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} | # What does this PR do ?
Fix mcore conversion script bug which affected GPT3 models. Also improve the UX of the script.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
python scripts/nlp_language_modeling/convert_nemo_gpt_to_mcore.py --in-folder=<input-nemo-folder> --out-file=<output-mcore-ckpt>
```
# 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 #7730
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
adds guard in case we pick up a distributed checkpoint
# Changelog
- Add specific line by line info of high level changes in this PR.
Fix issue when resuming topk from distributed checkpoint, the logic will [look](https://github.com/NVIDIA/NeMo/blob/8c061debd05837148e86fac19abf024e7210c35d/nemo/utils/callbacks/nemo_model_checkpoint.py#L92) for topk checkpoints and then delete them.
However, the [_saved_checkpoint_paths](https://github.com/NVIDIA/NeMo/blob/8c061debd05837148e86fac19abf024e7210c35d/nemo/utils/callbacks/nemo_model_checkpoint.py#L92C50-L92C73) returns paths without the .ckpt prefix as they are distributed formats, yet the code still calls [ckpt_to_dir](https://github.com/NVIDIA/NeMo/blob/8c061debd05837148e86fac19abf024e7210c35d/nemo/utils/callbacks/nemo_model_checkpoint.py#L226) which crashes since there is no .ckpt prefix on distributed checkpoints. This PR adds a guard to make sure if we see a distributed checkpoint we just return.
**PR Type**:
- [x] Bugfix
When resuming from topk with distributed checkpoint | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
- Fixes graphviz cropped plots for notebooks
- Fixes duplicated Megatron-LM installation
- Fixes k2 library for compatibility with latest PyTorch
**Collection**: [ASR]
# Changelog
- Now graphviz is built and installed from sources.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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Thanks to you guys I have succesfully finetuned a fast conformer transducer model and it is performing quite well. I wanted to see if I am able to continue finetuning for the Cache Aware Streaming Conformer model which looks it might be better for realtime asr.
**Steps/Code to reproduce bug**
I tried to load the dict from the finetuned Rnnt model into a streaming fast conformer moddel:
```
model = nemo_asr.models.EncDecHybridRNNTCTCBPEModel.from_pretrained(model_name="stt_en_fastconformer_hybrid_large_streaming_multi")
model2 = nemo_asr.models.EncDecRNNTBPEModel.restore_from(
saved_model + "/model3.nemo"
)
cfg = OmegaConf.create({ 'init_from_nemo_model': saved_model + "/model3.nemo",})
model.maybe_init_from_pretrained_checkpoint(cfg)
```
But it gives this error output:
```
RuntimeError: Error(s) in loading state_dict for EncDecHybridRNNTCTCBPEModel:
size mismatch for encoder.pre_encode.out.weight: copying a param with shape torch.Size([512, 2560]) from checkpoint, the shape in current model is torch.Size([512, 2816]).
```
Is there any way load the Rnnt Encoder into the Streaming one, perhaps by remodifying or something else?
**Environment details**
```
[NeMo I 2023-11-01 23:33:17 mixins:170] Tokenizer SentencePieceTokenizer initialized with 1024 tokens
[NeMo W 2023-11-01 23:33:18 modelPT:161] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.
Train config :
manifest_filepath: drive/Shareddrives/QuranResources/models/nemo-conformer-text-quran/train_manifest.json
sample_rate: 16000
batch_size: 12
shuffle: true
num_workers: 0
pin_memory: true
use_start_end_token: false
trim_silence: false
max_duration: 20.0
min_duration: 0.1
is_tarred: false
tarred_audio_filepaths: null
shuffle_n: 2048
bucketing_strategy: fully_randomized
bucketing_batch_size: null
channel_selector: 0
[NeMo W 2023-11-01 23:33:18 modelPT:168] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s).
Validation config :
manifest_filepath: drive/Shareddrives/QuranResources/models/nemo-conformer-text-quran/dev_manifest.json
sample_rate: 16000
batch_size: 12
shuffle: false
num_workers: 0
pin_memory: true
use_start_end_token: false
max_duration: 20
channel_selector: 0
is_tarred: false
[NeMo W 2023-11-01 23:33:18 modelPT:174] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).
Test config :
manifest_filepath: drive/Shareddrives/QuranResources/models/nemo-conformer-text-quran/test_manifest.json
sample_rate: 16000
batch_size: 12
shuffle: false
num_workers: 0
pin_memory: true
use_start_end_token: false
channel_selector: 0
[NeMo I 2023-11-01 23:33:18 features:289] PADDING: 0
[NeMo W 2023-11-01 23:33:18 nemo_logging:349] [/opt/homebrew/Caskroom/miniconda/base/envs/nemo/lib/python3.10/site-packages/torch/nn/modules/rnn.py:82](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniconda/base/envs/nemo/lib/python3.10/site-packages/torch/nn/modules/rnn.py:82): UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.2 and num_layers=1
warnings.warn("dropout option adds dropout after all but last "
[NeMo I 2023-11-01 23:33:19 rnnt_models:211] Using RNNT Loss : warprnnt_numba
Loss warprnnt_numba_kwargs: {'fastemit_lambda': 0.0, 'clamp': -1.0}
[NeMo I 2023-11-01 23:33:19 save_restore_connector:249] Model EncDecRNNTBPEModel was successfully restored from [/Users/nhossain/StudioProjects/AndroidStudioProjects/GenerateQuranTimeStamps/nemo/nemo-conformer-text-quran/model3.nemo.](https://file+.vscode-resource.vscode-cdn.net/Users/nhossain/StudioProjects/AndroidStudioProjects/GenerateQuranTimeStamps/nemo/nemo-conformer-text-quran/model3.nemo.)
`
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} | # What does this PR do ?
minor change to log the correct values during peft inference...
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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# Before your PR is "Ready for review"
**Pre checks**:
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] | closed | false | null | [] | null | null | 2023-10-31T18:19:55 | 2023-11-02T19:25:33 | 2023-11-02T19:25:32 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
I find that a JIT operation can have different outputs although the inputs are the same.
**Steps/Code to reproduce bug**
Suppose we have a checkpoint and load it to train a `MegatronGPTModel`.
We print the inputs and outputs of this function: https://github.com/NVIDIA/NeMo/blob/v1.20.0/nemo/collections/nlp/modules/common/megatron/transformer.py#L556
```
layernorm_input = bias_dropout_add_func(attention_output, attention_bias, residual, self.hidden_dropout)
if torch.distributed.get_rank()==0:
print(f"Layer: {self.layer_number} Attention checksum {layernorm_input.sum()}")
print(f"Layer: {self.layer_number} Attention checksum {attention_output.sum()}")
print(f"Layer: {self.layer_number} Attention checksum {attention_bias.sum()}")
print(f"Layer: {self.layer_number} Attention checksum {residual.sum()}")
print(f"Layer: {self.layer_number} Attention checksum {self.hidden_dropout}")
```
During the extra forward pass of the backward, we will get different outputs for this function despite the same inputs
For example, I get the following:
```
Layer: 1 Attention checksum -1.2981499433517456
Layer: 1 Attention checksum -0.10930676758289337
Layer: 1 Attention checksum 0.002560555934906006
Layer: 1 Attention checksum -1.1932671070098877
Layer: 1 Attention checksum 0
Layer: 2 Attention checksum -0.9067337512969971
Layer: 2 Attention checksum 0.4821169376373291
Layer: 2 Attention checksum 0.0022925958037376404
Layer: 2 Attention checksum -1.3912334442138672
Layer: 2 Attention checksum 0
Layer: 3 Attention checksum -1.014440894126892
Layer: 3 Attention checksum 0.02674156427383423
Layer: 3 Attention checksum -0.0017535686492919922
Layer: 3 Attention checksum -1.0347785949707031
Layer: 3 Attention checksum 0
Layer: 4 Attention checksum -1.6703314781188965
Layer: 4 Attention checksum -0.030592918395996094
Layer: 4 Attention checksum -0.0008977651596069336
Layer: 4 Attention checksum -1.6389871835708618
Layer: 4 Attention checksum 0
Layer: 1 Attention checksum -1.2990010976791382
Layer: 1 Attention checksum -0.10930676758289337
Layer: 1 Attention checksum 0.002560555934906006
Layer: 1 Attention checksum -1.1932671070098877
Layer: 1 Attention checksum 0
```
We can find that the outputs of `bias_dropout_add_func` in Layer 1 are different.
The key arguments I used are:
```
model.attention_dropout=0
model.hidden_dropout=0
model.transformer_engine=False
model.activations_checkpoint_granularity=full
model.activations_checkpoint_num_layers=1
```
However, when setting `PYTORCH_JIT=0`, I will get the same outputs for `bias_dropout_add_func`.
**Expected behavior**
The function should always yield the same output.
**Environment overview (please complete the following information)**
I find this issue on NeMov1.20 docker
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
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# Changelog
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# Usage
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```python
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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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https://api.github.com/repos/NVIDIA/NeMo/issues/7836 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7836/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7836/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7836/events | https://github.com/NVIDIA/NeMo/pull/7836 | 1,969,420,086 | PR_kwDOC_bI7s5eLVew | 7,836 | [Fix] Save best NeMo model only when necessary | {
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} | # What does this PR do ?
Currently, when using `save_best_model`, the best model will be saved every time, even when it is not necessary to update it.
This fix ensures NeMo checkpoints are saved only when necessary.
**Collection**: Core
# Changelog
- Fix member name when updating `previous_best_path`
- Added a debug log when saving is skipped when previous best is the same as the current best
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
Update codec configs to always save NeMo checkpoint with best `val_loss`.
**Collection**: TTS
# Changelog
- Updated `checkpoint_callback_params` to always save `NeMo` checkpoint with best `val_loss` and to save 5 best checkpoints
- Set `create_wandb_logger` to `true`, since `model.log_config.log_wandb` is set to `true` by default
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Add support for finetuning with HuggingFace datasets.
**Collection**: [ASR]
# Changelog
- Add dataset wrappers that cast HuggingFace dataset to be compatible with NeMo ASR models.
- Add yaml for finetuning ASR models with HuggingFace datasets.
# Usage
```python
<NEMO_ROOT>/examples/asr/speech_to_text_finetune.py \
--config-name='speech_to_text_hf_finetune'
```
Please refer to `speech_to_text_hf_finetune.yaml` for how to configure the dataset to use.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | null | null | null | **Describe the bug**
when calling `trainer.fit` the second time to train a NeMo model like:
```
trainer.fit(model)
trainer.fit_loop.epoch_loop.max_steps += 10
trainer.fit(model)
```
The second time would use more GPU memory than the first.
**Steps/Code to reproduce bug**
Create a `MegatronGPTModel` with `megatron_amp_O2=True` or with `distributed_fused_adam` and then use the above code for training.
When we print `torch.cuda.memory_allocated()` at the training step, we will find the GPU memory usage at the second `trainer.fit` is significantly higher.
**Expected behavior**
We expect the second `trainer.fit` should have the similar GPU memory usage to that in the first `trainer.fit`.
**Environment details**
Tried official Nemo v1.20 docker and it has this issue.
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} | null | null | null | **Describe the bug**
It seems stt_en_fastconformer_hybrid_large_pc is supported by the force alignment tool, but not the CTC segmentation tool. CTC segmentation tool works on older models, but not the ctc rnnt hybrid model
./NeMo/tools/ctc_segmentation$ ./run_segmentation.sh crash on latest stt_en_fastconformer_hybrid_large_pc model.
**Steps/Code to reproduce bug**
NeMo/tools/ctc_segmentation$ ./run_segmentation.sh --MODEL_NAME_OR_PATH="stt_en_fastconformer_hybrid_large_pc" --DATA_DIR=./data/timit --OUTPUT_DIR=./res/timit_fastconformer_hybrid_large_pc
**Expected behavior**
code crash with error message:
ctc_segmentation/scripts/prepare_data.py", line 357
omegaconf.errors.ConfigAttributeError: Key 'vocabulary' is not in struct
**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**
- OS version ubuntu "20.04.6 LTS (Focal Fossa)"
- PyTorch version 2.1.0 py3.10_cuda11.8_cudnn8.7.0_0
- Python version 3.10.12
**Additional context**
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} | # What does this PR do ?
Provide a fallback to CPU for `getKneighborsConnections` preventing OOM when applying to large cluster of embeddings (i.e when applying diarization to a long audio with multiple speakers).
**Collection**: ASR
# Changelog
- `getKneighborsConnections` now takes an optional argument `max_dim_on_gpu` that trigger a fallback to CPU when one of the dim is higher than `max_dim_on_gpu`.
# Usage
We were encountering OOMs when applying diarization to a longer audio (2h+) with multiple speakers. It appears that `getKneighborsConnections` double the VRAM needed to store the embedding when applying `binarized_affinity_mat = torch.zeros_like(affinity_mat).half()` as it use the same device as for `affinity_mat`.
This PR reduced the VRAM used when applying diarization on this same audio from 41Go (32 + 9Go) to 29Go by increase the inference time by only one seconde.
In order to allow user to use this param we either should add a kwargs to `getKneighborsConnections` (and all the function that called it) or use a more global param that's automatically propagated (i.e env var). As this choice need some thought on your side I prefer to only add `max_dim_on_gpu` to `getKneighborsConnections` for now and let you decide how to proceed (unless you already have some suggestion).
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] ~~Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)~~
- [x] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Update the multimodal/speech_cv branch to support audio-visual speech recognition (CTC, RNN-T and Hybrid)
**Collection**: [multimodal/speech_cv]
# Changelog
- Update speech_cv branch
- bug fix
- Add audio-visual datasets
- Add audio-visual models: (AudioVisualEncDecCTCModelBPE, AudioVisualEncDecCTCModel, AudioVisualEncDecHybridRNNTCTCBPEModel, AudioVisualEncDecHybridRNNTCTCModel, AudioVisualEncDecRNNTBPEModel, AudioVisualEncDecRNNTModel)
- Add audio-visual modules (FusionModule, InterCTCMixin)
# Usage
* You can potentially add a usage example below
```python
import nemo.collections.multimodal.speech_cv as nemo_cv
from omegaconf import OmegaConf
config_path = "model_config.yaml"
params = OmegaConf.load(config_path)
# hybrid CTC/RNN-T BPE audio-visual speech recognition model
model = nemo_cv.models.AudioVisualEncDecHybridRNNTCTCBPEModel(cfg=params.model)
```
# 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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# Changelog
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# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
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- [ ] 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.
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# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# 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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# Changelog
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# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | null | null | null | **Describe the bug**
when using the batch sampler such as [MegatronPretrainingBatchSampler](https://github.com/NVIDIA/NeMo/blob/177a67fa4a43ed6f7c4fb39a66c0a2998a27fef1/nemo/collections/nlp/data/language_modeling/megatron/megatron_batch_samplers.py#L141). The train_dataloader's drop_last argument is always set to False, which is accidentally picked up by this line of [code](https://github.com/NVIDIA/NeMo/blob/177a67fa4a43ed6f7c4fb39a66c0a2998a27fef1/nemo/core/optim/lr_scheduler.py#L877C47-L877C47) leading to our learning rate scheduler being off by 1step.
Instead we should use drop last from the Batch sampler itself [here](https://github.com/NVIDIA/NeMo/blob/177a67fa4a43ed6f7c4fb39a66c0a2998a27fef1/nemo/core/optim/lr_scheduler.py#L870) when we detect it
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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.
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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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Test
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Fixes errors using `BertDPRModel` and `BertJointIRModel`. Currently you get an exception due to an undefined variable `trainer` from method `get_lm_model_with_padded_embedding` in `BaseIRModel`.
**Collection**: nlp
# Changelog
- Fixes errors using `BertDPRModel` and `BertJointIRModel` from undefined trainer name.
# Usage
* You can potentially add a usage example below
```python
from omegaconf import OmegaConf
from nemo.collections.nlp.models.information_retrieval import BertJointIRModel
model_name_or_path = "intfloat/e5-small-v2"
cfg = OmegaConf.create({
"language_model": {"sim_score_dropout": 0.1, "pretrained_model_name": model_name_or_path, "config_file": None, "config": None},
"tokenizer": {"tokenizer_name": model_name_or_path, "vocab_file": None, "special_tokens": None, "tokenizer_model": None}
})
BertJointIRModel(cfg)
# => Raises NameError: name 'trainer' is not defined
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Do we implement phonetic lexicon support for ASR in the main code base, apart from BPE, char, and unigram?
Thanks :)
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} | # What does this PR do ?
Adds the mcore bert model to the nemo codebase.
**Collection**: nlp collection
# Usage
* Set mcore_bert config to True
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/7812 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7812/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7812/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7812/events | https://github.com/NVIDIA/NeMo/pull/7812 | 1,964,214,760 | PR_kwDOC_bI7s5d52Gv | 7,812 | Add safetensors | {
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} | # What does this PR do ?
By default, state_dicts are being saved as pytorch files which contain pickles. This is a security concern which this PR attempts to start the mitigation for while preserving backwards compatability.
**Collection**: all, but none specifically (core functionality)
# Changelog
- add nemo.utils.secure.torch_load and nemo.utils.secure.torch_save
- torch.load and torch.save removed from save_restore_connector and alternates of it
- secure.torch_load and torch_save use safetensors when available, however if safe=False torch.load and torch.save are used to allow for backwards compatability
- safe is a named parameter and passed through from save_to and restore_from. The default is safe=False to preserve backwards compatability
- added unit tests to double check backwards compatability and secure functions work correctly.
# Usage
* When using nemo with untrusted .nemo files this will greatly reduce the potential for the user to be attacked.
```python
# Existing usage should be preserved. although the result will include a .safetensor inside the .nemo now as well
model.save_to(save_path=model_save_path)
model.save_to(save_path=model_save_path, safe=False)
# the .nemo will only include the .safetensor.
model.save_to(save_path=model_save_path, safe=True)
# Existing usage should be preserved. although if the .nemo contains a .safetensor, it will be used instead of the pytorch version
model.restore_from(model_save_path)
model.restore_from(save_path=model_save_path, safe=False)
# The restore will fail if a .safetensor isn't available to prevent an untrusted .nemo file from resulting in an exploit
model.restore_from(save_path=model_save_path, safe=False)
```
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [X] Did you write any new necessary tests?
- [X] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [X] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | Is it ok that default model (I use ASR conformer transducer) has WER=0.26 but the same model that I have been training for 2 days has WER=0.32.
I use [Russian version](https://huggingface.co/nvidia/stt_ru_conformer_transducer_large) of the model.
My GPU is `V100DX-32Q`.
Here is the training config file:
```
cfg:
sample_rate: 16000
compute_eval_loss: true
log_prediction: true
skip_nan_grad: false
model_defaults:
enc_hidden: 512
pred_hidden: 640
joint_hidden: 640
train_ds:
manifest_filepath: data/processed/output_train_ds.json
sample_rate: 16000
batch_size: 32
shuffle: true
num_workers: 8
pin_memory: true
use_start_end_token: true
trim_silence: false
max_duration: 20.0
min_duration: 0.1
is_tarred: false
tarred_audio_filepaths: null
shuffle_n: 2048
bucketing_strategy: synced_randomized
bucketing_batch_size: null
bucketing_weights: ''
validation_ds:
manifest_filepath: data/processed/output_val_ds.json
sample_rate: 16000
batch_size: 32
shuffle: false
num_workers: 8
pin_memory: true
use_start_end_token: true
trim_silence: false
test_ds:
manifest_filepath: null
sample_rate: 16000
batch_size: 16
shuffle: false
num_workers: 8
pin_memory: true
use_start_end_token: false
tokenizer:
dir: /tokenizers/tokenizer_spe_unigram_v1024/
type: bpe
model_path: nemo:7e41df0fdb8f45388c8052fe688b7add_tokenizer.model
vocab_path: nemo:531574790a7146c3ac33709c3b0e6aff_vocab.txt
spe_tokenizer_vocab: nemo:8be1ca048a724049bfe9998715df9536_tokenizer.vocab
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
sample_rate: 16000
normalize: per_feature
window_size: 0.025
window_stride: 0.01
window: hann
features: 80
n_fft: 512
frame_splicing: 1
dither: 1.0e-05
pad_to: 0
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
freq_masks: 2
time_masks: 10
freq_width: 27
time_width: 0.05
encoder:
_target_: nemo.collections.asr.modules.ConformerEncoder
feat_in: 80
feat_out: -1
n_layers: 17
d_model: 512
subsampling: striding
subsampling_factor: 4
subsampling_conv_channels: 512
causal_downsampling: false
ff_expansion_factor: 4
self_attention_model: rel_pos
n_heads: 8
att_context_size:
- -1
- -1
att_context_style: regular
xscaling: true
untie_biases: true
pos_emb_max_len: 5000
conv_kernel_size: 31
conv_norm_type: batch_norm
conv_context_size: null
dropout: 0.1
dropout_emb: 0.0
dropout_att: 0.1
decoder:
_target_: nemo.collections.asr.modules.RNNTDecoder
normalization_mode: null
random_state_sampling: false
blank_as_pad: true
prednet:
pred_hidden: 640
pred_rnn_layers: 1
t_max: null
dropout: 0.1
vocab_size: 1024
joint:
_target_: nemo.collections.asr.modules.RNNTJoint
log_softmax: null
preserve_memory: false
fuse_loss_wer: true
fused_batch_size: 4
jointnet:
joint_hidden: 640
activation: relu
dropout: 0.1
encoder_hidden: 512
pred_hidden: 640
num_classes: 1024
vocabulary:
# there are 1024 symbols so I hide them
decoding:
strategy: greedy_batch
greedy:
max_symbols: 10
beam:
beam_size: 2
return_best_hypothesis: false
score_norm: true
tsd_max_sym_exp: 50
alsd_max_target_len: 2.0
loss:
loss_name: default
warprnnt_numba_kwargs:
fastemit_lambda: 0.0
clamp: -1.0
variational_noise:
start_step: 0
std: 0.0
optim:
name: novograd
lr: 0.0001
betas:
- 0.9
- 0.98
weight_decay: 0.001
sched:
name: CosineAnnealing
warmup_steps: 10000
warmup_ratio: null
min_lr: 1.0e-06
init_weights_from_model: /exp_init/ConformerL_rnnt_russian/Conformer_rnnt_trained_from_stt_en_ep_500_lr_2e-3_wd_1e-3_drc_cosine/Conformer_rnnt_trained_from_stt_en_ep_500_lr_2e-3_wd_1e-3_drc_cosine/checkpoints/Conformer_rnnt_en_finetuned_ep_500_lr_2e-3_wd_1e-3_drc_cosine.nemo
labels:
# there are 1024 labels so I hide them
target: nemo.collections.asr.models.rnnt_bpe_models.EncDecRNNTBPEModel
nemo_version: 1.12.0rc0
```
And I also set epochs_number to 1000 and accumulate_grad_batches to 8.
And for example this is val_wer for ctc (log_0410) and transducer (log_2410):
val_wer converges almost linear
And here is val_loss:
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When I train with modified vocab it always show me prediction and reference, but if it's a standard model nothing has shown.
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The TN option works just fine but when I use the mode of `ITN`, the model still output the Text Normalization instead.
The command I used:
```
cd NeMo/examples/nlp/duplex_text_normalization;
# run inference in interactive mode using pretrained tagger and decoder models
python duplex_text_normalization_infer.py \
tagger_pretrained_model=neural_text_normalization_t5 \
decoder_pretrained_model=neural_text_normalization_t5 \
inference.from_file=False \
lang=en \
mode=itn
```
The output:
```
Entering interactive mode.
Type "STOP" to exit.
Input a test input:$123
Skipping post-processing of one hundred twenty three dollars for '$'
Prediction (ITN): one hundred twenty three dollars
Type "STOP" to exit.
Input a test input:one hundred twenty three
Prediction (ITN): one hundred twenty three
Type "STOP" to exit.
Input a test input:one two three
Prediction (ITN): one two three
Type "STOP" to exit.
Input a test input:one hundred twenty three dollars
Prediction (ITN): one hundred twenty three dollars
```
As you can see, the model still returns TN result. | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | null | null | null | **Describe the bug**
Loading models trained with Nemo version `r1.21.0` using Nemo `r1.20.0` codebase fails with the following error:
```
[NeMo E 2023-10-23 16:21:42 common:506] Model instantiation failed!
Target class: nemo.collections.asr.models.hybrid_rnnt_ctc_bpe_models.EncDecHybridRNNTCTCBPEModel
Error(s): 'str' object has no attribute 'temperature'
```
This is due to changes in `nemo/collections/asr/parts/utils/asr_confidence_utils.py` - the newer [version ](https://github.com/NVIDIA/NeMo/blob/c0022aec9c8fedd59458935f0378a775b28d0444/nemo/collections/asr/parts/utils/asr_confidence_utils.py#L93-L120) supports the `str` value `DEPRECATED`:
**Steps/Code to reproduce bug**
The bug occurs when using Nemo `r1.21.0` and finetuning the model [here](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_multilingual_fastconformer_hybrid_large_pc) using the script `examples/asr/speech_to_text_finetune.py` and the config file `examples/asr/conf/speech_to_text_finetune.yaml` with minor modifications.
**Expected behavior**
The `r1.20.0` codebase should be able to load models trained with `r1.21.0` unless the models differ in ways that simply can't be supported by the old code base. In the later situation, a meaningful error message should indicate that code base is too old for the model.
**Environment overview (please complete the following information)**
- Environment location: Bare Metal
- Method of NeMo install: `python -m pip install 'git+https://github.com/NVIDIA/[email protected]#egg=nemo_toolkit[all]'`
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Ubuntu 20.04
- PyTorch version: pytorch-lightning 2.0.7
- Python version: Conda Python 3.10.13
**Additional context**
Add any other context about the problem here.
Example: GPU model: NVIDIA A100
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Updates Dockerfile to install Apex correctly.
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# Changelog
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# Usage
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```python
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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
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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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When running this colab we get error on the first few cells and fail to install requirements. We get the following error.
```site-packages (from requests->transformers->nemo_text_processing==0.1.6rc0->-r https://raw.githubusercontent.com/NVIDIA/NeMo/main/tools/ctc_segmentation/requirements.txt (line 3)) (2023.7.22)
Using cached num2words-0.5.13-py3-none-any.whl (143 kB)
Installing collected packages: setuptools, num2words, ctc_segmentation, nemo_text_processing
Attempting uninstall: setuptools
Found existing installation: setuptools 68.2.2
Uninstalling setuptools-68.2.2:
Successfully uninstalled setuptools-68.2.2
Attempting uninstall: nemo_text_processing
Found existing installation: nemo-text-processing 0.2.2rc0
Uninstalling nemo-text-processing-0.2.2rc0:
Successfully uninstalled nemo-text-processing-0.2.2rc0
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
nemo-toolkit 1.21.0rc0 requires setuptools>=65.5.1, but you have setuptools 59.5.0 which is incompatible.
Successfully installed ctc_segmentation-1.7.1 nemo_text_processing-0.1.6rc0 num2words-0.5.13 setuptools-59.5.0 | {
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I have tried out the [Joint Intent and Slot Classification Notebook](https://github.com/NVIDIA/NeMo/blob/stable/tutorials/nlp/Joint_Intent_and_Slot_Classification.ipynb) in Google Collab and it works great.
In the notebook it is mentioned that "[their] Joint Intent and Slot classification model is comprised of the pretrained [BERT](https://arxiv.org/pdf/1810.04805.pdf) model with an Intent and Slot Classification layer on top of it."
I assume this model is inherited from [BERTModule](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/modules/common/bert_module.py).
Is there a way to use a German-based BERT and put the "Intent and Slot Classification layer on top of it."? For instance to plug-in some German [SentenceTransformer](https://huggingface.co/sentence-transformers) model?
Any help in the right direction is much appreciated, thanks! | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/7800 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7800/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7800/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7800/events | https://github.com/NVIDIA/NeMo/issues/7800 | 1,961,116,495 | I_kwDOC_bI7s505ENP | 7,800 | Mixture of Expert with Expert Parallel -- initialization in nemo doesn't have expert_model_parallel flag | {
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} | null | null | null | **Describe the bug**
The model parallel initialisation in NeMo doesn't account for `expert_model_parallel_size` in `megatron_base_model.py` which is currently supported in the latest version of Megatron-LM.
```
initialize_model_parallel_for_nemo(
world_size=init_world_size,
global_rank=init_global_rank,
local_rank=init_local_rank,
tensor_model_parallel_size=cfg.get('tensor_model_parallel_size', 1),
pipeline_model_parallel_size=cfg.get('pipeline_model_parallel_size', 1),
virtual_pipeline_model_parallel_size=vp_size,
pipeline_model_parallel_split_rank=cfg.get('pipeline_model_parallel_split_rank', 0),
micro_batch_size=cfg.get('micro_batch_size'),
global_batch_size=cfg.get('global_batch_size'),
rampup_batch_size=cfg.get('rampup_batch_size', None),
use_fp8=cfg.get('fp8', False),
init_mpi_proc_group=cfg.get('ub_tp_comm_overlap', False),
seed=self.cfg.get('seed', 1234),
apex_transformer_log_level=self.cfg.get('apex_transformer_log_level', 30),
)
```
This should have the option to set expert_parallel_size here but it's currently not supported. Any ETA when changes to NeMo will add this?
Training will fail with the following error:
[1,1] File "~/Megatron-LM/megatron/core/parallel_state.py", line 550, in get_tensor_and_expert_parallel_group
[1,1]<stdout>: assert (
[1,1]<stdout>:AssertionError: tensor and expert parallel group is not initialized
which makes sense since the option for expert parallel is not supported in the initialize_model_parallel_for_nemo() function.
Setup for testing uses latest updates in both Megatron-LM and NeMo-Megatron
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} | -flash-decoding used for decoding if available
-change rope to use rotary_base_len as interpolation base instead of current max len
-optionally only interpolate decoding | {
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- Fix code for allowing multiple audios in a sample
- Add training support for multiple audios in a sample
- Fix num_workers=0 issue, but doesn't affect training speed
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} | # What does this PR do ?
Updates Dockerfile to install Apex correctly.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
(1) Add SHARP interface to M-CORE in the communicator initialization.
(2) use send/recv to send train loss to the first rank instead of b-cast. This mitigates the communication overhead.
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
The distributed Adam optimizer's performance and memory usage is sensitive to the bucket size:
1. Larger bucket sizes improve performance (less kernel overhead and NCCL latency overhead)
2. There is wasted memory and communication if the bucket sizes don't match the param sizes
3. The bucket size should be smaller than existing buffers in the caching memory allocator, e.g. from temporary activation tensors. Otherwise the allocator will need to allocate new buffers for temporary distopt buffers (e.g. for all-gathers).
This PR attempts to mitigate (2) by adding an option that looks for the bucket size in `[bucket_cap_mb/2, bucket_cap_mp]` that minimizes waste. Note that this `bucket_cap_mb` should still be chosen based on (3) and that the optimal choice will depend on the model.
Based on https://github.com/NVIDIA/NeMo/pull/7399. Pinging @shjwudp.
**Collection**: NLP
# Changelog
- Add option to tune distopt bucket sizes
# Usage
Configure the model with `model.optim.name=distributed_fused_adam`, `model.optim.tune_bucket_cap_mb=True`, `model.optim.bucket_cap_mb=<max buffer size in allocator>`:
https://github.com/NVIDIA/NeMo/blob/fb6fb2fc494afc6cbb13ab77e3829b69b6db6bb2/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml#L228
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Closes https://github.com/NVIDIA/NeMo/pull/7399 | {
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"merged_at": "2023-12-13T02:12:56"
} | This reverts commit d6900f9bc1922d086e2e388dcec6e3bd2b0f59dc.
# What does this PR do ?
Add All Multimodal Source Code
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Adds docs for subsampling args in ASR Fast Conformer and updates tokenization script
**Collection**: [ASR]
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [x] Documentation
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} | null | null | null | Hi. I have 34 hours of ASR dataset (split in an 80-20 ratio for training and validation) in the Armenian language and want to train the ASR model using Citrinet 256 (transfer-learning from stt_en_citrinet_256). After some epochs, WER is going up. How to improve this or must I use Citrinet 512? Below are hyperparameters of training:
`tokenizer type is unigram,`
`vocab size is 1024,`
`freeze_encoder: False,`
`batch_size: 4,`
`use_start_end_token: True,`
`trim_silence: False,`
`learning rate: 0.001,`
`betas: [0.95, 0.5],`
`weight_decay: 0.001,`
`warmup_steps: None,`
`warmup_ratio: 0.1,`
`minimum learning rate: 1e-5,`
`spec_augment.freq_masks: 2,`
`spec_augment.freq_width: 27,`
`spec_augment.time_masks: 10,`
`spec_augment.time_width: 0.05,`
`epochs: 200,`
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} | # What does this PR do ?
- Allow specifying the criterion with which the _reference audio_ is selected for a given training/validation example
**Collection**: TTS
# Changelog
- The current NeMo implementation selects the reference audio for a given training example as a random example from the same speaker. As discussed in #7777, this allows the GST module to extract speaker information, and _not_ speaking style information, from the reference spectrogram, thereby making it easier to fine-tune on new speakers with very little data
- On the other hand, the [GSTs paper](https://arxiv.org/abs/1803.09017) states that:
> During training, the reference signal is ground-truth audio.
- As discussed in #7777, it is desirable to support both use cases as opposed to just one of the two
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
## Who can review?
@subhankar-ghosh, @hsiehjackson
# Additional Information
* Related to #7777
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} | # What does this PR do ?
Updates `ema.py` for full Lightning 2.0 and 2.1 support, and also adds full docstrings for the EMA `Callback` and its components.
# Changelog
- Update imports for the `EMA` `Callback`
- Add docstrings to the `EMA` `Callback`
# Usage
* Being able to use the `EMA` `Callback` in Lightning 2.0 as well as Lightning 2.1 models.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [x] Documentation
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 ?
Fix bug in `nemo.collections.tts.modules.submodules.ConditionalInput.forward()`: concatenate `inputs` and `conditioning` along the _feature_ dimension, not the _batch_ dimension.
**Collection**: TTS
# Changelog
- When using `ConditionalInput` in `"concat"` mode, concatenate `inputs` and `conditioning` along the _feature_ dimension, not the _batch_ dimension as before
- Despite the misleading docstring in `ConditionalInput.forward()` (corrected in this PR), `inputs` and `conditioning` have shapes `B x T x H` and `B x T x C`, respectively (to be even more precise, `conditioning` has shape `B x 1 x C` _before_ the call to `conditioning.repeat()` and `B x T x C` _after_). Consequently, concatenating the two along the batch dimension has 2 problems:
- If `H != C`, the concatenation will fail
- If `H == C`, the concatenation results in a `2B x T x C` tensor
- This causes an error downstream when calling `self.concat_proj()`, which is defined as:
`self.concat_proj = torch.nn.Linear(hidden_dim + condition_dim, hidden_dim)`
- `self.concat_proj` is meant to map `B x T x (H+C)` tensors to `B x T x H` tensors, so passing a `2B x T x (H=C)` tensor to it results in the following error:
`RuntimeError: mat1 and mat2 shapes cannot be multiplied ((2B*T)x(H=C) and (H+C)xH)` (the actual error message contains constant values, but here I have extracted variables to make my point clearer)
- **Solution**: concatenate along the feature dimension to obtain a `B x T x (H+C)` tensor
# 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?
@subhankar-ghosh, @hsiehjackson
# Additional Information
* Related to #7777
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} | # What does this PR do ?
the context for memmap dataset is very slow with "spawn" so we set the context to "fork" just for memmap dataset loading.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | null | null | null | **Describe the bug**
There is a size mismatch between EncDec and Joint and Decoder modules when trying to finetune a Large Conformer-Transducer model.
**Steps/Code to reproduce bug**
I installed NeMo inside a docker container using the following command:
```
docker pull nvcr.io/nvidia/nemo:23.06
docker run --gpus all -it --rm -v /home/:/workspace --shm-size=8g -p 8888:8888 -p 6006:6006 --ulimit memlock=-1 --ulimit stack=67108864 --device=/dev/snd nvcr.io/nvidia/nemo:23.06
```
Inside the docker container I downloaded the Conformer-Transducer Large model using the following command:
```wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_transducer_large/versions/1.10.0/zip -O stt_en_conformer_transducer_large_1.10.0.zip
```
Then I tried to launch finetuning using the following command:
```
speech_to_text_rnnt_bpe.py --config-path=configs
--config-name=conformer_transducer_bpe_large
name=NEW-test
model.train_ds.batch_size=16
model.validation_ds.batch_size=16
model.test_ds.batch_size=16
trainer.devices=-1
trainer.accelerator=gpu
+init_from_nemo_model=non-finetuned/models/stt_en_conformer_transducer_large.nemo
~model.encoder.stochastic_depth_drop_prob
~model.encoder.stochastic_depth_mode
~model.encoder.stochastic_depth_start_layer
```
I get the following error:
```
Traceback (most recent call last):
File "/workspace/projects/nemo_asr/speech_to_text_rnnt_bpe_custom.py", line 114, in main
asr_model.maybe_init_from_pretrained_checkpoint(cfg)
File "/usr/local/lib/python3.10/dist-packages/lightning_utilities/core/rank_zero.py", line 32, in wrapped_fn
return fn(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/nemo/core/classes/modelPT.py", line 1219, in maybe_init_from_pretrained_checkpoint
self.load_state_dict(restored_model.state_dict(), strict=False)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 2040, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for EncDecRNNTBPEModel:
size mismatch for decoder.prediction.embed.weight: copying a param with shape torch.Size([1025, 640]) from checkpoint, the shape in current model is torch.Size([928, 640]).
size mismatch for joint.joint_net.2.weight: copying a param with shape torch.Size([1025, 640]) from checkpoint, the shape in current model is torch.Size([928, 640]).
size mismatch for joint.joint_net.2.bias: copying a param with shape torch.Size([1025]) from checkpoint, the shape in current model is torch.Size([928]).
```
My config file is as follows:
```
# It contains the default values for training a Conformer-Transducer ASR model, large size (~120M) with Transducer loss and sub-word encoding.
# Architecture and training config:
# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective
# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches.
# Here are the recommended configs for different variants of Conformer-Transducer, other parameters are the same as in this config file.
#
# +--------------+---------+---------+----------+------------------+--------------+--------------------------+-----------------+
# | Model | d_model | n_heads | n_layers | conv_kernel_size | weight_decay | pred_hidden/joint_hidden | pred_rnn_layers |
# +==============+=========+========+===========+==================+==============+==========================+=================+
# | Small (14M)| 176 | 4 | 16 | 31 | 0.0 | 320 | 1 |
# +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+
# | Medium (32M)| 256 | 4 | 16 | 31 | 1e-3 | 640 | 1 |
# +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+
# | Large (120M)| 512 | 8 | 17 | 31 | 1e-3 | 640 | 1 |
# +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+
# | XLarge (644M)| 1024 | 8 | 24 | 5 | 1e-3 | 640 | 2 |
# +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+
# Default learning parameters in this config are set for global batch size of 2K while you may use lower values.
# To increase the global batch size with limited number of GPUs, you may use higher accumulate_grad_batches.
# However accumulate_grad_batches is better to be avoided as long as the global batch size is large enough and training is stable.
# You may find more info about Conformer-Transducer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#conformer-transducer
# Pre-trained models of Conformer-Transducer can be found here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/results.html
# The checkpoint of the large model trained on NeMo ASRSET with this recipe can be found here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_transducer_large
# We suggest to use trainer.precision=bf16 for GPUs which support it otherwise trainer.precision=16 is recommended.
# Using bf16 or 16 would make it possible to double the batch size and speedup training/inference. If fp16 is not stable and model diverges after some epochs, you may use fp32.
# Here are the suggested batch size per GPU for each precision and memory sizes:
# +-----------+------------+------------+
# | Precision | GPU Memory | Batch Size |
# +===========+============+============+
# | 32 | 16GB | 8 |
# | | 32GB | 16 |
# | | 80GB | 32 |
# +-----------+------------+------------+
# | 16 or | 16GB | 16 |
# | bf16 | 32GB | 32 |
# | | 80GB | 64 |
# +-----------+------------+------------+
# Note: They are based on the assumption of max_duration of 20. If you have longer or shorter max_duration, then batch sizes may need to get updated accordingly.
name: "Conformer-Transducer-BPE"
model:
sample_rate: 16000
compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag.
log_prediction: true # enables logging sample predictions in the output during training
skip_nan_grad: false
model_defaults:
enc_hidden: ${model.encoder.d_model}
pred_hidden: 640
joint_hidden: 640
train_ds:
manifest_filepath: "/workspace/datasets/cmu_hf/train_manifest.json"
sample_rate: ${model.sample_rate}
batch_size: 16 # you may increase batch_size if your memory allows
shuffle: true
num_workers: 8
pin_memory: true
max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset
min_duration: 0.1
# tarred datasets
is_tarred: false
tarred_audio_filepaths: null
shuffle_n: 2048
# bucketing params
bucketing_strategy: "synced_randomized"
bucketing_batch_size: null
validation_ds:
manifest_filepath: "/workspace/datasets/cmu_hf/test_manifest.json"
sample_rate: ${model.sample_rate}
batch_size: 16
shuffle: false
use_start_end_token: false
num_workers: 8
pin_memory: true
test_ds:
manifest_filepath: "/workspace/datasets/cmu_hf/test_manifest.json"
sample_rate: ${model.sample_rate}
batch_size: 16
shuffle: false
use_start_end_token: false
num_workers: 8
pin_memory: true
# You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py
# recommend to use SPE Unigram tokenizer with vocab size of 1K to 4k when using 4x sub-sampling
tokenizer:
dir: "/workspace/projects/nemo_asr/tokenizers/cmu_hf/tokenizer_spe_unigram_v1024" # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe)
type: bpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer)
preprocessor:
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
sample_rate: ${model.sample_rate}
normalize: "per_feature"
window_size: 0.025
window_stride: 0.01
window: "hann"
features: 80
n_fft: 512
frame_splicing: 1
dither: 0.00001
pad_to: 0
spec_augment:
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
freq_masks: 2 # set to zero to disable it
time_masks: 10 # set to zero to disable it
freq_width: 27
time_width: 0.05
encoder:
_target_: nemo.collections.asr.modules.ConformerEncoder
feat_in: ${model.preprocessor.features}
feat_out: -1 # you may set it if you need different output size other than the default d_model
n_layers: 17
d_model: 512
# Sub-sampling parameters
subsampling: striding # vggnet, striding, stacking or stacking_norm, dw_striding
subsampling_factor: 4 # must be power of 2 for striding and vggnet
subsampling_conv_channels: -1 # set to -1 to make it equal to the d_model
causal_downsampling: false
# Reduction parameters: Can be used to add another subsampling layer at a given position.
# Having a 2x reduction will speedup the training and inference speech while keeping similar WER.
# Adding it at the end will give the best WER while adding it at the beginning will give the best speedup.
reduction: null # pooling, striding, or null
reduction_position: null # Encoder block index or -1 for subsampling at the end of encoder
reduction_factor: 1
# Feed forward module's params
ff_expansion_factor: 4
# Multi-headed Attention Module's params
self_attention_model: rel_pos # rel_pos or abs_pos
n_heads: 8 # may need to be lower for smaller d_models
# [left, right] specifies the number of steps to be seen from left and right of each step in self-attention
att_context_size: [-1, -1] # -1 means unlimited context
att_context_style: regular # regular or chunked_limited
xscaling: true # scales up the input embeddings by sqrt(d_model)
untie_biases: true # unties the biases of the TransformerXL layers
pos_emb_max_len: 5000
# Convolution module's params
conv_kernel_size: 31
conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups)
# conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size
# null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0]
conv_context_size: null
### regularization
dropout: 0.1 # The dropout used in most of the Conformer Modules
dropout_pre_encoder: 0.1 # The dropout used before the encoder
dropout_emb: 0.0 # The dropout used for embeddings
dropout_att: 0.1 # The dropout for multi-headed attention modules
# set to non-zero to enable stochastic depth
stochastic_depth_drop_prob: 0.0
stochastic_depth_mode: linear # linear or uniform
stochastic_depth_start_layer: 1
decoder:
_target_: nemo.collections.asr.modules.RNNTDecoder
normalization_mode: null # Currently only null is supported for export.
random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf
blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference.
prednet:
pred_hidden: ${model.model_defaults.pred_hidden}
pred_rnn_layers: 1
t_max: null
dropout: 0.2
joint:
_target_: nemo.collections.asr.modules.RNNTJoint
log_softmax: null # 'null' would set it automatically according to CPU/GPU device
preserve_memory: false # dramatically slows down training, but might preserve some memory
# Fuses the computation of prediction net + joint net + loss + WER calculation
# to be run on sub-batches of size `fused_batch_size`.
# When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size.
# `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss.
# Using small values here will preserve a lot of memory during training, but will make training slower as well.
# An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1.
# However, to preserve memory, this ratio can be 1:8 or even 1:16.
# Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow.
fuse_loss_wer: true
fused_batch_size: 4
jointnet:
joint_hidden: ${model.model_defaults.joint_hidden}
activation: "relu"
dropout: 0.2
decoding:
strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd.
# greedy strategy config
greedy:
max_symbols: 10
# beam strategy config
beam:
beam_size: 2
return_best_hypothesis: False
score_norm: true
tsd_max_sym_exp: 50 # for Time Synchronous Decoding
alsd_max_target_len: 2.0 # for Alignment-Length Synchronous Decoding
loss:
loss_name: "default"
warprnnt_numba_kwargs:
# FastEmit regularization: https://arxiv.org/abs/2010.11148
# You may enable FastEmit to reduce the latency of the model for streaming
fastemit_lambda: 0.0 # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start.
clamp: -1.0 # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only.
# Adds Gaussian noise to the gradients of the decoder to avoid overfitting
variational_noise:
start_step: 0
std: 0.0
optim:
name: adamw
lr: 3.0
# optimizer arguments
betas: [0.9, 0.98]
weight_decay: 1e-3
# scheduler setup
sched:
name: NoamAnnealing
d_model: ${model.encoder.d_model}
# scheduler config override
warmup_steps: 40000
warmup_ratio: null
min_lr: 1e-6
trainer:
devices: -1 # number of GPUs, -1 would use all available GPUs
num_nodes: 1
max_epochs: 500
max_steps: -1 # computed at runtime if not set
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
accelerator: auto
strategy: ddp
accumulate_grad_batches: 1
gradient_clip_val: 0.0
precision: 32 # 16, 32, or bf16
log_every_n_steps: 10 # Interval of logging.
enable_progress_bar: True
num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it
check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs
sync_batchnorm: true
enable_checkpointing: False # Provided by exp_manager
logger: false # Provided by exp_manager
benchmark: false # needs to be false for models with variable-length speech input as it slows down training
exp_manager:
exp_dir: null
name: ${name}
create_tensorboard_logger: true
create_checkpoint_callback: true
checkpoint_callback_params:
# in case of multiple validation sets, first one is used
monitor: "val_wer" # the objective function that determines model performance
mode: "min"
save_top_k: 2 # number of best performing .ckpt files to save (last epoch is always saved as a .ckpt file separately)
save_best_model: true # saves best model as nemo file instead of the default last checkpoint
always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints (last checkpoint is saved by default)
resume_if_exists: false
resume_ignore_no_checkpoint: false
create_wandb_logger: false
wandb_logger_kwargs:
name: null
project: null
```
**Additional context**
Add any other context about the problem here.
Example: A6000
I ran this exact command and successfully finetuned the model a few months ago last using the 23.03 image, but now I have no luck, coudl anyone please help me get this finetuning working? | {
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} | null | null | null | I am following this tutorial (https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/llama2peft.html) to convert llama2-70b-hf to a nemo file. I am encountering an issue that a number of weights are not in checkpoint but in model. I wonder if this has anything to do with the `megatron_llama_config.yaml` file, though I double checked every config allies with that of a 7b model.
Config:
```yaml
nemo_config: {'mcore_gpt': True, 'micro_batch_size': 4, 'global_batch_size': 8, 'tensor_model_parallel_size': 1, 'pipeline_model_parallel_size': 1, 'virtual_pipeline_model_parallel_size': None, 'encoder_seq_length': 4096, 'max_position_embeddings': 4096, 'num_layers': 32, 'hidden_size': 4096, 'ffn_hidden_size': 11008, 'num_attention_heads': 32, 'init_method_std': 0.02, 'use_scaled_init_method': True, 'hidden_dropout': 0.0, 'attention_dropout': 0.0, 'ffn_dropout': 0.0, 'kv_channels': None, 'apply_query_key_layer_scaling': True, 'normalization': 'rmsnorm', 'layernorm_epsilon': 1e-05, 'do_layer_norm_weight_decay': False, 'make_vocab_size_divisible_by': 128, 'pre_process': True, 'post_process': True, 'persist_layer_norm': True, 'bias': False, 'activation': 'fast-swiglu', 'headscale': False, 'transformer_block_type': 'pre_ln', 'openai_gelu': False, 'normalize_attention_scores': True, 'position_embedding_type': 'rope', 'rotary_percentage': 1.0, 'attention_type': 'multihead', 'share_embeddings_and_output_weights': False, 'overlap_p2p_comm': False, 'batch_p2p_comm': True, 'num_query_groups': 32, 'tokenizer': {'library': 'sentencepiece', 'type': None, 'model': './llama2-7b-hf/tokenizer.model', 'vocab_file': None, 'merge_file': None, 'delimiter': None, 'sentencepiece_legacy': False}, 'native_amp_init_scale': 4294967296, 'native_amp_growth_interval': 1000, 'hysteresis': 2, 'fp32_residual_connection': False, 'fp16_lm_cross_entropy': False, 'megatron_amp_O2': False, 'grad_allreduce_chunk_size_mb': 125, 'grad_div_ar_fusion': True, 'gradient_accumulation_fusion': False, 'bias_activation_fusion': False, 'bias_dropout_add_fusion': False, 'masked_softmax_fusion': True, 'get_attention_mask_from_fusion': True, 'seed': 1234, 'resume_from_checkpoint': None, 'use_cpu_initialization': True, 'onnx_safe': False, 'apex_transformer_log_level': 30, 'gradient_as_bucket_view': True, 'sync_batch_comm': False, 'activations_checkpoint_granularity': None, 'activations_checkpoint_method': None, 'activations_checkpoint_num_layers': None, 'num_micro_batches_with_partial_activation_checkpoints': None, 'activations_checkpoint_layers_per_pipeline': None, 'sequence_parallel': False, 'transformer_engine': True, 'fp8': False, 'fp8_e4m3': False, 'fp8_hybrid': False, 'fp8_margin': 0, 'fp8_interval': 1, 'fp8_amax_history_len': 1, 'fp8_amax_compute_algo': 'most_recent', 'reduce_amax': True, 'use_emha': False, 'data': {'index_mapping_dir': None, 'data_impl': 'mmap', 'splits_string': '900,50,50', 'seq_length': '${model.encoder_seq_length}', 'skip_warmup': True, 'num_workers': 2, 'dataloader_type': 'single', 'reset_position_ids': False, 'reset_attention_mask': False, 'eod_mask_loss': False, 'validation_drop_last': True, 'no_seqlen_plus_one_input_tokens': False, 'pad_samples_to_global_batch_size': False, 'shuffle_documents': True}, 'nsys_profile': {'enabled': False, 'start_step': 10, 'end_step': 10, 'ranks': [0], 'gen_shape': False}, 'optim': {'name': 'fused_adam', 'lr': 0.0002, 'weight_decay': 0.01, 'betas': [0.9, 0.98], 'sched': {'name': 'CosineAnnealing', 'warmup_steps': 500, 'constant_steps': 50000, 'min_lr': 2e-05}}, 'precision': 32}
```
Some of the missing weights:
```txt
'model.embedding.word_embeddings.weight', 'model.rotary_pos_emb.inv_freq', 'model.decoder.layers.0.self_attention.linear_qkv.weight', 'model.decoder.layers.0.self_attention.linear_proj.weight', 'model.decoder.layers.0.mlp.linear_fc1.weight', 'model.decoder.layers.0.mlp.linear_fc2.weight', 'model.decoder.layers.0.self_attention.linear_qkv.layer_norm_weight', 'model.decoder.layers.0.mlp.linear_fc1.layer_norm_weight'
....
```
**Environment details**
I am using the NVIDIA docker.
Thank you! | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/7781 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7781/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7781/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7781/events | https://github.com/NVIDIA/NeMo/pull/7781 | 1,957,148,274 | PR_kwDOC_bI7s5dh44b | 7,781 | Configure MCore logger | {
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"merged_at": "2023-10-25T20:17:27"
} | # What does this PR do ?
Configure MCore logger to print messages from MCore.
Note that currently only megatron.core.dist_checkpointing uses any logging.
In case of unidentified errors with dist checkpointing, might be helpful to run with NEMO_TESTING=1.
**Collection**: NLP
# Changelog
- Configure MCore logger to print messages from MCore.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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https://api.github.com/repos/NVIDIA/NeMo/issues/7780 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/7780/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/7780/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/7780/events | https://github.com/NVIDIA/NeMo/issues/7780 | 1,957,012,062 | I_kwDOC_bI7s50paJe | 7,780 | Error encountered at Step 24: 'set_logging_level' is not defined about tutorials/nlp/Relation_Extraction-BioMegatron.ipynb | {
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} | null | null | null | I followed the instructions provided in the documentation for setting up Colab. The instructions are as follows:
1. Open a new Python 3 notebook.
2. Import this notebook from GitHub (File -> Upload Notebook -> "GITHUB" tab -> copy/paste GitHub URL)
3. Connect to an instance with a GPU (Runtime -> Change runtime type -> select "GPU" for hardware accelerator)
4. Run this cell to set up dependencies.
However, when I reached Step 24(`model = nemo_nlp.models.TextClassificationModel(cfg=config.model, trainer=trainer)`), I encountered an error stating that name 'set_logging_level' is not defined. I double-checked the instructions and ensured that I followed them accurately. Below is the stack trace for the error encountered:
```python
[NeMo I 2023-10-23 11:51:10 megatron_init:294] All pipeline model parallel group ranks: [[0]]
[NeMo I 2023-10-23 11:51:10 megatron_init:295] Rank 0 has pipeline model parallel rank 0
[NeMo I 2023-10-23 11:51:10 megatron_init:296] All embedding group ranks: [[0]]
[NeMo I 2023-10-23 11:51:10 megatron_init:297] Rank 0 has embedding rank: 0
[NeMo E 2023-10-23 11:51:10 common:506] Model instantiation failed!
Target class: nemo.collections.nlp.models.language_modeling.megatron_bert_model.MegatronBertModel
Error(s): name 'set_logging_level' is not defined
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/nemo/core/classes/common.py", line 485, in from_config_dict
instance = imported_cls(cfg=config, trainer=trainer)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_bert_model.py", line 88, in __init__
super().__init__(cfg, trainer=trainer, no_lm_init=False)
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_base_model.py", line 158, in __init__
initialize_model_parallel_for_nemo(
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/modules/common/megatron/megatron_init.py", line 141, in initialize_model_parallel_for_nemo
set_logging_level(apex_transformer_log_level)
NameError: name 'set_logging_level' is not defined
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-24-543503bf2763> in <cell line: 1>()
----> 1 model = nemo_nlp.models.TextClassificationModel(cfg=config.model, trainer=trainer)
12 frames
/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/modules/common/megatron/megatron_init.py in initialize_model_parallel_for_nemo(world_size, global_rank, local_rank, tensor_model_parallel_size, pipeline_model_parallel_size, virtual_pipeline_model_parallel_size, pipeline_model_parallel_split_rank, micro_batch_size, global_batch_size, rampup_batch_size, use_fp8, init_mpi_proc_group, seed, apex_transformer_log_level)
139 app_state._is_megatron_initialized = True
140
--> 141 set_logging_level(apex_transformer_log_level)
142
143
```
Please advise on how to resolve this issue or provide further guidance if there are additional steps needed to avoid this error.
Thank you for your assistance. | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Use below steps to export Megatron GPT model:
1. Pull NeMo container [23.06](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo/tags)
2. Download megatron gpt model `wget https://huggingface.co/nvidia/nemo-megatron-gpt-1.3B/resolve/main/nemo_gpt1.3B_fp16.nemo`
3. Using command `python3 examples/nlp/language_modeling/megatron_export.py ++model_type=gpt ++onnx_model_file=nemo_gpt1.3B_fp16.onnx +gpt_model_file=nemo_gpt1.3B_fp16.nemo`
then it will report error:
```
Error executing job with overrides: ['++model_type=gpt', '++onnx_model_file=nemo_gpt1.3B_fp16.onnx', '+gpt_model_file=/mnt/robin.nemo']
Traceback (most recent call last):
File "/workspace/nemo/examples/nlp/language_modeling/megatron_export.py", line 142, in nemo_export
raise e
File "/workspace/nemo/examples/nlp/language_modeling/megatron_export.py", line 105, in nemo_export
model = ModelPT.restore_from(
File "/usr/local/lib/python3.10/dist-packages/nemo/core/classes/modelPT.py", line 435, in restore_from
instance = cls._save_restore_connector.restore_from(
File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/parts/nlp_overrides.py", line 393, in restore_from
loaded_params = super().load_config_and_state_dict(
File "/usr/local/lib/python3.10/dist-packages/nemo/core/connectors/save_restore_connector.py", line 164, in load_config_and_state_dict
instance = calling_cls.from_config_dict(config=conf, trainer=trainer)
File "/usr/local/lib/python3.10/dist-packages/nemo/core/classes/common.py", line 507, in from_config_dict
raise e
File "/usr/local/lib/python3.10/dist-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
```
The configuration in `nemo_gpt1.3B_fp16.nemo` is:
```
{
"micro_batch_size":3,
"tensor_model_parallel_size":1,
"encoder_seq_length":2048,
"max_position_embeddings":2048,
"num_layers":24,
"hidden_size":2048,
"ffn_hidden_size":3072,
"num_attention_heads":16,
"init_method_std":0.015,
"hidden_dropout":0.1,
"kv_channels":"None",
"apply_query_key_layer_scaling":true,
"layernorm_epsilon":1e-05,
"make_vocab_size_divisible_by":128,
"pre_process":true,
"post_process":true,
"tokenizer":{
"library":"megatron",
"type":"GPT2BPETokenizer",
"model":"gpt",
"vocab_file":"/artifacts/vocab.json",
"merge_file":"/artifacts/merges.txt"
},
"native_amp_init_scale":4294967296,
"native_amp_growth_interval":1000,
"fp32_residual_connection":false,
"fp16_lm_cross_entropy":false,
"seed":1234,
"use_cpu_initialization":false,
"onnx_safe":false,
"activations_checkpoint_method":"None",
"activations_checkpoint_num_layers":1,
...
```
"ModelPT" has abstract functions so it can not be instantiated. Therefore the solution is reading "model" attribute from the configuration and checking it for the class before restoring the model from the file.
# Changelog
- Fix error of exporting megatron-gpt-model.
# 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?
@XuesongYang @MaximumEntropy @ericharper @ekmb @yzhang123 @VahidooX @vladgets @okuchaiev
# Additional Information
* Related to #7673
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} | # What does this PR do ?
1. Fix bug in `nemo.collections.tts.modules.submodules.ConditionalInput.forward()`
2. Fix implementation of Global Style Tokens in FastPitch
**Collection**: TTS
# Changelog
- When using `ConditionalInput` in `"concat"` mode, concatenate `inputs` and `conditioning` along the _feature_ dimension, not the _batch_ dimension as before
- Despite the misleading docstring in `ConditionalInput.forward()` (corrected in this PR), `inputs` and `conditioning` have shapes `B x T x H` and `B x T x C`, respectively (to be even more precise, `conditioning` has shape `B x 1 x C` _before_ the call to `conditioning.repeat()` and `B x T x C` _after_). Consequently, concatenating the two along the batch dimension has 2 problems:
- If `H != C`, the concatenation will fail
- If `H == C`, the concatenation results in a `2B x T x C` tensor
- This causes an error downstream when calling `self.concat_proj()`, which is defined as:
`self.concat_proj = torch.nn.Linear(hidden_dim + condition_dim, hidden_dim)`
- `self.concat_proj` is meant to map `B x T x (H+C)` tensors to `B x T x H` tensors, so passing a `2B x T x (H=C)` tensor to it results in the following error:
`RuntimeError: mat1 and mat2 shapes cannot be multiplied ((2B*T)x(H=C) and (H+C)xH)` (the actual error message contains constant values, but here I have extracted variables to make my point clearer)
- **Solution**: concatenate along the feature dimension to obtain a `B x T x (H+C)` tensor
- Fix the implementation of GSTs in FastPitch, specifically regarding the selection of the _reference audio_ for a given training example
- The current NeMo implementation selects the reference audio for a given training example as a random example from the same speaker. Note that this also forces the user to add `speaker_id` to `sup_data_types` even when they don't want to use speaker embeddings
- However, the [GSTs paper](https://arxiv.org/abs/1803.09017) clearly states that:
> During training, the reference signal is ground-truth audio.
- While this is enough reason to justify amending the current NeMo implementation, I have expressed my thoughts on what selecting the reference audio at random may lead to in [this issue](https://github.com/NVIDIA/NeMo/issues/7420) I opened over a month ago (which went totally unnoticed)
# 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?
@blisc, @okuchaiev
# Additional Information
* Related to #7420
* I have run tests, but some failed or errored out. None of the failed tests makes use of my changes though. See [test_short_summary.txt](https://github.com/NVIDIA/NeMo/files/13063436/test_short_summary.txt) for further details
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ASR+VAD ?
How to achieve real-time dialogue with AI ?
ASR real-time detection, human speech, converted into text
Human beings stop talking,
Text submission to LLM
LLM sends text to TTS | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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# Changelog
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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N/A
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
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- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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fix typo in docstring | {
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Fix typo | {
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} | # What does this PR do ?
Nemo to HF converter for LLaMA 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
python3 convert_nemo_llama_to_hf.py
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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} | null | null | null | **Describe the bug**
I am following the tutorial: https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/llama2peft.html
to finetuning llama 2 on custom dataset.
when convert the llama 2 70b-hf to .nemo with the script
```
python3 ./scripts/nlp_language_modeling/convert_hf_llama_to_nemo.py --in-file=$1 --out-file=$2
```
the output of conversion is
but there exist no file named "llama2-70b.nemo" , only outputs a ``model_weights'':
the ``model_weights'' cannot feed to the training script:
How can we successfully run the tutorial ?
**Environment details**
- OS version: Ubuntu 18.04
- PyTorch version: '2.2.0.dev20231013+cu118'
- Python version: 3.8
- transformers: 4.33.0
- transformer_engine: stable branch
- CUDA : 11.8
- CUDNN: 8.6
**Additional context**
GPU: 8xA100
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} | null | null | null | Hi there!
The third-party dependency for the transformers package is outdated when looking at the requirements folder [./requirements/requirements_lightning.txt](https://github.com/NVIDIA/NeMo/blob/main/requirements/requirements_lightning.txt#L5)
Is it possible to update this dependency to the latest stable version transformers 4.34.1?
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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?
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
fix hybrid eval
**PR Type**:
- [ ] New Feature
- [ V] Bugfix
- [ ] Documentation
## Who can review?
@titu1994
# Additional Information
* Related to #7710
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fix hybrid eval
**PR Type**:
- [ ] New Feature
- [ V] Bugfix
- [ ] Documentation
## Who can review?
@titu1994
# Additional Information
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} | # What does this PR do ?
**PR Type**:
- [ ] New Feature
- [ V] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
@titu1994
# Additional Information
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} | null | null | null | **Describe the bug**
When I run a tuning job with default nvidia callback configuration. I get below error. If checkpoint_callback is disabled then epoch finishes fine. This seems to be an internal issue to me.
Image used: Nemo 28.03
A clear and concise description of what the bug is.Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
Traceback (most recent call last):
File "/opt/NeMo/examples/nlp/language_modeling/tuning/megatron_gpt_sft.py", line 212, in main
trainer.fit(model)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 532, in fit
call._call_and_handle_interrupt(
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py", line 42, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 93, in launch
return function(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 571, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 980, in _run
results = self._run_stage()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 1023, in _run_stage
self.fit_loop.run()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/fit_loop.py", line 202, in run
self.advance()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/fit_loop.py", line 355, in advance
self.epoch_loop.run(self._data_fetcher)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/training_epoch_loop.py", line 134, in run
self.on_advance_end()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/training_epoch_loop.py", line 249, in on_advance_end
self.val_loop.run()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/utilities.py", line 181, in _decorator
return loop_run(self, *args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/evaluation_loop.py", line 122, in run
return self.on_run_end()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/evaluation_loop.py", line 258, in on_run_end
self._on_evaluation_end()
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/evaluation_loop.py", line 303, in _on_evaluation_end
call._call_callback_hooks(trainer, hook_name, *args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py", line 194, in _call_callback_hooks
fn(trainer, trainer.lightning_module, *args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 311, in on_validation_end
self._save_topk_checkpoint(trainer, monitor_candidates)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 360, in _save_topk_checkpoint
self._save_monitor_checkpoint(trainer, monitor_candidates)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 663, in _save_monitor_checkpoint
self._update_best_and_save(current, trainer, monitor_candidates)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 714, in _update_best_and_save
self._save_checkpoint(trainer, filepath)
File "/opt/NeMo/nemo/utils/callbacks/nemo_model_checkpoint.py", line 272, in _save_checkpoint
super()._save_checkpoint(trainer, filepath)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 365, in _save_checkpoint
trainer.save_checkpoint(filepath, self.save_weights_only)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 1316, in save_checkpoint
self._checkpoint_connector.save_checkpoint(filepath, weights_only=weights_only, storage_options=storage_options)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py", line 506, in save_checkpoint
_checkpoint = self.dump_checkpoint(weights_only)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py", line 491, in dump_checkpoint
call._call_callbacks_on_save_checkpoint(trainer, checkpoint)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py", line 221, in _call_callbacks_on_save_checkpoint
callback.on_save_checkpoint(trainer, trainer.lightning_module, checkpoint)
File "/opt/NeMo/nemo/utils/callbacks/nemo_model_checkpoint.py", line 181, in on_save_checkpoint
pl_module.save_to(save_path=app_state.model_restore_path)
File "/opt/NeMo/nemo/core/classes/modelPT.py", line 385, in save_to
self._save_restore_connector.save_to(self, str(save_path)) # downstream tasks expect str, not Path
File "/opt/NeMo/nemo/collections/nlp/parts/nlp_overrides.py", line 489, in save_to
dist_checkpointing.save(sharded_state_dict=sharded_state_dict, checkpoint_dir=dist_ckpt_dir)
File "/opt/Megatron-LM/megatron/core/dist_checkpointing/serialization.py", line 174, in save
sharded_strategy.save(sharded_tensors, checkpoint_dir)
File "/opt/Megatron-LM/megatron/core/dist_checkpointing/strategies/zarr.py", line 49, in save
arrays = _create_or_open_zarr_arrays(sharded_tensors, checkpoint_dir)
File "/opt/Megatron-LM/megatron/core/dist_checkpointing/strategies/zarr.py", line 73, in _create_or_open_zarr_arrays
arr = zarr.open(checkpoint_dir / ten.key, 'r+', **open_kwargs)
File "/usr/local/lib/python3.10/dist-packages/zarr/convenience.py", line 130, in open
raise PathNotFoundError(path)
zarr.errors.PathNotFoundError: nothing found at path ''
**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.
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} | # What does this PR do ?
Bug fix for `MegatronPretrainingRandomSampler`, which may affect NLP collections in NeMo
**Collection**: NLP
# Changelog
**Changed Files**: `nemo/collections/nlp/data/language_modeling/megatron/data_samplers.py`
- [MegatronPretrainingRandomSampler](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/data/language_modeling/megatron/data_samplers.py#L162) requires `self.data_parallel_size` to calculate `bucket_offset`, but `self.data_parallel_size` is missing in the attributes.
- It will raise `AttributeError: 'MegatronPretrainingRandomSampler' object has no attribute 'data_parallel_size'. Did you mean: 'data_parallel_rank'?` when we tried to run llama2 models with `dataloader_type=cyclic`
# Reproduce steps
1. Use configuration `dataloader_type: cyclic` to launch llama2 models with [configuration file](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml#L210)
``` yaml
data:
data_impl: mmap
dataloader_type: cyclic
skip_warmup: true
splits_string: "99990,8,2"
seq_length: 4096
reset_position_ids: false
reset_attention_mask: false
eod_mask_loss: false
index_mapping_dir: null
data_prefix:
- ${data_dir}/my-llama_00_text_document
```
2. Then you will get the error msg shown below
``` bash
File "/mnt/petrelfs/caizheng/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1084, in __init__
self._reset(loader, first_iter=True)
File "/mnt/petrelfs/caizheng/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1117, in _reset
self._try_put_index()
File "/mnt/petrelfs/caizheng/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1351, in _try_put_index
index = self._next_index()
File "/mnt/petrelfs/caizheng/miniconda3/envs/nemo/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 623, in _next_index
return next(self._sampler_iter) # may raise StopIteration
File "/mnt/petrelfs/caizheng/dev/NeMo/nemo/collections/nlp/data/language_modeling/megatron/data_samplers.py", line 163, in __iter__
bucket_offset = current_epoch_samples // self.data_parallel_size
AttributeError: 'MegatronPretrainingRandomSampler' object has no attribute 'data_parallel_size'. Did you mean: 'data_parallel_rank'?
```
# 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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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Fixed issue with installation of ctc-decoders and Offline-ASR notebook
**Collection**: `tutorials/asr`
# Changelog
- Fixed issue with installation of ctc-decoders and Offline-ASR notebook
# Usage
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```python
# Add a code snippet demonstrating how to use this
```
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- [X] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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- [X] Bugfix
- [ ] Documentation
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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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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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add Adapter and IA3 for MCore PEFT tuning.
Previously, only LoRA and PTuning was supported for MCore models.
Verified training curve and eval results on Squad are identical as NeMo GPT model.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
```bash
model.peft.peft_scheme=adapter
# OR
model.peft.peft_scheme=ia3
# are now supported for mcore models (model.mcore_gpt=True)
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add dist checkpoint support for regular (non O2 and non-distributed) optimizers.
This change is required to support dist ckpt for FSDP training
**Collection**: NLP
# Changelog
- Add dist checkpoint support for regular (non O2 and non-distributed) optimizers.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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Tensorstore version 0.1.46 (published 10/18/23) breaks BF16 support for Zarr
**Collection**: NLP
# Changelog
- pin tensorstore version to 0.1.45
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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- [ ] Did you write any new necessary tests?
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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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Fixed issue with installation of ctc-decoders and Offline-ASR notebook
**Collection**: `tutorials/asr`
# Changelog
- Fixed issue with installation of ctc-decoders and Offline-ASR notebook
# Usage
* You can potentially add a usage example below
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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?
- [X] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
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- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
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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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* Related to # (issue)
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tune only a selected subset of the params of the base model.
**Collection**: [NLP]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
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- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
Add flash decoding from `flash-attn>=2.2` in this [repo](https://github.com/Dao-AILab/flash-attention/tree/main).
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* Add `model.use_flash_attention=True` when doing inference
* Support with container `nvcr.io/nvidian/ac-aiapps/nemo:swa101623`
# 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**:
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- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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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
```
# Before your PR is "Ready for review"
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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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
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```
# Before your PR is "Ready for review"
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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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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] New Feature
- [ ] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
Supports torch FSDP with Tensor Parallelism
# Usage
Set `model.fsdp=True` to enable FSDP. Gradient reduction data type can be set using `model.fsdp_grad_reduce_dtype`.
FSDP mode is not compatible with the `Distributed Optimizer` and `O2 optimizer wrapper`, thus should set `model.megatron_amp_O2=False` and do not use `model.optim.name="distributed_fused_adam"`
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
GPT training with long-context input (e.g., sequence length of 16, 32K, 64K) can easily overflow GPU memory with huge activations. Context parallelism splits long-context input along the dimension of sequence length, parallelizes partitioned sequence segments among multiple GPUs. In this way, each GPU only needs to store activations of a part of sequence length, so that we can avoid memory overflow.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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