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} | # What does this PR do ?
Support activation recomputation for SFT.
# Changelog
- Remove disabling recomputation setting reset at SFT/train.
# Usage
N/A
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] 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 ?
Revert `model.peft.lora_tuning.target_modules` back to `attention_qkv` since some community models does not support lora for all target modules.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [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 ?
Fix pipeline parallel for Gemma. When using PP, embedding scaling only applies to the first stage since `embedding` does not exist after the first stage.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [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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The NVIDIA/NeMo#8689 initializes the `PipelineMixedPrecisionPlugin` incorrectly. The `FSDPPrecision.__init__` is not called, and in result the `self._desired_input_dtype` field is not set.
Minimalistic repro:
```bash
python -c "from nemo.collections.nlp.parts.nlp_overrides import PipelineMixedPrecisionPlugin as PM; PM('16-mixed', 'cuda').tensor_init_context()"
```
The `FSDPPrecision.__init__` is not called, because it doesn't have the `device` argument. | {
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} | # What does this PR do ?
Add support for narrowband augmentations in lhotse dataloaders and fix small bug in noise augmentation
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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Removes mandatory installation of OpenAI Triton from requirements.txt
**Collection**: nlp
# Changelog
- Remove triton from requirements.txt
- Remove assertion errors for pinned triton versions
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Follow up on quantization docs.
**Collection**: NLP
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to https://github.com/NVIDIA/NeMo/pull/8730
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"merged_at": null
} | # What does this PR do ?
Update embedding method interface to match a change in mcore.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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"jenkins",
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} | # What does this PR do ?
Enable per sample val loss reduction with validation_drop_last=False.
Fixes seeding for Packed dataset
**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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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"Please use `python 3.10`, this is issue with mismatch of versions of python",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days."
] | 2024-04-03T09:57:06 | 2024-05-08T18:13:29 | 2024-05-08T18:13:29 | NONE | {
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} | null | null | null | import os
import wget
from omegaconf import OmegaConf
import json
import shutil
from faster_whisper import WhisperModel
import whisperx
import torch
from pydub import AudioSegment
from nemo.collections.asr.models.msdd_models import NeuralDiarizer
from deepmultilingualpunctuation import PunctuationModel
import re
import logging
import nltk
from whisperx.alignment import DEFAULT_ALIGN_MODELS_HF, DEFAULT_ALIGN_MODELS_TORCH
from whisperx.utils import LANGUAGES, TO_LANGUAGE_CODE
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [18], in <cell line: 10>()
8 import torch
9 from pydub import AudioSegment
---> 10 from nemo.collections.asr.models.msdd_models import NeuralDiarizer
11 from deepmultilingualpunctuation import PunctuationModel
12 import re
File ~/anaconda3/lib/python3.9/site-packages/nemo/collections/asr/__init__.py:15, in <module>
1 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License");
(...)
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
---> 15 from nemo.collections.asr import data, losses, models, modules
16 from nemo.package_info import __version__
18 # Set collection version equal to NeMo version.
File ~/anaconda3/lib/python3.9/site-packages/nemo/collections/asr/models/__init__.py:15, in <module>
1 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License");
(...)
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
---> 15 from nemo.collections.asr.models.asr_model import ASRModel
16 from nemo.collections.asr.models.audio_to_audio_model import AudioToAudioModel
17 from nemo.collections.asr.models.classification_models import EncDecClassificationModel, EncDecFrameClassificationModel
File ~/anaconda3/lib/python3.9/site-packages/nemo/collections/asr/models/aed_multitask_models.py:31, in <module>
28 from torchmetrics.text import SacreBLEUScore
29 from tqdm.auto import tqdm
---> 31 from nemo.collections.asr.data.audio_to_text_lhotse_prompted import (
32 PromptedAudioToTextLhotseDataset,
33 get_prompt_format_fn,
34 )
35 from nemo.collections.asr.models.asr_model import ASRModel, ExportableEncDecModel
36 from nemo.collections.asr.parts.mixins import ASRBPEMixin
File ~/anaconda3/lib/python3.9/site-packages/nemo/collections/asr/data/audio_to_text_lhotse_prompted.py:22, in <module>
19 from lhotse.dataset import AudioSamples
20 from lhotse.dataset.collation import collate_vectors
---> 22 from nemo.collections.asr.data.audio_to_text_lhotse import TokenizerWrapper
23 from nemo.collections.common.tokenizers import CanaryTokenizer, TokenizerSpec
26 class PromptedAudioToTextLhotseDataset(torch.utils.data.Dataset):
File ~/anaconda3/lib/python3.9/site-packages/nemo/collections/asr/data/audio_to_text_lhotse.py:59, in <module>
55 tokens = collate_vectors(tokens, padding_value=0)
56 return audio, audio_lens, tokens, token_lens
---> 59 class TokenizerWrapper:
60 """
61 Provide a unified interface for NeMo Tokenizer, AggregateTokenizer, and (char) Parser.
62 """
64 def __init__(self, tokenizer):
File ~/anaconda3/lib/python3.9/site-packages/nemo/collections/asr/data/audio_to_text_lhotse.py:73, in TokenizerWrapper()
70 else:
71 self._impl = self._call_parser
---> 73 def __call__(self, text: str, lang: str | None = None):
74 return self._impl(text, lang)
76 def _call_agg_tokenizer(self, text: str, lang: str | None = None):
TypeError: unsupported operand type(s) for |: 'type' and 'NoneType'
this is what is got while importing the above ones | {
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"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days."
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Enable per sample val loss reduction with validation_drop_last=False.
Fixes setting seed for Packed dataset
**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
```
# Jenkins CI
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# Usage
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```python
# Add a code snippet demonstrating how to use this
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# Jenkins CI
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# Before your PR is "Ready for review"
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If you haven't finished some of the above items you can still open "Draft" PR.
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you 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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# Changelog
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# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
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# Before your PR is "Ready for review"
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- [ ] 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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- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
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```python
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## Who can review?
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} | # What does this PR do ?
When training Megatron-core GPT with the distributed optimizer, the embedding gradients were reduced in the grad sync dtype (usually BF16). However, we want to reduce in FP32 to improve convergence.
**Collection**: NLP
# Changelog
- Make sure GPT embedding grads are reduced in FP32.
# Usage
Run GPT, e.g. with the config at https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml.
Enable mcore with `model.mcore_gpt=True` and the distributed optimizer with `model.optim.name=distributed_fused_adam`.
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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
* mcore support was added in https://github.com/NVIDIA/NeMo/pull/7093
* Distopt support for mixed grad dtypes was added in https://github.com/NVIDIA/NeMo/pull/5920 | {
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} | parallel_state.is_unitialized is deprecated, switching to parallel_state.is_initialized.
# What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"jenkins"
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} | # What does this PR do ?
Add an option to use NCCL memory allocator for AG buffer, which helps achieve zero-copy. A similar change has been done in DistOpt in Apex to support NCCL UB for RS. More details can be also found in the [documentation](https://gitlab-master.nvidia.com/dl/pytorch/apex/-/commit/80df9fc505fe0c2c07ffd054161674759c52ee87#351ce33af69db96f30d2878b31136de9b9400210_364_370) in this APEX [commit](https://gitlab-master.nvidia.com/dl/pytorch/apex/-/commit/80df9fc505fe0c2c07ffd054161674759c52ee87)
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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] | 2024-04-02T16:46:58 | 2024-04-26T01:44:56 | 2024-04-26T01:44:55 | NONE | {
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} | # What does this PR do ?
Makes sure the `map_location` parameter is used correctly.
**Collection**: only affects save_restore_connector.py
# Changelog
- use the function's `map_location` parameter instead of ignoring it
**PR Type**:
- [ ] New Feature
- [X ]"Bugfix"
- [ ] Documentation
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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"Please rebase to latest main, CI is broken -> stopped your pipeline. Thanks.",
"<!-- pylint-output-strict-mode-false -->\n\nbeep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please try helping us to improve the code base.\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module nemo.collections.nlp.data.language_modeling.megatron.gpt_sft_chat_dataset\nnemo/collections/nlp/data/language_modeling/megatron/gpt_sft_chat_dataset.py:113:0: C0301: Line too long (165/119) (line-too-long)\nnemo/collections/nlp/data/language_modeling/megatron/gpt_sft_chat_dataset.py:116:0: C0301: Line too long (144/119) (line-too-long)\nnemo/collections/nlp/data/language_modeling/megatron/gpt_sft_chat_dataset.py:147:0: C0301: Line too long (132/119) (line-too-long)\nnemo/collections/nlp/data/language_modeling/megatron/gpt_sft_chat_dataset.py:156:0: C0301: Line too long (165/119) (line-too-long)\nnemo/collections/nlp/data/language_modeling/megatron/gpt_sft_chat_dataset.py:177:0: C0301: Line too long (169/119) (line-too-long)\nnemo/collections/nlp/data/language_modeling/megatron/gpt_sft_chat_dataset.py:240:0: C0301: Line too long (124/119) (line-too-long)\nnemo/collections/nlp/data/language_modeling/megatron/gpt_sft_chat_dataset.py:59:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/data/language_modeling/megatron/gpt_sft_chat_dataset.py:182:0: C0116: Missing function or method docstring (missing-function-docstring)\nnemo/collections/nlp/data/language_modeling/megatron/gpt_sft_chat_dataset.py:311:0: C0115: Missing class docstring (missing-class-docstring)\n\n-----------------------------------\nYour code has been rated at 9.52/10\n```\n\n---\n\nMitigation guide:\n\n* Add sensible and useful docstrings to functions and methods\n* For trivial methods like getter/setters, consider adding `# pylint: disable=C0116` inside the function itself\n* To disable multiple functions/methods at once, put a `# pylint: disable=C0116` before the first and a `# pylint: enable=C0116` after the last.\n\nBy applying these rules, we reduce the occurance of this message in future.\n\nThank you for improving NeMo's documentation!"
] | 2024-04-02T13:57:39 | 2025-01-18T00:58:58 | 2025-01-18T00:58:58 | CONTRIBUTOR | {
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} | # What does this PR do ?
when we use nemo for sft FP8 training, it will reports
` File "/usr/local/lib/python3.10/dist-packages/torch/autograd/function.py", line 289, in apply
return user_fn(self, *args)
File "/usr/local/lib/python3.10/dist-packages/transformer_engine/pytorch/module/linear.py", line 447, in backward
wgrad, _ = fp8_gemm(
File "/usr/local/lib/python3.10/dist-packages/transformer_engine/pytorch/cpp_extensions/gemm.py", line 45, in fp8_gemm
assert_dim_for_fp8_exec(A)
File "/usr/local/lib/python3.10/dist-packages/transformer_engine/pytorch/utils.py", line 221, in assert_dim_for_fp8_exec
assert check_dim_for_fp8_exec(tensor), (
AssertionError: Tensor dimensions are not compatible for FP8 execution: (3584 % 8 != 0, 1912 % 16 != 0)`
fp8 GEMM requires tensor dimension be multiple of 16. This means the sequence dimension of input data should be padded to multiple of 16, which is not implemented in NeMo chat style data loader yet.
so need to change
`max_length = min(self.max_seq_length, self._ceil_to_nearest(max_length, 8)`
to
`max_length = min(self.max_seq_length, self._ceil_to_nearest(max_length, 16)`
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"I'm having the same problem for mistral 7B PEFT when running this command:\r\n`\r\npython3 /opt/NeMo/scripts/checkpoint_converters/convert_mistral_7b_hf_to_nemo.py --input_name_or_path=/workspace/mistral-7B-hf --output_path=mistral.nemo\r\n`\r\n\r\nThis is the error:\r\n`\r\n[NeMo I 2024-04-02 17:52:08 convert_mistral_7b_hf_to_nemo:149] loading checkpoint 1: /workspace/mistral-7B-hf\r\nin_dir: /workspace/mistral-7B-hf\r\nLoading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]\r\nTraceback (most recent call last):\r\n File \"/opt/NeMo/scripts/checkpoint_converters/convert_mistral_7b_hf_to_nemo.py\", line 339, in <module>\r\n convert(args)\r\n File \"/opt/NeMo/scripts/checkpoint_converters/convert_mistral_7b_hf_to_nemo.py\", line 151, in convert\r\n model_args, ckpt, tokenizer = load_mistral_ckpt(args.input_name_or_path)\r\n File \"/opt/NeMo/scripts/checkpoint_converters/convert_mistral_7b_hf_to_nemo.py\", line 140, in load_mistral_ckpt\r\n model = AutoModelForCausalLM.from_pretrained(in_dir)\r\n File \"/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py\", line 563, in from_pretrained\r\n return model_class.from_pretrained(\r\n File \"/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py\", line 3671, in from_pretrained\r\n ) = cls._load_pretrained_model(\r\n File \"/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py\", line 4078, in _load_pretrained_model\r\n state_dict = load_state_dict(shard_file, is_quantized=is_quantized)\r\n File \"/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py\", line 507, in load_state_dict\r\n with safe_open(checkpoint_file, framework=\"pt\") as f:\r\nFileNotFoundError: No such file or directory: \"/workspace/mistral-7B-hf/model-00001-of-00002.safetensors\"\r\n`\r\n\r\nI also tried to do fine tuning of llama2 7B but it gave me the same error, I cannot convert the checkpoints to .nemo format because the checkpoints cannot be loaded",
"@frankh077 \r\nCould you able to run the docker command\r\n```docker run --gpus device=1 --shm-size=2g --net=host --ulimit memlock=-1 --rm -it -v ${PWD}:/workspace -w /workspace -v ${PWD}/results:/results nvcr.io/ea-bignlp/ga-participants/nemofw-training:23.08.03 bash```\r\nspecified in the official documentation? - https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/llama2peft.html#step-2-convert-to-nemo",
"@pradeepdev-1995 \r\nYes but through the `nemo-framework-training` container, this is the command I used:\r\n\r\n`docker run --gpus device=1 --shm-size=2g --net=host --ulimit memlock=-1 --rm -it -v ${PWD}:/workspace -w /workspace -v ${PWD}/results:/results nvcr.io/nvaie/nemo-framework-training:23.08.03 bash`",
"is this container setup is mandatory? @frankh077 \r\nShall we do the fine-tuning directly in the Python console without using the container?",
"I think it is mandatory since the environment and the necessary tools are in the container, but if you can build the environment it should work, you can base on the NeMo containers available through the NGC",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-04-02T09:57:31 | 2024-05-11T01:45:03 | 2024-05-11T01:45:02 | NONE | {
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} | null | null | null | I am planning to finetune the llama model using the PEFT technique according to this official documentation
https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/llama2peft.html
However, I am facing some issues when I convert the huggingface model to .nemo format.
So is there any repository of ready-to-use **.nemo** models(llama.nemo, mistral.nemo, openchat.nemo....etc) that are compatible with PEFT training steps mentioned in this official documentation?
If so I can skip the huggingface to .nemo conversion step and resume the next steps of fine-tuning
@okuchaiev | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/8785 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8785/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8785/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8785/events | https://github.com/NVIDIA/NeMo/issues/8785 | 2,219,805,670 | I_kwDOC_bI7s6ET4vm | 8,785 | cannot load reward model from SFT model because of missing keys | {
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"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-04-02T07:46:37 | 2024-05-10T01:46:22 | 2024-05-10T01:46:21 | NONE | {
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} | null | null | null | I converted a llama model to nemo, with model dirs like below:
When I tried to load it to train a reward model, I got missing keys error. I load it from the default config, set `load_base_model_only=True`, the total load code is as below:
`ptl_model = load_from_nemo(
reward_model_cls,
cfg.model,
trainer,
strict=True,
load_base_model_only=True,
restore_path=cfg.pretrained_checkpoint.restore_from_path,
)`
And then I got the error below, any advice on how to load a pretrained non-reward model to train as a reward model in Nemo?
```
Error executing job with overrides: []
Traceback (most recent call last):
File "/checkpoint/binary/train_package/megatron/core/dist_checkpointing/serialization.py", line 206, in load_sharded_object
loaded_obj = torch.load(load_path)
File "/opt/conda/envs/python3.10.13/lib/python3.10/site-packages/torch/serialization.py", line 998, in load
with _open_file_like(f, 'rb') as opened_file:
File "/opt/conda/envs/python3.10.13/lib/python3.10/site-packages/torch/serialization.py", line 445, in _open_file_like
return _open_file(name_or_buffer, mode)
File "/opt/conda/envs/python3.10.13/lib/python3.10/site-packages/torch/serialization.py", line 426, in __init__
super().__init__(open(name, mode))
FileNotFoundError: [Errno 2] No such file or directory: '/mnt/workspace/models/LLaMA-2-7B-32K-Nemo-Official/model_weights/model.rm_head._extra_state/shard_0_1.pt'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/checkpoint/binary/train_package/train_reward_model.py", line 68, in main
ptl_model = load_from_nemo(
File "/checkpoint/binary/train_package/nemo_aligner/utils/utils.py", line 96, in load_from_nemo
model = cls.restore_from(
File "/checkpoint/binary/train_package/nemo/collections/nlp/models/nlp_model.py", line 465, in restore_from
return super().restore_from(
File "/checkpoint/binary/train_package/nemo/core/classes/modelPT.py", line 450, in restore_from
instance = cls._save_restore_connector.restore_from(
File "/checkpoint/binary/train_package/nemo_aligner/utils/utils.py", line 52, in restore_from
output = super().restore_from(*args, **kwargs)
File "/checkpoint/binary/train_package/nemo/collections/nlp/parts/nlp_overrides.py", line 1123, in restore_from
checkpoint = dist_checkpointing.load(
File "/checkpoint/binary/train_package/megatron/core/dist_checkpointing/serialization.py", line 120, in load
sharded_objects, sharded_state_dict = load_sharded_objects(
File "/checkpoint/binary/train_package/megatron/core/dist_checkpointing/serialization.py", line 221, in load_sharded_objects
return dict_list_map_inplace(load_sharded_object, sharded_objects), sharded_state_dict
File "/checkpoint/binary/train_package/megatron/core/dist_checkpointing/dict_utils.py", line 180, in dict_list_map_inplace
x[k] = dict_list_map_inplace(f, v)
File "/checkpoint/binary/train_package/megatron/core/dist_checkpointing/dict_utils.py", line 180, in dict_list_map_inplace
x[k] = dict_list_map_inplace(f, v)
File "/checkpoint/binary/train_package/megatron/core/dist_checkpointing/dict_utils.py", line 184, in dict_list_map_inplace
return f(x)
File "/checkpoint/binary/train_package/megatron/core/dist_checkpointing/serialization.py", line 218, in load_sharded_object
raise CheckpointingException(err_msg) from e
megatron.core.dist_checkpointing.core.CheckpointingException: Object shard /mnt/workspace/models/LLaMA-2-7B-32K-Nemo-Official/model_weights/model.rm_head._extra_state/shard_0_1.pt not found
```
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https://api.github.com/repos/NVIDIA/NeMo/issues/8784 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8784/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8784/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8784/events | https://github.com/NVIDIA/NeMo/issues/8784 | 2,219,350,020 | I_kwDOC_bI7s6ESJgE | 8,784 | No disk space left while loading llama2-70B for SFT | {
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"This is due to the `/tmp` folder inside your container does not have enough space. Because NeMo will untar the `.nemo` file into that folder, for 70B model, it needs a lot of space. You may mount an empty dir in host to `/tmp` in your container.",
"Right it's better to untar such large models with tar -xvf xyz.nemo /path and then use save restore connector to restore the model by explicitly stating the path of the extracted dir. There are some examples of this in inference scripts in LLM directories ",
"Here is an example - https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/megatron_gpt_eval.py#L185-L187 and pass the connector to restore_from \r\nhttps://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/megatron_gpt_eval.py#L238",
"Thanks @qijiaxing and @titu1994 for reply. \r\napplying tar command on .nemo files and updating model.restore_from_path to path solves issue I was facing. ",
"@qijiaxing @titu1994 , reopening as getting following error after training all steps.\r\n\r\ncmd\r\n\r\n```\r\n WORLD_SIZE=16 srun --kill-on-bad-exit=0 -N 2 --ntasks-per-node=8 --cpus-per-task=24 --ntasks=16 --container-image=\"docker://nvcr.io#nvidia/nemo:24.01.01.framework\" --container-name=nemo_llama_slurm --container-mounts=\"${_cont_mounts}\" python /opt/NeMo/examples/nlp/language_modeling/tuning/megatron_gpt_sft.py trainer.precision=bf16 trainer.devices=8 trainer.num_nodes=2 trainer.val_check_interval=1.0 trainer.max_steps=5 model.restore_from_path=${MODEL} model.micro_batch_size=1 model.global_batch_size=128 model.tensor_model_parallel_size=${TP_SIZE} model.activations_checkpoint_num_layers=1 model.pipeline_model_parallel_size=${PP_SIZE} model.megatron_amp_O2=True model.sequence_parallel=False model.activations_checkpoint_granularity=full model.activations_checkpoint_method=uniform model.optim.name=distributed_fused_adam model.optim.lr=5e-6 model.answer_only_loss=True model.data.train_ds.file_names=${TRAIN} model.data.validation_ds.file_names=${VALID} model.data.test_ds.file_names=${TEST} model.data.train_ds.concat_sampling_probabilities=${CONCAT_SAMPLING_PROBS} model.data.train_ds.max_seq_length=512 model.data.validation_ds.max_seq_length=512 model.data.train_ds.micro_batch_size=1 model.data.train_ds.global_batch_size=128 model.data.validation_ds.micro_batch_size=1 model.data.validation_ds.global_batch_size=128 model.data.test_ds.micro_batch_size=1 model.data.test_ds.global_batch_size=256 model.data.train_ds.num_workers=0 model.data.validation_ds.num_workers=0 model.data.test_ds.num_workers=0 model.data.validation_ds.metric.name=loss model.data.test_ds.metric.name=loss exp_manager.create_wandb_logger=False exp_manager.explicit_log_dir=/workspace/result exp_manager.resume_if_exists=True exp_manager.resume_ignore_no_checkpoint=True exp_manager.create_checkpoint_callback=True exp_manager.checkpoint_callback_params.monitor=validation_loss exp_manager.checkpoint_callback_params.save_best_model=False exp_manager.checkpoint_callback_params.save_nemo_on_train_end=False ++cluster_type=BCP\r\n```\r\n\r\n```\r\nSet the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace. \r\nTraceback (most recent call last): \r\n File \"/opt/NeMo/examples/nlp/language_modeling/tuning/megatron_gpt_sft.py\", line 236, in main \r\n trainer.fit(model) \r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py\", line 532, in fit \r\n call._call_and_handle_interrupt( \r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py\", line 42, in _call_and_handle_interrupt \r\n return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs) \r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/strategies/launchers/subprocess_script.py\", line 93, in launch \r\n return function(*args, **kwargs) \r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py\", line 571, in _fit_impl \r\n self._run(model, ckpt_path=ckpt_path) \r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py\", line 980, in _run \r\n results = self._run_stage() \r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py\", line 1023, in _run_stage \r\n self.fit_loop.run() \r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/fit_loop.py\", line 202, in run \r\n self.advance() \r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/fit_loop.py\", line 355, in advance \r\n self.epoch_loop.run(self._data_fetcher) \r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/training_epoch_loop.py\", line 134, in run \r\n self.on_advance_end() \r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/training_epoch_loop.py\", line 249, in on_advance_end \r\n self.val_loop.run() \r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/utilities.py\", line 181, in _decorator \r\n return loop_run(self, *args, **kwargs) \r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/evaluation_loop.py\", line 122, in run \r\n return self.on_run_end() \r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/evaluation_loop.py\", line 258, in on_run_end \r\n self._on_evaluation_end() \r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/evaluation_loop.py\", line 303, in _on_evaluation_end \r\n call._call_callback_hooks(trainer, hook_name, *args, **kwargs) \r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py\", line 194, in _call_callback_hooks \r\n fn(trainer, trainer.lightning_module, *args, **kwargs) \r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/callbacks/model_checkpoint.py\", line 311, in on_validation_end \r\n self._save_topk_checkpoint(trainer, monitor_candidates) \r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/callbacks/model_checkpoint.py\", line 360, in _save_topk_checkpoint \r\n self._save_monitor_checkpoint(trainer, monitor_candidates) \r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/callbacks/model_checkpoint.py\", line 663, in _save_monitor_checkpoint \r\n self._update_best_and_save(current, trainer, monitor_candidates) \r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/callbacks/model_checkpoint.py\", line 714, in _update_best_and_save \r\n self._save_checkpoint(trainer, filepath) \r\n File \"/opt/NeMo/nemo/utils/callbacks/nemo_model_checkpoint.py\", line 383, in _save_checkpoint \r\n super()._save_checkpoint(trainer, filepath) \r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/callbacks/model_checkpoint.py\", line 365, in _save_checkpoint \r\n trainer.save_checkpoint(filepath, self.save_weights_only) \r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py\", line 1316, in save_checkpoint \r\n self._checkpoint_connector.save_checkpoint(filepath, weights_only=weights_only, storage_options=storage_options) \r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py\", line 507, in save_checkpoint \r\n self.trainer.strategy.save_checkpoint(_checkpoint, filepath, storage_options=storage_options) \r\n File \"/opt/NeMo/nemo/collections/nlp/parts/nlp_overrides.py\", line 356, in save_checkpoint \r\n dist_checkpointing.save(sharded_state_dict=checkpoint, checkpoint_dir=checkpoint_dir) \r\n File \"/opt/megatron-lm/megatron/core/dist_checkpointing/serialization.py\", line 278, in save \r\n save_config( \r\n File \"/opt/megatron-lm/megatron/core/dist_checkpointing/core.py\", line 76, in save_config \r\n with config_path.open('w') as f:\r\n File \"/opt/megatron-lm/megatron/core/dist_checkpointing/core.py\", line 76, in save_config \r\n with config_path.open('w') as f:\r\n File \"/usr/lib/python3.10/pathlib.py\", line 1119, in open\r\n return self._accessor.open(self, mode, buffering, encoding, errors,\r\nFileNotFoundError: [Errno 2] No such file or directory: '/workspace/result/checkpoints/megatron_gpt_sft--validation_loss=1.526-step=5-consumed_samples=640.0/metadata.json'\r\n```\r\n",
"Also how can I adapt Tiny Shakespeare dataset?",
"By default, there is no `/workspace/result` folder inside NeMo container. Can you try give an existing dir to `exp_manager.explicit_log_dir`",
"> Also how can I adapt Tiny Shakespeare dataset?\r\n\r\nSFT normally requires data to be in style of <instruction, response>. But the dataset you mentioned is not this type. Maybe you can use it as pretrain?",
"> By default, there is no `/workspace/result` folder inside NeMo container. Can you try give an existing dir to `exp_manager.explicit_log_dir`\r\n\r\nI mount my current working directory as /workspace and I already have result directory created at that path. so it should be valid path. ",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale.",
"```py\nimport nemo.collections.asr as nemo_asr\nfrom nemo.core.connectors.save_restore_connector import SaveRestoreConnector\n\nconnector = SaveRestoreConnector()\nconnector.model_extracted_dir = \"./model_dir_extracted\"\n\nasr_model = nemo_asr.models.EncDecRNNTBPEModel.restore_from(\"model.nemo\",\n save_restore_connector=connector)\n```\n\nThis worked for me"
] | 2024-04-02T00:38:03 | 2024-11-07T07:33:54 | 2024-05-13T01:46:22 | NONE | {
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} | null | null | null | **Describe the bug**
https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/llama2sft.html
docker image: nvcr.io/nvidia/nemo:24.01.01.framework
converted llama2-70B hf model to Nemo using above doc, which is 129GB in size. I have disk space of 1.2T. while running on 2xH100 with 16Gpus total, I getting following error.
```
Traceback (most recent call last):
File "/opt/NeMo/examples/nlp/language_modeling/tuning/megatron_gpt_sft.py", line 225, in main
model = load_from_nemo(MegatronGPTSFTModel, cfg, trainer, gpt_cfg, modify_confg_fn=_modify_config)
File "/opt/NeMo/examples/nlp/language_modeling/tuning/megatron_gpt_sft.py", line 116, in load_from_nemo
model = cls.restore_from(
File "/opt/NeMo/nemo/collections/nlp/models/nlp_model.py", line 465, in restore_from
return super().restore_from(
File "/opt/NeMo/nemo/core/classes/modelPT.py", line 450, in restore_from
instance = cls._save_restore_connector.restore_from(
File "/opt/NeMo/nemo/collections/nlp/parts/nlp_overrides.py", line 1067, in restore_from
loaded_params = super().load_config_and_state_dict(
File "/opt/NeMo/nemo/core/connectors/save_restore_connector.py", line 143, in load_config_and_state_dict
self._unpack_nemo_file(
File "/opt/NeMo/nemo/core/connectors/save_restore_connector.py", line 572, in _unpack_nemo_file
tar.extractall(path=out_folder)
File "/usr/lib/python3.10/tarfile.py", line 2257, in extractall
self._extract_one(tarinfo, path, set_attrs=not tarinfo.isdir(),
File "/usr/lib/python3.10/tarfile.py", line 2324, in _extract_one
self._handle_fatal_error(e)
File "/usr/lib/python3.10/tarfile.py", line 2320, in _extract_one
self._extract_member(tarinfo, os.path.join(path, tarinfo.name),
File "/usr/lib/python3.10/tarfile.py", line 2403, in _extract_member
self.makefile(tarinfo, targetpath)
File "/usr/lib/python3.10/tarfile.py", line 2456, in makefile
copyfileobj(source, target, tarinfo.size, ReadError, bufsize)
File "/usr/lib/python3.10/tarfile.py", line 255, in copyfileobj
dst.write(buf)
OSError: [Errno 28] No space left on device
```
A clear and concise description of what the bug is.
**Steps/Code to reproduce bug**
followed doc
https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/llama2sft.html
```
WORLD_SIZE=16 srun --kill-on-bad-exit=0 -N 2 --ntasks-per-node=8 --cpus-per-task=24 --ntasks=16
--container-image="docker://nvcr.io#nvidia/nemo:24.01.01.framework" --container-name=nemo_llama_slurm --container-mounts="${_cont_mounts}"
python /opt/NeMo/examples/nlp/language_modeling/tuning/megatron_gpt_sft.py trainer.precision=bf16 trainer.devices=8 trainer.num_nodes=2
trainer.val_check_interval=0.1 trainer.max_steps=50 model.restore_from_path=${MODEL} model.micro_batch_size=1 model.global_batch_size=128
model.tensor_model_parallel_size=${TP_SIZE} model.activations_checkpoint_num_layers=1 model.pipeline_model_parallel_size=${PP_SIZE} model.megatron_amp_O2=True
model.sequence_parallel=True model.activations_checkpoint_granularity=full model.activations_checkpoint_method=uniform model.optim.name=distributed_fused_adam
model.optim.lr=5e-6 model.answer_only_loss=True model.data.train_ds.file_names=${TRAIN} model.data.validation_ds.file_names=${VALID}
model.data.test_ds.file_names=${TEST} model.data.train_ds.concat_sampling_probabilities=${CONCAT_SAMPLING_PROBS} model.data.train_ds.max_seq_length=2048
model.data.validation_ds.max_seq_length=2048 model.data.train_ds.micro_batch_size=1 model.data.train_ds.global_batch_size=128
model.data.validation_ds.micro_batch_size=1 model.data.validation_ds.global_batch_size=128 model.data.test_ds.micro_batch_size=1
model.data.test_ds.global_batch_size=256 model.data.train_ds.num_workers=0 model.data.validation_ds.num_workers=0 model.data.test_ds.num_workers=0
model.data.validation_ds.metric.name=loss model.data.test_ds.metric.name=loss exp_manager.create_wandb_logger=False exp_manager.explicit_log_dir=/tmp/results
exp_manager.resume_if_exists=True exp_manager.resume_ignore_no_checkpoint=True exp_manager.create_checkpoint_callback=True
exp_manager.checkpoint_callback_params.monitor=validation_loss exp_manager.checkpoint_callback_params.save_best_model=False
exp_manager.checkpoint_callback_params.save_nemo_on_train_end=True ++cluster_type=BCP
```
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version Ubuntu 20.04
- docker image: nvcr.io/nvidia/nemo:24.01.01.framework
**Additional context**
Add any other context about the problem here.
Example: GPU model
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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
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# Usage
* You can potentially add a usage example below
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
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```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
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# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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We have been experiencing poor convergence when running PEFT in FP8. The root cause is because the frozen model is set in evaluation mode, and so the FP8 scaling factors are never updated from their initial value of 1.0.
**Collection**: NLP
# Changelog
- PEFT keeps frozen model in train mode instead of eval mode
# Usage
Run GPT finetuning, e.g. with the config at https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py.
Enable FP8 support with model.fp8=True.
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [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
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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"Can you show steps on how you installed NeMo. Chances are you probably installed one NeMo version but using different version code.",
"NeMo version: '1.23.0rc0' \r\nI followed the instructions as under Conda :\r\n\r\n\r\n# clone the repo and install in development mode\r\ngit clone https://github.com/NVIDIA/NeMo\r\ncd NeMo\r\npip install 'nemo_toolkit[all]'",
"If you're cloning the repo you need to follow the installation from source part of the readme - pip install -e .[asr]",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-04-01T17:11:54 | 2024-05-15T01:47:33 | 2024-05-15T01:47:32 | NONE | {
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} | null | null | null | **Describe the bug**
NeMo version: '1.23.0rc0'
Running on MAC OS <Sonoma 14.3.1> , Conda environment
import nemo.collections.asr as nemo_asr gives the following error:
---> 31 class ASRDatasetConfig(nemo.core.classes.dataset.DatasetConfig):
32 manifest_filepath: Optional[Any] = None
33 sample_rate: int = MISSING
AttributeError: module 'nemo.core.classes' has no attribute 'dataset'
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"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-04-01T11:10:49 | 2024-05-09T01:45:58 | 2024-05-09T01:45:58 | NONE | {
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} | null | null | null | **Describe the bug**
1. `GPTDatasetConfig` got unexpected keyword `mmap_bin_files` (can be solved if I install main of Megatron LM instead of megatron-core-r0.5)
2. `GPTDatasetConfig` got unexpected keyword `is_build_on_rank`
3. merge file and vocab file seem to be useless, since `AutoTokenizer` downloads `gpt2` repo anyway.
**Steps/Code to reproduce bug**
follow https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/nemo_megatron/gpt/gpt_training.html
**Environment overview (please complete the following information)**
Cuda 12
Torch 2.1
Megatron-LM 9de386d08770d7296263a590171ace4ae45348ad
NeMo e64b2227b4759665187d061784927d2f9d0868b3 | {
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"Yes it's quite easily doable. What you can do is build a sentence piece \"char\" tokenizer instead of \"bpe\" or \"unigram\" - the char spe tokenizer provides a character encoding but with similar API to subword one so model can continue working with just change_vocabulary(). \r\n\r\nThe only think to watch out for is if you're using CTC models, then you need to test that your audio length / text length ratio is always about 1.1 or 1.2 (we have a test for this in the eng to japanese tutorial). If not, your model will fail to train. ",
"Thank you ! Now I'm having a 70-hours Japanese dataset and I want to finetune from Character based ConformerTranducer pretrained on Mandarin. Can you give me some advice about hyper params selection (lr, batch size, warm up steps , ...) ?"
] | 2024-04-01T11:07:35 | 2024-04-10T03:21:11 | 2024-04-10T03:21:11 | NONE | {
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} | null | null | null | Hi. First of all, thank you for your great work!
I 've followed the ASR language finetuning tutorial. In this tutorial, you finetuned character-based model and sub-word based model from English to corresponding model type in Japanese. I have a question, can I finetune from a sub-word base model in English to a character-based model in Japanese ?
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} | # What does this PR do ?
- Applies suggested change in https://github.com/NVIDIA/NeMo/issues/3421 to fix pickle error when running speaker diarisation
**Collection**: [common]
# Changelog
- Use defined SpeechLabelEntity class instead of `collections.namedtuple`
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [x] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [x] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to [[# (issue)](https://github.com/NVIDIA/NeMo/issues/3421)](https://github.com/NVIDIA/NeMo/issues/3421)
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"In some tutorials I've used grad accumulation 8 without modifying any LR etc and the model is able to trim and converge fine. You need a decently large global batch size, otherwise model will not begin to train unless you start with a pretrained encoder ",
"yes, I used batch size of 256 with 8 accumulation steps for a total of 2,048. I got good result for the fastconformer CTC model but not yet for the fastconformer transducer. Perhaps there's some other issues. Thanks!",
"When you get batch size of 256, not necessary to have such large grad accumulation but it does not hurt, just maybe slow down convergence a little bit at the beginning. \r\nHave you tried hybrid models? \r\nWhat do you mean by not good results? You mean wer is worse than CTC?",
"I haven't tried the hybrid models. I couldn't get the fastconformer transducer's wer close to the Conformer-L Transducer's wer yet. But I'm working with Japanese, which have longer average acoustic length for each character (especially kanji), leading to higher loss when using mean_volume reduction than in other languages. I guess that I might need to tune the learning rate.",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days."
] | 2024-04-01T02:36:40 | 2024-05-08T18:14:09 | 2024-05-08T18:14:09 | NONE | {
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} | null | null | null | Hi, I see that the fastconformer model is trained with mean_volume loss reduction (both for ctc and transducer loss). However, when using gradient accumulation (such as 8), the mean_volume loss is aggregated for each single batch rather for all of the 8 batches. I haven't successfully trained fastconformer transducer so I suspect that this is a possible issue. Do you have any suggestion on changing the reduction method or changing the learning rate when using gradient accumulation? | {
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"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-03-31T16:29:34 | 2024-05-09T01:46:00 | 2024-05-09T01:46:00 | NONE | {
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} | null | null | null | **Description**:
While training with the default configuration in the NVIDIA NeMo Framework, I encountered the following warning message:
```
[NeMo W 2024-03-31 19:25:05 tacotron2:338 rank:2] Reached max decoder steps 1000.
```
This warning suggests that the decoder reached the maximum number of steps (1000) during training. It's important to investigate why this limit is being reached and whether it impacts the model's performance.
**Steps to Reproduce**:
1. Train Tacotron2 using the default configuration.
2. Observe the warning message in the logs.
**Expected Behavior**:
The decoder should not reach the maximum step limit unless there is a specific reason for doing so. It's crucial to identify the cause and address it appropriately.
**Additional Context**:
LJSpeech-1.1 dadaset | {
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"Please feel free to put a PR with this added lr. \r\nYou can add it directly from pt scheduler similar to other schedulers as shown here: https://github.com/NVIDIA/NeMo/blob/6ca22351ea7dd231dfb94284d141de938cdb2822/nemo/core/optim/lr_scheduler.py#L963 ",
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"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-03-31T16:20:39 | 2024-05-12T01:48:00 | 2024-05-12T01:47:59 | NONE | {
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} | null | null | null | The NVIDIA NeMo Framework, a powerful generative AI framework, currently lacks support for the MultiStepLR learning rate scheduler, which is available in PyTorch’s torch.optim.lr_scheduler module. This scheduler is commonly used for adjusting learning rates at specific milestones during training.
Expected Behavior: NeMo should include an implementation or provide guidance on how to use MultiStepLR effectively within its training pipelines.
Steps to Reproduce:
Attempt to use MultiStepLR with NeMo conf yaml.
Observe that it is not directly supported.
**Additional Context:** Support for MultiStepLR would enhance the flexibility and adaptability of NeMo’s training process, especially for researchers and developers working on large language models, multimodal models, automatic speech recognition, and text-to-speech synthesis.
how to fix:
add MultiStepLR to https://github.com/NVIDIA/NeMo/blob/main/nemo/core/config/schedulers.py | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"Values mentioned at that link are the default values and the functionality is enabled by default.\r\nIn case you want to change the values, you can follow the document, regenerate the RMIR and redeploy the RMIR.\r\n\r\nThere is also a shortcut method if you want to quickly modify the values and experiment. Assuming you are using the latest 2.15.0 release, you can edit the file `riva_bls_config.yaml` in your model repository and there you will find relevant parameters (listed below). You can play with these to suit your use case. Once you modify this file, make sure to restart the Riva server for the modified values to take effect.\r\n\r\n```\r\nendpointing:\r\n endpointing_type: greedy_ctc\r\n residue_blanks_at_end: 0\r\n residue_blanks_at_start: -16\r\n start_history: 300\r\n start_th: 0.2\r\n stop_history: 800\r\n stop_th: 0.98\r\n```\r\n",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale.",
"> Values mentioned at that link are the default values and the functionality is enabled by default. In case you want to change the values, you can follow the document, regenerate the RMIR and redeploy the RMIR.\r\n> \r\n> There is also a shortcut method if you want to quickly modify the values and experiment. Assuming you are using the latest 2.15.0 release, you can edit the file `riva_bls_config.yaml` in your model repository and there you will find relevant parameters (listed below). You can play with these to suit your use case. Once you modify this file, make sure to restart the Riva server for the modified values to take effect.\r\n> \r\n> ```\r\n> endpointing:\r\n> endpointing_type: greedy_ctc\r\n> residue_blanks_at_end: 0\r\n> residue_blanks_at_start: -16\r\n> start_history: 300\r\n> start_th: 0.2\r\n> stop_history: 800\r\n> stop_th: 0.98\r\n> ```\r\n\r\nThank you very much for the response @virajkarandikar , but I cannot find the file. In which path should it be?\r\n\r\nAnother question is that in Spanish, I have found that the transcriber often adds false words. Do you know if I can add VAD detection from the configuration file? Or do I have to uninstall Riva, perform a fresh installation, and deploy a model with VAD enabled to test it?"
] | 2024-03-30T02:45:24 | 2024-05-23T13:13:05 | 2024-05-12T01:48:00 | NONE | {
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I am currently integrating ASR, LLM, and TTS technologies, with a specific interest in detecting the completion of sentences for more realistic interactions. I believe an effective approach to this could be the detection of speech disfluencies, such as detecting an "umm", which could then delay the response slightly to allow more time for thought.
Before tackling this issue, I have the following questions:
Do the NeMo/Riva models incorporate any similar approaches or techniques for sentence completion detection that I could leverage?
I have found some open-source projects with models that include weights, but I am keen on developing something compatible with Nvidia Riva. What approach would you recommend for achieving this, considering this may deviate from the standard, and I'm unsure if I can utilize the Riva client for this purpose?
On the other hand, while reading about Riva, I found this ([link](https://docs.nvidia.com/deeplearning/riva/archives/180-b/user-guide/docs/service-asr.html#beginning-end-of-utterance-detection)), which seems to indicate that it already utilizes an algorithm to detect the end of sentences to trigger a call to the punctuator model. According to the documentation, it marks the end of a sentence when 98% of the frames in an 800 ms window are silent characters. It would be very interesting to be able to modify this in order to provide more realistic experiences. However, it's not clear to me if this is the default behavior, what the default values are in this case, or if it's necessary to redeploy the models to set these values.
Thank you in advance for your guidance and suggestions.
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"I saw someone had the same issue with ecapa tdnn model way back and it has not been solved",
"It might not be possible to convert MSDD straightaway to onnx, as it depends on other models. \r\n@tango4j can you pls provide steps to achieve this. ",
"Thank you for responding, it would be very helpful for me if this can be solved. \r\n",
"NeMo neural diarization class (clustering+MSDD) is not ONNX exportable. \r\nThis is because pytorch linear algebra library is not fully ONNX exportable.\r\nWe are working on fully neural diarization and only after fully neural diarization is released, the diarization pipeline will be ONNX exportable.\r\n",
"Thank you for the quick response. Is there a way to export it to any other format that can be uploaded to triton?. I am trying to run a diarization pipeline on triton and i need to upload this model.",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-03-30T00:21:06 | 2024-05-12T01:48:02 | 2024-05-12T01:48:02 | NONE | {
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} | null | null | null | **Describe the bug**
I am trying to export the diarization MSDD telephonic model to onnx format but i get the following error:
File "/home/chidalgo/.local/lib/python3.10/site-packages/nemo/core/classes/exportable.py", line 113, in export
out, descr, out_example = model._export(
File "/home/chidalgo/.local/lib/python3.10/site-packages/nemo/core/classes/exportable.py", line 177, in _export
input_example = self.input_module.input_example()
File "/home/chidalgo/.local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1695, in __getattr__
raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'")
AttributeError: 'MyExportableModel' object has no attribute 'input_module'. Did you mean: 'output_module'?.
**Steps/Code to reproduce bug**
Here is the code i used.
import nemo
from nemo.collections.asr.models import EncDecDiarLabelModel
from nemo.core.classes import ModelPT, Exportable
class MyExportableModel(EncDecDiarLabelModel, ModelPT, Exportable):
pass
sd_model = MyExportableModel.from_pretrained(model_name = "diar_msdd_telephonic")
sd_model.eval()
sd_model.to('cuda')
sd_model.export('sd_model.onnx')
**Expected behavior**
To get a file named sd_model.onnx with the model in onnx format
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal]
- Method of NeMo install: pip install nemo_toolkit['all']
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- Ubuntu 20.04
- 2.1.2
- Python 3.10.12
**Additional context**
i also tried with the export.py script but i get the following:
File "/home/chidalgo/.local/lib/python3.10/site-packages/nemo/core/classes/common.py", line 507, in from_config_dict
raise e
File "/home/chidalgo/.local/lib/python3.10/site-packages/nemo/core/classes/common.py", line 499, in from_config_dict
instance = cls(cfg=config, trainer=trainer)
TypeError: Can't instantiate abstract class ModelPT with abstract methods list_available_models, setup_training_data, setup_validation_data
thank you, i have seen other issues with the same bug but it has not been solved
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] | 2024-03-29T19:04:15 | 2024-04-30T01:44:53 | 2024-04-30T01:44:53 | NONE | {
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} | # What does this PR do ?
Fix `ModuleNotFoundError: No module named 'megatron.core.deploy'` error due to [a recent refractor of Megatron-LM](https://github.com/NVIDIA/Megatron-LM/commit/38644dd756c46142787b1bd5dc08ed82ca0e6de1).
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [X] Did you write any new necessary tests?
- [X] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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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- Set proper path to have workflows execute in self-container, separate/independent manner
- Use of common (shared) storage between runners
- Use of common container registry across all jobs | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/8762 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8762/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8762/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8762/events | https://github.com/NVIDIA/NeMo/pull/8762 | 2,214,978,812 | PR_kwDOC_bI7s5rJaTF | 8,762 | update README fix frame_vad_infer_postprocess no have dateset Field | {
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"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.",
"There's no changes in this PR, can we close it?",
"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.",
"This PR was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-03-29T09:13:20 | 2024-08-18T01:54:37 | 2024-08-18T01:54:36 | NONE | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [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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https://api.github.com/repos/NVIDIA/NeMo/issues/8761 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8761/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8761/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8761/events | https://github.com/NVIDIA/NeMo/issues/8761 | 2,214,884,072 | I_kwDOC_bI7s6EBHLo | 8,761 | FileNotFoundError: Model stt_zh_citrinet_1024_gamma_0_25 was not found. Check cls.list_available_models() for the list of all available models. | {
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"I try restory form local print error info :\r\nValueError: `cfg` must have `tokenizer` config to create a tokenizer !\r\n",
"@weiqingw4ng will submit a PR to fix this. Thank you Weiqing ",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale.",
"@weiqingw4ng is working on this, he is working to improve the code for other non-english languages as well. ",
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"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-03-29T08:02:14 | 2024-06-21T01:47:37 | 2024-06-21T01:47:36 | NONE | {
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} | null | null | null | **Describe the bug**
I try offline_diar_with_asr_infer wish zh asr model
!python ./clustering_diarizer/offline_diar_with_asr_infer.py \
diarizer.manifest_filepath="/home/zhibo/Eval_Ali/Eval_Ali_far/audio_dir/temp_manifest_input.json" \
diarizer.out_dir='demo_asr_output' \
diarizer.speaker_embeddings.parameters.save_embeddings=False \
diarizer.asr.parameters.asr_based_vad=True \
diarizer.asr.model_path="stt_zh_citrinet_1024_gamma_0_25"
**Steps/Code to reproduce bug**
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
Add any other context about the problem here.
Example: GPU model
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"I'm having this same issue!",
"The environment you are deploying the NeMo diarization seems like CUDA is not enabled.\r\nIf your CUDA setting is not properly set, it will run it on CPU then it will get very slow.\r\nCheck `torch.cuda.is_available()` is True in your container environment on the instance you are using.",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days."
] | 2024-03-28T04:18:35 | 2024-05-08T18:14:35 | 2024-05-08T18:14:35 | NONE | {
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} | null | null | null | Hi, im having the same problem as above,
I tried to create the model with:
```
model = NeuralDiarizer.from_pretrained('diar_msdd_telephonic', map_location='cuda')
```
```
model = NeuralDiarizer.from_pretrained('diar_msdd_telephonic', map_location='cuda:0')
```
```
model = NeuralDiarizer.from_pretrained('diar_msdd_telephonic', map_location='cuda').to('cuda')
```
```
model = NeuralDiarizer.from_pretrained('diar_msdd_telephonic', map_location='cuda:0').to('cuda:0')
```
and even
```
model = NeuralDiarizer.from_pretrained('diar_msdd_telephonic').to('cuda:0')
```
On a certain machine (the jupyter notebook on AWS Sagemaker, running on Data Science 3.0, ml.g4dn.xlarge machine), all of these option worked with GPU flawlessly, this only takes 6 seconds for my audio file.
But when i deploy it on AWS Sagemaker Endpoint(with the container [PyTorch 2.0.0 with HuggingFace transformers](763104351884.dkr.ecr.ap-southeast-1.amazonaws.com/huggingface-pytorch-inference:2.0.0-transformers4.28.1-gpu-py310-cu118-ubuntu20.04), running on ml.g4dn.xlarge), Nemo keeps telling me:
```
... speaker_utils:464] cuda=False, using CPU for eigen decomposition. This might slow down the clustering process.
```
just as above. This took 112 seconds to complete with the same audio file.
i dont understand why with the same audio file, same code, it could result in two completely different outcomes?
I appreciate any help, thank you in advanced | {
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Added Nvidia Learning to the readme file just before publications
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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# Jenkins CI
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# Before your PR is "Ready for review"
**Pre checks**:
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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**Collection**: Docs
# Changelog
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# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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# Jenkins CI
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- [ ] 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.
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**Collection**: requirements
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# Usage
* You can potentially add a usage example below
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# Jenkins CI
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# Before your PR is "Ready for review"
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**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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"The converter `convert_llama_hf_to_nemo.py` should produce a `.nemo` file, not a dir. Can you try using NeMo docker image? https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-03-27T08:28:58 | 2024-05-10T01:46:27 | 2024-05-10T01:46:27 | NONE | {
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} | null | null | null | While I am trying to convert llama-hf model to Nemo via scripts/nlp_language_modeling/convert_llama_hf_to_nemo.py, the checkpoints are saved with each params type as a separate folder, as below
<img width="1284" alt="image" src="https://github.com/NVIDIA/NeMo/assets/157778011/cb83ef47-eb43-4c08-8093-c169c628d8f7">
However, when I am running Nemo-aligner, I was told that a model_weights.ckpt file is required, what is the problem here?
btw: I am using the latest version of Nemo
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"@erastorgueva-nv can you look at this issue pls. ",
"Hi @Jesteinbe, thanks for bringing this to our attention. You are correct, unfortunately NeMo CTC Segmentation does not currently support Hybrid models. \r\n\r\nYour fix of \"`mdl.cfg.aux_ctc.decoder.vocabulary`\" is a reasonable partial fix for this. The remaining issue of `list indices must be integers or slices, not tuple` can be fixed if you add the following lines in `run_ctc_segmentation.py` after `asr_model` is instantiated:\r\n\r\n```\r\n if isinstance(asr_model, nemo_asr.models.EncDecHybridRNNTCTCModel):\r\n asr_model.change_decoding_strategy(decoder_type=\"ctc\")\r\n```\r\n",
"Thanks @erastorgueva-nv ! I figured that out too. \r\n\r\nI'm also seeing that I get the same results no matter what window size I use when calling `run_ctc_segmentation.py`. I've tried values ranging from 10 (which I thought should result in bad alignment if not break it), 8000, 16000, 32000, and 48000. i am using single channel audio with limited amounts of silence so i didn't expect a bigger window size was necessary but I was surprised to find identical results. I saw this behavior on both 2 recordings, one English and the other Spanish, that were about ~5min long. it seems like the window size isn't getting update properly but the log/segment file names are all showing the expected values. i haven't tested on a larger amount of audio yet so this could be an anomaly but thought it was worth mentioning. ",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"@erastorgueva-nv could we close this issue? "
] | 2024-03-26T12:58:34 | 2024-05-08T18:22:00 | 2024-05-08T18:22:00 | NONE | {
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} | null | null | null | Following the steps outlined in [CTC_Segmentation_Tutorial.ipynb](https://github.com/NVIDIA/NeMo/blob/main/tutorials/tools/CTC_Segmentation_Tutorial.ipynb) I'm trying to align text and audio. If I use a CTC-only model like [stt_en_fastconformer_ctc_large](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_fastconformer_ctc_large) then things work fine. However, if I try to use a EncDecHybridRNNTCTCBPEModel model like [stt_en_fastconformer_hybrid_large_pc](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_fastconformer_hybrid_large_pc) then things break. As far as I can tell, there are at least two problems.
The first issue is that hybrid models' vocabularies are `mdl.cfg.aux_ctc.decoder.vocabulary`, not `mdl.cfg.decoder.vocabulary` which is what is used by [prepare_data.py](https://github.com/NVIDIA/NeMo/blob/main/tools/ctc_segmentation/scripts/prepare_data.py) and [run_ctc_segmentation.py](https://github.com/NVIDIA/NeMo/blob/main/tools/ctc_segmentation/scripts/run_ctc_segmentation.py). Once I fixed this, prepare_data.py seems to work fine but the segmentation still fails.
I'm not sure what the second issue is yet but the alignment fails and I just get a generic error like:
```
INFO:root:Processing 1st_call.wav...
Transcribing: 100%|███████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00, 1.21s/it]
ERROR:root:list indices must be integers or slices, not tuple
ERROR:root:Skipping 1st_call.wav
```
I'm guessing the call to the forward pass of the ASR decoder is different for the hybrid RNNT-CTC models.
As for expected behavior, the ctc_segmentation should work regardless of which CTC-based model you use. In this case, the bug shouldn't have anything to do with my environment but I'm running on bare metal (Ubuntu 20.04) using a conda environment in which i installed Nemo v.1.22.0 via pip.
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"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-03-26T12:19:48 | 2024-05-03T01:45:46 | 2024-05-03T01:45:45 | NONE | {
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} | null | null | null | **Describe the bug**
I tried to run the `basic_usage.py` file under `/examples/quantization` and reported an error.
**Steps/Code to reproduce bug**
`python basic_usage.py`
```shell
# error report info
return self.fget.__get__(instance, owner)()
2024-03-26 12:05:10 INFO [auto_gptq.modeling._base] Start quantizing layer 1/12
2024-03-26 12:05:11 INFO [auto_gptq.modeling._base] Quantizing self_attn.k_proj in layer 1/12...
Traceback (most recent call last):
File "/root/autoGPTQ/AutoGPTQ/examples/quantization/basic_usage_local.py", line 74, in <module>
main()
File "/root/autoGPTQ/AutoGPTQ/examples/quantization/basic_usage_local.py", line 29, in main
model.quantize(examples)
File "/usr/local/corex-3.2.1.20231222.40/lib64/python3/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/root/anaconda3/envs/auto_gptq/lib/python3.10/site-packages/auto_gptq/modeling/_base.py", line 368, in quantize
scale, zero, g_idx = gptq[name].fasterquant(
File "/root/anaconda3/envs/auto_gptq/lib/python3.10/site-packages/auto_gptq/quantization/gptq.py", line 110, in fasterquant
H = torch.linalg.cholesky(H)
RuntimeError: cusolver error: Unknown cusolver error number, when calling `cusolverDnCreate(handle)`. If you keep seeing this error, you may use `torch.backends.cuda.preferred_linalg_library()` to try linear algebra operators with other supported backends. See https://pytorch.org/docs/stable/backends.html#torch.backends.cuda.preferred_linalg_library
```
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
- Environment location: `Docker`
- Method of NeMo install: `BUILD_CUDA_EXT=0 pip install auto-gptq==0.5.1`
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: `Linux aarch64`
- PyTorch version: `2.1.0`
- Python version: `3.10`
**Additional context**
Add any other context about the problem here.
Example: GPU model
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https://api.github.com/repos/NVIDIA/NeMo/issues/8748 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8748/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8748/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8748/events | https://github.com/NVIDIA/NeMo/issues/8748 | 2,208,046,776 | I_kwDOC_bI7s6DnB64 | 8,748 | Nemo ASR + NFA + TN + Seq2Seq | {
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} | null | null | null | Hello!
Has anyone already used technology "[NeMo Forced Aligner](https://nvidia.github.io/NeMo/blogs/2023/2023-08-forced-alignment/), [Text Normalization](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/text_normalization/wfst/wfst_text_normalization.html) and Seq2Seq" in one project?
Which direction should we go? In what order?
Most likely, when normalizing the text, the alignment will not be as accurate | {
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"jenkins",
"I'm closing this as there are CI issues here and **int4_awq** case will be tested elsewhere"
] | 2024-03-26T09:20:15 | 2024-04-08T09:58:33 | 2024-04-08T09:58:32 | COLLABORATOR | {
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} | # What does this PR do ?
Add **int4_awq** quantization test in Jenkinsfile.
**Collection**: [NLP]
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] 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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first covert mixtral-8x7B-hf to test2.nemo:
python3 ./scripts/checkpoint_converters/convert_mixtral_hf_to_nemo.py --input_name_or_path mixtral-8x7B-hf --output_path test2.nemo
then
python ./examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py \
trainer.precision=bf16 \
trainer.devices=8 \
trainer.num_nodes=1 \
trainer.val_check_interval=0.1 \
trainer.max_steps=50 \
model.restore_from_path= test2.nemo \
model.micro_batch_size=1 \
model.global_batch_size=8 \
model.tensor_model_parallel_size=1\
model.pipeline_model_parallel_size=8 \
model.megatron_amp_O2=True \
model.sequence_parallel=True \
model.activations_checkpoint_granularity=selective \
model.activations_checkpoint_method=uniform \
model.optim.name=distributed_fused_adam \
model.optim.lr=5e-6 \
model.answer_only_loss=True \
model.data.train_ds.file_names=./training.jsonl \
model.data.validation_ds.file_names=./validation.jsonl \
model.data.test_ds.file_names=./test.jsonl \
model.data.train_ds.concat_sampling_probabilities=[1] \
model.data.train_ds.max_seq_length=2048 \
model.data.validation_ds.max_seq_length=2048 \
model.data.train_ds.micro_batch_size=1 \
model.data.train_ds.global_batch_size=8 \
model.data.validation_ds.micro_batch_size=1 \
model.data.validation_ds.global_batch_size=8 \
model.data.test_ds.micro_batch_size=1 \
model.data.test_ds.global_batch_size=256 \
model.data.train_ds.num_workers=0 \
model.data.validation_ds.num_workers=0 \
model.data.test_ds.num_workers=0 \
model.data.validation_ds.metric.name=loss \
model.data.test_ds.metric.name=loss \
exp_manager.create_wandb_logger=False \
exp_manager.explicit_log_dir=/results \
exp_manager.resume_if_exists=True \
exp_manager.resume_ignore_no_checkpoint=True \
exp_manager.create_checkpoint_callback=True \
exp_manager.checkpoint_callback_params.monitor=validation_loss \
exp_manager.checkpoint_callback_params.save_best_model=False \
exp_manager.checkpoint_callback_params.save_nemo_on_train_end=True \
exp_manager.checkpoint_callback_params.mode=min
Error parsing override 'test2.nemo'
missing EQUAL at '<EOF>'
See https://hydra.cc/docs/1.2/advanced/override_grammar/basic for details
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
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"Which version of NeMo is installed? If you're using NeMo 1.23, you need to change branch to r1.23.0 before selecting and using the script ",
"> Which version of NeMo is installed? If you're using NeMo 1.23, you need to change branch to r1.23.0 before selecting and using the script\r\n\r\nI try install pip3 install 'nemo_toolkit[asr]==1.23.0' print errer info :\r\n\r\nINFO: pip is looking at multiple versions of nemo-toolkit to determine which version is compatible with other requirements. This could take a while.\r\nERROR: Ignored the following versions that require a different python version: 1.21.2 Requires-Python >=3.7,<3.11; 1.21.3 Requires-Python >=3.7,<3.11; 1.21.4 Requires-Python >=3.7,<3.11; 1.21.5 Requires-Python >=3.7,<3.11; 1.21.6 Requires-Python >=3.7,<3.11\r\nERROR: Could not find a version that satisfies the requirement triton (from nemo-toolkit) (from versions: none)\r\nERROR: No matching distribution found for triton\r\n\r\nI am using an M series mac which does not support triron.",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-03-26T01:03:15 | 2024-05-03T01:45:50 | 2024-05-03T01:45:49 | NONE | {
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when I run speech_to_text_with_vad.py with some parameters below
!python /Users/whs/PycharmProjects/NeMo/examples/asr/asr_vad/speech_to_text_with_vad.py \
manifest_filepath=/Users/whs/PycharmProjects/ava-speech-downloader/temp_manifest_input.json \
vad_model=vad_multilingual_frame_marblenet \
vad_config=../conf/vad/frame_vad_infer_postprocess.yaml
**Steps/Code to reproduce bug**
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version mac 14.2.1
- PyTorch version
- Python version 3.11
**Additional context**
Add any other context about the problem here.
Example: GPU model
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"jenkins",
"Does this fix work with all 4 classes of Samplers inside the train dataloader? (MegatronPretrainingSampler, MegatronPretrainingRandomSampler, MegatronPretrainingBatchSampler, MegatronPretrainingRandomBatchSampler)",
"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.",
"This PR was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-03-25T21:00:34 | 2024-04-16T01:44:24 | 2024-04-16T01:44:24 | COLLABORATOR | {
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} | # What does this PR do ?
This fixes a bug in #7518 where `max_steps` wasn't computed properly when resuming an experiment in the middle of an epoch (my fault -- I hadn't realized the training dataloader had a smaller length on resume).
I have tested it locally to confirm both the bug and the fix.
I don't really like that we need to mess with the sampler but that's the best I could think of. Happy to take other suggestions as well.
NB: I also added an extra safety check for `limit_train_batches` but this is unrelated to the bug being fixed.
**Collection**: core
# Changelog
- Fix learning rate schedule in Megatron models when max_steps is not set and an experiment is resumed
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to #7518
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"@titu1994 Thanks for your review and all of the suggestions. Tried to address all of your comments. I agree with most of them and updated the code based on your suggestion. Some of them, I left comment. Can you please do another review? Would love to merge this asap because there are many tasks depend on this PR.\r\n\r\n@ericharper Please let us know what you think as well.",
"@titu1994 Added the deploy and the export to exclude package list here https://github.com/oyilmaz-nvidia/NeMo/blob/oss-export-deploy/setup.py#L237 ",
"Jenkins ",
"jenkins",
"jenkins",
"jenkins",
"jenkins",
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} | # What does this PR do ?
Open sourcing the export and deploy module that exports a nemo checkpoint to TensorRT-LLM and serves it using PyTriton.
**Collection**: [None]
# Changelog
- Added export and deploy folders under the main folder.
# Usage
* You can potentially add a usage example below
```python
# A simple example is as follows.
# Run the following on the server side
from nemo.export import TensorRTLLM
from nemo.deploy import DeployPyTriton
# To export to TRT-LLM
trt_llm_exporter = TensorRTLLM(model_dir="/path/to/trt_llm_engine_folder")
trt_llm_exporter.export(
nemo_checkpoint_path="/path/to/nemo/ckpt.nemo",
model_type="model_type",
n_gpus=1,
)
# To start serving on Triton server
nm = DeployPyTriton(model=trt_llm_exporter, triton_model_name="LLM")
nm.deploy()
nm.run()
# To query the model, please run the following on a the client side
from nemo.deploy import NemoQuery
nq = NemoQuery(url="localhost:8000", model_name="LLM")
output = nq.query_llm(
prompts=["what is the color of a banana?"], , top_k=1, top_p=0.0, temperature=1.0,
)
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests? (Test location: https://github.com/oyilmaz-nvidia/NeMo/blob/oss-export/tests/export/test_nemo_export.py)
- [x] Did you add or update any necessary documentation? (Added documentation on the gitlab repo for the docs. Will need to make some updates though)
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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"@titu1994 @nithinraok could you please take another look to see if your comments have been addressed? Thanks~",
"Steve, can you look at CodeQL comments. ",
"@titu1994 @zhehuaichen I've refactored the dataset such that the input and output keys can be configured dynamically by setting `context_key` and `answer_key` in the dataset. For example, if we want to use `input_text` and `output_text` as the text input and output keys in manifest, we can set `context_key='input_text'` and `answer_key='output_text'`. The defaults are `context` and `answer`, and I also added a backward compatibility check for the old `question` field. ",
"@zhehuaichen FYI I removed the `random context training` trick from the dataset, since it only makes sense for word-boosting and not other tasks. It's better to actually generate those word-boosting manifests instead of doing the trick which may hurt other tasks.",
"> @zhehuaichen FYI I removed the `random context training` trick from the dataset, since it only makes sense for word-boosting and not other tasks. It's better to actually generate those word-boosting manifests instead of doing the trick which may hurt other tasks.\r\n\r\nsg for removing but is it possible to still include that part of the training in the release ckpt?",
"> > @zhehuaichen FYI I removed the `random context training` trick from the dataset, since it only makes sense for word-boosting and not other tasks. It's better to actually generate those word-boosting manifests instead of doing the trick which may hurt other tasks.\r\n> \r\n> sg for removing but is it possible to still include that part of the training in the release ckpt?\r\n\r\n@zhehuaichen Yes the checkpoint trained for release has that included",
"jenkins",
"@titu1994 how to invoke CI tests? I tried `jenkins` but it didn't seem to work...",
"@aklife97 Could you please review the changes to the NLP collection? There're mainly two changes:\r\n- Modifying `get_iterator_k_split` to support splitting non-tensor objects (e.g., lists), while the behavior is same as before if the batch only has tensor objects.\r\n- Changing hard `assert` to raising warnings when loading adapters that have different params than the actual adapters in LLM. This is needed since we store the ASR encoders in the same checkpoint as the GPT adapters, where it leads to additional params when loading the adapter checkpoint for the GPT adapter.\r\n\r\nPlease let me know if you have any questions, thanks~!\r\n",
"Hi @ericharper, could you please help reviewing (or assign someone else available to review) the small changes to the NLP collection? There're mainly two changes:\r\n\r\n- Modifying `get_iterator_k_split` to support splitting non-tensor objects (e.g., lists), while the behavior is same as before if the batch only has tensor objects.\r\n- Changing hard `assert` to raising warnings when loading adapters that have different params from the actual adapters in LLM. This is needed since we store the ASR encoders in the same checkpoint as the GPT adapters, where it leads to additional params when loading the adapter checkpoint for the GPT adapter.\r\n\r\nPlease let me know if you have any questions, thanks~!",
"@arendu suggested by Abhinav, could you please help review the small changes to the NLP collection? There're mainly two changes:\r\n\r\n- Modifying get_iterator_k_split to support splitting non-tensor objects (e.g., lists), while the behavior is same as before if the batch only has tensor objects.\r\n- Changing hard assert to raising warnings when loading adapters that have different params from the actual adapters in LLM. This is needed since we store the ASR encoders in the same checkpoint as the GPT adapters, where it leads to additional params when loading the adapter checkpoint for the GPT adapter.\r\n\r\nPlease let us know if you have any questions, thanks~!\r\n\r\n"
] | 2024-03-25T15:59:45 | 2024-05-11T02:24:12 | 2024-05-11T02:24:11 | COLLABORATOR | {
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} | # What does this PR do ?
Add SpeechLLM training/inference scripts to NeMo, along with dataset, model, examples and test.
### Main features
- Model class for SALM-style architecture, which supports SFT&PEFT.
- Some auxiliary modules to support multi-layer feature extraction, and multiple audio encoders.
- Dataset class for audio-text-question-answering tasks (generalized for any audio-to-text tasks)
- Detailed examples on training and evaluating SpeechLLMs
- Minor updates to data utils that move dict data to cuda and split into micro batches.
**Collection**: [common,nlp,multimodal]
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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"This issue was closed because it has been inactive for 7 days since being marked as stale.",
"Were you able to fix this?",
"Thank you for checking in. unfortunately, I haven't been able to fix it."
] | 2024-03-25T15:42:53 | 2024-05-15T09:45:52 | 2024-05-03T01:45:50 | NONE | {
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} | null | null | null | **Describe the bug**
assertion error on calling model_eval.load_adapters() in lora tutorial
**Steps/Code to reproduce bug**
I'm basically just running every cells in [lora tutorial](https://github.com/NVIDIA/NeMo/blob/11b7a733cbd4b8311eacba581323f88c7cd4bac4/tutorials/nlp/lora.ipynb)
and got this error on the inference part.
the error seems to be from this line of code
`model_eval.load_adapters(config_eval.model.peft.restore_from_path, LoraPEFTConfig(eval_model_cfg))`
error
```
AssertionError Traceback (most recent call last)
Cell In[32], [line 2](vscode-notebook-cell:?execution_count=32&line=2)
[1](vscode-notebook-cell:?execution_count=32&line=1) model_eval = MegatronGPTSFTModel.restore_from(config_eval.model.restore_from_path, eval_model_cfg, trainer=trainer_eval)
----> [2](vscode-notebook-cell:?execution_count=32&line=2) model_eval.load_adapters(config_eval.model.peft.restore_from_path, LoraPEFTConfig(eval_model_cfg))
[3](vscode-notebook-cell:?execution_count=32&line=3) model_eval.freeze()
[5](vscode-notebook-cell:?execution_count=32&line=5) print("Parameter count manually:\n", model_eval.summarize())
File /opt/NeMo/nemo/collections/nlp/parts/mixins/nlp_adapter_mixins.py:296, in NLPAdapterModelMixin.load_adapters(self, filepath, peft_cfgs, map_location)
[294](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f4e654d6f32227d.vscode-resource.vscode-cdn.net/opt/NeMo/nemo/collections/nlp/parts/mixins/nlp_adapter_mixins.py:294) peft_cfgs = [PEFT_CONFIG_MAP[conf.peft.peft_scheme](conf)]
[295](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f4e654d6f32227d.vscode-resource.vscode-cdn.net/opt/NeMo/nemo/collections/nlp/parts/mixins/nlp_adapter_mixins.py:295) self.add_adapter(peft_cfgs)
--> [296](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f4e654d6f32227d.vscode-resource.vscode-cdn.net/opt/NeMo/nemo/collections/nlp/parts/mixins/nlp_adapter_mixins.py:296) assert set(state_dict.keys()) == self.adapter_keys.union(self.tunable_base_param_keys)
[297](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f4e654d6f32227d.vscode-resource.vscode-cdn.net/opt/NeMo/nemo/collections/nlp/parts/mixins/nlp_adapter_mixins.py:297) super().load_state_dict(state_dict, strict=False)
AssertionError:
```
**Environment overview (please complete the following information)**
- Environment location: NeMo Docker container on WSL2 Ubuntu
- Method of NeMo install: docker pull nvcr.io/nvidia/nemo:24.01.01.framework && docker run -it -d --name my_nemo_container --gpus all -p 8888:8888 -p 6006:6006 nvcr.io/nvidia/nemo:24.01.01.framework
**Environment details**
NVIDIA docker image
**Additional context**
GPU Model: RTX4070 Super
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This PR addresses this issue by proposing a fix to ensure that the overwritten `audio` variable does not cause incorrect processing of subsequent segments. | {
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} | # What does this PR do ?
Converts the limit_train_batches in terms of num of micro batches.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/8737 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8737/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8737/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8737/events | https://github.com/NVIDIA/NeMo/issues/8737 | 2,204,002,324 | I_kwDOC_bI7s6DXmgU | 8,737 | parakeet-tdt-1.1b unable to calculate ASR confidence scores! | {
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"TDTs are a recent model, and not all models support all features (due to architecture or training loss). We will look into it, but for now confidence metrics are computable only on rnnt and ctc models. ",
"Thank you! The TDT model is very impressive, and would love to switch to it, but our application requires confidence metrics. Thanks for looking into it!",
"Hey NeMo team! Just following up on using confidence scores from TDT-1.1b model. This is a key enabler for our project.",
"Hi @asifjahmed\r\nWe are working on it. However, please consider that TDT confidence estimation is neither formally defined nor easily supported by the NeMo design. So, my estimate for this feature is ~2 weeks.",
"Hey @GNroy -\r\n\r\nThanks very much for the update! With a timeframe in mind I can plan accordingly. Thanks and looking forward to migrating to the awesome TDT model soon.",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"Hi, I saw this was just marked as completed! Great! Can you link to the commit or LMK how to use this? Thank you!",
"You need to add `include_duration_confidence=True` to the decoding config. @GNroy are docs updated on how to use this feature. ",
"TDT confidence does not require additional parameters by default. It runs just like a regular confidence estimation.\r\n`tdt_include_duration` is an optional flag indicating that TDT confidence will be computed with consideration of duration probabilities as well.",
"Awesome work @GNroy ! We need to add the comment about tdt include duration flag to the docs for confidence estimation ",
"I did add documentation about the flag (e.g. https://github.com/NVIDIA/NeMo/blob/b1628cf231eff0ca96a94b5f840b0dcbb7f2d667/nemo/collections/asr/parts/submodules/rnnt_decoding.py#L99) and also mentioned it in the confidence tutorial.",
"Perfect thanks !"
] | 2024-03-23T18:50:03 | 2024-05-14T16:51:05 | 2024-05-08T18:16:20 | NONE | {
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} | null | null | null | When following the NeMo tutorial for ASR Confidence (https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/ASR_Confidence_Estimation.ipynb)
I am able to obtain confidence scores from both "parakeet-rnnt-1.1b" as well as "parakeet-ctc-0.6b". However I am unable to get the same results when using "parakeet-tdt-1.1b" and I get the following exception:
```
Transcribing: 0%| | 0/1 [00:00<?, ?it/s]
INFO: 127.0.0.1:58692 - "POST /v1/audio/transcriptions HTTP/1.1" 500 Internal Server Error
ERROR: Exception in ASGI application
Traceback (most recent call last):
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/uvicorn/protocols/http/h11_impl.py", line 407, in run_asgi
result = await app( # type: ignore[func-returns-value]
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/uvicorn/middleware/proxy_headers.py", line 69, in __call__
return await self.app(scope, receive, send)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/fastapi/applications.py", line 1054, in __call__
await super().__call__(scope, receive, send)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/starlette/applications.py", line 123, in __call__
await self.middleware_stack(scope, receive, send)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/starlette/middleware/errors.py", line 186, in __call__
raise exc
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/starlette/middleware/errors.py", line 164, in __call__
await self.app(scope, receive, _send)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/starlette/middleware/cors.py", line 83, in __call__
await self.app(scope, receive, send)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/starlette/middleware/exceptions.py", line 62, in __call__
await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/starlette/_exception_handler.py", line 64, in wrapped_app
raise exc
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
await app(scope, receive, sender)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/starlette/routing.py", line 758, in __call__
await self.middleware_stack(scope, receive, send)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/starlette/routing.py", line 778, in app
await route.handle(scope, receive, send)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/starlette/routing.py", line 299, in handle
await self.app(scope, receive, send)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/starlette/routing.py", line 79, in app
await wrap_app_handling_exceptions(app, request)(scope, receive, send)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/starlette/_exception_handler.py", line 64, in wrapped_app
raise exc
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
await app(scope, receive, sender)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/starlette/routing.py", line 74, in app
response = await func(request)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/fastapi/routing.py", line 278, in app
raw_response = await run_endpoint_function(
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/fastapi/routing.py", line 191, in run_endpoint_function
return await dependant.call(**values)
File "/home/jamie/nvidia_asr/parakeet_streaming_2.0_confidence.py", line 116, in transcriptions
transcript = transcribe(audio_path=upload_name)
File "/home/jamie/nvidia_asr/parakeet_streaming_2.0_confidence.py", line 157, in transcribe
transcript = transcriber.transcribe([audio_path], return_hypotheses=True)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/nemo/collections/asr/models/rnnt_models.py", line 304, in transcribe
best_hyp, all_hyp = self.decoding.rnnt_decoder_predictions_tensor(
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/nemo/collections/asr/parts/submodules/rnnt_decoding.py", line 502, in rnnt_decoder_predictions_tensor
hypotheses = self.compute_confidence(hypotheses)
File "/opt/conda/envs/nemo/lib/python3.10/site-packages/nemo/collections/asr/parts/submodules/rnnt_decoding.py", line 582, in compute_confidence
[hyp.frame_confidence[ts][offset]]
IndexError: list index out of range
``` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/8736 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8736/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8736/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8736/events | https://github.com/NVIDIA/NeMo/issues/8736 | 2,203,993,187 | I_kwDOC_bI7s6DXkRj | 8,736 | Restoring from pretrained checkpoint is broke for "stt_ua_fastconformer_hybrid_large_pc" | {
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"I figured how to load the parameters, though with the hack.\r\n\r\n1. I obtained model's config by doing this:\r\n```\r\nckpt_name = \"stt_ua_fastconformer_hybrid_large_pc\"\r\ncfg = nemo_asr.models.EncDecHybridRNNTCTCBPEModel.from_pretrained(model_name=ckpt_name, return_config=True)\r\nomegaconf.OmegaConf.save(cfg, f=\"cfg.yaml\")\r\n```\r\n\r\n2. then I commented out the `conv_pointwise_type` attribute of encoder's parameters.\r\n\r\n3. loaded model by providing a patched config:\r\n```\r\nmodel = nemo_asr.models.EncDecHybridRNNTCTCBPEModel.from_pretrained(\r\n model_name=ckpt_path,\r\n override_config_path=\"./cfg_modified.yaml\",\r\n```\r\n\r\nThe above hack works, though I wonder if the loaded model works correctly? Also it will be good to update the official checkpoints in the NGC registry.\r\n\r\nSame issue happens to `stt_ru_fastconformer_hybrid_large_pc` russian checkpoint.\r\n",
"Thank you for reporting the issue and the fix ! We'll take a look soon. I think we updated the huggingface version but not NGC ",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"Still relevant for https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_ru_fastconformer_hybrid_large_pc\r\ndocker: nvcr.io/nvidia/nemo:24.01.speech\r\n\r\nAnd this model has a different tokenizer from the one in ngc.\r\nhttps://huggingface.co/nvidia/stt_ru_fastconformer_hybrid_large_pc/tree/main",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-03-23T18:24:06 | 2024-06-01T01:49:22 | 2024-06-01T01:49:21 | NONE | {
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} | null | null | null | Hi folks! I'm trying to restore `EncDecHybridRNNTCTCModel` from checkpoint trained on Ukrainian
```
In [4]: from nemo.collections.asr.models import EncDecHybridRNNTCTCBPEModel
In [5]: model = EncDecHybridRNNTCTCBPEModel.from_pretrained("stt_ua_fastconformer_hybrid_large_pc")
```
model can't be restored because of keyword argument `conv_pointwise_type`
```
InstantiationException: Error in call to target 'nemo.collections.asr.modules.conformer_encoder.ConformerEncoder':
TypeError("ConformerEncoder.__init__() got an unexpected keyword argument 'conv_pointwise_type'")
```
Search through NeMo repository I only found `conv_pointwise_type` keyword in one [PR](https://github.com/NVIDIA/NeMo/pull/6476), and nowhere in the code base, that looks suspicious. Is there any recommendation how one would make that checkpoint compatible?
Env:
- Nemo version: nemo_toolkit==1.23.0
- OS: Ubuntu 22.04 | {
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"Hi for these proposes I wrote a tiny script for my ASR models, that accepts `.ckpt` model and saves it as `.nemo` model\r\n\r\nI think something similar should work for NLP models too.\r\n\r\n```\r\nimport sys\r\nfrom pathlib import Path\r\n\r\nimport nemo.collections.asr as nemo_asr\r\n\r\n\r\ndef get_nemo_ckpt_path(ckpt_path: Path) -> Path:\r\n ckpt_name = ckpt_path.stem\r\n name, other = ckpt_name.split(\"--\")\r\n epoch = other.split(\"=\")[-1]\r\n out_path = ckpt_path.with_name(f\"{name}-epoch-{epoch}.nemo\")\r\n return out_path\r\n\r\ndef export_to_nemo(lightning_ckpt_path: Path):\r\n model = nemo_asr.models.EncDecCTCModelBPE.load_from_checkpoint(\r\n str(lightning_ckpt_path)\r\n )\r\n ckpt_name = get_nemo_ckpt_path(lightning_ckpt_path)\r\n out_path = get_nemo_ckpt_path(lightning_ckpt_path)\r\n model.save_to(str(out_path))\r\n print(f\"Succesfully saved model checkpoint: {out_path} in nemo format\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n in_path = Path(sys.argv[1])\r\n assert in_path.exists()\r\n export_to_nemo(in_path)\r\n```",
"> Hi for these proposes I wrote a tiny script for my ASR models, that accepts `.ckpt` model and saves it as `.nemo` model\r\n> \r\n> I think something similar should work for NLP models too.\r\n> \r\n> ```\r\n> import sys\r\n> from pathlib import Path\r\n> \r\n> import nemo.collections.asr as nemo_asr\r\n> \r\n> \r\n> def get_nemo_ckpt_path(ckpt_path: Path) -> Path:\r\n> ckpt_name = ckpt_path.stem\r\n> name, other = ckpt_name.split(\"--\")\r\n> epoch = other.split(\"=\")[-1]\r\n> out_path = ckpt_path.with_name(f\"{name}-epoch-{epoch}.nemo\")\r\n> return out_path\r\n> \r\n> def export_to_nemo(lightning_ckpt_path: Path):\r\n> model = nemo_asr.models.EncDecCTCModelBPE.load_from_checkpoint(\r\n> str(lightning_ckpt_path)\r\n> )\r\n> ckpt_name = get_nemo_ckpt_path(lightning_ckpt_path)\r\n> out_path = get_nemo_ckpt_path(lightning_ckpt_path)\r\n> model.save_to(str(out_path))\r\n> print(f\"Succesfully saved model checkpoint: {out_path} in nemo format\")\r\n> \r\n> \r\n> if __name__ == \"__main__\":\r\n> in_path = Path(sys.argv[1])\r\n> assert in_path.exists()\r\n> export_to_nemo(in_path)\r\n> ```\r\n\r\nThanks for your reply.\r\n\r\nIt seems like changing the import nemo.collections.asr as nemo_asr might work.\r\n\r\nHave you tried converting the Nemo format to gguf? Alternatively, how do you build a model from your Nemo format?\r\n\r\nThank you."
] | 2024-03-23T08:37:49 | 2024-03-28T11:09:59 | 2024-03-28T11:09:59 | NONE | {
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I'm using the code [colab](https://github.com/NVIDIA/NeMo/blob/stable/tutorials/nlp/Token_Classification-BioMegatron.ipynb) to train my own model. The performance looks promising in the first epoch. However, I'm struggling to figure out how to convert the checkpoint to the NeMo format.
```python
[NeMo I 2024-03-22 15:19:31 token_classification_model:161]
label precision recall f1 support
O (label_id: 0) 77.87 55.56 64.85 171
B-DIS (label_id: 1) 2.65 31.25 4.88 16
I-DIS (label_id: 2) 13.33 1.44 2.60 139
-------------------
micro avg 31.29 31.29 31.29 326
macro avg 31.28 29.41 24.11 326
weighted avg 46.66 31.29 35.36 326
[NeMo W 2024-03-22 15:19:32 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/connectors/data_connector.py:438: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 256 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
rank_zero_warn(
Epoch 1: 33%
117250/356647 [4:12:20<8:35:13, 7.74it/s, v_num=2-07, lr=4.08e-5, train_step_timing in s=0.104, val_loss=0.00559]
[NeMo I 2024-03-22 15:19:32 preemption:56] Preemption requires torch distributed to be initialized, disabling preemption
[NeMo I 2024-03-23 04:23:00 token_classification_model:161]
label precision recall f1 support
O (label_id: 0) 99.77 99.82 99.80 6081509
B-DIS (label_id: 1) 98.21 97.71 97.96 608465
I-DIS (label_id: 2) 100.00 100.00 100.00 3735127
-------------------
micro avg 99.76 99.76 99.76 10425101
macro avg 99.33 99.18 99.25 10425101
weighted avg 99.76 99.76 99.76 10425101
```
Could you please advise on how to convert the checkpoint to the NeMo format?
And
When running a full epoch, will it automatically save the model with the lowest loss or the model of the last epoch? | {
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} | # What does this PR do ?
Add sdxl support
**Collection**: NeMo multimodal
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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Remove unnecessary attention masks. [Related MCore MR is here.](https://gitlab-master.nvidia.com/ADLR/megatron-lm/-/merge_requests/1259) | {
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} | # What does this PR do ?
* fixing AED transcribe failure case when input is manifest
* Fixing pos encoding ids bug during inference.
**Collection**: [ASR]
# Jenkins CI
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
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# Before your PR is "Ready for review"
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**PR Type**:
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- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Documentation for model quantization using AMMO.
**Collection**: NLP
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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"Thanks @yunbin, we'll take a look at adding this functionality over the next couple weeks. We'll post future updates here.",
"@anteju Any update on getting the channel feature implemented in NeMo training scripts?",
"@yunbin, please check #9007.\r\nThe change there enables using channel selector from manifest for `nvidia/canary-1b` model."
] | 2024-03-22T16:56:44 | 2024-05-08T18:18:14 | 2024-05-08T18:18:13 | NONE | {
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} | null | null | null | **Describe the bug**
NeMo training and decoding scripts do not support multi-channel audio data.
**Steps/Code to reproduce bug**
It does not support specifying which channel to use for each audio file in each line in train.manifest.json or test.manifest.json file.
I was able to run ./examples/asr/speech_to_text_eval.py with "channel_selector=" to specify the channel for all the audio in a manifest.json file, but I can't find a way to specifying them for each audio file inside the manifest.json file.
**Expected behavior**
Can NeMo team add this useful feature to work with a diverse set of multi-channel training and testing audio data so data from different channel can be mixed within a manifest.json file?
**Environment overview (please complete the following information)**
NeMo was installed by pip in a conda environment. It works for single channel audio data.
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} | # What does this PR do ?
The PR adds functionality to defer the embedding table wgrad GEMM, so that it happens during pipeline flush. This is only done on the last pipeline stage.
The embedding wgrad compute is drained by a function in megatron.core.utils which NeMo will import and use. | {
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"If you are interested, I opened a PR yesterday (https://github.com/NVIDIA/NeMo/pull/8723) which contains a tutorial notebook you could download and try https://github.com/NVIDIA/NeMo/blob/79ffef97d2993b716406227f00fc234c19541610/tutorials/tts/Audio_Codec_Training.ipynb. Change the branch from 'main' to 'codec_tutorial'.\r\n\r\nThe training manifest only needs `{\"audio_filepath\", \"duration\"}`\r\n\r\nThe metadata structure is defined at https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/tts/data/vocoder_dataset.py#L45. For each dataset you use it asks for the `manifest_path` to the manifest file, and the root `audio_dir` where the audio is stored. If training on multiple datasets you can specify `sample_weight` to determine how often it samples from each.\r\n\r\n`train_ds_meta` configures training data, `val_ds_meta` configures data for computing validation statistics/loss, and `log_ds_meta` is used with this callback (https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/tts/parts/utils/callbacks.py#L331) to configure logging of various artifacts during training (eg. reconstructed audio, embedding plots). If doing local logging then `log_dir` specifies the local directory.\r\n\r\nA full training command (copied from the tutorial), with 1 dataset \"vctk\", looks like:\r\n\r\n```\r\npython /content/nemo/examples/tts/audio_codec.py\r\n--config-path=/content/nemo/examples/tts/conf/audio_codec\r\n--config-name=audio_codec_24000.yaml\r\nmax_epochs=10\r\nweighted_sampling_steps_per_epoch=10\r\nbatch_size=4\r\nlog_dir=/content/exps/EnCodec/test_run/logs\r\nexp_manager.exp_dir=/content/exps\r\n+exp_manager.version=test_run\r\nmodel.log_config.log_wandb=False\r\nmodel.log_config.log_tensorboard=False\r\nmodel.log_config.generators.0.log_dequantized=True\r\ntrainer.accelerator=gpu\r\n+train_ds_meta.vctk.manifest_path=/content/data/vctk_subset_multispeaker/train_manifest.json\r\n+train_ds_meta.vctk.audio_dir=/content/data/vctk_subset_multispeaker/audio_preprocessed\r\n+val_ds_meta.vctk.manifest_path=/content/data/vctk_subset_multispeaker/dev_manifest.json\r\n+val_ds_meta.vctk.audio_dir=/content/data/vctk_subset_multispeaker/audio_preprocessed\r\n+log_ds_meta.vctk.manifest_path=/content/data/vctk_subset_multispeaker/dev_manifest.json\r\n+log_ds_meta.vctk.audio_dir=/content/data/vctk_subset_multispeaker/audio_preprocessed\r\n```\r\n\r\nLet me know if you have any other questions.",
"Thank you for your kind reply. It was so helpful to me !!\r\n\r\n",
"> If you are interested, I opened a PR yesterday (#8723) which contains a tutorial notebook you could download and try https://github.com/NVIDIA/NeMo/blob/79ffef97d2993b716406227f00fc234c19541610/tutorials/tts/Audio_Codec_Training.ipynb. Change the branch from 'main' to 'codec_tutorial'.\r\n> \r\n> The training manifest only needs `{\"audio_filepath\", \"duration\"}`\r\n> \r\n> The metadata structure is defined at https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/tts/data/vocoder_dataset.py#L45. For each dataset you use it asks for the `manifest_path` to the manifest file, and the root `audio_dir` where the audio is stored. If training on multiple datasets you can specify `sample_weight` to determine how often it samples from each.\r\n> \r\n> `train_ds_meta` configures training data, `val_ds_meta` configures data for computing validation statistics/loss, and `log_ds_meta` is used with this callback (https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/tts/parts/utils/callbacks.py#L331) to configure logging of various artifacts during training (eg. reconstructed audio, embedding plots). If doing local logging then `log_dir` specifies the local directory.\r\n> \r\n> A full training command (copied from the tutorial), with 1 dataset \"vctk\", looks like:\r\n> \r\n> ```\r\n> python /content/nemo/examples/tts/audio_codec.py\r\n> --config-path=/content/nemo/examples/tts/conf/audio_codec\r\n> --config-name=audio_codec_24000.yaml\r\n> max_epochs=10\r\n> weighted_sampling_steps_per_epoch=10\r\n> batch_size=4\r\n> log_dir=/content/exps/EnCodec/test_run/logs\r\n> exp_manager.exp_dir=/content/exps\r\n> +exp_manager.version=test_run\r\n> model.log_config.log_wandb=False\r\n> model.log_config.log_tensorboard=False\r\n> model.log_config.generators.0.log_dequantized=True\r\n> trainer.accelerator=gpu\r\n> +train_ds_meta.vctk.manifest_path=/content/data/vctk_subset_multispeaker/train_manifest.json\r\n> +train_ds_meta.vctk.audio_dir=/content/data/vctk_subset_multispeaker/audio_preprocessed\r\n> +val_ds_meta.vctk.manifest_path=/content/data/vctk_subset_multispeaker/dev_manifest.json\r\n> +val_ds_meta.vctk.audio_dir=/content/data/vctk_subset_multispeaker/audio_preprocessed\r\n> +log_ds_meta.vctk.manifest_path=/content/data/vctk_subset_multispeaker/dev_manifest.json\r\n> +log_ds_meta.vctk.audio_dir=/content/data/vctk_subset_multispeaker/audio_preprocessed\r\n> ```\r\n> \r\n> Let me know if you have any other questions.\r\n\r\nI'm using my custom data to fine-tune the pre-trained model. There is no problem when I start training from scratch, but when I fine-tune the pre-trained model, I get an error. Below are my dataset format and error encountered.\r\n\r\n\r\n\r\n"
] | 2024-03-22T08:19:24 | 2024-06-25T06:14:22 | 2024-03-29T04:08:43 | NONE | {
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} | null | null | null | Hello, I'm trying to train an audio codec using my custom data. The configuration requires several parameters, including train_ds_meta, val_ds_meta, log_ds_meta, and log_dir. I'm curious about the meaning and detailed structure of each.
For example, in the case of Speech-to-Text (STT), the training dataset (train_ds) requires a manifest. This manifest is structured with the fields {"audio_filepath", "duration", "text"}, and for STT, the manifest can be specified as a .json file.
I would like to understand specifically how to format these parameters for my audio codec training configuration.
Please provide examples of the file formats and internal structures for train_ds_meta, val_ds_meta, log_ds_meta, and log_dir. | {
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"jenkins"
] | 2024-03-22T07:56:43 | 2024-03-22T22:51:07 | 2024-03-22T22:51:07 | CONTRIBUTOR | {
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} | # What does this PR do ?
Setup CP Groups in GPTSFT model
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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# Jenkins CI
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# Before your PR is "Ready for review"
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Fix an CLIP training issue found in nightly MM CI
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
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# Jenkins CI
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# Before your PR is "Ready for review"
**Pre checks**:
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
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```python
# Add a code snippet demonstrating how to use this
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# Jenkins CI
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
Changes required to run FP8 debug in TransformerEngine.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
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# Before your PR is "Ready for review"
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} | # What does this PR do ?
Fix the nsys profiling start and end step comparison condition to support different initial micro-batch start points.
# Changelog
- Change the `current_step == target_step` to `current_step >= target_step`
# Usage
N/A
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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**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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- Mistral/Mixtral PEFT support
- Update converter path
- Add packed sequence docs
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [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
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} | # What does this PR do ?
bug fix for base models that use position encoding.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [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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"There was a temporary error due to the `stable` tag pointing to NeMo main instead of NeMo r1.23.0. Please try again, it should work now. ",
"That worked. Thanks!"
] | 2024-03-20T21:41:44 | 2024-03-21T00:19:29 | 2024-03-21T00:19:29 | NONE | {
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} | null | null | null | **Describe the bug**
The NeMo [tutorials ](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/starthere/tutorials.html) on Google Colab do not run due to a dependency issue with Megatron-Core. Currently, NeMo is pinned to Megatron-Core 0.5.0, but references the `IndexedDataset` class. In 0.5.0 this class is called `MMIndexedDataset`. In Megatron-Core `main` branch is is now `IndexedDataset`.
**Steps/Code to reproduce bug**
1. Load the tutorial at https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/00_NeMo_Primer.ipynb
2. Run the first three cells.
On the third cell:
```
import nemo.collections.asr as nemo_asr
import nemo.collections.nlp as nemo_nlp
import nemo.collections.tts as nemo_tts
```
You get the error:
```
ImportError Traceback (most recent call last)
[<ipython-input-6-424875f6207b>](https://localhost:8080/#) in <cell line: 2>()
1 import nemo.collections.asr as nemo_asr
----> 2 import nemo.collections.nlp as nemo_nlp
3 import nemo.collections.tts as nemo_tts
5 frames
[/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/data/language_modeling/megatron/gpt_fim_dataset.py](https://localhost:8080/#) in <module>
17 import numpy as np
18 from megatron.core.datasets.gpt_dataset import GPTDataset, GPTDatasetConfig
---> 19 from megatron.core.datasets.indexed_dataset import IndexedDataset
20 from megatron.core.datasets.utils import Split
21
ImportError: cannot import name 'IndexedDataset' from 'megatron.core.datasets.indexed_dataset' (/usr/local/lib/python3.10/dist-packages/megatron/core/datasets/indexed_dataset.py)
```
It looks like I see the same error on any of the NLP tutorials
**Expected behavior**
The tutorial should run to completion.
**Environment overview (please complete the following information)**
Google Collab
Tutorials loaded from https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/starthere/tutorials.html
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"jenkins"
] | 2024-03-20T21:40:28 | 2024-10-02T22:11:43 | 2024-03-21T16:32:09 | CONTRIBUTOR | {
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} | # What does this PR do ?
Fix the wrong expert model parallel rank when tensor_model_parallel_size >1.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
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**PR Type**:
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
minor fix...
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
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# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
fixes precision attribute during conversion
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
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# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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- [x] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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} | Bumps [black](https://github.com/psf/black) from 19.10b0 to 24.3.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/psf/black/releases">black's releases</a>.</em></p>
<blockquote>
<h2>24.3.0</h2>
<h3>Highlights</h3>
<p>This release is a milestone: it fixes Black's first CVE security vulnerability. If you
run Black on untrusted input, or if you habitually put thousands of leading tab
characters in your docstrings, you are strongly encouraged to upgrade immediately to fix
<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2024-21503">CVE-2024-21503</a>.</p>
<p>This release also fixes a bug in Black's AST safety check that allowed Black to make
incorrect changes to certain f-strings that are valid in Python 3.12 and higher.</p>
<h3>Stable style</h3>
<ul>
<li>Don't move comments along with delimiters, which could cause crashes (<a href="https://redirect.github.com/psf/black/issues/4248">#4248</a>)</li>
<li>Strengthen AST safety check to catch more unsafe changes to strings. Previous versions
of Black would incorrectly format the contents of certain unusual f-strings containing
nested strings with the same quote type. Now, Black will crash on such strings until
support for the new f-string syntax is implemented. (<a href="https://redirect.github.com/psf/black/issues/4270">#4270</a>)</li>
<li>Fix a bug where line-ranges exceeding the last code line would not work as expected
(<a href="https://redirect.github.com/psf/black/issues/4273">#4273</a>)</li>
</ul>
<h3>Performance</h3>
<ul>
<li>Fix catastrophic performance on docstrings that contain large numbers of leading tab
characters. This fixes
<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2024-21503">CVE-2024-21503</a>.
(<a href="https://redirect.github.com/psf/black/issues/4278">#4278</a>)</li>
</ul>
<h3>Documentation</h3>
<ul>
<li>Note what happens when <code>--check</code> is used with <code>--quiet</code> (<a href="https://redirect.github.com/psf/black/issues/4236">#4236</a>)</li>
</ul>
<h2>24.2.0</h2>
<h3>Stable style</h3>
<ul>
<li>Fixed a bug where comments where mistakenly removed along with redundant parentheses
(<a href="https://redirect.github.com/psf/black/issues/4218">#4218</a>)</li>
</ul>
<h3>Preview style</h3>
<ul>
<li>Move the <code>hug_parens_with_braces_and_square_brackets</code> feature to the unstable style
due to an outstanding crash and proposed formatting tweaks (<a href="https://redirect.github.com/psf/black/issues/4198">#4198</a>)</li>
<li>Fixed a bug where base expressions caused inconsistent formatting of ** in tenary
expression (<a href="https://redirect.github.com/psf/black/issues/4154">#4154</a>)</li>
<li>Checking for newline before adding one on docstring that is almost at the line limit
(<a href="https://redirect.github.com/psf/black/issues/4185">#4185</a>)</li>
<li>Remove redundant parentheses in <code>case</code> statement <code>if</code> guards (<a href="https://redirect.github.com/psf/black/issues/4214">#4214</a>).</li>
</ul>
<h3>Configuration</h3>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a href="https://github.com/psf/black/blob/main/CHANGES.md">black's changelog</a>.</em></p>
<blockquote>
<h2>24.3.0</h2>
<h3>Highlights</h3>
<p>This release is a milestone: it fixes Black's first CVE security vulnerability. If you
run Black on untrusted input, or if you habitually put thousands of leading tab
characters in your docstrings, you are strongly encouraged to upgrade immediately to fix
<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2024-21503">CVE-2024-21503</a>.</p>
<p>This release also fixes a bug in Black's AST safety check that allowed Black to make
incorrect changes to certain f-strings that are valid in Python 3.12 and higher.</p>
<h3>Stable style</h3>
<ul>
<li>Don't move comments along with delimiters, which could cause crashes (<a href="https://redirect.github.com/psf/black/issues/4248">#4248</a>)</li>
<li>Strengthen AST safety check to catch more unsafe changes to strings. Previous versions
of Black would incorrectly format the contents of certain unusual f-strings containing
nested strings with the same quote type. Now, Black will crash on such strings until
support for the new f-string syntax is implemented. (<a href="https://redirect.github.com/psf/black/issues/4270">#4270</a>)</li>
<li>Fix a bug where line-ranges exceeding the last code line would not work as expected
(<a href="https://redirect.github.com/psf/black/issues/4273">#4273</a>)</li>
</ul>
<h3>Performance</h3>
<ul>
<li>Fix catastrophic performance on docstrings that contain large numbers of leading tab
characters. This fixes
<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2024-21503">CVE-2024-21503</a>.
(<a href="https://redirect.github.com/psf/black/issues/4278">#4278</a>)</li>
</ul>
<h3>Documentation</h3>
<ul>
<li>Note what happens when <code>--check</code> is used with <code>--quiet</code> (<a href="https://redirect.github.com/psf/black/issues/4236">#4236</a>)</li>
</ul>
<h2>24.2.0</h2>
<h3>Stable style</h3>
<ul>
<li>Fixed a bug where comments where mistakenly removed along with redundant parentheses
(<a href="https://redirect.github.com/psf/black/issues/4218">#4218</a>)</li>
</ul>
<h3>Preview style</h3>
<ul>
<li>Move the <code>hug_parens_with_braces_and_square_brackets</code> feature to the unstable style
due to an outstanding crash and proposed formatting tweaks (<a href="https://redirect.github.com/psf/black/issues/4198">#4198</a>)</li>
<li>Fixed a bug where base expressions caused inconsistent formatting of ** in tenary
expression (<a href="https://redirect.github.com/psf/black/issues/4154">#4154</a>)</li>
<li>Checking for newline before adding one on docstring that is almost at the line limit
(<a href="https://redirect.github.com/psf/black/issues/4185">#4185</a>)</li>
<li>Remove redundant parentheses in <code>case</code> statement <code>if</code> guards (<a href="https://redirect.github.com/psf/black/issues/4214">#4214</a>).</li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li>See full diff in <a href="https://github.com/psf/black/commits/24.3.0">compare view</a></li>
</ul>
</details>
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"Up!\r\nI wish to try it as well to check if reported results are as good as they say.\r\nCan we already find it and if no, when will it be available?",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"Still waiting for the answer, thank you.",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"Up",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale.",
"wait for it"
] | 2024-03-20T15:25:31 | 2024-07-17T09:44:40 | 2024-07-17T01:50:49 | NONE | {
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} | null | null | null | The article ["Conformer-based Target-Speaker Automatic Speech Recognition for Single-Channel Audio"](https://arxiv.org/pdf/2308.05218v1.pdf) states that the model CONF-TSASR will be open-sourced through NVIDIA NeMo toolkit. Is it already open-sourced? Will pretrained weights be available? If yes, where can I found it? | {
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"@Slyne (myself) to check this issue.\r\n\r\n@nirraviv89 Meanwhile could you help try tensorrt 10.0:\r\nhttps://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.0.0/tensorrt-10.0.0.6.linux.x86_64-gnu.cuda-12.4.tar.gz\r\n\r\nThe installation guide:\r\nhttps://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html#installing-tar\r\n",
"Thanks @Slyne this is the error I get with tensorrt 10.0:\r\n```\r\n[04/08/2024-07:16:08] [TRT] [W] Tactic Device request: 24316MB Available: 22723MB. Device memory is insufficient to use tactic.\r\n[04/08/2024-07:16:08] [TRT] [W] UNSUPPORTED_STATE: Skipping tactic 0 due to insufficient memory on requested size of 24316 detected for tactic 0x0000000000000000.\r\n[04/08/2024-07:16:08] [TRT] [E] 10: Could not find any implementation for node {ForeignNode[ONNXTRT_squeezeTensor_643 + /layers.0/conv/Transpose_1.../layers.1/self_attn/MatMul_1]}.\r\n[04/08/2024-07:16:08] [TRT] [E] 10: [optimizer.cpp::computeCosts::4103] Error Code 10: Internal Error (Could not find any implementation for node {ForeignNode[ONNXTRT_squeezeTensor_643 + /layers.0/conv/Transpose_1.../layers.1/self_attn/MatMul_1]}.)\r\n```",
"> Thanks @Slyne this is the error I get with tensorrt 10.0:\r\n> \r\n> ```\r\n> [04/08/2024-07:16:08] [TRT] [W] Tactic Device request: 24316MB Available: 22723MB. Device memory is insufficient to use tactic.\r\n> [04/08/2024-07:16:08] [TRT] [W] UNSUPPORTED_STATE: Skipping tactic 0 due to insufficient memory on requested size of 24316 detected for tactic 0x0000000000000000.\r\n> [04/08/2024-07:16:08] [TRT] [E] 10: Could not find any implementation for node {ForeignNode[ONNXTRT_squeezeTensor_643 + /layers.0/conv/Transpose_1.../layers.1/self_attn/MatMul_1]}.\r\n> [04/08/2024-07:16:08] [TRT] [E] 10: [optimizer.cpp::computeCosts::4103] Error Code 10: Internal Error (Could not find any implementation for node {ForeignNode[ONNXTRT_squeezeTensor_643 + /layers.0/conv/Transpose_1.../layers.1/self_attn/MatMul_1]}.)\r\n> ```\r\n\r\nIf you only want to export conformer to Tensorrt, you may check this file to export nemo conformer to onnx: \r\n```\r\nhttps://github.com/Slyne/NeMo/blob/dgalvez/triton-inference-server/examples/asr/triton-inference-server/scripts/export_asr_ctc_onnx.py\r\n```\r\nConvert onnx model to tensorrt model by using trtexec:\r\n```\r\ntrtexec --onnx=onnx_model/model.onnx \\\r\n --minShapes=audio_signal:1x80x100,length:1 \\\r\n --optShapes=audio_signal:16x80x1000,length:16 \\\r\n --maxShapes=audio_signal:32x80x6000,length:32 \\\r\n --fp16 \\\r\n --saveEngine=encoder.trt\r\n```\r\nFor this quantization script issue, I haven't get a chance to start it. I think I can check this issue this week. (Based on my previous experience, I didn't see perf gain by using trt + quantization.)",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-03-20T08:53:01 | 2024-05-18T01:45:24 | 2024-05-18T01:45:24 | NONE | {
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} | null | null | null | Hi,
I'm trying to export conformer CTC to tensorRT by using this script: `examples/asr/quantization/speech_to_text_quant_infer_trt.py`
OS: Linux 20.04
PyTorch 1.13.1
CUDA: 11.7
tensorrt: 8.6.1
I modified the min and max input shapes to fit the model num_features and added another input (because otherwise it fails):
```
profile.set_shape("length", min=(min_input_shape[0],), opt=(max_input_shape[0],), max=(max_input_shape[0],))
```
But now I get the error below. Any idea what is wrong?
```
[03/20/2024-08:46:08] [TRT] [W] /layers.0/self_attn/Reshape_7: IShuffleLayer with zeroIsPlaceHolder=true has reshape dimension at position 3 that might or might not be zero. TensorRT resolves it at runtime, but this may cause excessive memory consumption and is usually a sign of a bug in the network.
[03/20/2024-08:46:08] [TRT] [W] /layers.1/self_attn/Reshape_7: IShuffleLayer with zeroIsPlaceHolder=true has reshape dimension at position 3 that might or might not be zero. TensorRT resolves it at runtime, but this may cause excessive memory consumption and is usually a sign of a bug in the network.
[03/20/2024-08:46:08] [TRT] [W] /layers.2/self_attn/Reshape_7: IShuffleLayer with zeroIsPlaceHolder=true has reshape dimension at position 3 that might or might not be zero. TensorRT resolves it at runtime, but this may cause excessive memory consumption and is usually a sign of a bug in the network.
[03/20/2024-08:46:08] [TRT] [W] /layers.3/self_attn/Reshape_7: IShuffleLayer with zeroIsPlaceHolder=true has reshape dimension at position 3 that might or might not be zero. TensorRT resolves it at runtime, but this may cause excessive memory consumption and is usually a sign of a bug in the network.
[03/20/2024-08:46:08] [TRT] [W] /layers.4/self_attn/Reshape_7: IShuffleLayer with zeroIsPlaceHolder=true has reshape dimension at position 3 that might or might not be zero. TensorRT resolves it at runtime, but this may cause excessive memory consumption and is usually a sign of a bug in the network.
[03/20/2024-08:46:08] [TRT] [W] /layers.5/self_attn/Reshape_7: IShuffleLayer with zeroIsPlaceHolder=true has reshape dimension at position 3 that might or might not be zero. TensorRT resolves it at runtime, but this may cause excessive memory consumption and is usually a sign of a bug in the network.
[03/20/2024-08:46:08] [TRT] [W] /layers.6/self_attn/Reshape_7: IShuffleLayer with zeroIsPlaceHolder=true has reshape dimension at position 3 that might or might not be zero. TensorRT resolves it at runtime, but this may cause excessive memory consumption and is usually a sign of a bug in the network.
[03/20/2024-08:46:08] [TRT] [W] /layers.7/self_attn/Reshape_7: IShuffleLayer with zeroIsPlaceHolder=true has reshape dimension at position 3 that might or might not be zero. TensorRT resolves it at runtime, but this may cause excessive memory consumption and is usually a sign of a bug in the network.
[03/20/2024-08:46:08] [TRT] [W] /layers.8/self_attn/Reshape_7: IShuffleLayer with zeroIsPlaceHolder=true has reshape dimension at position 3 that might or might not be zero. TensorRT resolves it at runtime, but this may cause excessive memory consumption and is usually a sign of a bug in the network.
[03/20/2024-08:46:08] [TRT] [W] /layers.9/self_attn/Reshape_7: IShuffleLayer with zeroIsPlaceHolder=true has reshape dimension at position 3 that might or might not be zero. TensorRT resolves it at runtime, but this may cause excessive memory consumption and is usually a sign of a bug in the network.
[03/20/2024-08:46:08] [TRT] [W] /layers.10/self_attn/Reshape_7: IShuffleLayer with zeroIsPlaceHolder=true has reshape dimension at position 3 that might or might not be zero. TensorRT resolves it at runtime, but this may cause excessive memory consumption and is usually a sign of a bug in the network.
[03/20/2024-08:46:08] [TRT] [W] /layers.11/self_attn/Reshape_7: IShuffleLayer with zeroIsPlaceHolder=true has reshape dimension at position 3 that might or might not be zero. TensorRT resolves it at runtime, but this may cause excessive memory consumption and is usually a sign of a bug in the network.
[03/20/2024-08:46:08] [TRT] [W] /layers.12/self_attn/Reshape_7: IShuffleLayer with zeroIsPlaceHolder=true has reshape dimension at position 3 that might or might not be zero. TensorRT resolves it at runtime, but this may cause excessive memory consumption and is usually a sign of a bug in the network.
[03/20/2024-08:46:08] [TRT] [W] /layers.13/self_attn/Reshape_7: IShuffleLayer with zeroIsPlaceHolder=true has reshape dimension at position 3 that might or might not be zero. TensorRT resolves it at runtime, but this may cause excessive memory consumption and is usually a sign of a bug in the network.
[03/20/2024-08:46:08] [TRT] [W] /layers.14/self_attn/Reshape_7: IShuffleLayer with zeroIsPlaceHolder=true has reshape dimension at position 3 that might or might not be zero. TensorRT resolves it at runtime, but this may cause excessive memory consumption and is usually a sign of a bug in the network.
[03/20/2024-08:46:08] [TRT] [W] /layers.15/self_attn/Reshape_7: IShuffleLayer with zeroIsPlaceHolder=true has reshape dimension at position 3 that might or might not be zero. TensorRT resolves it at runtime, but this may cause excessive memory consumption and is usually a sign of a bug in the network.
[03/20/2024-08:46:08] [TRT] [W] /layers.16/self_attn/Reshape_7: IShuffleLayer with zeroIsPlaceHolder=true has reshape dimension at position 3 that might or might not be zero. TensorRT resolves it at runtime, but this may cause excessive memory consumption and is usually a sign of a bug in the network.
[03/20/2024-08:46:08] [TRT] [W] /layers.17/self_attn/Reshape_7: IShuffleLayer with zeroIsPlaceHolder=true has reshape dimension at position 3 that might or might not be zero. TensorRT resolves it at runtime, but this may cause excessive memory consumption and is usually a sign of a bug in the network.
[03/20/2024-08:46:48] [TRT] [E] 10: Could not find any implementation for node {ForeignNode[/pos_enc/Constant_output_0 + (Unnamed Layer* 57) [Shuffle].../layers.0/self_attn/MatMul_1]}.
[03/20/2024-08:46:48] [TRT] [E] 10: [optimizer.cpp::computeCosts::3869] Error Code 10: Internal Error (Could not find any implementation for node {ForeignNode[/pos_enc/Constant_output_0 + (Unnamed Layer* 57) [Shuffle].../layers.0/self_attn/MatMul_1]}.)
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"Please install NeMo with pip install nemo_toolkit[all] ",
"> Please install NeMo with pip install nemo_toolkit[all]\r\n\r\nThank you ,I solved it. It's an environmental problem."
] | 2024-03-20T08:45:12 | 2024-03-22T01:10:10 | 2024-03-22T01:10:10 | NONE | {
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} | null | null | null | **Describe the bug**
I run Speaker_Diarization_Inference.ipynb on my mac book pro(M2)
**Steps/Code to reproduce bug**
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version. 14.2.1
- PyTorch version 2.0.1
- Python version 3.11
**Additional context**
Add any other context about the problem here.
Example: GPU model
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} | # What does this PR do ?
All checkpoints statedicts will be switched to safetensors following security recommendations against use of pkl (torch internally uses pickle format).
Additionaly, fix the model filter logic for HF based API search.
**Collection**: [Core]
# Changelog
- Add the support of "ckpt_security_level" flag to SaveRestoreConnector
- Add compact and standard security modes
- In standard mode, only safetensor files can be read and written. If a legacy .ckpt checkpoint is attempted to be loaded into NeMos restoration engine, it will error out.
- In compat mode (the default), both pickle files and safetensors can be loaded for model restoration, however only safetensor files will be written to disk.
- Compat mode is the default and will enable full backward compatibility with all older checkpoints.
- It is the default mode (set as default inside the constructor). It must be explicitly updated to "standard" security mode that is stricter and breaks backward compatibility for .ckpt files.
- Fix a bug in an older test for Huggingface filters
# Usage
```python
from nemo.core.connectors.save_restore_connector import SaveRestoreConnector
connector = SaveRestoreConnector()
connector.ckpt_security_level = "standard"
# Will succeed if the checkpoint was saved with safetensors, fail if saved with .ckpt pickle file
model = ModelPT.restore_from("path/model.nemo", save_restore_connector=connector)
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Add NeMo FW diagrams to the github io website and add mention of NVIDIA NIM.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [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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