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Fix LoRA convergence bug, and add a parameter count print out after freezing base model, similar to NeMo 1.
**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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**Collection**: LLM
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"A few notes:\r\n- You might need to adapt this class to support additional modules: https://github.com/zhehuaichen/NeMo/blob/speechllm-develop/nemo/collections/nlp/parts/nlp_overrides.py#L626\r\n - E.g., adding new modules here: https://github.com/zhehuaichen/NeMo/blob/speechllm-develop/nemo/collections/nlp/parts... | 2024-08-06T21:27:15 | 2024-08-09T18:58:09 | 2024-08-09T18:58:09 | COLLABORATOR | {
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} | # What does this PR do ?
Support FSDP in SALM and BESTOW. Thank @pyf98 for early trying
# Usage
Elaborated and tested here https://docs.google.com/document/d/1fr2UlqThlzqvqL_lpc_HIyrh0yCzL27A-QX1eCI_ojg/edit#heading=h.9o9g0iu9eu69
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There is still a remaining issue. pack_hypotheses() appears to be copying data from GPU to CPU for the dec_state tensor, on... | {
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These lines of code do not handle a cut's duration correctly when the offset is not zero:
From `nemo/collections/common/data/lhotse/nemo_adapters.py` for the class `LazyNeMoIterator`:
```
def __iter__(self) -> Generator[Cut, None, None]:
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**Collection**: nlp
# Changelog
- Add specific line by line info of high level changes in this PR.
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Fix imports and loading of the packed, distributed NeMo checkpoints during conversion to TRT LLM.
**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
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convert(local_rank, rank, world_size, args)
File "/share//code//NeMo/examples/nlp/language_modeling/megatron_ckpt_to_nemo.py", line 198, in convert
model = MegatronGPTModel.load_from_checkpoint(checkp... | {
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Fix imports and loading of the packed distributed checkpoints.
**Collection**: nlp
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**Collection**: audio
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📝 Please remember the following to-do's before merge:
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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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Update dev doc for features section
**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 ... | {
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"This has been added here: https://github.com/NVIDIA/NeMo/pull/9815\r\n\r\nPlease re-open or create a new issue if needed."
] | 2024-08-05T20:56:46 | 2024-08-21T17:57:54 | 2024-08-21T17:57:54 | NONE | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
LLAMA3.1 introduces a new ROPE implementation, while NeMo hasn't supported it.
https://github.com/huggingface/transformers/blob/51ab25e2932da15511ced35bcbdfa92d25c4794c/src/transformers/modeling_rope_utils.py#L298
**Describe the solution you'd li... | {
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"@ar-nowaczynski Can you check if https://github.com/NVIDIA/NeMo/pull/10125 fixes this issue."
] | 2024-08-05T20:04:38 | 2024-08-20T18:50:50 | 2024-08-20T18:50:50 | COLLABORATOR | {
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} | # What does this PR do ?
Fixes basic error in recently introduced #9932 FLOPsMeasurementCallback on_train_end method when `pl_module.logger.experiment` is None.
**Collection**: common
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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"> LGTM, thanks! I like this approach and the possibility of using an explicitly instantiated strategy. Also, learned about `Mapping`, thanks :)\r\n\r\nThanks! I added a test and one fix, should be OK to merge now."
] | 2024-08-05T19:58:59 | 2024-08-19T21:47:52 | 2024-08-19T21:47:51 | COLLABORATOR | {
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This change allows, amongst other things, to specify options for DDP such as `gradient_as_bucket_view=True` which saves a considerable amount of memory for 1B+ models.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high lev... | {
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📝 Please remember the following to-do's before merge:
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} | # What does this PR do ?
This PR recursively finds the leaves of the trainer config and calls `track_io` on the leaves. This handles the case of adding io functionality to plugins which are dictionaries.
**Collection**: llm
# Changelog
- Add specific line by line info of high level changes in this PR.
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Use hard-coded chat-template for known models only, need a better strategy.
**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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} | Add support for overlapped gradient and parameter synchronization for GPT SFT model
# What does this PR do ?
Add support for overlapped gradient and parameter synchronization
**Collection**: nlp
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentiall... | {
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"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-08-05T12:37:40 | 2024-09-12T01:55:27 | 2024-09-12T01:55:27 | CONTRIBUTOR | {
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I would like to train an Ecapa-TDNN model on Voxceleb2 and Voxceleb2+Cnceleb2 for speaker verification/identification. I don't want it to be SOA (ex: complicated pretraining and then large margin finetuning), but I want it to be moderately performant as fast as possible, as the model performance is ... | {
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"Update:\r\n\r\nApologies. I opened it in the wrong repository. My bad."
] | 2024-08-05T06:46:47 | 2024-08-05T06:55:28 | 2024-08-05T06:55:28 | CONTRIBUTOR | {
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I have noticed a couple of small changes in the code that can improve its performance:
By refactoring `score_document` as suggested, it will run between 2x and 3x faster, and will reduce by half the memory footprint.
By refactoring '_line_statistics' as suggested, it can run about 21% faster.
I will sub... | {
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} | # What does this PR do ?
Disables gradients in `swap_tensors()` within `ema.py` to prevent unintended leaf variable gradient modifications (should they arise). This was found to be necessary in certain projects that use PyTorch Lightning 2+ such as [AlphaFold3-Lightning-Hydra](https://github.com/amorehead/alphafold3... | {
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"Hi, since you want to run only inference, you could modify the prediction_step in [model code](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/models/ctc_models.py#L572) and run .predict on any model (that uses the feature extractor you are looking for) \nReference: https://lightning.ai/docs/pytorch/... | 2024-08-03T04:42:14 | 2024-10-30T01:59:27 | 2024-10-30T01:59:26 | NONE | {
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I want to run the NeMo ASR pipeline to analyze the features using different parameters, datasets, and strategies. However, I do not want to run the conformer training. Instead, I only want to extract the features from the input JSON files and store them.
**Current Understanding:**
From my understand... | {
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"@pzelasko I checked, caching `TarInfo`s with a single linear scan lets then extract in O(1):\r\n```python\r\ndef taropen_build(tar_path):\r\n import tarfile\r\n tar = tarfile.open(tar_path)\r\n tarinfo_index = {tarinfo.name : tarinfo for tarinfo in tar.getmembers()}\r\n return lambda member_path, mode:... | 2024-08-03T02:28:21 | 2024-08-30T19:54:52 | 2024-08-07T15:59:35 | COLLABORATOR | {
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} | # What does this PR do ?
Support for NeMo tarred manifests with offset field in lhotse dataloading.
**Collection**: ASR
# 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 h... | {
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"I'm curious if by retraining you might mean continued training? https://docs.nvidia.com/nemo-framework/user-guide/latest/llms/allmodels/continuetraining.html?highlight=continued%2520training#configure-continual-learning\r\n\r\nRegarding your error, the decoder module will be `MegatronGPTModel.model.decoder` I beli... | 2024-08-03T01:28:47 | 2024-09-30T02:00:32 | 2024-09-30T02:00:32 | NONE | {
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I am retraining a LLaMA3 model. Due to the limited size of my dataset, I attempted to use `freeze_updates` as referenced in the [NVIDIA NeMo documentation](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/core/neural_modules.html#dynamic-layer-freezing). My configuration is as fo... | {
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} | # What does this PR do ?
Default interval for lr_schedulers was `epoch` which means the lr was never getting updated for nemo.collections.llm examples.
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 l... | {
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"Please update base branch to `main`.",
"stopping this until NeMo CI is out of maintenance mode"
] | 2024-08-02T23:12:31 | 2024-08-16T19:39:49 | 2024-08-16T19:39:47 | COLLABORATOR | {
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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
- Remove `rank_zero_only=True` flag, unless used as [documented](https://lightning.ai/docs/pytorch/stable/visualize/logging_advanced.html#rank-zero-only... | {
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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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# What does this PR do ?
Hi, sorry to bother.
There might be some dataset license issues. So need to remove the dataset downloading and preprocessing script.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
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"Which version of nemo are you using? The model card: https://huggingface.co/nvidia/nemo-megatron-gpt-5B is using v1.11.0. \r\n\r\nI would recommend using a newer model if possible. This model is quite out of date and likely won't perform as well as more recent models.",
"This issue is stale because it has been o... | 2024-08-02T19:06:06 | 2024-09-25T18:11:59 | 2024-09-25T18:11:59 | NONE | {
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When attempting to launch training using the specified configuration files and code snippet, a key mismatch error occurs between the model and the dictionary. How to proceed?
**Model:**
https://huggingface.co/nvidia/nemo-megatron-gpt-5B/resolve/main/nemo_gpt5B_fp16_tp1.nemo
**Steps/Code... | {
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} | # What does this PR do ?
Due to license issue, we need to remove the data downloading and preprocessing script.
**Collection**: [Note which collection this PR will affect]
None of the collection will be affected;
# Changelog
- Add specific line by line info of high level changes in this PR.
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} | # What does this PR do ?
Update continual pre-training argument override strategy. Input args are going to be merged with existing ones inside the checkpoint.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
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Replacing export functions with the mcore ones.
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2. Adds drop-layers feature support.
3. Adds script which loads the ckpt, trims needed layers, and saves trimmed checkpoint.
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For instruct models fallback to HF tokenizer.
**Collection**: [Note which collection this PR will affect]
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This is closed. New PR here: https://github.com/NVIDIA/NeMo/pull/10485
Adds support for upcycling pretrained weights to a MoE model. This follows the paper: SPARSE UPCYCLING: TRAINING MIXTURE-OF-EXPERTS FROM DENSE CHECKPOINTS ([https://arxiv.org/pdf/2212.05055](https://arxiv.org/pdf/2212.05... | {
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Adds support for multiple hard negatives in GPT Embedding model during training
**Collection**: [Note which collection this PR will affect]
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"I have the same issue...",
"@XugZhao @Luxur @RadhaGulhane13 The issue is the current Llama 3.1 model on HF (which you fetch when running the conversion script) is built with the new version of the Transformers library, which was released after our `nvcr.io/nvidia/nemo:24.05.llama3.1... | 2024-08-01T21:12:17 | 2024-08-24T14:39:55 | 2024-08-24T14:39:55 | NONE | {
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I'm trying to convert **LLama3.1-8b huggingface** checkpoint into **nemo** checkpoint using `convert_llama_hf_to_nemo.py` script. It's giving `ValueError: `rope_scaling` must be a dictionary with two fields, `type` and `factor`, got {'factor': 8.0, 'low_freq_factor': 1.0, 'high_freq_factor': 4.0, ... | {
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} | # What does this PR do ?
Add the interface to pass the argument for gradient average reduction to MegatronCore
# Changelog
- Add `average_in_collective`
# Usage
- Set by `model.optim.average_in_collective` to enable
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-h... | {
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] | 2024-08-01T18:20:31 | 2024-09-19T00:15:06 | 2024-08-30T10:02:36 | CONTRIBUTOR | {
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} | # What does this PR do ?
Adds support for using LoRA adapters on checkpoints exported to vLLM.
**Collection**: NLP
# Changelog
- Moved the LoRA conversion logic from the `convert_nemo_to_canonical.py` script to a reusable module
- Implemented on-load conversion of Nemo format LoRA checkpoints into HF format ... | {
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} | # What does this PR do ?
Move pooler into the flag of post processing so that it doesn't get built on all the pp ranks.
Add a one line overview of what this PR aims to accomplish.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potent... | {
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When you restart a job, you typically set consumed_samples to a non-zero value. This has the side effect of changing the length returned of the sampler. If the trainer current step is restored to some value ... | {
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"Hi, thank you for your question and investigating the root cause.\r\nThere is a typo in the model card (cc @nithinraok) but this will work:\r\n`asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name=\"nvidia/parakeet-ctc-0.6b\")`\r\nor even this:\r\n`asr_model = nemo_asr.models.ASRModel.from_pret... | 2024-08-01T15:38:50 | 2024-08-22T21:03:13 | 2024-08-22T21:03:13 | NONE | {
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I installed nemo with:
`pip install nemo_toolkit['asr']`
According to the Huggingface repo, I use this following command to load the model:
```
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCBPEModel.fro... | {
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"@zhehuaichen Glad you asked, as it turns out I forgot to push a commit that does it (see https://github.com/NVIDIA/NeMo/pull/10004/commits/8017db4a1d9d5a3db8a9c63a7aaff77061e0a261). \r\n\r\nNow it's all good and ready to merge. "
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} | # What does this PR do ?
Utility to help people re-build tarred datasets when they want only a small subset of them, based on a filtered manifest.
**Collection**: ASR
# 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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"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-08-01T14:48:54 | 2024-09-09T01:56:55 | 2024-09-09T01:56:54 | NONE | {
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} | null | null | null | **Describe the bug**
The Docker build command in the README does not build.
**Steps/Code to reproduce bug**
```sh
DOCKER_BUILDKIT=1 docker build -f Dockerfile -t nemo:latest
```
**Expected behavior**
You will get the following error message
```sh
ERROR: "docker buildx build" requires exactly 1 argu... | {
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} | # What does this PR do ?
1. Adds `deploy` method from NeMo 1.0 as a `task` to NeMo 2.0
2. This PR aims at minimum viable product to deploy and expose REST API with NeMo 2.0 and hence only adds `TRTLLM` backend route of deploy module to NeMo 2.0. Also, it only supports the basic arguments for the export method. Mo... | {
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1. Adds a feature similar to mcore's --skip-train flag.
3. Adds drop-layers feature support.
4. Adds script which loads the ckpt, trims needed layers, and saves trimmed checkpoint.
**Collection**: [Note which collection this PR will affect]
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"@yaoyu-33 could we merge this [fix on sig/clip](https://github.com/NVIDIA/NeMo/pull/9842) pr to main? so that I could rebase and remove the similar fix in this PR. Thanks!"
] | 2024-08-01T00:27:34 | 2024-08-20T19:49:47 | 2024-08-20T19:49:47 | CONTRIBUTOR | {
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Add blend dataset in NeVA for both unpacked and packed sequence.
**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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📝 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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added support for data blending in NeVA.
**Collection**: [Note which collection this PR will affect]
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# Usage
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} | # What does this PR do ?
This introduces a new `MegatronProgressPrinter` class which simply prints the metrics to stdout in a similar style to Megatron. This implementation is more robust as the TQDM progress bar is not well-suited for non-interactive settings.
The train step logging looks like this:
```
Epoch... | {
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Build pooler only in the last pp rank (Dist checkpoint will fail if you build in more than one rank)
**Collection**: nlp
# Changelog
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# Usage
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} | Use kv_channels to enable cases where head_dim != hidden_size // head_num
for example as is the case with the 2407 modes: https://huggingface.co/mistralai/Mistral-Large-Instruct-2407
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To add fix for r2.0.0rc1.
ORG PR: https://github.com/NVIDIA/NeMo/pull/9973
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"> One name change, fix import flags from the CI.\r\n\r\nOK\r\n\r\n> Btw, how are you feeding temperature to the MLP now?\r\n\r\nIt's applied directly in `Categorical` "
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} | # What does this PR do ?
TL;DR: fix for a bug I accidentally introduced in #9901
Context:
* #9901 changed multi-task beam search decoding module to strip the prompt and EOS away form recognitions; it removed "remove_special_tokens" sprinkled in various places in our codebase (transcribe, wer, bleu, etc.)
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} | # What does this PR do ?
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**Collection**: [Note which collection this PR will affect]
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"This PR was closed because it has been inactive for 7 days since being marked as stale."
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PR introduces default beam search and mAES (with Ngram LM fusion) decoding implementations for TDT models.
**Collection**: ASR
# Changelog
- [nemo/collections/asr/parts/submodules/tdt_beam_decoding.py]: Default Beam Search and mAES implementations for TDT.
# Usage
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Summary of changes:
* using directly `trtllm-build` for quantized checkpoints
* removing dependency on `nvidia-modelopt` for this
* updating export code to TensorRT-LLM v0.11.0
* bumping ModelOpt 0.15.0
* several other minor updates
**Collection**: NLP
# Changelog
- Add specifi... | {
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"Thanks for reporting this. This workload -- [examples/nlp/language_modeling/megatron_gpt_ptq.py](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/megatron_gpt_ptq.py) -- was tested on 1xH100 80GB GPU but I agree that it looks excessive, we'll take a look. For completeness: could you perhaps ... | 2024-07-31T14:17:17 | 2024-09-09T08:04:56 | 2024-09-09T08:04:56 | NONE | {
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Running FP8 PTQ of Llama3-8b on 1x 4090 (24GB) goes OOM? Is this expected? vLLM FP8 quantization works on the same GPU.
What are the minimum requirements to run this quantization?
I have even tried setting batch size to 1 and it still goes OOM.
**Steps/Code to reproduce bug**
```bash
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} | # What does this PR do ?
To add fix for r2.0.0rc1.
ORG PR: https://github.com/NVIDIA/NeMo/pull/9973
**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 ?
Changes for the updated version of resiliency features:
- Fault Tolerance
- Straggler Detection
**Collection**: [Note which collection this PR will affect]
# Changelog
- Updated/clarified documentation
- Removed duplicated "straggler detection" section from the documentation
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} | Want to enable the following
`
model = llm.MixtralModel(llm.MixtralConfig8x22B())
hf_ckpt_path = "hf://mistralai/Mixtral-8x22B-v0.1"
hf_ckpt_path = 'hf:///mnt/4tb/mixtral_8x22b/mixtral-8x22B-hf/'
ckpt_path = model.import_ckpt(hf_ckpt_path)
`
- pass via hf identifier, ie: `hf_ckpt_path = "hf://m... | {
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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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Add support for HF tokenizer (e.g. Llama 3 models)
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Fix lita tutorial typo.
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} | Makes the `convert` function of the checkpoint converter an iterator. This allows for reduced memory usage during conversion, in fact less memory usage than the total RAM requirements of a full checkpoint.
If the `--low-mem` CLI option is used, the converter will load only the requested layers from the HF checkpoint... | {
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📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
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I'd like to be able to get per-dataset metrics during training & eval(testing).
Currently even if I configure the `model.data` with multiple datasets, the val_loss still only shows the total loss not the per-dataset losses. See attached.
Looking at ... | {
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"Adding decreasing related support in separate PR later ",
"Besides the comment about the in-line imports, LGTM",
"@terrykong This is actually an legacy setting inherited from previous version. They use try except t... | 2024-07-30T18:36:34 | 2024-09-04T18:10:32 | 2024-08-01T22:37:15 | COLLABORATOR | {
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} | # What does this PR do ?
Add Mcore microbatches calculator supports. Added backward compatibility. Merged `init_num_mb_calculator` and `reconfigure_num_mb_calculator`
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
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"This issue was closed because it has been inactive for 7 days since being marked as stale.",
"PR is merged: https://github.com/NVIDIA/NeMo/pull/10098"
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Currently when training LLMs, the checkpoints are saved with all of the optimizer states, which generally increases the checkpoint size about 6 times. When the LLM itself is very large (e.g. > 30B parameters) and we are running many training jobs at ... | {
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Change equivalent to https://github.com/NVIDIA/NeMo/pull/9717 but targeted to **r2.0.0rc1** branch.
**Collection**: [Note which collection this PR will affect]
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Call `_build_num_microbatches_calculator` instead of `build_num_microbatches_calculator` & pass False to `decrease_batch_size_if_needed`
**Collection**: [Note which collection this PR will affect]
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📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
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"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days."
] | 2024-07-30T00:00:08 | 2024-08-19T16:39:56 | 2024-08-19T16:39:54 | MEMBER | {
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} | # What does this PR do ?
Import `packaging` instead of `pkg_resources`;
In later versions of `pkg_resources`, `packaging` is removed from the module and in Python 3.12 `pkg_resources` itself is deprecated.
Sol: use the `packaging` package.
Note: this is mostly needed to query TE's version which is a subopt... | {
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fix mem in refit | {
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