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} | 🚀 PR to bump `NVIDIA/Megatron-LM` in `Dockerfile.ci` to `MCORE_TAG=71c394b172ce4f1b9466e1d728bad5cc6314d15d`.
📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
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- 32 GB for model params
- 32 GB for gradients
- 32 * 2 GB for optimizer states
This will sum up to 128 GB, however, in pr... | null | {
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} | null | null | null | Hi,
Even though the Nemo2 finetuning recipes have been released for Mamba2, there is no code for converting the checkpoints from nemo1 to nemo2 for Mamba2 or from Megatron checkpoint to nemo2. Is there an estimate of when this code will be released? It would be greatly appreciated!
Thanks! | null | {
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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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} | 🚀 PR to bump `NVIDIA/Megatron-LM` in `Dockerfile.ci` to `MCORE_TAG=3f5d5d4657fa2467ef578d852cb86bbfe8512309`.
📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
🙏 Please merge this PR only if the CI workflow completed successfully. | {
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} | # What does this PR do ?
this PR introduces VAE training into NeMo that allows users to customize a 16x or 8x reduction VAE on their in house data. Besides, we also introduce a feature that automatically designs VAE based on the GPU memory and latency requirements.
**Collection**: NeMo/Diffusion/VAE
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"@pzelasko Can you approve again? Rebased the repo for the tests.",
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"<!-- pylint-output-strict-mode-false -->\n\nbeep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please t... | 2024-12-13T23:31:17 | 2024-12-20T18:05:01 | 2024-12-20T18:05:01 | COLLABORATOR | {
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**Collection**: [Note which collection this PR will affect]
# Changelog
- Filter out transcription with text by regex
- Tokenize word by word and insert start and end timestamps for each word
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we've cherry picked #11571 into for you! 🚀
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we've cherry picked #11574 into for you! 🚀
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we've cherry picked #11560 into for you! 🚀
Please review and approve this cherry pick by your convenience! | {
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📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
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} | # What does this PR do ?
1. Fix PEFT inference (bug due to trainer not attached during peft inference)
2. Add CI test for PEFT inference
3. Expose top_k to enable greedy sampling in inference script
**Collection**: [Note which collection this PR will affect]
# Changelog
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Add VAE training, allowing users to customize VAE compression ratio to 16x or 8x and train the customized VAE with the script.
**Collection**:
NeMo/collections/diffusion/vae
# Changelog
- Add VAE training on customized datasets
- Add autoVAE that automatically searches for a good... | {
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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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Neva updates and bug fixes
**Collection**: [multimodal]
# 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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# Add a code snippet demonstrating how to use this
```
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} | # What does this PR do ?
This PR adds some documentation in the code for packed dataset preparation with context parallel.
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
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# What does this PR do ?
micro_batch_size and global_batch_size as class attributes in llava next mock data module. This is to fix breaking change introduced in this PR (asserts datamodule to have these attributes) = https://github.com/NVIDIA/NeMo/pull/10954
**Collection**:
Multimodal
# Changelog
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📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
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} | This reverts commit 6784db56a03f19f37bc4f37bdf87dabb3fc1acee.
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} | # What does this PR do ?
In HF you can do:
```
from datasets import Dataset
my_data = {"a": [1, 2, 3]}
dataset = Dataset.from_dict(my_data)
```
Adding support for the following:
```
data = {'text': "Below is an instruction that describes a task, paired with an input that "}
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
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# Usage
* You can potentially add a usage example below
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
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- 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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"I just wanted to chime in that if there is any way to get this pull request merged into the main branch and a release branch that would be hugely helpful. I can hobble along using my clone of this branch, but that's obviously not ideal. Thank you guys for you great work and for this really helpful contribution i... | 2024-12-11T19:58:01 | 2025-01-10T02:07:28 | null | COLLABORATOR | {
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vLLM export for NeMo 2.0 models
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Should allow `transcribe_speech_parallel.py` to produce results that are not duplicated.
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When autocast region is enabled, the FilterbankFeatures featurizer, during the forward pass may generate NaN frames.
**Steps/Code to reproduce bug**
As probably is known, on CUDA devices, matmul operations are cast to half when feature compat allows for this data type.
So the following is expect... | null | {
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1. Add script for extracting embeddings from different layers of NEST model (also works for other Conformer-based models).
2. Fixes ModelPT.py when defer_setup is True, since the current code will skip builing validation dataloaders as they are lists and not None.
3. Update ASR manifest p... | {
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I'm trying to do supervised learning given labeled data of an input token sequence mapping to a probability output. Does Nemo support a simple way to do this? For example, via soft targets (predicting one of two tokens each with a certain probability t... | {
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Synchronizes validation metrics across DDP ranks at the end of validation epoch. Fixes critical issue with corrupted checkpoints for multi-GPU Canary runs. Follows up on https://github.com/NVIDIA/NeMo/pull/10257
**Collection**: ASR
# Changelog
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Fixes export of NeMo 2.0 mixtral
**Collection**: nlp/llm
# 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
```
# GitHub Actio... | {
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- Only Megatron Core-based GPT models are supported
- Only logit-pair distillation is supported for now
- Pipeline parallelism not yet supported... | null | {
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Add STDiT(Spatial-Temporal DiT) model for nemo training. Support megatron parallel pattern in training pipeline.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add stdit model
- Add stdit layer
- Add stdit embedding
- Add stdit training factory function... | null | {
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📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
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Add documentation for audio and mel codec Hugging Face models
**Collection**: [TTS]
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
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Modify the tutorial about training audio codec models so that the user can download and fine tune from a hugging face checkpoint.
**Collection**: [TTS]
# Changelog
- Change previous logic using `list_available_models` to instead use `from_pretrained()`
- Replace default 16khz audio ... | {
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} | # What does this PR do ?
Fix up after https://github.com/NVIDIA/Megatron-LM/commit/fa0dcc48720ed9f440f36c7973fe5e7a54c7d208
Users of megatron's TransformerConfig should no longer disable TransformerEngine attention backends through environment variables like `NVTE_FUSED_ATTN`, `NVTE_FLASH_ATTN`, and `NVTE_UNFUSED_A... | {
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For some reason, the `transcribe` method causes a core dump with quartznet or conformer. I'm running this in docker. My code is tweaked from one of the tutorials.
Mike H
Here's my code:
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import os,glob,os,subprocess,tarfile
import wget,nemo,librosa,json
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} | # What does this PR do ?
* ~~Bugfix to correctly support Mixtral for PTQ in NeMo 2.0.~~ done in https://github.com/NVIDIA/NeMo/pull/11607
* Updating `nvidia-modelopt=0.21.0`
**Collection**: LLM / NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potenti... | {
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} | null | null | null | **Describe the bug**
An error occurs when exporting T5G2PModel to ONNX.
**Steps/Code to reproduce bug**
> import torch
> from transformers import PreTrainedTokenizerBase, AutoTokenizer
>
> from nemo.collections.tts.g2p.models.t5 import T5G2PModel
> model_name = "T5G2P.nemo"
> model = T5G2PModel.restore_from(model_n... | {
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} | # What does this PR do ?
Add diarization Mixin class to enable "one click" executable function (`diarize()`) as in ASR collections' `transcribe()` functions.
1. wav filepath string
2. a list of wav filepath strings
3. json manifest filepath string
Three types of input data is supported for
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"@njfamirm what version did you fix numpy to? I'm having the same problem. I tried to downgrade to numpy 2.0.2 but had other compatibility issues. Is [ASR] not compatible with numpy 2 at all or just the latest release?\n\nEDIT: in the end it worked with 1.23.5",
"> [@n... | 2024-12-07T19:14:48 | 2024-12-19T14:10:48 | 2024-12-16T17:47:18 | NONE | {
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**Describe the bug**
Compatibility error with NumPy v2.0: AttributeError: `np.sctypes was removed in the NumPy 2.0 release. Access dtypes explicitly instead.. Did you mean: 'dtypes'?`
**Steps/Code to reproduce bug**
Run this code
```python
import nemo.collections.asr as nemo_asr
# 1. Download and instantiate t... | {
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] | 2024-12-07T08:49:50 | 2025-01-15T01:56:43 | 2025-01-15T01:56:42 | CONTRIBUTOR | {
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Is this a mistake since on ngc the container is already at version 24.09? If not, c... | {
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} | # What does this PR do ?
Compatibility with https://github.com/NVIDIA/Megatron-LM/commit/210162aebcfc68d72f39049d5cf84a83d3b11dea
**Collection**: nlp
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
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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
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📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
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**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
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**Collection**: [Note which collection this PR will affect]
# Changelog
- intro.rst
# Usage
no code changes
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"datamodule = llm.HFDatasetDataModule(\"rajpurkar/squad\", split=\"train\", pad_token_id=tokenizer.eos_token_id) - this solution enforces using a particular dataset split, \r\n\r\nlooking into finetune_default:\r\n```\r\n # datamodule = run.Config(llm.SquadDataModule, seq_length=2048, global_batch_size=128, micro_b... | 2024-12-06T19:11:34 | 2024-12-11T08:37:48 | 2024-12-11T08:37:45 | MEMBER | {
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```
dataset = load_dataset("rajpurkar/squad", split="train")
datamodule = llm.HFDatasetDataModule(dataset, pad_token_id=tokenizer.eos_token_id)
```
After:
```
datamodule = llm.HFDatasetDataModule("rajpurkar/squad", split="train", pad_token_id=tokenizer.eos_token_id)
```
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Updated the readme Latest News section to Q4 2024, revised links, changed playbook references to tutorial references, and made a few copyedits to the text. These updates were requested by Shashank.
**Collection**: [Note which collection this PR will affect]
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