+|\\\\|\/|:|~|@|\?|>|\*|\$|\%[0-9]{2}|[0-9]' \
--input_type text \
--output_file regex_tokenizer -- \
data_file1.txt data_file2.txt
```
To build vocabulary from CSV files ("smiles" column):
```
python -- scripts/nlp_language_modeling/build_regex_tokenizer.py \
--regex '\[[^\]]+]|Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p|\(|\)|\.|=|#|-|\+|\\\\|\/|:|~|@|\?|>|\*|\$|\%[0-9]{2}|[0-9]' \
--input_type csv \
--input_csv_col smiles \
--output_file regex_tokenizer -- \
data_file1.csv data_file2.csv
``` | {
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} | Signed-off-by: MaximumEntropy <[email protected]>
# What does this PR do ?
Fixes a bug with T5 decoding present with encoder mask computation.
NOTE: This does not affect fine-tuning that calls `.decode()` directly. It only affects `.complete()`.
**Collection**: NLP
# Changelog
- One line removal of encoder_mask = encoder_mask < 0.5
# Usage
N/A
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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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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} | Signed-off-by: ericharper <[email protected]>
# 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?
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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 ?
Fixes the default values for the tokenizer type and add_start_end_token in the config files of Conformer models.
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When instantiating a new model, in `nemo.utils.model_utils.parse_dataset_as_name`, if the name of the manifest passed is `manifest.json`, then eventually the manifest name will be empty and an index error will occur during this check:
```py
if '_' != name[-1]:
name = name + '_'
```
**Steps/Code to reproduce bug**
Run any ASR example/tutorial with a cfg value `model.(train/val)_ds.manifest_filepath = "/path/to/manifest.json"`
```py
from omegaconf import OmegaConf
import pytorch_lightning as pl
from nemo.collections.asr.models import EncDecCTCModelBPE
cfg = OmegaConf.load("/path/to/cfg.yaml")
trainer = pl.Trainer(**cfg.trainer)
asr_model = EncDecCTCModelBPE(cfg=cfg.model, trainer=trainer)
```
**Expected behavior**
I should be able to create a model with train/val_ds whose manifest file name is `manifest.json`
**Environment overview (please complete the following information)**
- Environment location: Docker
- Method of NeMo install: `pip install nemo_tookit[all]==1.7.0`
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
Docker image: `nvcr.io/nvidia/pytorch:22.02-py3`
I can of course change the name of my manifest file, but I don't think this is valid assumption that we will not be naming our files `manifest.json` and not sure why this parsing is necessary.
Simple resolution is to just make sure the `name` string never becomes empty and early return if it will.
If it does, maybe use the parent directory name as the dataset name?
If this is necessary, can make the change on my end, but at least provide a different error informing the user to change their manifest name. | {
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} | # What does this PR do?
Added the text_generation_util to support the MegatronLM way of text generation. It supports top_k, top_p sampling.
Added the Tabular Data tokenizer which recognizes the tabular data structure.
Added the unit test for column type encode and decode.
Added tabular data generation which considers the tabular data structure.
To run text generation. here is one example.
```python
python examples/nlp/language_modeling/megatron_gpt_eval.py \
model_file=/path/gpt_model.nemo
```
the inference parameters are set in file
```
megatron_gpt_inference.yaml
```
Update:
Included GPT inference server. To use it
```python
python examples/nlp/language_modeling/megatron_gpt_eval.py \
model_file=/path/gpt_model.nemo \
server=True \
port=5555
```
Here is the example code to run inference as a client
```python
import json
import requests
batch_size = 8
port_num = 5555
headers = {"Content-Type": "application/json"}
def request_data(data):
resp = requests.put('http://localhost:{}/generate'.format(port_num),
data=json.dumps(data),
headers=headers)
sentences = resp.json()['sentences']
return sentences
data = {
"sentences": [""] * batch_size,
"tokens_to_generate": 300,
"temperature": 1.0,
"add_BOS": True,
"top_k": 0,
"top_p": 0.9,
"greedy": False,
"all_probs": False,
"repetition_penalty": 1.2,
"min_tokens_to_generate": 2,
}
sentences = request_data(data)
for i in range(batch_size):
s = sentences[i]
print(s)
``` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3833 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3833/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3833/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3833/events | https://github.com/NVIDIA/NeMo/pull/3833 | 1,166,753,744 | PR_kwDOC_bI7s40UQ5r | 3,833 | fix: multilingual transcribe does not require lang id param | {
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} | # What does this PR do ?
Fixes model.transcribe() for multilingual models.
**Collection**: ASR
# Changelog
- multilingual models require language id for manifests during training. The transcribe method used to generate a transient
manifest that had 'nothing' as placeholder text. Replacing this placeholder text with ''. If this empty string is detected , the manifest language id requirement gets lifted in this PR.
# Usage
* No changes to interface
```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
- [ 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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https://api.github.com/repos/NVIDIA/NeMo/issues/3832 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3832/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3832/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3832/events | https://github.com/NVIDIA/NeMo/issues/3832 | 1,166,697,113 | I_kwDOC_bI7s5FimKZ | 3,832 | Cant load model from path with 1.6 | {
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"name": "bug",
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] | closed | false | null | [] | null | null | 2022-03-11T17:52:13 | 2022-04-05T15:53:08 | 2022-04-05T15:53:08 | NONE | {
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} | null | null | null | I have a 1.6 model trying to load with the following line:
`nemo_asr.models.EncDecCTCModelBPE.restore_from`
And I get this:
```
[NeMo E 2022-03-11 17:35:11 common:504] Traceback (most recent call last):
File "/root/.cache/pypoetry/virtualenvs/--IulmO6hw-py3.7/lib/python3.7/site-packages/nemo/core/classes/common.py", line 477, in from_config_dict
instance = imported_cls(cfg=config, trainer=trainer)
File "/root/.cache/pypoetry/virtualenvs/--IulmO6hw-py3.7/lib/python3.7/site-packages/nemo/collections/asr/models/ctc_bpe_models.py", line 194, in __init__
self._setup_tokenizer(cfg.tokenizer)
File "/root/.cache/pypoetry/virtualenvs/--IulmO6hw-py3.7/lib/python3.7/site-packages/nemo/collections/asr/parts/mixins/mixins.py", line 84, in _setup_tokenizer
self.tokenizer = tokenizers.SentencePieceTokenizer(model_path=model_path)
File "/root/.cache/pypoetry/virtualenvs/--IulmO6hw-py3.7/lib/python3.7/site-packages/nemo/utils/exceptions.py", line 31, in __init__
raise LightningNotInstalledException(self)
nemo.utils.exceptions.LightningNotInstalledException: You are trying to use <nemo.collections.common.tokenizers.SentencePieceTokenizer object at 0x7f582a0ad950> without installing all of pytorch_lightning, hydra, and omegaconf. Please install those packages before trying to access <nemo.collections.common.tokenizers.SentencePieceTokenizer object at 0x7f582a0ad950>.
```
All there of those packages are installed and importable | {
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} | null | null | null | Hi, I notice that the variational noise for RNNT model is implemented as gradient noise addition (line 878 of `nemo/collections/asr/models/rnnt_models.py`). The variational noise cited by the Conformer-Transducer paper is actually adding noise to weights. So, is this a mistake or because injecting gradient noise works better than adding weight noise?
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https://api.github.com/repos/NVIDIA/NeMo/issues/3830 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3830/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3830/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3830/events | https://github.com/NVIDIA/NeMo/pull/3830 | 1,166,535,380 | PR_kwDOC_bI7s40TjUY | 3,830 | Add new pretrained Spanish ASR models | {
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} | Signed-off-by: Elena Rastorgueva <[email protected]>
# What does this PR do ?
Add new pretrained Spanish ASR models (Citrinet 1024, Conformer CTC Large, Conformer Transducer Large, ContextNet 1024) to `list_available_models` and to documentation
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
* Add new pretrained Spanish ASR models (Citrinet 1024, Conformer CTC Large, Conformer Transducer Large, ContextNet 1024) to the method `list_available_models` in `EncDecCTCModelBPE` and `EncDecRNNTBPEModel`.
* Added the 4 models also to the list of Spanish ASR models in the documentation.
# Usage
# Before your PR is "Ready for review"
**Pre checks**:
- [y] 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?
- [y] Did you add or update any necessary documentation?
- [n] 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**:
- [y] New Feature
- [ ] Bugfix
- [y] 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 | Hi all,
Thank you for your effort for these work. I have a little question about the split point of the input source. As mentioned in T5 paper, they choose a random point to split it into prefix and target portions. However, you set a specific length to split text sequence into two parts as shown in the following code
https://github.com/NVIDIA/NeMo/blob/49183b7c59c3b4243671d5eb4d3f202a4e4739ce/nemo/collections/nlp/data/language_modeling/megatron/lm_adapted_t5_dataset.py#L49-L52
I am curious about this part. Could you please explain why this set is different from T5 paper? Thank you in advance.
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} | # What does this PR do ?
This PR adds functions and a tutorial about how to perform offline VAD before ASR.
Pipeline: audio -> [VAD] -> speech segments -> [ASR] -> segmented ASR output -> stitched ASR output
**Collection**: ASR
# Changelog
- Transcribe partial audio (for example speech segments) with manifest.
- Stitch segmented ASR output.
- Construct the manifest that aligns stitched ASR outputs and ground truth text.
- And a walkthrough tutorial for the whole process.
# 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?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | Thank you for providing a very good framework and solution for speaker recognition.
To understand more about it, I read through source code and paper. I want to clarify one of the calculation in `get_statistics_with_mask`. To calculate statistics, the variable name and description is `mean` while the implementation is `sum` when calculating input and mask.
May I confirm whether it is `mean` or `sum` operation?
https://github.com/NVIDIA/NeMo/blob/v1.7.0/nemo/collections/asr/parts/submodules/tdnn_attention.py#L87 | {
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} | # What does this PR do ?
Use `data_parallel_size` appropriately.
**Collection**: [Note which collection this PR will affect]
# Changelog
-
# 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
- [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.
ccing @ericharper @MaximumEntropy
# Additional Information
* Related to #3665
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} | null | null | null | Hi, thanks for the great toolkit!
I'm training the conformer-transducer-medium on librispeech-960hr from scratch, following the config in `examples/asr/conf/conformer/conformer_transducer_bpe.yaml`
I use a sentencepiece unigram tokenizer with a vocab size of 1024 generated using the script in NeMo. The batch size is set at 32 on each GPU for a global batch size of 256 (8 A100 GPUs are used). I use automatic mixed precision with amp_level O1. I tried a few different settings for the learning rate and weight decay to adjust for the batch size of 256, which is smaller than the default 2k batch size, and here are the results:
1. Learning rate = 2, weight decay = 1e-6. The training went well and got 2.8/7.6 WER on the dev-clean/dev-other sets at epoch 144. But suddenly got NaN error at epoch 145. I tried resuming the training from checkpoint but got NaN error again at the end of epoch 145.
2. Learning rate = 5, weight decay = 1e-3 (the default setting). The training ran for 25 epoch and got NaN error at epoch 26.
3. Learning rate = 2, weight decay = 1e-4. The training ran for 58 epochs and got NaN error at epoch 59.
Given that the NaN error occurred so late in the training, I doubt that the error was caused by some training example. So, any suggestions to find the root causes would be really appreciated!
Thank you! | {
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} | # What does this PR do ?
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**Collection**: [Note which collection this PR will affect]
# Changelog
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# 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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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Check what tests failed in FeatureFilterbanks.
**Collection**: [ASR]
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
GPT sampler has a typo.
**Collection**: NLP
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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https://api.github.com/repos/NVIDIA/NeMo/issues/3821 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3821/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3821/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3821/events | https://github.com/NVIDIA/NeMo/pull/3821 | 1,164,469,293 | PR_kwDOC_bI7s40MwVE | 3,821 | GPT multinode eval | {
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} | # What does this PR do ?
This PR adds multi-node eval for GPT. Also, it adds the ability to do GPT eval from PTL checkpoint. This is useful when models are very large.
**Collection**: NLP
# Usage
```python
python /code/examples/nlp/language_modeling/megatron_gpt_eval.py \
--devices ${SLURM_NTASKS_PER_NODE} \
--num_nodes ${SLURM_JOB_NUM_NODES} \
--checkpoint_dir "/checkpoint_dir" \
--checkpoint_name ${CHECKPOINT_NAME} \
--hparams_file /hparams_dir/${HPARAMS_FILE} \
--prompt "In critical moments, people sometimes see exactly" \
--tensor_model_parallel_size ${TENSOR_MODEL_PARALLEL_SIZE} \
--pipeline_model_parallel_size ${PIPELINE_MODEL_PARALLEL_SIZE} \
--tokens_to_generate 32 \
--stop_after_sentence True \
--precision ${PRECISION}
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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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} | null | null | null | Hi all.
I've trained FastPitch using both LJSpeech and my dataset, as it is suggested in the FastPitch_finetuning.ipynb colab. I've balanced the datasets by time instead of by number of samples. It turns out that the voice generated is from the woman of LJSpeech, not from my dataset.
While trying to execute the inference, with the function `str_to_audio()`:
```
def str_to_audio(inp, pace=1.0, durs=None, pitch=None):
with torch.no_grad():
tokens = fastpitch.parse(inp)
spec, _, durs_pred, _, pitch_pred, *_ = fastpitch(text=tokens, durs=durs, pitch=pitch, speaker=None, pace=pace)
audio = hifigan.convert_spectrogram_to_audio(spec=spec).to('cpu').numpy()
return spec, audio, durs_pred, pitch_pred
```
I want to change the parameter speaker to 0 or 1, but I get the following error:
```
[NeMo E 2022-03-09 19:12:15 vocabs:324] Torch distributed needs to be initialized before you initialized <nemo.collections.asr.data.vocabs.Phonemes object at 0x7f876a7febd0>. This class is prone to data access race conditions. Now downloading corpora from global rank 0. If other ranks pass this before rank 0, errors might result.
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
[<ipython-input-69-2fd102bd26e0>](https://localhost:8080/#) in <module>()
5 phraseNumber = i+1
6 with torch.no_grad():
----> 7 spec_norm, audio_norm, durs_norm_pred, pitch_norm_pred = str_to_audio(p)
8
9 # Let's try to make the speech more solemn
9 frames
[/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py](https://localhost:8080/#) in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
2042 # remove once script supports set_grad_enabled
2043 _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
-> 2044 return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
2045
2046
TypeError: embedding(): argument 'indices' (position 2) must be Tensor, not int
```
Thank you in advance. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3818 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3818/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3818/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3818/events | https://github.com/NVIDIA/NeMo/pull/3818 | 1,164,241,042 | PR_kwDOC_bI7s40MBZ6 | 3,818 | patch librosa deprecation and fix | {
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} | Signed-off-by: fayejf <[email protected]>
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
Fix the deprecation of librosa get_duration in vad_utils.py and fix the error happened in http://10.110.40.131:8080/blue/organizations/jenkins/NeMo/detail/PR-3771/8/pipeline
**Collection**: [Note which collection this PR will affect]
ASR
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# 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)
http://10.110.40.131:8080/blue/organizations/jenkins/NeMo/detail/PR-3771/8/pipeline | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/3815 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/3815/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/3815/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/3815/events | https://github.com/NVIDIA/NeMo/pull/3815 | 1,163,818,296 | PR_kwDOC_bI7s40Knx7 | 3,815 | bert_module: fix inputs of export model | {
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} | [] | closed | false | null | [] | null | null | 2022-03-09T11:27:57 | 2022-03-11T18:06:45 | 2022-03-11T18:06:45 | CONTRIBUTOR | {
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} | Exported ONNX model had to be passed with "attention_mask" and
"token_type_ids" inputs swapped to get correct output.
Changing the input order fixes this issue.
Also return a dict instead of list for correctly passing inputs.
# What does this PR do ?
Fix swapped model inputs for exported ONNX/TRT model.
**Collection**: NLP
# Changelog
- Fix order of inputs returned via input_types()
# 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? - Updated related tests.
- [x] Did you add or update any necessary documentation? - Not required
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - No
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] Bugfix
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
@MaximumEntropy, @ericharper, @ekmb, @yzhang123
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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