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* Fix T5 mask when using FA
* Add additional configs
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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# Changelog
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# Usage
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```python
# Add a code snippet demonstrating how to use this
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# Before your PR is "Ready for review"
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**PR Type**:
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- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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adds a warning for multiple peft scripts.
**Collection**: [NLP]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | null | null | null | **Describe the bug**
A clear and concise description of what the bug is.
Hello, we are running a Dutch trained version of the conformer RNNT large model.
However, in a small amount of cases we receive the following list index out of range error.

I saw that in [https://github.com/NVIDIA/NeMo/issues/4981 ](url) the issue seemed solved for the word timings, but maybe this issue still exists for the confidence scores?
We use the following to enable the confidence scores:
`
decoding_config = copy.deepcopy(conformer.cfg.decoding)
`
`
confidence_cfg = ConfidenceConfig(preserve_word_confidence=True, preserve_token_confidence=False)
`
`
with open_dict(decoding_config):
`
`
decoding_config.preserve_alignments = True,
`
`
decoding_config.confidence_cfg = confidence_cfg
`
`
conformer.change_decoding_strategy(decoding_config)
`
**Expected behavior**
Transcription succeeding without errors
**Environment overview (please complete the following information)**
Docker nvcr.io/nvidia/pytorch:23.04-py3
NeMo installed from main with git clone and ./reinstall.sh ( cloned today )
**Additional context**
Ran on a 4090 GPU
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} | In the `_create_buckets` method of the `DistributedBucketSampler` class, a ZeroDivisionError was being raised when `len_bucket` was zero. This fix adds a conditional check to skip the problematic division if `len_bucket` is zero, thus avoiding the error and ensuring that the rest of the code functions as expected.
# What does this PR do ?
Fixes a ZeroDivisionError in the DistributedBucketSampler class to ensure proper functionality.
**Collection**: TTS Collection
# Changelog
- Added a conditional check in the `_create_buckets` method to prevent division by zero when `len_bucket` is zero.
# Usage
```python
from nemo.collections.tts.data.dataset import DistributedBucketSampler
# Initialize the sampler with your dataset, batch_size, and boundaries
sampler = DistributedBucketSampler(dataset, batch_size, boundaries)
# Use the sampler in your training loop
for batch in sampler:
# Process the batch
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [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.
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} | Currently, `AudioCodecModel` training fails on `main` due to changes in PTL 2.0.
# What does this PR do ?
This PR is fixing two issues when using PTL 2.0.
**Collection**: TTS
# Changelog
- Support for `training_epoch_end` has been removed in v2.0.0, and we use `on_train_epoch_end`
- Reference: https://github.com/Lightning-AI/lightning/pull/16520
- Enable the detection of unused parameters in DDP, either by setting the string value `strategy='ddp_find_unused_parameters_true'`
- Reference: https://github.com/Lightning-AI/lightning/pull/16520
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to #6433
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} | # What does this PR do ?
Write an extra line at the end of the ASS files which will ensure the subtitles of the final segment are visible until the end of the video. Without this, it means that if we create an MP4 file from an audio file by writing the subtitles on a plain background, at the end of the video we are always left with a blank screen, which is not a nice UX as the text feels to disappear very suddenly.
**Collection**: `tools/nemo_forced_aligner.py`
# Changelog
- extra code to get the duration of the audio file and write the extra lines at the end of the ASS files.
# Usage
No change in usage.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ N/A ] Did you write any new necessary tests?
- [ N/A ] Did you add or update any necessary documentation?
- [ N/A ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Update TTS configs so they run with PTL 2.0
**Collection**: [TTS]
# Changelog
- Replace _ddp_ with _ddp_find_unused_parameters_true_ for all vocoder recipes, otherwise it fails because there are unused parameters during the alternating generator and discriminator training steps.
- Update training_epoch_end() for AudioCodec to be compatible with new PTL interface.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | null | null | null | The code of `end_of_generation_condition()` https://github.com/NVIDIA/NeMo/blob/13791d27ec8b322da11e8324ff07124aa4e6c55f/nemo/collections/nlp/modules/common/text_generation_strategy.py#L138 seems weird to me => opening an issue to clarify whether it is working as intended (cc @gshennvm @yidong72)
I have two concerns:
1. I find it weird to see `<extra_id_1>` hardcoded in this function -- I thought that the goal of providing `end_strings` as argument would be to avoid having to hardcode special strings
2. I am confused by something that is best explained through an example. Let's say I want to use `end_strings = ["abc"]`, which is tokenized into [1, 2, 3] (tokens for "a", "b", "c"), while `<extra_id_1>` is tokenized into [0]. It seems to me that this code will lead to `ids_1 = tokenizer.text_to_ids(f'<extra_id_1>abc') = [0, 1, 2, 3]`, `ids_2 = [0]`, and `token_id = ids_1[len(ids_2) :][0] = 1`. As a result, as soon as `p` (the last token generated) is equal to 1 (i.e., as soon as an `"a"` is generated), we consider that the generation has ended. Am I missing something? | {
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} | # What does this PR do ?
Rename spk_emb api to speaker_encoder
**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)
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do?
Fixed samples processing:
- removed multiple conversions from text_to_ids and ids_to_tokens which causes the difference in the token amounts between these operations
- changed the prompt template processing: tokens inside the prompt template (eg "<extra_token_1") are converted to token_ids and after merging with "input" and "output" token_ids lists
- due to some tokenizers have no specified space tokens (like " " and "\n") the prompt_template is mandatory (or it will be set by default as "{input} <extra_id_1> {output}"
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [+] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
Refer to the [comments](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py#L677), Layernorm parameters need to be all-reduced across model parallel nodes when sequence parallelism is used.
NeMo find layernorm parameters through '`sequence_parallel` attribute. Code is [here](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py#L665).
But layernorm set the attribute with `sequence_parallel_enabled` in apex. Code is [here](https://github.com/NVIDIA/apex/blob/master/apex/transformer/layers/layer_norm.py#L30).
**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
- [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 ?
NeMo uses `apex.normalization.fused_layer_norm.MixedFusedLayerNorm` when the `persistent_layernorm` is false.
But the `apex.normalization.fused_layer_norm.MixedFusedLayerNorm` doesn't set sequence_parallel.
And the `apex.transformer.layers.layer_norm.MixedFusedLayerNorm` will set sequence_parallel.
This PR use the `apex.transformer.layers.layer_norm.MixedFusedLayerNorm` instead of `apex.normalization.fused_layer_norm.MixedFusedLayerNorm`.
**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
- [x] Bugfix
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I tried to run my MSDD model (using TITANET-large as speaker embedding model) in serving mode, meaning loading the model once and inference it in several processes, and in order to do so I need to use torch share_memory method.
when I use share_memory method I get the following error when I start the process:
AttributeError: Can't pickle local object 'FilterbankFeatures.__init__.<locals>.<lambda>'
Is there any guidelines how to share memory for all MSDD modules or any utils script to do so?
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} | # What does this PR do ?
Not sure why missing lib import did not raise any errors.
* added missing import for `Trainer` and `NemoConfig`;
* unify `'Trainer'` into `Trainer` as a variable type hint.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | null | null | null | ## ASR Tutorial - Online_Offline_Speech_Commands_Demo.ipynb
I tried to execute the notebook in google colab (`https://github.com/NVIDIA/NeMo/blob/stable/tutorials/asr/Online_Offline_Speech_Commands_Demo.ipynb`)
While running the code block:
```python
wave_file = demo_wave
CHANNELS = 1
audio, sample_rate = librosa.load(wave_file, sr=SAMPLE_RATE)
dur = librosa.get_duration(audio)
print(dur)
```
The below error is produced:
```bash
TypeError Traceback (most recent call last)
[<ipython-input-20-1dea7463f5dd>](https://al43ym753q8-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20230804-060128-RC00_553764177#) in <cell line: 5>()
3 CHANNELS = 1
4 audio, sample_rate = librosa.load(wave_file, sr=SAMPLE_RATE)
----> 5 dur = librosa.get_duration(audio)
6 print(dur)
TypeError: get_duration() takes 0 positional arguments but 1 was given
```
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} | # What does this PR do ?
The PR fixes the progress bar stdout in megatron models to remove the `s/it` or `it/s` and instead display the actual cuda synchronized train step timing.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
Fix the following issues with the previous lightning 2.0 upgrade PR:
1) Decrease `val_check_interval` to 1 while resuming from a ckpt in some CI tests where max_steps is same as that of training and lightning 2.0 errors: [fit_loop.py#L259](https://github.com/Lightning-AI/lightning/blob/2.0.6/src/lightning/pytorch/loops/fit_loop.py#L259). Lightning 2.0 calls `setup_data` right at the beginning of fit_loop.run unlike 1.9 that did it only when `self.skip` for fit was False, hence the error din't show up with 1.9
2) Remove the usage of `trainer._checkpoint_connector` as it's redundant in lightning 2.0 and use `trainer.ckpt_path` directly. Also having `trainer._checkpoint_connector = _CheckpointConnector(trainer)` overrode ckpt_path with None leading to starting training from scratch during resuming from a ckpt.
3) Remove `optimizer_idx` arg in `optimizer_step` of `MegatronHalfPrecisionPlugin` class as the parent function in lightning does not have it anymore.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
update readme regarding container version and python version
**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 ?
When TensorRT optimizes Nemo GPT models it uses a different mask shape in the Context phase (triangular mask, i.e. causal) and in the Generation phase (1D mask, i.e. padding).
To support this TensorRT needs to control GPT's encoder mask type (which defaults to causal) via the model configuration.
**Collection**: NLP
# Changelog
- `MegatronGPTModel::model_provider_func` reads an optional `encoder_attn_mask_type` argument from the config file and passes it to the GPTModel constructor. The default value is a causal mask.
- Add an optional `encoder_attn_mask_type` argument to GPTModel constructor. The argument defaults to `AttnMaskType.causal`.
- Pass this argument to `get_language_model` (previously hard-coded: `encoder_attn_mask_type=AttnMaskType.causal`)
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. | {
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} | null | null | null | I am looking at the citrinet paper (https://arxiv.org/pdf/2104.01721.pdf) and I am trying to understand the architecture. Simultaneously I downloaded citrinet model (stt_en_citrinet_1024_gamma_0_24) and printed the model. Part of it looks like follows
---------------------
(1): JasperBlock(
(mconv): ModuleList(
(0): MaskedConv1d(
(conv): Conv1d(1024, 1024, kernel_size=(3,), stride=(1,), padding=(1,), groups=1024, bias=False)
)
(1): MaskedConv1d(
(conv): Conv1d(1024, 1024, kernel_size=(1,), stride=(1,), bias=False)
)
(2): BatchNorm1d(1024, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): Dropout(p=0.1, inplace=False)
(5): MaskedConv1d(
(conv): Conv1d(1024, 1024, kernel_size=(3,), stride=(1,), padding=(1,), groups=1024, bias=False)
)
(6): MaskedConv1d(
(conv): Conv1d(1024, 1024, kernel_size=(1,), stride=(1,), bias=False)
)
........................
---------------------
There are 2 conv1d blocks before batchnorm where as there is only 1 conv1d block as per the paper.
Is this observation correct ?
I saw few lines of the code , it looks like there is residual module for every sub block in the megablock . Am I correct ? But as per paper, there is only residual connecting from beginning to end of the the megablock.
Kindly clarify | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
Current `hydra-core` version (`hydra-core>=1.2.0,<1.3` from `requirements/requirements_lightning.txt`) doesn't work with Python 3.11: see #7166
**Describe the solution you'd like**
Now, after [Upgrade to pytorch lightning 2.0](https://github.com/NVIDIA/NeMo/commit/67de251e980ef8d8aec3b8339e70e96f6c952122) commit, would it be safe to upgrade `hydra-core` past `1.3.0`? `hydra-core==1.3.2` at least imports fine without `1.2.0` `dataclasses` related issues.
**Describe alternatives you've considered**
The only alternative I can think of is creating a fork of `1.2.0` version and patching it, but I think it's counter-productive since it has to be maintained separately.
Thanks :smiley:
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} | # What does this PR do ?
GPT training with long-context input (e.g., sequence length of 16, 32K, 64K) can easily overflow GPU memory with huge activations. Context parallelism splits long-context input along the dimension of sequence length, parallelizes partitioned sequence segments among multiple GPUs. In this way, each GPU only needs to store activations of a part of sequence length, so that we can avoid memory overflow.
# Usage
Add a new config in yaml file, e.g., "context_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?
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# Changelog
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After installing latest stable `1.19.1` from pipI, or the latest current commit with `[asr]` extras, I'm getting this error from `hydra-core==1.2.0` when trying to import `nemo.collections.asr`:
```
Traceback (most recent call last):
File "/home/alex/T7/src/speaker_verification/nemo_speaker/test.py", line 5, in <module>
import nemo.collections.asr as nemo_asr
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/nemo/collections/asr/__init__.py", line 15, in <module>
from nemo.collections.asr import data, losses, models, modules
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/nemo/collections/asr/losses/__init__.py", line 15, in <module>
from nemo.collections.asr.losses.angularloss import AngularSoftmaxLoss
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/nemo/collections/asr/losses/angularloss.py", line 18, in <module>
from nemo.core.classes import Loss, Typing, typecheck
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/nemo/core/__init__.py", line 16, in <module>
from nemo.core.classes import *
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/nemo/core/classes/__init__.py", line 16, in <module>
import hydra
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/hydra/__init__.py", line 5, in <module>
from hydra import utils
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/hydra/utils.py", line 8, in <module>
import hydra._internal.instantiate._instantiate2
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 12, in <module>
from hydra._internal.utils import _locate
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/hydra/_internal/utils.py", line 18, in <module>
from hydra.core.utils import get_valid_filename, validate_config_path
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/hydra/core/utils.py", line 20, in <module>
from hydra.core.hydra_config import HydraConfig
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/hydra/core/hydra_config.py", line 6, in <module>
from hydra.conf import HydraConf
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/hydra/conf/__init__.py", line 46, in <module>
class JobConf:
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/hydra/conf/__init__.py", line 75, in JobConf
@dataclass
^^^^^^^^^
File "/usr/lib/python3.11/dataclasses.py", line 1230, in dataclass
return wrap(cls)
^^^^^^^^^
File "/usr/lib/python3.11/dataclasses.py", line 1220, in wrap
return _process_class(cls, init, repr, eq, order, unsafe_hash,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/dataclasses.py", line 958, in _process_class
cls_fields.append(_get_field(cls, name, type, kw_only))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/dataclasses.py", line 815, in _get_field
raise ValueError(f'mutable default {type(f.default)} for field '
ValueError: mutable default <class 'hydra.conf.JobConf.JobConfig.OverrideDirname'> for field override_dirname is not allowed: use default_factory
```
If I then manually upgrade `hydra-core` to the current latest (`1.3.2`), I get similar errors from `nemo-toolkit`:
```
Traceback (most recent call last):
File "/home/alex/T7/src/speaker_verification/nemo_speaker/test.py", line 5, in <module>
import nemo.collections.asr as nemo_asr
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/nemo/collections/asr/__init__.py", line 15, in <module>
from nemo.collections.asr import data, losses, models, modules
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/nemo/collections/asr/losses/__init__.py", line 16, in <module>
from nemo.collections.asr.losses.audio_losses import SDRLoss
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/nemo/collections/asr/losses/audio_losses.py", line 21, in <module>
from nemo.collections.asr.parts.preprocessing.features import make_seq_mask_like
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/nemo/collections/asr/parts/preprocessing/__init__.py", line 16, in <module>
from nemo.collections.asr.parts.preprocessing.features import FeaturizerFactory, FilterbankFeatures, WaveformFeaturizer
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/nemo/collections/asr/parts/preprocessing/features.py", line 44, in <module>
from nemo.collections.asr.parts.preprocessing.perturb import AudioAugmentor
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/nemo/collections/asr/parts/preprocessing/perturb.py", line 50, in <module>
from nemo.collections.common.parts.preprocessing import collections, parsers
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/nemo/collections/common/__init__.py", line 16, in <module>
from nemo.collections.common import data, losses, parts, tokenizers
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/nemo/collections/common/parts/__init__.py", line 15, in <module>
from nemo.collections.common.parts.adapter_modules import LinearAdapter, LinearAdapterConfig
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/nemo/collections/common/parts/adapter_modules.py", line 147, in <module>
@dataclass
^^^^^^^^^
File "/usr/lib/python3.11/dataclasses.py", line 1230, in dataclass
return wrap(cls)
^^^^^^^^^
File "/usr/lib/python3.11/dataclasses.py", line 1220, in wrap
return _process_class(cls, init, repr, eq, order, unsafe_hash,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/dataclasses.py", line 958, in _process_class
cls_fields.append(_get_field(cls, name, type, kw_only))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/dataclasses.py", line 815, in _get_field
raise ValueError(f'mutable default {type(f.default)} for field '
ValueError: mutable default <class 'nemo.core.classes.mixins.adapter_mixin_strategies.ResidualAddAdapterStrategyConfig'> for field adapter_strategy is not allowed: use default_factory
```
It's easy to fix with a patch like this:
```
--- nemo/collections/common/parts/adapter_modules.py.orig 2023-08-04 13:55:53.464534800 +0200
+++ nemo/collections/common/parts/adapter_modules.py 2023-08-04 14:05:45.579537700 +0200
@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-from dataclasses import dataclass, is_dataclass
+from dataclasses import dataclass, is_dataclass, field
from typing import Any, Optional
from hydra.utils import instantiate
@@ -151,5 +151,5 @@ class LinearAdapterConfig:
activation: str = 'swish'
norm_position: str = 'pre'
dropout: float = 0.0
- adapter_strategy: Optional[Any] = adapter_mixin_strategies.ResidualAddAdapterStrategyConfig()
+ adapter_strategy: Optional[Any] = field(default_factory=adapter_mixin_strategies.ResidualAddAdapterStrategyConfig)
_target_: str = "{0}.{1}".format(LinearAdapter.__module__, LinearAdapter.__name__)
```
However then another error of the kind comes up:
```
Traceback (most recent call last):
File "/home/alex/T7/src/speaker_verification/nemo_speaker/test.py", line 5, in <module>
import nemo.collections.asr as nemo_asr
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/nemo/collections/asr/__init__.py", line 15, in <module>
from nemo.collections.asr import data, losses, models, modules
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/nemo/collections/asr/models/__init__.py", line 18, in <module>
from nemo.collections.asr.models.clustering_diarizer import ClusteringDiarizer
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/nemo/collections/asr/models/clustering_diarizer.py", line 29, in <module>
from nemo.collections.asr.metrics.der import score_labels
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/nemo/collections/asr/metrics/der.py", line 24, in <module>
from nemo.collections.asr.metrics.wer import word_error_rate
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/nemo/collections/asr/metrics/wer.py", line 27, in <module>
from nemo.collections.asr.parts.submodules import ctc_beam_decoding, ctc_greedy_decoding
File "/home/alex/T7/src/speaker_verification/nemo_speaker/venv11/lib/python3.11/site-packages/nemo/collections/asr/parts/submodules/ctc_beam_decoding.py", line 593, in <module>
@dataclass
^^^^^^^^^
File "/usr/lib/python3.11/dataclasses.py", line 1230, in dataclass
return wrap(cls)
^^^^^^^^^
File "/usr/lib/python3.11/dataclasses.py", line 1220, in wrap
return _process_class(cls, init, repr, eq, order, unsafe_hash,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/dataclasses.py", line 958, in _process_class
cls_fields.append(_get_field(cls, name, type, kw_only))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/dataclasses.py", line 815, in _get_field
raise ValueError(f'mutable default {type(f.default)} for field '
ValueError: mutable default <class 'nemo.collections.asr.parts.submodules.ctc_beam_decoding.FlashlightConfig'> for field flashlight_cfg is not allowed: use default_factory
```
This can also be fixed accordingly, but all in all, it appears these issues are pretty common in the code base.
Looks like NeMo isn't Python 3.11 ready, at least the `asr` collection.
**Steps/Code to reproduce bug**
With Python 3.11
1. Install `nemo-toolkit[asr]` either 1.19.1, or current HEAD from git (b498d438fc4c35ebf364a9a1c5cd3e29a2c0fe50)
2. Run `import nemo.collections.asr`
**Expected behavior**
Import `nemo.collections.asr` without errors
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: `pip install nemo-toolkit==1.19.1` or from source b498d438fc4c35ebf364a9a1c5cd3e29a2c0fe50
**Environment details**
- Ubuntu 22.04
- PyTorch version 2.0.1
- Python version 3.11.4
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python /workspaces/TTS/nemo/examples/tts/hifigan_finetune.py --config-path /workspaces/TTS/nemo/examples/tts/conf/hifigan --config-name hifigan.yaml model.max_steps=200000 model.optim.lr=0.00001 ~model.optim.sched train_dataset=/workspaces/TTS/data/train_manifest_mel_test.json validation_datasets=/workspaces/TTS/data/val_manifest_mel_test.json exp_manager.exp_dir=experimentsHifigan +init_from_pretrained_model=tts_en_hifigan +trainer.val_check_interval=100 trainer.check_val_every_n_epoch=null model/train_ds=train_ds_finetune model/validation_ds=val_ds_finetune exp_manager.resume_if_exists=false
And I get:
[NeMo W 2023-08-04 10:01:13 optimizers:54] Apex was not found. Using the lamb or fused_adam optimizer will error out.
[NeMo W 2023-08-04 10:01:13 experimental:27] Module <class 'nemo.collections.asr.modules.audio_modules.SpectrogramToMultichannelFeatures'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-08-04 10:01:13 experimental:27] Module <class 'nemo.collections.tts.models.fastpitch_ssl.FastPitchModel_SSL'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-08-04 10:01:13 experimental:27] Module <class 'nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.IPATokenizer'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-08-04 10:01:13 experimental:27] Module <class 'nemo.collections.tts.models.radtts.RadTTSModel'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-08-04 10:01:14 experimental:27] Module <class 'nemo.collections.tts.models.ssl_tts.SSLDisentangler'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-08-04 10:01:14 experimental:27] Module <class 'nemo.collections.tts.models.vits.VitsModel'> is experimental, not ready for production and is not fully supported. Use at your own risk.
[NeMo W 2023-08-04 10:01:14 nemo_logging:349] /opt/conda/envs/nemo/lib/python3.8/site-packages/hydra/_internal/defaults_list.py:251: UserWarning: In 'hifigan.yaml': Defaults list is missing `_self_`. See https://hydra.cc/docs/upgrades/1.0_to_1.1/default_composition_order for more information
warnings.warn(msg, UserWarning)
[NeMo W 2023-08-04 10:01:14 nemo_logging:349] /opt/conda/envs/nemo/lib/python3.8/site-packages/hydra/_internal/hydra.py:119: UserWarning: Future Hydra versions will no longer change working directory at job runtime by default.
See https://hydra.cc/docs/next/upgrades/1.1_to_1.2/changes_to_job_working_dir/ for more information.
ret = run_job(
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
[NeMo I 2023-08-04 10:01:14 exp_manager:374] Experiments will be logged at experimentsHifigan/HifiGan/2023-08-04_10-01-14
[NeMo I 2023-08-04 10:01:14 exp_manager:797] TensorboardLogger has been set up
[NeMo W 2023-08-04 10:01:14 exp_manager:893] The checkpoint callback was told to monitor a validation value and trainer's max_steps was set to 200000. Please ensure that max_steps will run for at least 1 epochs to ensure that checkpointing will not error out.
[NeMo I 2023-08-04 10:01:14 dataset:1068] Loading dataset from /root/../workspaces/TTS/data/train_manifest_mel_test.json.
20869it [00:00, 213363.10it/s]
[NeMo I 2023-08-04 10:01:14 dataset:1092] Loaded dataset with 20869 files.
[NeMo I 2023-08-04 10:01:14 dataset:1094] Dataset contains 29.45 hours.
[NeMo I 2023-08-04 10:01:14 dataset:376] Pruned 0 files. Final dataset contains 20869 files
[NeMo I 2023-08-04 10:01:14 dataset:378] Pruned 0.00 hours. Final dataset contains 29.45 hours.
[NeMo I 2023-08-04 10:01:14 dataset:1068] Loading dataset from /root/../workspaces/TTS/data/val_manifest_mel_test.json.
460it [00:00, 199419.10it/s]
[NeMo I 2023-08-04 10:01:14 dataset:1092] Loaded dataset with 460 files.
[NeMo I 2023-08-04 10:01:14 dataset:1094] Dataset contains 0.76 hours.
[NeMo I 2023-08-04 10:01:14 dataset:376] Pruned 13 files. Final dataset contains 447 files
[NeMo I 2023-08-04 10:01:14 dataset:378] Pruned 0.01 hours. Final dataset contains 0.76 hours.
[NeMo I 2023-08-04 10:01:14 features:291] PADDING: 0
[NeMo I 2023-08-04 10:01:14 features:299] STFT using exact pad
[NeMo I 2023-08-04 10:01:14 features:291] PADDING: 0
[NeMo I 2023-08-04 10:01:14 features:299] STFT using exact pad
[NeMo I 2023-08-04 10:01:15 cloud:58] Found existing object /root/.cache/torch/NeMo/NeMo_1.19.0/tts_hifigan/e6da322f0f7e7dcf3f1900a9229a7e69/tts_hifigan.nemo.
[NeMo I 2023-08-04 10:01:15 cloud:64] Re-using file from: /root/.cache/torch/NeMo/NeMo_1.19.0/tts_hifigan/e6da322f0f7e7dcf3f1900a9229a7e69/tts_hifigan.nemo
[NeMo I 2023-08-04 10:01:15 common:913] Instantiating model from pre-trained checkpoint
[NeMo W 2023-08-04 10:01:17 modelPT:161] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.
Train config :
dataset:
_target_: nemo.collections.tts.data.datalayers.MelAudioDataset
manifest_filepath: /home/fkreuk/data/train_finetune.txt
min_duration: 0.75
n_segments: 8192
dataloader_params:
drop_last: false
shuffle: true
batch_size: 64
num_workers: 4
[NeMo W 2023-08-04 10:01:17 modelPT:168] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s).
Validation config :
dataset:
_target_: nemo.collections.tts.data.datalayers.MelAudioDataset
manifest_filepath: /home/fkreuk/data/val_finetune.txt
min_duration: 3
n_segments: 66150
dataloader_params:
drop_last: false
shuffle: false
batch_size: 5
num_workers: 4
[NeMo W 2023-08-04 10:01:17 features:268] Using torch_stft is deprecated and has been removed. The values have been forcibly set to False for FilterbankFeatures and AudioToMelSpectrogramPreprocessor. Please set exact_pad to True as needed.
[NeMo I 2023-08-04 10:01:17 features:291] PADDING: 0
[NeMo W 2023-08-04 10:01:17 features:268] Using torch_stft is deprecated and has been removed. The values have been forcibly set to False for FilterbankFeatures and AudioToMelSpectrogramPreprocessor. Please set exact_pad to True as needed.
[NeMo I 2023-08-04 10:01:17 features:291] PADDING: 0
[NeMo I 2023-08-04 10:01:18 save_restore_connector:249] Model HifiGanModel was successfully restored from /root/.cache/torch/NeMo/NeMo_1.19.0/tts_hifigan/e6da322f0f7e7dcf3f1900a9229a7e69/tts_hifigan.nemo.
[NeMo I 2023-08-04 10:01:18 modelPT:1268] Model checkpoint restored from pretrained checkpoint with name : `tts_en_hifigan`
Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/1
Added key: store_based_barrier_key:1 to store for rank: 0
Rank 0: Completed store-based barrier for key:store_based_barrier_key:1 with 1 nodes.
----------------------------------------------------------------------------------------------------
distributed_backend=nccl
All distributed processes registered. Starting with 1 processes
----------------------------------------------------------------------------------------------------
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [1]
| Name | Type | Params
------------------------------------------------------------------------
0 | audio_to_melspec_precessor | FilterbankFeatures | 0
1 | trg_melspec_fn | FilterbankFeatures | 0
2 | generator | Generator | 13.9 M
3 | mpd | MultiPeriodDiscriminator | 41.1 M
4 | msd | MultiScaleDiscriminator | 29.6 M
5 | feature_loss | FeatureMatchingLoss | 0
6 | discriminator_loss | DiscriminatorLoss | 0
7 | generator_loss | GeneratorLoss | 0
------------------------------------------------------------------------
84.7 M Trainable params
0 Non-trainable params
84.7 M Total params
338.643 Total estimated model params size (MB)
Sanity Checking: 0it [00:00, ?it/s]Error executing job with overrides: ['model.max_steps=200000', 'model.optim.lr=0.00001', '~model.optim.sched', 'train_dataset=/root/../workspaces/TTS/data/train_manifest_mel_test.json', 'validation_datasets=/root/../workspaces/TTS/data/val_manifest_mel_test.json', 'exp_manager.exp_dir=experimentsHifigan', '+init_from_pretrained_model=tts_en_hifigan', '+trainer.val_check_interval=100', 'trainer.check_val_every_n_epoch=1', 'model/train_ds=train_ds_finetune', 'model/validation_ds=val_ds_finetune', 'model.train_ds.dataloader_params.batch_size=16', 'exp_manager.resume_if_exists=false']
Traceback (most recent call last):
File "/root/../workspaces/TTS/nemo/examples/tts/hifigan_finetune.py", line 32, in <module>
main() # noqa pylint: disable=no-value-for-parameter
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/nemo/core/config/hydra_runner.py", line 105, in wrapper
_run_hydra(
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/hydra/_internal/utils.py", line 389, in _run_hydra
_run_app(
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/hydra/_internal/utils.py", line 452, in _run_app
run_and_report(
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/hydra/_internal/utils.py", line 216, in run_and_report
raise ex
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/hydra/_internal/utils.py", line 213, in run_and_report
return func()
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/hydra/_internal/utils.py", line 453, in <lambda>
lambda: hydra.run(
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/hydra/_internal/hydra.py", line 132, in run
_ = ret.return_value
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/hydra/core/utils.py", line 260, in return_value
raise self._return_value
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/hydra/core/utils.py", line 186, in run_job
ret.return_value = task_function(task_cfg)
File "/root/../workspaces/TTS/nemo/examples/tts/hifigan_finetune.py", line 28, in main
trainer.fit(model)
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 608, in fit
call._call_and_handle_interrupt(
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/call.py", line 36, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 88, in launch
return function(*args, **kwargs)
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 650, in _fit_impl
self._run(model, ckpt_path=self.ckpt_path)
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1112, in _run
results = self._run_stage()
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1191, in _run_stage
self._run_train()
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1204, in _run_train
self._run_sanity_check()
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1276, in _run_sanity_check
val_loop.run()
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 152, in advance
dl_outputs = self.epoch_loop.run(self._data_fetcher, dl_max_batches, kwargs)
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 121, in advance
batch = next(data_fetcher)
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/utilities/fetching.py", line 184, in __next__
return self.fetching_function()
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/utilities/fetching.py", line 265, in fetching_function
self._fetch_next_batch(self.dataloader_iter)
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/utilities/fetching.py", line 280, in _fetch_next_batch
batch = next(iterator)
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 633, in __next__
data = self._next_data()
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1345, in _next_data
return self._process_data(data)
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1371, in _process_data
data.reraise()
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/torch/_utils.py", line 644, in reraise
raise exception
ValueError: Caught ValueError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop
data = fetcher.fetch(index)
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 51, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 51, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/opt/conda/envs/nemo/lib/python3.8/site-packages/nemo/collections/tts/data/dataset.py", line 1144, in __getitem__
start = random.randint(0, mel.shape[1] - frames - 1)
File "/opt/conda/envs/nemo/lib/python3.8/random.py", line 248, in randint
return self.randrange(a, b+1)
File "/opt/conda/envs/nemo/lib/python3.8/random.py", line 226, in randrange
raise ValueError("empty range for randrange() (%d, %d, %d)" % (istart, istop, width))
ValueError: empty range for randrange() (0, -16, -16)
I am using main branch of nemo. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/7162 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7162/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7162/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7162/events | https://github.com/NVIDIA/NeMo/issues/7162 | 1,835,969,566 | I_kwDOC_bI7s5tbqwe | 7,162 | swiglu activation doesn't support sequence_parallel | {
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] | closed | false | null | [] | null | null | 2023-08-04T02:30:54 | 2023-09-11T01:44:26 | 2023-09-11T01:44:25 | NONE | {
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} | null | null | null | **Describe the bug**
When changing activation to `swiglu` and enable `sequence_parallel`, the training crashes.
**Steps/Code to reproduce bug**
1. Checkout out repo tag: `git checkout v1.19.1`
2. Config modifications:
```diff
--- a/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml
+++ b/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml
@@ -51,7 +51,7 @@ model:
virtual_pipeline_model_parallel_size: null # interleaved pipeline
# model architecture
- encoder_seq_length: 512
+ encoder_seq_length: 8192
max_position_embeddings: ${.encoder_seq_length}
num_layers: 12
hidden_size: 768
@@ -71,8 +71,8 @@ model:
pre_process: True # add embedding
post_process: True # add pooler
persist_layer_norm: True # Use of persistent fused layer norm kernel.
- bias: True # Whether to use bias terms in all weight matrices.
- activation: 'gelu' # Options ['gelu', 'geglu', 'swiglu', 'reglu', 'squared-relu', 'fast-geglu', 'fast-swiglu', 'fast-reglu']
+ bias: False # Whether to use bias terms in all weight matrices.
+ activation: 'swiglu' # Options ['gelu', 'geglu', 'swiglu', 'reglu', 'squared-relu', 'fast-geglu', 'fast-swiglu', 'fast-reglu']
headscale: False # Whether to learn extra parameters that scale the output of the each self-attention head.
transformer_block_type: 'pre_ln' # Options ['pre_ln', 'post_ln', 'normformer']
openai_gelu: False # Use OpenAI's GELU instead of the default GeLU
@@ -88,8 +88,8 @@ model:
library: 'megatron'
type: 'GPT2BPETokenizer'
model: null
- vocab_file: null
- merge_file: null
+ vocab_file: /data/meta/gpt2/vocab.json
+ merge_file: /data/meta/gpt2/merges.txt
delimiter: null # only used for tabular tokenizer
sentencepiece_legacy: False # Legacy=True allows you to add special tokens to sentencepiece tokenizers.
@@ -107,8 +107,8 @@ model:
# Fusion
grad_div_ar_fusion: True # Fuse grad division into torch.distributed.all_reduce. Only used with O2 and no pipeline parallelism..
gradient_accumulation_fusion: False # Fuse weight gradient accumulation to GEMMs. Only used with pipeline parallelism and O2.
- bias_activation_fusion: True # Use a kernel that fuses the bias addition from weight matrices with the subsequent activation function.
- bias_dropout_add_fusion: True # Use a kernel that fuses the bias addition, dropout and residual connection addition.
+ bias_activation_fusion: False # Use a kernel that fuses the bias addition from weight matrices with the subsequent activation function.
+ bias_dropout_add_fusion: False # Use a kernel that fuses the bias addition, dropout and residual connection addition.
masked_softmax_fusion: True # Use a kernel that fuses the attention softmax with it's mask.
get_attention_mask_from_fusion: True # When using fused softmax it will create the attention mask so we won't copy it to the pipeline stages.
@@ -187,7 +187,9 @@ model:
# Dictionary: can override from CLI "model.data.data_prefix"={"train":[1.0, /path/to/data], "validation":/path/to/data, "test":/path/to/test}
# Or see example below:
# "model.data.data_prefix: {train:[1.0,/path/to/data], validation:[/path/to/data], test:[/path/to/test]}"
- data_prefix: ???
+ data_prefix:
+ - 1.0
+ - /data/pretrain/owt2_clean_v2/hfbpe_gpt_training_data_text_document
index_mapping_dir: null # path to save index mapping .npy files, by default will save in the same location as data_prefix
data_impl: mmap
splits_string: 900,50,50
```
3. Run command:
```
python examples/nlp/language_modeling/megatron_gpt_pretraining.py --config-path=/workspace/project/NeMo-upstream/examples/nlp/language_modeling/conf --config-name=megatron_gpt_config trainer.devices=4 trainer.num_nodes=1 model.micro_batch_size=1 model.global_batch_size=8 model.tensor_model_parallel_size=4 model.pipeline_model_parallel_size=1 model.sequence_parallel=True
```
4. The error trackback:
```
Traceback (most recent call last):
File "/workspace/project/NeMo-upstream/examples/nlp/language_modeling/megatron_gpt_pretraining.py", line 86, in main
trainer.fit(model)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 608, in fit
call._call_and_handle_interrupt(
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/call.py", line 38, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 650, in _fit_impl
self._run(model, ckpt_path=self.ckpt_path)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1112, in _run
results = self._run_stage()
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1191, in _run_stage
self._run_train()
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1214, in _run_train
self.fit_loop.run()
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/fit_loop.py", line 267, in advance
self._outputs = self.epoch_loop.run(self._data_fetcher)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 213, in advance
batch_output = self.batch_loop.run(kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 88, in advance
outputs = self.optimizer_loop.run(optimizers, kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/loop.py", line 199, in run
self.advance(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 202, in advance
result = self._run_optimization(kwargs, self._optimizers[self.optim_progress.optimizer_position])
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 249, in _run_optimization
self._optimizer_step(optimizer, opt_idx, kwargs.get("batch_idx", 0), closure)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 370, in _optimizer_step
self.trainer._call_lightning_module_hook(
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1356, in _call_lightning_module_hook
output = fn(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/core/module.py", line 1742, in optimizer_step
optimizer.step(closure=optimizer_closure)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/core/optimizer.py", line 169, in step
step_output = self._strategy.optimizer_step(self._optimizer, self._optimizer_idx, closure, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/strategies/ddp.py", line 280, in optimizer_step
optimizer_output = super().optimizer_step(optimizer, opt_idx, closure, model, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/strategies/strategy.py", line 234, in optimizer_step
return self.precision_plugin.optimizer_step(
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/plugins/precision/native_amp.py", line 75, in optimizer_step
closure_result = closure()
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 149, in __call__
self._result = self.closure(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 135, in closure
step_output = self._step_fn()
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 419, in _training_step
training_step_output = self.trainer._call_strategy_hook("training_step", *kwargs.values())
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py", line 1494, in _call_strategy_hook
output = fn(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/strategies/ddp.py", line 351, in training_step
return self.model(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1533, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/parallel/distributed.py", line 1137, in forward
output = self._run_ddp_forward(*inputs, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/parallel/distributed.py", line 1094, in _run_ddp_forward
return self.module(*inputs, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1533, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/overrides/base.py", line 98, in forward
output = self._forward_module.training_step(*inputs, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/nemo/utils/model_utils.py", line 380, in wrap_training_step
output_dict = wrapped(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 469, in training_step
self.allreduce_sequence_parallel_gradients()
File "/usr/local/lib/python3.8/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 571, in allreduce_sequence_parallel_gradients
coalesced = torch._utils._flatten_dense_tensors(grads)
File "/usr/local/lib/python3.8/dist-packages/torch/_utils.py", line 459, in _flatten_dense_tensors
return torch._C._nn.flatten_dense_tensors(tensors)
RuntimeError: torch.cat(): expected a non-empty list of Tensors
```
**Expected behavior**
The training can proceed normally
**Environment overview (please complete the following information)**
- Docker images: `nvcr.io/nvidia/nemo:23.04`
- Start docker container: `sudo nvidia-docker run -it --shm-size 64g --network host --hostname nemo-2304 --name nemo-2304 -v /path/on/host:/path/on/container/ nvcr.io/nvidia/nemo:23.04 bash`
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
GPU: 4 V100
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} | null | null | null | Hi I am trying to install NeMo on aarch64 RasberryPi 4 64bit Bulleye. I wonder if anyone have a feasible way to install NeMo and make it run on Raspberry Pi 4?
I try the method **Pip from source**, and I run the `./reinstall.sh`, I found it fails while installing the pynini. Then I follow methods on https://pypi.org/project/pynini/ to install the pynini on my Pi step by step, I have installed the required openfst and then when I run the `pip install pynini`, I got such error
```
error: invalid cast from type ‘std::string’ {aka ‘std::__cxx11::basic_string<char>’} to type ‘PyObject*’ {aka ‘_object*’}
```
The full error log during installation is shown as below
```
extensions/_pywrapfst.cpp:11529:40: error: invalid cast from type ‘std::string’ {aka ‘std::__cxx11::basic_string<char>’} to type ‘PyObject*’ {aka ‘_object*’}
11529 | __pyx_t_2 = __Pyx_PyObject_AsDouble(((PyObject *)__pyx_t_1)); if (unlikely(__pyx_t_2 == ((double)((double)-1)) && PyErr_Occurred())) __PYX_ERR(0, 497, __pyx_L1_error)
| ^~~~~~~~~~~~~~~~~~~~~
extensions/_pywrapfst.cpp:1400:43: note: in definition of macro ‘likely’
1400 | #define likely(x) __builtin_expect(!!(x), 1)
| ^
/usr/include/python3.9/object.h:130:42: note: in expansion of macro ‘_PyObject_CAST_CONST’
130 | #define Py_IS_TYPE(ob, type) _Py_IS_TYPE(_PyObject_CAST_CONST(ob), type)
| ^~~~~~~~~~~~~~~~~~~~
/usr/include/python3.9/floatobject.h:24:32: note: in expansion of macro ‘Py_IS_TYPE’
24 | #define PyFloat_CheckExact(op) Py_IS_TYPE(op, &PyFloat_Type)
| ^~~~~~~~~~
extensions/_pywrapfst.cpp:3196:10: note: in expansion of macro ‘PyFloat_CheckExact’
3196 | ((likely(PyFloat_CheckExact(obj))) ? PyFloat_AS_DOUBLE(obj) :\
| ^~~~~~~~~~~~~~~~~~
extensions/_pywrapfst.cpp:11529:15: note: in expansion of macro ‘__Pyx_PyObject_AsDouble’
11529 | __pyx_t_2 = __Pyx_PyObject_AsDouble(((PyObject *)__pyx_t_1)); if (unlikely(__pyx_t_2 == ((double)((double)-1)) && PyErr_Occurred())) __PYX_ERR(0, 497, __pyx_L1_error)
| ^~~~~~~~~~~~~~~~~~~~~~~
In file included from /usr/include/python3.9/Python.h:101,
from extensions/_pywrapfst.cpp:46:
extensions/_pywrapfst.cpp:11529:40: error: invalid cast from type ‘std::string’ {aka ‘std::__cxx11::basic_string<char>’} to type ‘PyObject*’ {aka ‘_object*’}
11529 | __pyx_t_2 = __Pyx_PyObject_AsDouble(((PyObject *)__pyx_t_1)); if (unlikely(__pyx_t_2 == ((double)((double)-1)) && PyErr_Occurred())) __PYX_ERR(0, 497, __pyx_L1_error)
| ^~~~~~~~~~~~~~~~~~~~~
/usr/include/python3.9/floatobject.h:51:51: note: in definition of macro ‘PyFloat_AS_DOUBLE’
51 | #define PyFloat_AS_DOUBLE(op) (((PyFloatObject *)(op))->ob_fval)
| ^~
extensions/_pywrapfst.cpp:11529:15: note: in expansion of macro ‘__Pyx_PyObject_AsDouble’
11529 | __pyx_t_2 = __Pyx_PyObject_AsDouble(((PyObject *)__pyx_t_1)); if (unlikely(__pyx_t_2 == ((double)((double)-1)) && PyErr_Occurred())) __PYX_ERR(0, 497, __pyx_L1_error)
| ^~~~~~~~~~~~~~~~~~~~~~~
extensions/_pywrapfst.cpp:11529:40: error: invalid cast from type ‘std::string’ {aka ‘std::__cxx11::basic_string<char>’} to type ‘PyObject*’ {aka ‘_object*’}
11529 | __pyx_t_2 = __Pyx_PyObject_AsDouble(((PyObject *)__pyx_t_1)); if (unlikely(__pyx_t_2 == ((double)((double)-1)) && PyErr_Occurred())) __PYX_ERR(0, 497, __pyx_L1_error)
| ^~~~~~~~~~~~~~~~~~~~~
extensions/_pywrapfst.cpp:1400:43: note: in definition of macro ‘likely’
1400 | #define likely(x) __builtin_expect(!!(x), 1)
| ^
/usr/include/python3.9/object.h:130:42: note: in expansion of macro ‘_PyObject_CAST_CONST’
130 | #define Py_IS_TYPE(ob, type) _Py_IS_TYPE(_PyObject_CAST_CONST(ob), type)
| ^~~~~~~~~~~~~~~~~~~~
/usr/include/python3.9/longobject.h:16:31: note: in expansion of macro ‘Py_IS_TYPE’
16 | #define PyLong_CheckExact(op) Py_IS_TYPE(op, &PyLong_Type)
| ^~~~~~~~~~
extensions/_pywrapfst.cpp:3197:9: note: in expansion of macro ‘PyLong_CheckExact’
3197 | likely(PyLong_CheckExact(obj)) ?\
| ^~~~~~~~~~~~~~~~~
extensions/_pywrapfst.cpp:11529:15: note: in expansion of macro ‘__Pyx_PyObject_AsDouble’
11529 | __pyx_t_2 = __Pyx_PyObject_AsDouble(((PyObject *)__pyx_t_1)); if (unlikely(__pyx_t_2 == ((double)((double)-1)) && PyErr_Occurred())) __PYX_ERR(0, 497, __pyx_L1_error)
| ^~~~~~~~~~~~~~~~~~~~~~~
extensions/_pywrapfst.cpp:11529:40: error: invalid cast from type ‘std::string’ {aka ‘std::__cxx11::basic_string<char>’} to type ‘PyObject*’ {aka ‘_object*’}
11529 | __pyx_t_2 = __Pyx_PyObject_AsDouble(((PyObject *)__pyx_t_1)); if (unlikely(__pyx_t_2 == ((double)((double)-1)) && PyErr_Occurred())) __PYX_ERR(0, 497, __pyx_L1_error)
| ^~~~~~~~~~~~~~~~~~~~~
extensions/_pywrapfst.cpp:3198:18: note: in definition of macro ‘__Pyx_PyObject_AsDouble’
3198 | PyLong_AsDouble(obj) : __Pyx__PyObject_AsDouble(obj))
| ^~~
extensions/_pywrapfst.cpp:11529:40: error: invalid cast from type ‘std::string’ {aka ‘std::__cxx11::basic_string<char>’} to type ‘PyObject*’ {aka ‘_object*’}
11529 | __pyx_t_2 = __Pyx_PyObject_AsDouble(((PyObject *)__pyx_t_1)); if (unlikely(__pyx_t_2 == ((double)((double)-1)) && PyErr_Occurred())) __PYX_ERR(0, 497, __pyx_L1_error)
| ^~~~~~~~~~~~~~~~~~~~~
extensions/_pywrapfst.cpp:3198:50: note: in definition of macro ‘__Pyx_PyObject_AsDouble’
3198 | PyLong_AsDouble(obj) : __Pyx__PyObject_AsDouble(obj))
```
My GCC version is `gcc (Debian 10.2.1-6) 10.2.1 20210110`, and my `python version is 3.9.2`, I am trying to install `openfst 1.8.2` and install `pynini 2.1.5` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/7160 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7160/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7160/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7160/events | https://github.com/NVIDIA/NeMo/pull/7160 | 1,835,756,596 | PR_kwDOC_bI7s5XJWoP | 7,160 | NFA subtitle file config - specify colors and vertical alignment | {
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} | # What does this PR do ?
Modify the `ASSFileConfig` so we can specify the colors of the text and its vertical alignment.
**Collection**: `tools/nemo_forced_aligner`
# Changelog
For specifying vertical alignment, removed `ass_file_config.marginv` parameter, which was being used as a rough way to modify the vertical placement of the text. Now, there is a parameter `ass_file_config.vertical_alignment`, which can take the values `"center"` (default), `"top"`, or `"bottom"`
For specifying the colors of the text, added 3 new parameters:
- `ass_file_config.text_already_spoken_rgb`
- `ass_file_config.text_being_spoken_rgb`
- `ass_file_config.text_not_yet_spoken_rgb`
# Usage
Can specify new parameters in `ASSFileConfig` e.g.:
```bash
python <path to NeMo>/tools/nemo_forced_aligner/align.py \
pretrained_name="stt_en_fastconformer_ctc_large" \
manifest_filepath="manifest.json" \
output_dir=nfa_output/ \
additional_segment_grouping_separator="|" \
ass_file_config.text_already_spoken_rgb=[252,186,3] \ # bright yellow/orange
ass_file_config.text_being_spoken_rgb=[240,26,240] \ # bright pink
ass_file_config.text_not_yet_spoken_rgb=[62,26,240] \ # bright blue
ass_file_config.vertical_alignment="top"
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ N/A ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ N/A ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [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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https://api.github.com/repos/NVIDIA/NeMo/issues/7159 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7159/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7159/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7159/events | https://github.com/NVIDIA/NeMo/pull/7159 | 1,835,671,566 | PR_kwDOC_bI7s5XJFAl | 7,159 | Refactor LLM pretraining examples | {
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} | # What does this PR do ?
Simplify LLM pretraining example scripts by moving common logic into a new `TrainerBuilder` and `exp_manager`.
**Collection**: [Note which collection this PR will affect]
# Changelog
- add a `TrainerBuilder` type that hides some common logic for setting up a `Trainer`
- ~~move logic to handle `resume_from_checkpoint` arg to `exp_manager`~~ Current disabling `resume_from_checkpoint` logic and leaving a TODO comment since its current behavior is not a desirable user experience. Will make a separate PR to improve this.
- utilize above refactors to reduce length of Megatron LLM pretraining example scripts
# 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?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
- [x] Refactor
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/7158 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7158/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7158/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7158/events | https://github.com/NVIDIA/NeMo/issues/7158 | 1,835,279,279 | I_kwDOC_bI7s5tZCOv | 7,158 | Incorrect padding for get_seq_len of features.py | {
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I don't know what was intended here, but to me it's clear there is redundency or error, because `self.n_fft // 2 * 2` is the same as just `self.n_fft`. Perhaps it was intended to be the power of two (aka `2 ** 2`)?
```
# Assuming that center is True is stft_pad_amount = 0
pad_amount = self.stft_pad_amount * 2 if self.stft_pad_amount is not None else self.n_fft // 2 * 2
```
What was the intended purpose of the line? Perhaps we want n_fft to be even?
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# Changelog
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# Usage
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```python
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# Before your PR is "Ready for review"
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**PR Type**:
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- [ ] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
This PR contains all of the changes needed to support training TTS models with large, multi-utterance files.
Related features are all experimental, and the change is not backwards compatible as I had to change feature files from _.pt_ to _.npy_ so I could memory map them to avoid reading the entire feature file into memory.
**Collection**: [TTS]
# Changelog
- Add support for "offset" and "duration" in manifest (similar to ASR) to allow reading a subset of audio in the data loader.
- Modify featurizer classes so that they save features as .npy files, and reads subsets of the files using numpy memory mapping when "offset" is present in the manifest.
- Add logic for computing pitch in small chunks for large audio files (to avoid out of memory issues).
- Add on-the-fly volume normalization to featurizers and data loader, as we can not do it as a preprocessing step for multi-utterance files. Volume effects feature computation, as energy is proportional to volume, and volume can change the voiced/unvoiced classification during pitch computation.
- Unit tests to validate that the saving/loading logic works as expected.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Bugfix to prevent errors being thrown if the substring specified as an `additional_segment_grouping_separator` is placed at the beginning or end of reference text, or if 2+ of these substrings are placed next to each other in the reference text.
**Collection**: tools/nemo_forced_aligner
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
No usage changes
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ N/A ] Did you write any new necessary tests?
- [ N/A ] Did you add or update any necessary documentation?
- [ N/A ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
In a separate PR (https://github.com/NVIDIA/NeMo/pull/6852) it was brought up that TTS is using nested hydra config, which is against NeMo 1 config file per model policy.
This PR removes nested config from our experimental FastPitch and HiFiGan in favor of a flat structure with duplicated code between 22kHz and 44kHz versions.
Our older non-experimental configs are left unchanged for backwards compatibility.
**Collection**: [TTS]
# Changelog
- Remove nested configuration from FastPitch and HiFiGan configs using new data loader
- Add separate 22kHz and 44kHz examples for each model.
- Moved hifigan_data.yaml config to separate "hifigan_dataset" folder (not sure if that is a good name) now that it does not need to reuse the generator config files.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
In the case where we are training GPT with distopt, BF16 grads, and pipeline parallelism, we are not properly synchronizing the embedding grads between the first and last pipeline stage. The embeddings were being accumulated into BF16 buffers and the pipeline-parallel reduction was on FP32 buffers (so just all-reducing zeros). Note that we synchronize these buffers before the data-parallel reduction, so that is still being done properly in FP32.
This PR fixes the issue by eagerly accumulating the embedding grads into FP32 buffers.
**Collection**: NLP
# Changelog
- Distopt accumulates grads for FP32 params into FP32 buffers.
# Usage
In the config file, set the optimizer with `name: distributed_fused_adam` and `grad_sync_dtype: bf16`.
# 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
* Depends/includes https://github.com/NVIDIA/NeMo/pull/6886
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} | # What does this PR do ?
adds a self.freeze() during peft model init to prevent memory spike during startup.
**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
- [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 ?
Support FSDP + (Tensor parallelism) for Transformer models.
What's missing in the PR
1. Sharded checkpoint store and load (not critical at the moment)
2. Decouple the module granularity of full-activation recomputation and FSDP sharding to remove AG in recomputation
3. CPU initialization with TE enabled
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} | # What does this PR do ?
* Add support for multiple contexts and truncation_field
* Add support for truncation in the middle of prompt_template
* Arguments
* Remove context_key
* Remove separate_prompt_and_response_with_newline
* Add truncate_method
* Change the truncation logic to the following
1. Split prompt_template by placeholder
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4. Concatenate all part
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```python
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} | null | null | null | **Describe the bug**
I am creating an MTEncDecModel model and the gpu memory allocated never gets freed.
Specifically, I want to create an MTEncDecModel model for english to german, perform some translations, delete then model, then create an MTEncDecModel model for english to french, perform some translations, delete the model, and continue this process for many language pairs. However, the gpu memory allocated to each model instantiated is not freed, so I get a `RuntimeError: CUDA Out of memory` error.
I want a way to clear the gpu memory allocated without terminating the python program or restarting the jupyter kernel.
**Steps/Code to reproduce bug**
```
import torch
from nemo.collections.nlp.models import MTEncDecModel
from transformers import MarianMTModel
def memory_stats():
print('Memory allocated:', torch.cuda.memory_allocated()/1024**2)
print('Memory cached:', torch.cuda.memory_cached()/1024**2, '\n')
print('Initia memory stats')
memory_stats()
model = MTEncDecModel.from_pretrained('nmt_en_es_transformer24x6').cuda()
print('Model created')
memory_stats()
del model
print('Model deleted')
memory_stats()
torch.cuda.empty_cache()
print('Cache emptied')
memory_stats()
```
The output from this code is
```
Initial memory stats
Memory allocated: 0.0
Memory cached: 0.0
Model created
Memory allocated: 1793.8173828125
Memory cached: 3714.0
Model deleted
Memory allocated: 1793.8173828125
Memory cached: 3714.0
Cache emptied
Memory allocated: 1793.8173828125
Memory cached: 1802.0
```
**Expected behavior**
Deleting all objects and references pointing to objects allocating GPU memory should free the memory. Link:
https://discuss.pytorch.org/t/free-all-gpu-memory-used-in-between-runs/168202/2
For example, If I create a PyTorch tensor instead of the MTEncDecModel, then the output is as expected:
```
print('Initial memory stats')
memory_stats()
x = torch.randn(1024*1024, device='cuda')
print('Tensor created')
memory_stats()
del x
print('Tensor deleted')
memory_stats()
torch.cuda.empty_cache()
print('Cache emptied')
memory_stats()
```
Output:
```
Initial memory stats
Memory allocated: 0.0
Memory cached: 0.0
Tensor created
Memory allocated: 4.0
Memory cached: 20.0
Tensor deleted
Memory allocated: 0.0
Memory cached: 20.0
Cache emptied
Memory allocated: 0.0
Memory cached: 0.0
```
Also, if I create a Marian machine translation model from HuggingFace instead of the MTEncDecModel model then again the output is as expected:
```
print('Initial memory stats')
memory_stats()
model = MarianMTModel.from_pretrained('Helsinki-NLP/opus-mt-tc-big-en-it').cuda()
print('Model created')
memory_stats()
del model
print('Model deleted')
memory_stats()
torch.cuda.empty_cache()
print('Cache emptied')
memory_stats()
```
Output
```
Initial memory stats
Memory allocated: 0.0
Memory cached: 0.0
Model created
Memory allocated: 893.5439453125
Memory cached: 896.0
Model deleted
Memory allocated: 0.0
Memory cached: 896.0
Cache emptied
Memory allocated: 0.0
Memory cached: 0.0
```
In fact, even instantiating an MTEncDecModel without sending to model to `cuda()` and without storing the model to a variable, causes the memory allocated to increate every time:
```
print('Initial memory stats')
memory_stats()
MTEncDecModel.from_pretrained('nmt_en_es_transformer24x6')
torch.cuda.empty_cache()
print('After one instantiation')
memory_stats()
MTEncDecModel.from_pretrained('nmt_en_es_transformer24x6')
torch.cuda.empty_cache()
print('After two instantiations')
memory_stats()
MTEncDecModel.from_pretrained('nmt_en_es_transformer24x6')
torch.cuda.empty_cache()
print('After three instantiations')
memory_stats()
```
Output:
```
Initial memory stats
Memory allocated: 0.0
Memory cached: 0.0
After one instantiation
Memory allocated: 1793.8173828125
Memory cached: 1802.0
After two instantiations
Memory allocated: 3587.634765625
Memory cached: 3588.0
After three instantiations
Memory allocated: 5381.4521484375
Memory cached: 5390.0
```
**Environment overview (please complete the following information)**
- Environment location: Docker on Kubernetes
- Method of NeMo install: using helm chart using nvcr.io/nvidia/nemo: 1.5.1
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] | closed | false | null | [] | null | null | 2023-08-01T10:08:03 | 2023-10-17T01:17:33 | 2023-08-29T14:00:22 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
I am using nemo docker v23.04 , but I also observe the following behavior in a conda environment too.
I am training several conformer models ( same behavior using fastconformer transducer or conformer transducer) with several perturbations ( speed and noise from musan) and at the end of the epoch, when validation is performed, i observe the following behavior:
```
[NeMo I 2023-08-01 09:14:24 rnnt_wer_bpe:395] reference :el își joacă postul în meciul cu sportul studențesc
[NeMo I 2023-08-01 09:14:24 rnnt_wer_bpe:396] predicted : ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇
⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇
⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇
⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇
⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇
⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇
⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇
⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇
Epoch 0: 100%|██████████████████████████████| 16235/16235 [2:21:02<00:00, 1.92it/s, loss=5.14, v_num=2]Epoch 0, global step 503: 'val_wer' reached 57.20575 (best 57.20575), saving model to '/mnt/zfs-mirror-02/homes/gpirlogeanu/git_repos/nemoasr/examples/asr/train_dir/nemo_experiments/fastconformer_BAS/checkpoints/fastconformer_BAS--val_wer=57.2057-epoch=0.ckpt' as top 5
```
If I don't use augmentations, it shows the predictions and works ok. The val_loss is NaN and the val_wer displays a random value.
I think it might be a problem with my noise manifest, but I am unsure why, when I was augmenting VAD audios, I remember making the segments equal to 0.63s, but I thought in case of ASR, that perturb automatically cuts the noise audios. It looks like this:
```
{"audio_filepath": "/home/gpirlogeanu/musan/noise/all_noise/noise-free-sound-0000.wav", "duration": 17.65, "label": "noise", "text": "_", "offset": 0.0}
{"audio_filepath": "/home/gpirlogeanu/musan/noise/all_noise/noise-free-sound-0001.wav", "duration": 40.53, "label": "noise", "text": "_", "offset": 0.0}
{"audio_filepath": "/home/gpirlogeanu/musan/noise/all_noise/noise-free-sound-0002.wav", "duration": 71.21, "label": "noise", "text": "_", "offset": 0.0}
{"audio_filepath": "/home/gpirlogeanu/musan/noise/all_noise/noise-free-sound-0003.wav", "duration": 14.23, "label": "noise", "text": "_", "offset": 0.0}
{"audio_filepath": "/home/gpirlogeanu/musan/noise/all_noise/noise-free-sound-0004.wav", "duration": 2.98, "label": "noise", "text": "_", "offset": 0.0}
{"audio_filepath": "/home/gpirlogeanu/musan/noise/all_noise/noise-free-sound-0005.wav", "duration": 21.18, "label": "noise", "text": "_", "offset": 0.0}
{"audio_filepath": "/home/gpirlogeanu/musan/noise/all_noise/noise-free-sound-0006.wav", "duration": 7.77, "label": "noise", "text": "_", "offset": 0.0}
{"audio_filepath": "/home/gpirlogeanu/musan/noise/all_noise/noise-free-sound-0007.wav", "duration": 16.61, "label": "noise", "text": "_", "offset": 0.0}
```
This is my config file:
```
# It contains the default values for training a Fast Conformer-Transducer ASR model, large size (~120M) with Transducer loss and sub-word encoding.
# You may find more info about FastConformer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#fast-conformer
# We suggest to use trainer.precision=bf16 for GPUs which support it otherwise trainer.precision=16 is recommended.
# Using bf16 or 16 would make it possible to double the batch size and speedup training/inference. If fp16 is not stable and model diverges after some epochs, you may use fp32.
# Here are the suggested batch size per GPU for each precision and memory sizes:
# +-----------+------------+------------+
# | Precision | GPU Memory | Batch Size |
# +===========+============+============+
# | 32 | 16GB | 16 |
# | | 32GB | 32 |
# | | 80GB | 64 |
# +-----------+------------+------------+
# | fp16 or | 16GB | 32 |
# | bf16 | 32GB | 64 |
# | | 80GB | 128 |
# +-----------+------------+------------+
# Here are the recommended configs for different variants of FastConformer-Transducer-BPE, other parameters are the same as in this config file.
#
# +--------------+---------+---------+----------+----------------+--------------+--------------------------+-----------------+------------+
# | Model | d_model | n_heads | n_layers |conv_kernel_size| weight_decay | pred_hidden/joint_hidden | pred_rnn_layers | xscaling |
# +==============+=========+========+===========+================+==============+==========================+=================+============+
# | Small (14M) | 176 | 4 | 16 | 9 | 0.0 | 320 | 1 | True |
# +--------------+---------+--------+-----------+----------------+--------------+--------------------------+-----------------+------------+
# | Medium (32M) | 256 | 4 | 16 | 9 | 1e-3 | 640 | 1 | True |
# +--------------+---------+--------+-----------+----------------+--------------+--------------------------+-----------------+------------+
# | Large (120M) | 512 | 8 | 17 | 9 | 1e-3 | 640 | 1 | True |
# +--------------+---------+--------+-----------+----------------+--------------+--------------------------+-----------------+------------+
# | XLarge (616M)| 1024 | 8 | 24 | 9 | 1e-3 | 640 | 2 | True |
# +--------------+---------+--------+-----------+----------------+--------------+--------------------------+-----------------+------------+
# | XXLarge(1.2B)| 1024 | 8 | 42 | 5 | 1e-3 | 640 | 2 | False |
# +--------------------------------------------------------------+--------------+--------------------------+-----------------+------------+
# Note: They are based on the assumption of max_duration of 20. If you have longer or shorter max_duration, then batch sizes may need to get updated accordingly.
# Default learning parameters in this config are set for global batch size of 2K while you may use lower values.
# To increase the global batch size with limited number of GPUs, you may use higher accumulate_grad_batches.
# However accumulate_grad_batches is better to be avoided as long as the global batch size is large enough and training is stable.
name: "FastConformer-Transducer-BPE"
init_from_nemo_model: "models/stt_en_fastconformer_transducer_large.nemo"
init_strict: false
model:
sample_rate: 16000
compute_eval_loss: true # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag.
log_prediction: true # enables logging sample predictions in the output during training
rnnt_reduction: 'mean_volume'
skip_nan_grad: false
model_defaults:
enc_hidden: ${model.encoder.d_model}
pred_hidden: 640
joint_hidden: 640
train_ds:
manifest_filepath: "manifests/RSC_train.json"
sample_rate: ${model.sample_rate}
batch_size: 32 # you may increase batch_size if your memory allows
shuffle: true
num_workers: 8
pin_memory: true
max_duration: 20 # it is set for LibriSpeech, you may need to update it for your dataset
min_duration: 0.1
# tarred datasets
is_tarred: false
tarred_audio_filepaths: null
shuffle_n: 2048
# bucketing params
bucketing_strategy: "fully_randomized"
bucketing_batch_size: null
augmentor:
speed:
prob: 0.4
sr: 16000
resample_type: "kaiser_best"
min_speed_rate: 0.9
max_speed_rate: 1.1
noise:
prob: 0.4
manifest_path: "manifests/noise_musan.json"
min_snr_db: 10
max_snr_db: 30
rng: null
orig_sr: 16000
validation_ds:
manifest_filepath: "manifests/RSC_eval.json"
sample_rate: ${model.sample_rate}
batch_size: 16
shuffle: false
use_start_end_token: false
num_workers: 8
pin_memory: true
test_ds:
manifest_filepath: null
sample_rate: ${model.sample_rate}
batch_size: 16
shuffle: false
use_start_end_token: false
num_workers: 8
pin_memory: true
# You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py
tokenizer:
dir: "tokenizers/5_normal_BAS+news_unigram/tokenizer_spe_unigram_v1024_max_5" # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe)
type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer)
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
sample_rate: ${model.sample_rate}
normalize: "per_feature"
window_size: 0.025
window_stride: 0.01
window: "hann"
features: 80
n_fft: 512
frame_splicing: 1
dither: 0.00001
pad_to: 0
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
freq_masks: 2 # set to zero to disable it
time_masks: 10 # set to zero to disable it
freq_width: 27
time_width: 0.05
encoder:
_target_: nemo.collections.asr.modules.ConformerEncoder
feat_in: ${model.preprocessor.features}
feat_out: -1 # you may set it if you need different output size other than the default d_model
n_layers: 17
d_model: 512
# Sub-sampling parameters
subsampling: dw_striding # vggnet, striding, stacking or stacking_norm, dw_striding
subsampling_factor: 8 # must be power of 2 for striding and vggnet
subsampling_conv_channels: 256 # set to -1 to make it equal to the d_model
causal_downsampling: false
# Reduction parameters: Can be used to add another subsampling layer at a given position.
# Having a 2x reduction will speedup the training and inference speech while keeping similar WER.
# Adding it at the end will give the best WER while adding it at the beginning will give the best speedup.
reduction: null # pooling, striding, or null
reduction_position: null # Encoder block index or -1 for subsampling at the end of encoder
reduction_factor: 1
# Feed forward module's params
ff_expansion_factor: 4
# Multi-headed Attention Module's params
self_attention_model: rel_pos # rel_pos or abs_pos
n_heads: 8 # may need to be lower for smaller d_models
# [left, right] specifies the number of steps to be seen from left and right of each step in self-attention
att_context_size: [-1, -1] # -1 means unlimited context
att_context_style: regular # regular or chunked_limited
xscaling: true # scales up the input embeddings by sqrt(d_model)
untie_biases: true # unties the biases of the TransformerXL layers
pos_emb_max_len: 5000
# Convolution module's params
conv_kernel_size: 9
conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups)
# conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size
# null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0]
conv_context_size: null
### regularization
dropout: 0.1 # The dropout used in most of the Conformer Modules
dropout_pre_encoder: 0.1 # The dropout used before the encoder
dropout_emb: 0.0 # The dropout used for embeddings
dropout_att: 0.1 # The dropout for multi-headed attention modules
# set to non-zero to enable stochastic depth
stochastic_depth_drop_prob: 0.0
stochastic_depth_mode: linear # linear or uniform
stochastic_depth_start_layer: 1
decoder:
_target_: nemo.collections.asr.modules.RNNTDecoder
normalization_mode: null # Currently only null is supported for export.
random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf
blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference.
prednet:
pred_hidden: ${model.model_defaults.pred_hidden}
pred_rnn_layers: 1
t_max: null
dropout: 0.2
# if a large vocabulary size is desired, you may wish to use SampleRNNTJoint module
# _target_: nemo.collections.asr.modules.SampledRNNTJoint
# n_samples: 500 # Specifies the minimum number of tokens to sample from the vocabulary space, excluding
# the RNNT blank token. If a given value is larger than the entire vocabulary size, then the full
# vocabulary will be used
joint:
_target_: nemo.collections.asr.modules.RNNTJoint
log_softmax: null # 'null' would set it automatically according to CPU/GPU device
preserve_memory: false # dramatically slows down training, but might preserve some memory
# Fuses the computation of prediction net + joint net + loss + WER calculation
# to be run on sub-batches of size `fused_batch_size`.
# When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size.
# `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss.
# Using small values here will preserve a lot of memory during training, but will make training slower as well.
# An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1.
# However, to preserve memory, this ratio can be 1:8 or even 1:16.
# Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow.
fuse_loss_wer: true
fused_batch_size: 4
jointnet:
joint_hidden: ${model.model_defaults.joint_hidden}
activation: "relu"
dropout: 0.2
decoding:
strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd.
# greedy strategy config
greedy:
max_symbols: 10
# beam strategy config
beam:
beam_size: 2
return_best_hypothesis: False
score_norm: true
tsd_max_sym_exp: 50 # for Time Synchronous Decoding
alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding
loss:
loss_name: "default"
warprnnt_numba_kwargs:
# FastEmit regularization: https://arxiv.org/abs/2010.11148
# You may enable FastEmit to reduce the latency of the model for streaming
fastemit_lambda: 0.0 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start.
clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only.
optim:
name: adamw
lr: 5e-3
# optimizer arguments
betas: [0.9, 0.98]
weight_decay: 1e-3
# scheduler setup
sched:
name: CosineAnnealing
# scheduler config override
warmup_steps: 15000
warmup_ratio: null
min_lr: 5e-4
trainer:
devices: -1 # number of GPUs, -1 would use all available GPUs
num_nodes: 1
max_epochs: 500
max_steps: -1 # computed at runtime if not set
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
accelerator: auto
strategy: ddp
accumulate_grad_batches: 16
gradient_clip_val: 0.0
precision: bf16 # 16, 32, or bf16
log_every_n_steps: 100 # Interval of logging.
enable_progress_bar: True
resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc.
num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it
check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs
sync_batchnorm: true
enable_checkpointing: False # Provided by exp_manager
logger: false # Provided by exp_manager
benchmark: false # needs to be false for models with variable-length speech input as it slows down training
exp_manager:
exp_dir: fastconformer
name: ${name}
create_tensorboard_logger: true
create_checkpoint_callback: true
checkpoint_callback_params:
# in case of multiple validation sets, first one is used
monitor: "val_wer"
mode: "min"
save_top_k: 5
always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints
resume_if_exists: True
resume_ignore_no_checkpoint: True
create_wandb_logger: false
wandb_logger_kwargs:
name: null
project: null
```
Thanks in advance!
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} | # What does this PR do ?
There is a bug when performing RNNT/CTC decoding with hybrid models.
By default `att_context_size` should be [-1,-1] which is what set as default, but if None is passed through cfg, it replaces the current value, which shouldn't be the case.
**Collection**: ASR
# Changelog
- change value only if `cfg.att_context_size` is not None
# 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 ?
There is a bug when performing RNNT/CTC decoding with hybrid models.
By default `att_context_size` should be [-1,-1] which is what set as default, but if None is passed through cfg, it replaces the current value, which shouldn't be the case.
**Collection**: ASR
# Changelog
- change value only if `cfg.att_context_size` is not None
# 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 ?
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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} | null | null | null | I have searched for documentation on how can I use NeMo for downstream tasks, I found this :
https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/v1.0.0/nlp/megatron_finetuning.html
However, I haven't found any steps to get started with that do I need to make any code changes so that I can run the code on multinode-multiple config?
Thanks | {
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Rename `megatron_amp_o2` to `megatron_amp_O2` in source code to be consistent
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# Usage
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## Who can review?
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```python
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# Before your PR is "Ready for review"
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If you haven't finished some of the above items you can still open "Draft" PR.
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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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**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"
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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} | null | null | null | I am currently looking into your code base and am wondering where to find the above-mentioned script that you are referring to [here](https://github.com/NVIDIA/NeMo/blob/2fa7abfb7311f1a4115dfa47afb80339697ec88d/examples/nlp/spellchecking_asr_customization/run_training.sh#L34C29-L34C29). I have already searched in your repo but can't seem to find it.
Any help would be greatly appreciated.
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I was hoping the NVIDIA team would release a script to evaluate the generated synthetic data appropriately, Fine grained , course grained and medium grained evaluations shown in the video was done very nicely. Please release the script for that.
Best Regards | {
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} | # What does this PR do ?
Support grouped query attention for language modelling, with torch attention and flash attention 2.
**Collection**: NLP
# Changelog
Support grouped query attention for language modelling, with torch attention and flash attention 2.
- Added parameter parse to enable GQA for gpt like models (change in nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py, nemo/collections/nlp/models/language_modeling/megatron/gpt_model.py)
- Added support for grouped query attention for language modeling (change in nemo/collections/nlp/modules/common/megatron/language_model.py, nemo/collections/nlp/modules/common/megatron/transformer.py, nemo/collections/nlp/modules/common/megatron/attention.py)
- Not implemented for flash attention triton yet, as it is marked as experimental
- Not implemented for xpos yet, as it is marked as experimental
# Usage
Configure num_kv_attention_heads in model config yaml file, e.g. add in examples/nlp/language_modeling/conf/megatron_gpt_config.yaml, in model configuration.
num_kv_attention_heads is optional configuraiton, if not configured or configured same as num_attention_heads, the existing multi head attention will be used instead.
Requires num_attention_heads as same or multiple of num_kv_attention_heads (same -> multi head attention), num_kv_attention_heads as same or multiple of tensor_model_parallel_size.
Same convention as flash attention, e.g. if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head 0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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I trained msdd model but I stopped the training (not waited until the max_epoch value), is there any script to convert the ckpt file into nemo file, the ckpt file contain all the optimizer params and gradients.
please advise. | {
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} | # What does this PR do ?
Add updated FC-XXL RNNT and CTC models
**Collection**: ASR
# Changelog
- Add pre trained models to code
- Add to docs
# Usage
```python
# FC Transducer BPE models
from nemo.collections.asr.models import EncDecRNNTBPEModel
fc_xxl_rnnt = EncDecRNNTBPEModel.from_pretrained('stt_en_fastconformer_transducer_xxlarge')
# FC CTC BPE models
from nemo.collections.asr.models import EncDecCTCModelBPE
fc_xxl_ctc = EncDecCTCModelBPE.from_pretrained('stt_en_fastconformer_ctc_xxlarge')
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [x] Documentation | {
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Add updated FC-XXL RNNT and CTC models
**Collection**: ASR
# Changelog
- Add pre trained models to code
- Add to docs
# Usage
```python
# FC Transducer BPE models
from nemo.collections.asr.models import EncDecRNNTBPEModel
fc_xxl_rnnt = EncDecRNNTBPEModel.from_pretrained('stt_en_fastconformer_transducer_xxlarge')
# FC CTC BPE models
from nemo.collections.asr.models import EncDecCTCModelBPE
fc_xxl_ctc = EncDecCTCModelBPE.from_pretrained('stt_en_fastconformer_ctc_xxlarge')
```
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [x] Documentation | {
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} | # What does this PR do ?
Fix vad_utils.py for r1.12.0
Redoing https://github.com/NVIDIA/NeMo/pull/7113 for r1.20.0
**Collection**: ASR
# Changelog
The lines that are treating "None" in vad_utils.py
# Usage
Run Speaker_Diarization_Inference.ipynb
# 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 NeMo ASR
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Fix import guards properly
**Collection**: [ASR, NLP]
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Fix import guards for flash attention and DALI
**Collection**: [ASR, NLP]
# Changelog
- Add AttributeError support for import where classpath resolution fails.
- Add import guards to pynvml
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
Closes #7122 | {
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} | # What does this PR do ?
Refactoring to make MegatronGPTModel dataset building more flexible.
In particular this allows subclasses to use a different `build_datasets_strategy` without having to copy / paste the full `build_train_valid_test_datasets()` method.
**Collection**: `nlp`
# Changelog
- Add a new attribute `build_datasets_strategy` to the constructor of `MegatronGPTModel` to make it easier to override the dataset building logic
# Usage
```python
from my_module import my_build_datasets_strategy
class MyClass(MegatronGPTModel):
def __init__(self):
super().__init__(build_datasets_strategy=my_build_datasets_strategy)
```
# Discussion
There are at least two potential controversial design choices, happy to discuss them here (marking PR as draft in the meantime):
1. Naming: the most "natural" name would be `build_train_valid_test_datasets_strategy`, but I felt it was starting to get annoyingly long, which is why I picked a shorter variant
2. Implementation: alternatively, we could (a) avoid changing the signature of `__init__()`, letting subclasses override `self._build_datasets_strategy` as they see fit (I didn't go this route because I tend to prefer customization options to be clearly exposed through the API), or (b) make it part of the config, using Hydra's instantiate feature to obtain the strategy (although I actually like this option better, I wanted to discuss it here before making changes to the config). [edit: see [comment below](https://github.com/NVIDIA/NeMo/pull/7123#issuecomment-1665501843) for a follow-up discussion]
I'll look into adding a test once the implementation is approved.
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [X] New Feature
- [ ] Bugfix
- [ ] Documentation | {
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I wanted to setup a dev conda environment for NeMo, so I followed steps at https://github.com/NVIDIA/NeMo/tree/main#from-source
Afterwards `pytest --cpu` was failing (before it could even run any test) with two errors:
* `module 'nvidia' has no attribute 'dali'`
* `No module named 'pynvml'`
**Describe the solution you'd like**
After manually installing both libraries with
* pip install --extra-index-url https://developer.download.nvidia.com/compute/redist --upgrade nvidia-dali-cuda120
* pip install pynvml
the tests were able to pass (`1420 passed, 304 skipped, 261 warnings`).
Ideally these libraries would be installed automatically by the `reinstall.sh` script. | {
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} | * Push changes
* Fixes
* add continue training script
* [WIP] nonlinear interp
* Fix
* override encoder_seq_len
* Remove nonlinear
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* sft with pi (#7006)
* sft with pi
* update values only if not None"
---------
* Address comments
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Add info
* Empty
---------
# 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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} | null | null | null | **Describe the bug**
I was training a MegatronGPTModel using `megatron_gpt_pretraining.py` and a machine with H100 chips. When I set `transformer_engine=true` I later noticed that my requested `activation=fast-swiglu` was ignored.
I traced the issue back to the [transformer.py](./nemo/collections/nlp/modules/common/megatron/transformer.py) file, in the definition of the class `AutocastTransformerLayer`. In the initializer there is no `activation` parameter being accepted or passed into the layer.
**Steps/Code to reproduce bug**
python megatron_gpt_pretraining.py model.activation=swiglu
**Expected behavior**
The model to use the SwiGLU activation function, which can be seen by the `layernorm_mlp.fc1_weight` parameter (or `mlp.dense_h_to_4h` in the non transformer_engine code) being of dimension `2 * config.ffn_hidden_size` .
**Environment overview (please complete the following information)**
- Environment location: [Docker]
- Method of NeMo install: [from source]. Please specify exact commands you used to install.
```bash
git clone https://github.com/NVIDIA/NeMo.git
DOCKER_BUILDKIT=1 docker build -f Dockerfile -t nemo:latest .
```
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} | # What does this PR do ?
Correctly pass `rotary_pos_emb` and `relative_position_bias` to TE.
**Collection**: [Note which collection this PR will affect]
[NLP]
# Changelog
- nemo/collections/nlp/modules/common/megatron/transformer.py
# 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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"merged_at": "2023-07-28T17:08:07"
} | # What does this PR do ?
Fixes a small indexing bug inside the NoisePerturbationWithNormalization class in perturb.py
**Collection**: ASR
# Changelog
- nemo/collections/asr/parts/preprocessing/perturb.py: added safety check to ensure you don't index into scalar value
# Before your PR is "Ready for review"
**Pre checks**:
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**PR Type**:
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- [X] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
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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"
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If you haven't finished some of the above items you can still open "Draft" PR.
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} | This PR needs mcore dist ckpt for GPT PR to be pushed before merging.
# What does this PR do ?
Adds distributed checkpointing when using mcore gpt.
Distributed checkpointing enables training runs to restart automatically with different model parallel configs.
The checkpoint is saved to disk according to the `sharded_state_dict`:
Below is a sample of what the checkpoint looks like on disk.
``` bash
common.pt model.decoder.layers.self_attention.linear_qkv.weight optimizer.state.exp_avg.model.embedding.word_embeddings.weight optimizer.state.fp32_from_fp16.model.decoder.final_layernorm.bias
metadata.json model.embedding.position_embeddings.weight optimizer.state.exp_avg.model.output_layer.weight optimizer.state.fp32_from_fp16.model.decoder.final_layernorm.weight
model.decoder.final_layernorm.bias model.embedding.word_embeddings.weight optimizer.state.exp_avg_sq.model.decoder.final_layernorm.bias optimizer.state.fp32_from_fp16.model.decoder.layers.input_layernorm.bias
model.decoder.final_layernorm.weight model.output_layer.weight optimizer.state.exp_avg_sq.model.decoder.final_layernorm.weight optimizer.state.fp32_from_fp16.model.decoder.layers.input_layernorm.weight
model.decoder.layers.input_layernorm.bias optimizer.state.exp_avg.model.decoder.final_layernorm.bias optimizer.state.exp_avg_sq.model.decoder.layers.input_layernorm.bias optimizer.state.fp32_from_fp16.model.decoder.layers.mlp.linear_fc1.bias
model.decoder.layers.input_layernorm.weight optimizer.state.exp_avg.model.decoder.final_layernorm.weight optimizer.state.exp_avg_sq.model.decoder.layers.input_layernorm.weight optimizer.state.fp32_from_fp16.model.decoder.layers.mlp.linear_fc1.weight
model.decoder.layers.mlp.linear_fc1.bias optimizer.state.exp_avg.model.decoder.layers.input_layernorm.bias optimizer.state.exp_avg_sq.model.decoder.layers.mlp.linear_fc1.bias optimizer.state.fp32_from_fp16.model.decoder.layers.mlp.linear_fc2.bias
model.decoder.layers.mlp.linear_fc1._extra_state optimizer.state.exp_avg.model.decoder.layers.input_layernorm.weight optimizer.state.exp_avg_sq.model.decoder.layers.mlp.linear_fc1.weight optimizer.state.fp32_from_fp16.model.decoder.layers.mlp.linear_fc2.weight
model.decoder.layers.mlp.linear_fc1.weight optimizer.state.exp_avg.model.decoder.layers.mlp.linear_fc1.bias optimizer.state.exp_avg_sq.model.decoder.layers.mlp.linear_fc2.bias optimizer.state.fp32_from_fp16.model.decoder.layers.post_self_attn_layernorm.bias
model.decoder.layers.mlp.linear_fc2.bias optimizer.state.exp_avg.model.decoder.layers.mlp.linear_fc1.weight optimizer.state.exp_avg_sq.model.decoder.layers.mlp.linear_fc2.weight optimizer.state.fp32_from_fp16.model.decoder.layers.post_self_attn_layernorm.weight
model.decoder.layers.mlp.linear_fc2._extra_state optimizer.state.exp_avg.model.decoder.layers.mlp.linear_fc2.bias optimizer.state.exp_avg_sq.model.decoder.layers.post_self_attn_layernorm.bias optimizer.state.fp32_from_fp16.model.decoder.layers.self_attention.linear_proj.bias
model.decoder.layers.mlp.linear_fc2.weight optimizer.state.exp_avg.model.decoder.layers.mlp.linear_fc2.weight optimizer.state.exp_avg_sq.model.decoder.layers.post_self_attn_layernorm.weight optimizer.state.fp32_from_fp16.model.decoder.layers.self_attention.linear_proj.weight
model.decoder.layers.post_self_attn_layernorm.bias optimizer.state.exp_avg.model.decoder.layers.post_self_attn_layernorm.bias optimizer.state.exp_avg_sq.model.decoder.layers.self_attention.linear_proj.bias optimizer.state.fp32_from_fp16.model.decoder.layers.self_attention.linear_qkv.bias
model.decoder.layers.post_self_attn_layernorm.weight optimizer.state.exp_avg.model.decoder.layers.post_self_attn_layernorm.weight optimizer.state.exp_avg_sq.model.decoder.layers.self_attention.linear_proj.weight optimizer.state.fp32_from_fp16.model.decoder.layers.self_attention.linear_qkv.weight
model.decoder.layers.self_attention.linear_proj.bias optimizer.state.exp_avg.model.decoder.layers.self_attention.linear_proj.bias optimizer.state.exp_avg_sq.model.decoder.layers.self_attention.linear_qkv.bias optimizer.state.fp32_from_fp16.model.embedding.position_embeddings.weight
model.decoder.layers.self_attention.linear_proj._extra_state optimizer.state.exp_avg.model.decoder.layers.self_attention.linear_proj.weight optimizer.state.exp_avg_sq.model.decoder.layers.self_attention.linear_qkv.weight optimizer.state.fp32_from_fp16.model.embedding.word_embeddings.weight
model.decoder.layers.self_attention.linear_proj.weight optimizer.state.exp_avg.model.decoder.layers.self_attention.linear_qkv.bias optimizer.state.exp_avg_sq.model.embedding.position_embeddings.weight optimizer.state.fp32_from_fp16.model.output_layer.weight
model.decoder.layers.self_attention.linear_qkv.bias optimizer.state.exp_avg.model.decoder.layers.self_attention.linear_qkv.weight optimizer.state.exp_avg_sq.model.embedding.word_embeddings.weight
model.decoder.layers.self_attention.linear_qkv._extra_state optimizer.state.exp_avg.model.embedding.position_embeddings.weight optimizer.state.exp_avg_sq.model.output_layer.weight
```
Then inside a module directory we have the sharded tensor:
``` bash
ls model.decoder.layers.mlp.linear_fc1.weight/
0.0.0 1.0.0 10.0.0 11.0.0 12.0.0 13.0.0 14.0.0 15.0.0 2.0.0 3.0.0 4.0.0 5.0.0 6.0.0 7.0.0 8.0.0 9.0.0
```
To implement distributed checkpointing for a model, the `sharded_state_dict` has to be defined.
This is done in megatron core so that in NeMo, if the module is from mcore, we only have to call `module.sharded_state_dict()`.
**Collection**: NLP
# Usage
Usage is automatic when using mcore:
``` bash
model.mcore_gpt=True
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] 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 ?
Set a default value when getting `rampup_batch_size`. Otherwise, configs without setting the value would result in errors.
**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**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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I was trying an online diarizer with reference to this PR(https://github.com/NVIDIA/NeMo/pull/5609). I am using the Nemo Frame VAD and modified it to directly output vad_timestamps (e.g., tensor([[0.0000, 1.5200, 1.5300]])). Then, I call diarize_step function every two seconds, as I'm not quite sure what one "step" (1 frame) represents. However, I only get one speaker output each time, despite calling diarize_step multiple times. Could you please help me identify what might be going wrong? I will provide some relevant code snippets and logs below. Thank you in advance.
```python
MODEL_CONFIG = "/workspace/diar_infer.yaml"
cfg = OmegaConf.load(MODEL_CONFIG)
online_diar = OnlineClusteringDiarizer(cfg)
filepath = "/workspace/test_60s.wav"
waveform, _ = torchaudio.load(filepath)
vad_model = fvad("/workspace/model/vad_multilingual_frame_marblenet.nemo", "/workspace/diar_infer.yaml")
print(f"waveform.size() {waveform.size())}")
online_diar.buffer_end = 60
scale = 2
for step in range(20):
start = step * scale
online_diar.frame_start = start
s = start * 16000
e = s + scale * 16000 #(step + 1) * scale * 16000
cur_wav = waveform[:,: e]
vad_timestamps = vad_model.infer(cur_wav[:, s: ]) + start
print(f"vad_timestamps: {vad_timestamps}")
diar_hyp = online_diar.diarize_step(cur_wav.squeeze(0), vad_timestamps)
print(f"step: {step}, diar_hyp: {diar_hyp}")
```
[log_online1.txt](https://github.com/NVIDIA/NeMo/files/12179113/log_online1.txt)
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https://api.github.com/repos/NVIDIA/NeMo/issues/7112 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7112/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7112/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7112/events | https://github.com/NVIDIA/NeMo/pull/7112 | 1,823,221,886 | PR_kwDOC_bI7s5WfEjI | 7,112 | Include the scripts for preprocessing OAST and unit tests for chat sft datasets | {
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1. include scripts for preprocess OAST datasets.
2. annotate the datasets using reward model.
3. clean up the data for training.
4. include the unit tests to make sure the Chat SFT datasets produced expected masks.
5. add support in the UI to run attribute condition generations.
6. add HF dataset as JSON dataset backend. | {
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} | null | null | null | **Describe the bug**
`nemo.collections.asr.models.EncDecSpeakerLabelModel.verify_speakers` has excessive embeddings normalization. First computed ambeddings are normalized here:
```
# Length Normalize
X = embs1 / torch.linalg.norm(embs1)
Y = embs2 / torch.linalg.norm(embs2)
```
(https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/models/label_models.py#L510)
Then during similarity computing the dot product is divided by `X` and `Y` norms (which are mathematically identical to `1`; computationally very close to `1`):
```
similarity_score = torch.dot(X, Y) / ((torch.dot(X, X) * torch.dot(Y, Y)) ** 0.5)
```
(https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/models/label_models.py#L513)
**Steps/Code to reproduce bug**
Just use `verify_speakers()`. The result is accurate, however doing something excessive doesn't look good.
**Expected behavior**
Don't normalize embeddings twice. Either normalize them first, or divide the dot product of (not normalized) embeddings by the norms. No sense to do both.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal]
- Method of NeMo install: `pip install nemo-toolkit[asr]`.
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS: Ubuntu 22.04
- PyTorch version: 2.0.1
- Python version: 3.10.12
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"html_url": "https://github.com/NVIDIA/NeMo/pull/7110",
"diff_url": "https://github.com/NVIDIA/NeMo/pull/7110.diff",
"patch_url": "https://github.com/NVIDIA/NeMo/pull/7110.patch",
"merged_at": "2023-07-26T19:55:17"
} | # What does this PR do ?
Hopefully fix the microphone access error for the transcription demo on the ASR docs main page.
**Collection**: docs
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | I kept on trying to install nemo toolkit but I keep on getting this error:
```jupyter-notebook
Collecting wget
Downloading wget-3.2.zip (10 kB)
Preparing metadata (setup.py) ... done
Building wheels for collected packages: wget
Building wheel for wget (setup.py) ... done
Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9657 sha256=9d056575abefb14615c70a2e0e473b362f9821047bd7a3da45bbb2d36aab773f
Stored in directory: /root/.cache/pip/wheels/8b/f1/7f/5c94f0a7a505ca1c81cd1d9208ae2064675d97582078e6c769
Successfully built wget
Installing collected packages: wget
Successfully installed wget-3.2
Reading package lists... Done
Building dependency tree... Done
Reading state information... Done
libsndfile1 is already the newest version (1.0.31-2build1).
ffmpeg is already the newest version (7:4.4.2-0ubuntu0.22.04.1).
The following additional packages will be installed:
libid3tag0 libmad0 libopencore-amrnb0 libopencore-amrwb0 libsox-fmt-alsa
libsox-fmt-base libsox3 libwavpack1
Suggested packages:
libsox-fmt-all
The following NEW packages will be installed:
libid3tag0 libmad0 libopencore-amrnb0 libopencore-amrwb0 libsox-fmt-alsa
libsox-fmt-base libsox-fmt-mp3 libsox3 libwavpack1 sox
0 upgraded, 10 newly installed, 0 to remove and 8 not upgraded.
Need to get 729 kB of archives.
After this operation, 2,146 kB of additional disk space will be used.
Get:1 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libid3tag0 amd64 0.15.1b-14 [31.3 kB]
Get:2 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libmad0 amd64 0.15.1b-10ubuntu1 [63.1 kB]
Get:3 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libopencore-amrnb0 amd64 0.1.5-1 [94.8 kB]
Get:4 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libopencore-amrwb0 amd64 0.1.5-1 [49.1 kB]
Get:5 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libsox3 amd64 14.4.2+git20190427-2+deb11u2build0.22.04.1 [240 kB]
Get:6 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libsox-fmt-alsa amd64 14.4.2+git20190427-2+deb11u2build0.22.04.1 [11.2 kB]
Get:7 http://archive.ubuntu.com/ubuntu jammy/main amd64 libwavpack1 amd64 5.4.0-1build2 [83.7 kB]
Get:8 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libsox-fmt-base amd64 14.4.2+git20190427-2+deb11u2build0.22.04.1 [33.7 kB]
Get:9 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libsox-fmt-mp3 amd64 14.4.2+git20190427-2+deb11u2build0.22.04.1 [17.4 kB]
Get:10 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 sox amd64 14.4.2+git20190427-2+deb11u2build0.22.04.1 [104 kB]
Fetched 729 kB in 2s (368 kB/s)
Selecting previously unselected package libid3tag0:amd64.
(Reading database ... 129824 files and directories currently installed.)
Preparing to unpack .../0-libid3tag0_0.15.1b-14_amd64.deb ...
Unpacking libid3tag0:amd64 (0.15.1b-14) ...
Selecting previously unselected package libmad0:amd64.
Preparing to unpack .../1-libmad0_0.15.1b-10ubuntu1_amd64.deb ...
Unpacking libmad0:amd64 (0.15.1b-10ubuntu1) ...
Selecting previously unselected package libopencore-amrnb0:amd64.
Preparing to unpack .../2-libopencore-amrnb0_0.1.5-1_amd64.deb ...
Unpacking libopencore-amrnb0:amd64 (0.1.5-1) ...
Selecting previously unselected package libopencore-amrwb0:amd64.
Preparing to unpack .../3-libopencore-amrwb0_0.1.5-1_amd64.deb ...
Unpacking libopencore-amrwb0:amd64 (0.1.5-1) ...
Selecting previously unselected package libsox3:amd64.
Preparing to unpack .../4-libsox3_14.4.2+git20190427-2+deb11u2build0.22.04.1_amd64.deb ...
Unpacking libsox3:amd64 (14.4.2+git20190427-2+deb11u2build0.22.04.1) ...
Selecting previously unselected package libsox-fmt-alsa:amd64.
Preparing to unpack .../5-libsox-fmt-alsa_14.4.2+git20190427-2+deb11u2build0.22.04.1_amd64.deb ...
Unpacking libsox-fmt-alsa:amd64 (14.4.2+git20190427-2+deb11u2build0.22.04.1) ...
Selecting previously unselected package libwavpack1:amd64.
Preparing to unpack .../6-libwavpack1_5.4.0-1build2_amd64.deb ...
Unpacking libwavpack1:amd64 (5.4.0-1build2) ...
Selecting previously unselected package libsox-fmt-base:amd64.
Preparing to unpack .../7-libsox-fmt-base_14.4.2+git20190427-2+deb11u2build0.22.04.1_amd64.deb ...
Unpacking libsox-fmt-base:amd64 (14.4.2+git20190427-2+deb11u2build0.22.04.1) ...
Selecting previously unselected package libsox-fmt-mp3:amd64.
Preparing to unpack .../8-libsox-fmt-mp3_14.4.2+git20190427-2+deb11u2build0.22.04.1_amd64.deb ...
Unpacking libsox-fmt-mp3:amd64 (14.4.2+git20190427-2+deb11u2build0.22.04.1) ...
Selecting previously unselected package sox.
Preparing to unpack .../9-sox_14.4.2+git20190427-2+deb11u2build0.22.04.1_amd64.deb ...
Unpacking sox (14.4.2+git20190427-2+deb11u2build0.22.04.1) ...
Setting up libsox3:amd64 (14.4.2+git20190427-2+deb11u2build0.22.04.1) ...
Setting up libid3tag0:amd64 (0.15.1b-14) ...
Setting up libopencore-amrwb0:amd64 (0.1.5-1) ...
Setting up libsox-fmt-alsa:amd64 (14.4.2+git20190427-2+deb11u2build0.22.04.1) ...
Setting up libmad0:amd64 (0.15.1b-10ubuntu1) ...
Setting up libwavpack1:amd64 (5.4.0-1build2) ...
Setting up libopencore-amrnb0:amd64 (0.1.5-1) ...
Setting up libsox-fmt-base:amd64 (14.4.2+git20190427-2+deb11u2build0.22.04.1) ...
Setting up libsox-fmt-mp3:amd64 (14.4.2+git20190427-2+deb11u2build0.22.04.1) ...
Setting up sox (14.4.2+git20190427-2+deb11u2build0.22.04.1) ...
Processing triggers for man-db (2.10.2-1) ...
Processing triggers for libc-bin (2.35-0ubuntu3.1) ...
Requirement already satisfied: text-unidecode in /usr/local/lib/python3.10/dist-packages (1.3)
DEPRECATION: git+https://github.com/NVIDIA/[email protected]#egg=nemo_toolkit[all] contains an egg fragment with a non-PEP 508 name pip 25.0 will enforce this behaviour change. A possible replacement is to use the req @ url syntax, and remove the egg fragment. Discussion can be found at https://github.com/pypa/pip/issues/11617
Collecting nemo_toolkit[all]
Cloning https://github.com/NVIDIA/NeMo.git (to revision r1.19.1) to /tmp/pip-install-z_sduiuz/nemo-toolkit_20856122c60348d8bf748ed8dbe6478b
Running command git clone --filter=blob:none --quiet https://github.com/NVIDIA/NeMo.git /tmp/pip-install-z_sduiuz/nemo-toolkit_20856122c60348d8bf748ed8dbe6478b
Running command git checkout -b r1.19.1 --track origin/r1.19.1
Switched to a new branch 'r1.19.1'
Branch 'r1.19.1' set up to track remote branch 'r1.19.1' from 'origin'.
Resolved https://github.com/NVIDIA/NeMo.git to commit 55f8ce5a32ee2a2ec2c50c6df3eb8af585ea2fae
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing metadata (pyproject.toml) ... done
Collecting huggingface-hub (from nemo_toolkit[all])
Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 268.8/268.8 kB 4.7 MB/s eta 0:00:00
Requirement already satisfied: numba in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit[all]) (0.56.4)
Requirement already satisfied: numpy>=1.22 in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit[all]) (1.22.4)
Collecting onnx>=1.7.0 (from nemo_toolkit[all])
Downloading onnx-1.14.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (14.6 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 14.6/14.6 MB 85.3 MB/s eta 0:00:00
Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit[all]) (2.8.2)
Collecting ruamel.yaml (from nemo_toolkit[all])
Downloading ruamel.yaml-0.17.32-py3-none-any.whl (112 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 112.2/112.2 kB 18.6 MB/s eta 0:00:00
Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit[all]) (1.2.2)
Collecting setuptools==65.5.1 (from nemo_toolkit[all])
Downloading setuptools-65.5.1-py3-none-any.whl (1.2 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.2/1.2 MB 24.9 MB/s eta 0:00:00
Requirement already satisfied: tensorboard in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit[all]) (2.12.3)
Requirement already satisfied: text-unidecode in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit[all]) (1.3)
Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit[all]) (2.0.1+cu118)
Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit[all]) (4.65.0)
Requirement already satisfied: wget in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit[all]) (3.2)
Requirement already satisfied: wrapt in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit[all]) (1.14.1)
Collecting black==19.10b0 (from nemo_toolkit[all])
Downloading black-19.10b0-py36-none-any.whl (97 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 97.5/97.5 kB 14.7 MB/s eta 0:00:00
Collecting click==8.0.2 (from nemo_toolkit[all])
Downloading click-8.0.2-py3-none-any.whl (97 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 97.6/97.6 kB 16.4 MB/s eta 0:00:00
Collecting isort<6.0.0,>5.1.0 (from nemo_toolkit[all])
Downloading isort-5.12.0-py3-none-any.whl (91 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 91.2/91.2 kB 9.8 MB/s eta 0:00:00
Collecting parameterized (from nemo_toolkit[all])
Downloading parameterized-0.9.0-py2.py3-none-any.whl (20 kB)
Requirement already satisfied: pytest in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit[all]) (7.2.2)
Collecting pytest-runner (from nemo_toolkit[all])
Downloading pytest_runner-6.0.0-py3-none-any.whl (7.2 kB)
Requirement already satisfied: sphinx in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit[all]) (3.5.4)
Collecting sphinxcontrib-bibtex (from nemo_toolkit[all])
Downloading sphinxcontrib_bibtex-2.5.0-py3-none-any.whl (39 kB)
Collecting wandb (from nemo_toolkit[all])
Downloading wandb-0.15.7-py3-none-any.whl (2.1 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.1/2.1 MB 76.5 MB/s eta 0:00:00
Collecting hydra-core<1.3,>=1.2.0 (from nemo_toolkit[all])
Downloading hydra_core-1.2.0-py3-none-any.whl (151 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 151.1/151.1 kB 23.8 MB/s eta 0:00:00
Collecting omegaconf<2.3,>=2.2 (from nemo_toolkit[all])
Downloading omegaconf-2.2.3-py3-none-any.whl (79 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 79.3/79.3 kB 7.9 MB/s eta 0:00:00
Collecting pytorch-lightning<=1.9.4,>=1.9.0 (from nemo_toolkit[all])
Downloading pytorch_lightning-1.9.4-py3-none-any.whl (827 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 827.8/827.8 kB 80.1 MB/s eta 0:00:00
Collecting pyyaml<6 (from nemo_toolkit[all])
Downloading PyYAML-5.4.1.tar.gz (175 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 175.1/175.1 kB 25.8 MB/s eta 0:00:00
Installing build dependencies ... done
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
Getting requirements to build wheel ... error
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> See above for output.
```
**Environment details**
- Python version 3.10.6
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I am using Ubuntu 22.04.2 LTS**
File "/home/deb/.local/lib/python3.10/site-packages/nemo/core/classes/common.py", line 852, in from_pretrained
instance = class_.restore_from(
File "/home/deb/.local/lib/python3.10/site-packages/nemo/core/classes/modelPT.py", line 435, in restore_from
instance = cls._save_restore_connector.restore_from(
File "/home/deb/.local/lib/python3.10/site-packages/nemo/core/connectors/save_restore_connector.py", line 248, in restore_from
self.load_instance_with_state_dict(instance, state_dict, strict)
File "/home/deb/.local/lib/python3.10/site-packages/nemo/core/connectors/save_restore_connector.py", line 203, in load_instance_with_state_dict
instance.load_state_dict(state_dict, strict=strict)
File "/home/deb/.local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2041, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for PunctuationCapitalizationModel:
Unexpected key(s) in state_dict: "bert_model.embeddings.position_ids". | {
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} | # What does this PR do ?
Add BLOOM model support to NeMo.
**Collection**: [Note which collection this PR will affect]
[NLP]
# Changelog
- Add BLOOM model architecture.
- Add input embedding layernorm to the language model.
- Add model configurations.
- A conversion script to convert HuggingFace checkpoints to `.nemo` file.
- Configurations and scripts for pertaining, evaluation, and SFT.
# 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 ?
Fix quotes causing syntax error in notebook json
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
Fix alibi position embedding for causal attention.
**Collection**: NLP
# Changelog
Existing: returns (1, num_heads, 1, key_length)
Fixed: returns (1, num_heads, query_length, key_length), where for ith query, the bias is m · [−(i − 1), ..., −2, −1, 0] for the first i keys, as per the alibi paper
**PR Type**:
- [ ] Bugfix
## 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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```
(...)
File "/asr/asr_solution.py", line 34, in <module>
import nemo.collections.asr as nemo_asr # NeMo's ASR library
File "/usr/local/lib/python3.10/site-packages/nemo/collections/asr/__init__.py", line 15, in <module>
from nemo.collections.asr import data, losses, models, modules
File "/usr/local/lib/python3.10/site-packages/nemo/collections/asr/losses/__init__.py", line 15, in <module>
from nemo.collections.asr.losses.angularloss import AngularSoftmaxLoss
File "/usr/local/lib/python3.10/site-packages/nemo/collections/asr/losses/angularloss.py", line 18, in <module>
from nemo.core.classes import Loss, Typing, typecheck
File "/usr/local/lib/python3.10/site-packages/nemo/core/__init__.py", line 16, in <module>
from nemo.core.classes import *
File "/usr/local/lib/python3.10/site-packages/nemo/core/classes/__init__.py", line 16, in <module>
from nemo.core.classes.common import (
File "/usr/local/lib/python3.10/site-packages/nemo/core/classes/common.py", line 31, in <module>
from nemo.utils import logging
File "/usr/local/lib/python3.10/site-packages/nemo/utils/__init__.py", line 22, in <module>
from nemo.utils.lightning_logger_patch import add_memory_handlers_to_pl_logger
File "/usr/local/lib/python3.10/site-packages/nemo/utils/lightning_logger_patch.py", line 18, in <module>
import pytorch_lightning as pl
File "/usr/local/lib/python3.10/site-packages/pytorch_lightning/__init__.py", line 20, in <module>
from pytorch_lightning import metrics # noqa: E402
File "/usr/local/lib/python3.10/site-packages/pytorch_lightning/metrics/__init__.py", line 15, in <module>
from pytorch_lightning.metrics.classification import ( # noqa: F401
File "/usr/local/lib/python3.10/site-packages/pytorch_lightning/metrics/classification/__init__.py", line 14, in <module>
from pytorch_lightning.metrics.classification.accuracy import Accuracy # noqa: F401
File "/usr/local/lib/python3.10/site-packages/pytorch_lightning/metrics/classification/accuracy.py", line 18, in <module>
from pytorch_lightning.metrics.utils import deprecated_metrics, void
File "/usr/local/lib/python3.10/site-packages/pytorch_lightning/metrics/utils.py", line 22, in <module>
from torchmetrics.utilities.data import get_num_classes as _get_num_classes
ImportError: cannot import name 'get_num_classes' from 'torchmetrics.utilities.data' (/usr/local/lib/python3.10/site-packages/torchmetrics/utilities/data.py)
```
This does not happen in my local virtual environment, but in a docker container, so I am guessing that somehow not all necessary files have been installed when building the container. However, `nemo_toolkit[asr]` is part of the libs being pipped via Dockerfile, and I thought all necessary requirements were contained in that installation. Should I add anything specific to the requirements?
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} | # What does this PR do ?
In certain scenarios(ex: single stage embeddings) there are no gradients for a rank with sequence parallel. This PR will skip the all reduce on those ranks.
**Collection**: [NLP]
# Changelog
- skip seqparallel all reduce on no grads
# 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.
# Additional Information
* Related to # (issue)
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Fix quotes causing syntax error in notebook json
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | I run the MSDD model on Nvidia A10 (24GB), but the inference is too slow, I looked on the code and there is a lot of traffic between the CPU and GPU and vice versa.
most of the time GPU utilization is on 0%
First the data is split into short segments according to the number of scales (I have 5 scales).
After each scale splitting the embedding extraction is applied and save the embedding to pkl file.
Then the the clustering is applied and finally the MSDD is applied.
Is there something that can be done in order to speed up the inference?
Is there any flag for parallelism the embedding extraction stage?
please help.
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} | # What does this PR do ?
This fixes a regression introduced in 39aff5ca3d52247e136a86429d567255c02a5d44 when joining paths was changed from string concatenation to `os.path.join`. At the moment the call disregards the first part (`frame_pred_dir`) and always returns a path starting with `/overlap_smoothing_output`.
**Collection**: asr
# Changelog
- Remove leading slash in argument to `os.path.join`
# 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
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I was trying to find a place where SLU metrics are computed. AFAIU this is going to be [here](https://github.com/NVIDIA/NeMo/blob/main/examples/slu/speech_intent_slot/eval_utils/evaluator.py#L92). But I can't find where ```ErrorMetric ``` class is defined. Are you planning to make this code publicly available?
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} | null | null | null | I think currently, the RNN-t loss supports any padding for every example in batch except the longest example. What is the nature of such limitation? (as it seems that the underlying code is already able to handle padding, so not clear why the longest example is different)
Original discussion: https://github.com/NVIDIA/NeMo/issues/6797#issuecomment-1589149555 | {
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} | # What does this PR do ?
Add GQA config in GPT config file.
__Note__ that this PR merge into `mcore_gpt_path` not `main`.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* Set `num_query_groups` in `MegatronGPTModel` `cfg.model`. If `None`, normal (multi-head) attention is used. If `1`, multi-query attention is used.
# 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 fixes a bug reported by @wdykas, where https://github.com/NVIDIA/NeMo/pull/5684 significantly increased the size of GPT checkpoints.
I think this is because this PR configures the model parameters so they are all views into a single large buffer. However, PyTorch tensors save their underlying memory buffer when pickled, so checkpointing the model results in multiple redundant copies of all the parameters. This PR disables this behavior by default, at the expense of adding an extra copy after each param all-gather.
**Collection**: NLP
# Changelog
- Disable distopt contiguous param buffer by default.
# Usage
This fix is applied by default. To restore the old behavior, configure the optimizer with `contiguous_param_buffer=True`.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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.
Pinging @wdykas.
# Additional Information
* Fixes bug from https://github.com/NVIDIA/NeMo/pull/5684.
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} | # What does this PR do ?
This PR adds the following items related to multi-lookahead cache-aware streaming models:
+ documentation
+ support of multi-lookahead for transcribe_speech.py, transcribe_speech_parallel.py, and speech_to_text_eval.py,
+ one pre-trained model which is uploaded on NGC and supports 0s, 80ms, 480ms and 1040ms:
https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_fastconformer_hybrid_large_streaming_multi
# Changelog
- Added a multi-lookahead cache-aware streaming model which supports multiple look-ahead in one single model.
# Usage
You may switch between different supported look-aheads by:
1- calling the set_default_att_context_size method of the model's encoder like this:
```python
asr_model.encoder.set_default_att_context_size(att_context_size=[70,1])
```
2- set att_context_size when using transcribe_speech.py, transcribe_speech_parallel.py, or speech_to_text_eval.py.
**PR Type**:
- [X] New Feature
- [ ] Bugfix
- [X] Documentation
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} | # What does this PR do ?
This PR adds a path to use GPTModel from Megatron Core.
Requirements:
Make sure that megatron core is installed from a recent commit:
```
git clone https://github.com/NVIDIA/Megatron-LM.git && \
cd Megatron-LM && \
git checkout 3316e811cc5335ee24c2d203416d864edcf2f7a8 && \
pip install -e .
```
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
Set `mcore_gpt=True` in the `MegatronGPTModel` config.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adds a script which extracts pure CTC or pure RNNT models from ASR hybrid models in .nemo format
**Collection**: ASR
# Changelog
- Added new script in examples/asr/asr_hybrid_transducer_ctc/helpers/convert_nemo_asr_hybrid_to_ctc.py
# Usage
* You can potentially add a usage example below
```shell
python convert_nemo_asr_hybrid_to_ctc.py -i /path/to/hybrid.nemo -o /path/to/saved_ctc_model.nemo -m 'ctc'|'rnnt'
```
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [X] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Megatron-LM has changed its function APIs in tensor_parallel, where a lot of params (e.g., `use_cpu_initialization`) are moved into a config object (e.g., `megatron.core.model_parallel_config.ModelParallelConfig`), for example [here](https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/tensor_parallel/layers.py#L148)
This PR fixes such errors when trying to use those functions with the latest Megatron-LM codebase, but there may be some other missed functions.
**Collection**: [NLP]
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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**:
- [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
- [ ] 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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