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Fix mcore conversion script bug which affected GPT3 models. Also improve the UX of the script.
**Collection**: NLP
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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:/... | {
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**Collection**: [Note which collection this PR will affect]
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- Add specific line by line info of high level changes in this PR.
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- 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.
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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*... | {
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minor change to log the correct values during peft inference...
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
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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`.
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**Collection**: [Note which collection this PR will affect]
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
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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`
- Add... | {
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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.l... | {
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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
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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 `m... | {
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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_... | {
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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 `ma... | {
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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, Au... | {
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Add a one line overview of what this PR aims to accomplish.
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Test
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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... | {
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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... | {
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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 ne... | {
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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:
```
cf... | {
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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 j... | {
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Add a one line overview of what this PR aims to accomplish.
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* You can potentially add a usage example below
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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
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} | # What does this PR do ?
Updates Dockerfile to install Apex correctly.
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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.
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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 comprise... | {
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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,
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-change rope to use rotary_base_len as interpolation base instead of current max len
-optionally only interpolate decoding | {
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} | # What does this PR do ?
- 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 demo... | {
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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 ... | {
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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 b... | {
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# 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
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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)
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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. ... | {
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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
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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` ... | {
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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 p... | {
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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 ru... | {
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} | # 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 lo... | {
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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 ty... | {
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**Collection**: [Note which collection this PR will affect]
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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 `pyth... | {
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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... | {
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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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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
* You can potentially add a usage example below
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Add some foundational NeMo docs + improve ASR docs
**Collection**: docs
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
N/A
# Before your PR is "Ready for review"
**Pre checks**:
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fix typo in docstring | {
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Nemo to HF converter for LLaMA model
**Collection**: [Note which collection this PR will affect]
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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
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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 versi... | {
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Fixes an issue that forgets to update cur_decoder of rnnt bpe model
**Collection**: [ASR]
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- Fix change decoding strategy bug
- Add test to check for repeated change to strategy
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- Add test to check for repeated change to strategy
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fix hybrid eval
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"merged_at": "2023-10-19T17:10:10"
} | # 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
* Related to #7710
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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 variab... | {
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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/NeM... | {
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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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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
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**Collection**: NLP
# Changelog
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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
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Fixed issue with installation of ctc-decoders and Offline-ASR notebook
**Collection**: `tutorials/asr`
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tune only a selected subset of the params of the base model.
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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]
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
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} | # 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.megat... | {
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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... | {
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