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```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
There's no need to comment `jenkins` on the PR to trigger Jenkins CI.
The GitHub Actions CI will run automatically when the PR is opened.
To run CI on an untrusted fork, a NeMo user with write access must click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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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This PR sets the ground for more advanced distributed checkpointing features, like fully parallel save/load or async save which will be configured and implemented as part of DistributedCheckpointIO plugin. It doesn't introduce any new features, just refactors existing behavior.
**Collection**: NLP
# Changelog
- implement DistributedCheckpointIO plugin for save/load of distributed checkpoints
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
There's no need to comment `jenkins` on the PR to trigger Jenkins CI.
The GitHub Actions CI will run automatically when the PR is opened.
To run CI on an untrusted fork, a NeMo user with write access must click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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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} | # What does this PR do ?
This PR fixes the behavior of encountering an existing distributed checkpoint. Previously the save was skipped based on logic in `NLPDDPStrategy.save_checkpoint`, which is not a good place for such logic (strategy should just save the checkpoint). This PR reuses the PTL versioning mechanism to adjust the checkpoint name accordingly (add `-v1` suffix to ckpt name)
**Collection**: NLP
# Changelog
- fix the behavior of skipping duplicate checkpoint by reusing PTL versioning mechanism
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
There's no need to comment `jenkins` on the PR to trigger Jenkins CI.
The GitHub Actions CI will run automatically when the PR is opened.
To run CI on an untrusted fork, a NeMo user with write access must click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] 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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"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.",
"This PR was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-04-23T08:27:12 | 2024-05-15T01:47:22 | 2024-05-15T01:47:22 | COLLABORATOR | {
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} | # What does this PR do ?
Simplify NLPDDPStrategy.save_checkpoint methods by using native PTL mechanism for state dict construction.
**Collection**: NLP
# Changelog
- Create optimizer state dict with `optimizer_state` method
- Create model state entirely in `on_save_checkpoint` method
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
There's no need to comment `jenkins` on the PR to trigger Jenkins CI.
The GitHub Actions CI will run automatically when the PR is opened.
To run CI on an untrusted fork, a NeMo user with write access must click "Approve and run".
# 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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"fixed here: https://github.com/NVIDIA/NeMo/pull/9010"
] | 2024-04-23T07:49:01 | 2024-04-23T16:32:34 | 2024-04-23T16:32:34 | NONE | {
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} | # What does this PR do ?
This PR fix the following error when build form `Dockerfile`:
```
[+] Building 3256.2s (33/41) docker:default
=> [internal] load build definition from Dockerfile 0.0s
=> => transferring dockerfile: 6.72kB 0.0s
=> resolve image config for docker-image://docker.io/docker/dockerfile:experimental 0.3s
=> [auth] docker/dockerfile:pull token for registry-1.docker.io 0.0s
=> CACHED docker-image://docker.io/docker/dockerfile:experimental@sha256:600e5c62eedff338b3f7a0850beb7c05866e0ef27b2d2e8c02a 0.0s
=> [internal] load .dockerignore 0.0s
=> => transferring context: 226B 0.0s
=> [internal] load build definition from Dockerfile 0.0s
=> [internal] load metadata for nvcr.io/nvidia/pytorch:24.01-py3 0.7s
=> [internal] load build context 3.4s
=> => transferring context: 105.39MB 3.4s
=> [nemo-deps 1/22] FROM nvcr.io/nvidia/pytorch:24.01-py3@sha256:afd682405d620a620f61f38cb9d9bbc6a5230817699a48e9ed193546e8 0.0s
=> CACHED [nemo-deps 2/22] RUN apt-get update && apt-get upgrade -y && apt-get install -y libsndfile1 sox libfreety 0.0s
=> CACHED [nemo-deps 3/22] RUN apt-get update && apt-get install -y libtool libltdl-dev automake autoconf bison 0.0s
=> CACHED [nemo-deps 4/22] WORKDIR /workspace/ 0.0s
=> CACHED [nemo-deps 5/22] RUN git clone https://github.com/NVIDIA/Megatron-LM.git && cd Megatron-LM && git checkout 36 0.0s
=> [nemo-deps 6/22] RUN git clone https://github.com/NVIDIA/apex.git && cd apex && git checkout f058162b215791b15507 2267.0s
=> [nemo-src 1/1] COPY . . 0.2s
=> [nemo-deps 7/22] RUN git clone https://github.com/NVIDIA/TransformerEngine.git && cd TransformerEngine && git fetc 293.9s
=> [nemo-deps 8/22] WORKDIR /tmp/ 0.6s
=> [nemo-deps 9/22] RUN pip3 uninstall -y sacrebleu torchtext 2.0s
=> [nemo-deps 10/22] WORKDIR /tmp/torchaudio_build 0.1s
=> [nemo-deps 11/22] COPY scripts/installers /tmp/torchaudio_build/scripts/installers/ 0.1s
=> [nemo-deps 12/22] RUN INSTALL_MSG=$(/bin/bash /tmp/torchaudio_build/scripts/installers/install_torchaudio_latest.sh); I 176.7s
=> [nemo-deps 13/22] COPY scripts /tmp/nemo/scripts/ 0.1s
=> [nemo-deps 14/22] RUN INSTALL_MSG=$(/bin/bash /tmp/nemo/scripts/installers/install_graphviz.sh --docker); INSTALL_CODE=$ 84.6s
=> [nemo-deps 15/22] COPY scripts /tmp/nemo/scripts/ 0.1s
=> [nemo-deps 16/22] RUN INSTALL_MSG=$(/bin/bash /tmp/nemo/scripts/installers/install_k2.sh); INSTALL_CODE=$?; echo ${IN 201.3s
=> [nemo-deps 17/22] WORKDIR /tmp/nemo 0.1s
=> [nemo-deps 18/22] COPY requirements . 0.0s
=> [nemo-deps 19/22] RUN for f in $(ls requirements*.txt); do pip3 install --disable-pip-version-check --no-cache-dir -r $ 151.3s
=> [nemo-deps 20/22] RUN pip install flash-attn 5.2s
=> [nemo-deps 21/22] RUN pip install numba>=0.57.1 4.3s
=> [nemo-deps 22/22] RUN pip install nvidia-ammo~=0.7.0 --extra-index-url https://pypi.nvidia.com --no-cache-dir 10.9s
=> [nemo 1/10] RUN /usr/bin/test -n "2.0.0" && /bin/echo "export NEMO_VERSION=2.0.0" >> /root/.bashrc && /bin/echo "exp 0.6s
=> ERROR [nemo 2/10] RUN --mount=from=nemo-src,target=/tmp/nemo,rw cd /tmp/nemo && pip install ".[all]" 14.5s
------
> [nemo 2/10] RUN --mount=from=nemo-src,target=/tmp/nemo,rw cd /tmp/nemo && pip install ".[all]":
3.533 Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
3.540 Processing /tmp/nemo
3.543 Installing build dependencies: started
9.221 Installing build dependencies: finished with status 'done'
9.222 Getting requirements to build wheel: started
9.526 Getting requirements to build wheel: finished with status 'done'
9.528 Preparing metadata (pyproject.toml): started
9.891 Preparing metadata (pyproject.toml): finished with status 'done'
10.15 Requirement already satisfied: huggingface-hub>=0.20.3 in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (0.22.2)
10.15 Requirement already satisfied: numba in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (0.57.1+1.g1ff679645)
10.15 Requirement already satisfied: numpy>=1.22 in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (1.24.4)
10.15 Requirement already satisfied: onnx>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (1.15.0rc2)
10.15 Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (2.8.2)
10.15 Requirement already satisfied: ruamel.yaml in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (0.18.6)
10.15 Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (1.2.0)
10.15 Requirement already satisfied: setuptools>=65.5.1 in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (68.2.2)
10.15 Requirement already satisfied: tensorboard in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (2.9.0)
10.15 Requirement already satisfied: text-unidecode in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (1.3)
10.15 Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (2.2.0a0+81ea7a4)
10.15 Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (4.66.1)
10.15 Requirement already satisfied: wget in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (3.2)
10.15 Requirement already satisfied: wrapt in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (1.16.0)
10.23 Requirement already satisfied: black==19.10b0 in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (19.10b0)
10.23 Requirement already satisfied: click==8.0.2 in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (8.0.2)
10.23 Requirement already satisfied: isort<6.0.0,>5.1.0 in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (5.13.2)
10.23 Requirement already satisfied: parameterized in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (0.9.0)
10.23 Requirement already satisfied: pytest in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (7.4.4)
10.23 Requirement already satisfied: pytest-mock in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (3.14.0)
10.23 Requirement already satisfied: pytest-runner in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (6.0.1)
10.23 Requirement already satisfied: sphinx in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (7.3.7)
10.24 Requirement already satisfied: sphinxcontrib-bibtex in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (2.6.2)
10.24 Requirement already satisfied: wandb in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (0.16.6)
10.24 Requirement already satisfied: hydra-core<=1.3.2,>1.3 in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (1.3.2)
10.24 Requirement already satisfied: omegaconf<=2.3 in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (2.3.0)
10.24 Requirement already satisfied: pytorch-lightning>=2.2.1 in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (2.2.2)
10.24 Requirement already satisfied: torchmetrics>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (1.3.2)
10.24 Requirement already satisfied: transformers>=4.36.0 in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (4.40.0)
10.24 Requirement already satisfied: webdataset>=0.2.86 in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (0.2.86)
10.24 Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (2.19.0)
10.24 Requirement already satisfied: inflect in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (7.2.0)
10.25 Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (1.5.3)
10.25 Requirement already satisfied: sacremoses>=0.0.43 in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (0.1.1)
10.25 Requirement already satisfied: sentencepiece<1.0.0 in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (0.2.0)
10.25 Requirement already satisfied: braceexpand in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (0.1.7)
10.25 Requirement already satisfied: editdistance in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (0.8.1)
10.25 Requirement already satisfied: g2p-en in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (2.1.0)
10.25 Requirement already satisfied: ipywidgets in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (8.1.2)
10.25 Requirement already satisfied: jiwer in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (3.0.3)
10.25 Requirement already satisfied: kaldi-python-io in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (1.2.2)
10.25 Requirement already satisfied: kaldiio in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (2.18.0)
10.25 Requirement already satisfied: lhotse>=1.22.0 in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (1.22.0)
10.26 Requirement already satisfied: librosa>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (0.10.1)
10.26 Requirement already satisfied: marshmallow in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (3.21.1)
10.26 Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (3.8.2)
10.26 Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (23.2)
10.26 Requirement already satisfied: pyannote.core in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (5.0.0)
10.26 Requirement already satisfied: pyannote.metrics in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (3.2.1)
10.26 Requirement already satisfied: pydub in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (0.25.1)
10.26 Requirement already satisfied: pyloudnorm in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (0.1.1)
10.26 Requirement already satisfied: resampy in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (0.4.3)
10.26 Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (1.12.0)
10.26 Requirement already satisfied: soundfile in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (0.12.1)
10.27 Requirement already satisfied: sox in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (1.5.0)
10.27 Requirement already satisfied: texterrors in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (0.4.4)
10.27 Requirement already satisfied: boto3 in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (1.34.89)
10.27 Requirement already satisfied: einops in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (0.7.0)
10.79 Collecting faiss-cpu (from nemo_toolkit==2.0.0rc0)
10.88 Downloading faiss_cpu-1.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.6 kB)
11.20 Collecting fasttext (from nemo_toolkit==2.0.0rc0)
11.22 Downloading fasttext-0.9.2.tar.gz (68 kB)
11.24 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 68.8/68.8 kB 3.8 MB/s eta 0:00:00
11.25 Preparing metadata (setup.py): started
11.55 Preparing metadata (setup.py): finished with status 'done'
11.87 Collecting flask-restful (from nemo_toolkit==2.0.0rc0)
11.89 Downloading Flask_RESTful-0.3.10-py2.py3-none-any.whl.metadata (1.0 kB)
11.89 Requirement already satisfied: ftfy in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (6.2.0)
12.22 Collecting gdown (from nemo_toolkit==2.0.0rc0)
12.23 Downloading gdown-5.1.0-py3-none-any.whl.metadata (5.7 kB)
12.58 Collecting h5py (from nemo_toolkit==2.0.0rc0)
12.60 Downloading h5py-3.11.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.5 kB)
12.96 Collecting ijson (from nemo_toolkit==2.0.0rc0)
12.98 Downloading ijson-3.2.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)
12.98 Requirement already satisfied: jieba in /usr/local/lib/python3.10/dist-packages (from nemo_toolkit==2.0.0rc0) (0.42.1)
13.30 Collecting markdown2 (from nemo_toolkit==2.0.0rc0)
13.32 Downloading markdown2-2.4.13-py2.py3-none-any.whl.metadata (2.0 kB)
13.64 INFO: pip is looking at multiple versions of nemo-toolkit[all] to determine which version is compatible with other requirements. This could take a while.
13.64 ERROR: Could not find a version that satisfies the requirement megatron-core>0.6.0; extra == "all" (from nemo-toolkit[all]) (from versions: 0.1.0, 0.2.0, 0.3.0, 0.4.0, 0.5.0, 0.6.0)
13.64 ERROR: No matching distribution found for megatron-core>0.6.0; extra == "all"
13.98
13.98 [notice] A new release of pip is available: 23.3.2 -> 24.0
13.98 [notice] To update, run: python -m pip install --upgrade pip
------
Dockerfile:153
--------------------
151 |
152 | # Install NeMo
```
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
There's no need to comment `jenkins` on the PR to trigger Jenkins CI.
The GitHub Actions CI will run automatically when the PR is opened.
To run CI on an untrusted fork, a NeMo user with write access must click "Approve and run".
# 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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- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
There's no need to comment `jenkins` on the PR to trigger Jenkins CI.
The GitHub Actions CI will run automatically when the PR is opened.
To run CI on an untrusted fork, a NeMo user with write access must click "Approve and run".
# 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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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
There's no need to comment `jenkins` on the PR to trigger Jenkins CI.
The GitHub Actions CI will run automatically when the PR is opened.
To run CI on an untrusted fork, a NeMo user with write access must click "Approve and run".
# 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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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
There's no need to comment `jenkins` on the PR to trigger Jenkins CI.
The GitHub Actions CI will run automatically when the PR is opened.
To run CI on an untrusted fork, a NeMo user with write access must click "Approve and run".
# 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 ?
Support training of multi-turn interaction trajectories in ChatML template format.
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
The following example shows how to process a file with multi-turn chat trajectories in OpenAI Format to NeMo's packed SFT format:
Example script to download the data:
```python
import os
import pathlib
from datasets import load_dataset
ds = load_dataset("xingyaoww/code-act")
if not os.path.exists("data/datasets"):
pathlib.Path("data/datasets").mkdir(parents=True, exist_ok=True)
print("Created data/datasets")
codeact_ds = ds["codeact"]
codeact_df = codeact_ds.to_pandas()
codeact_df.to_json("data/datasets/codeact.jsonl", orient="records", lines=True)
print(f"Saved {len(codeact_df)} examples to data/datasets/codeact.jsonl")
general_ds = ds["general"]
general_df = general_ds.to_pandas()
general_df.to_json("data/datasets/general.jsonl", orient="records", lines=True)
print(f"Saved {len(general_df)} examples to data/datasets/general.jsonl")
print("Done")
```
You will get two files `data/datasets/codeact.jsonl,data/datasets/general.jsonl`.
Then you run this example script to convert the data to `codeact-mixture.nemo.jsonl`:
`python3 scripts/data/convert_openai_to_nemo_chat_format.py data/datasets/codeact.jsonl,data/datasets/general.jsonl --output_file data/datasets/codeact-mixture.nemo.jsonl`
```python
"""Convert OpenAI chat format to NeMo chat format.
Example usage:
python3 scripts/data/convert_openai_to_nemo_chat_format.py data/datasets/codeact.jsonl,data/datasets/general.jsonl --output_file data/datasets/codeact-mixture.nemo.jsonl
============
OpenAI chat format:
source = {
'messages': [
{'role': 'system', 'content': '{system message}'}, // this is optional
{'role': 'user', 'content': '{turn 1 user message}'},
{'role': 'assistant', 'content': '{turn 1 assistant message}'},
{'role': 'user', 'content': '{turn 2 user message}'},
],
}
Nemo chat format:
source = {
'system': '{system message}',
'conversations': [
{'from': 'user', 'value': '{turn 1 user message}', 'label': None},
{'from': 'assistant', 'value': '{turn 1 assistant message}', 'label': '{turn 1 assistant label}'},
{'from': 'user', 'value': '{turn 2 user message}', 'label': None},
{'from': 'assistant', 'value': '{turn 2 assistant message}', 'label': '{turn 2 assistant label}'},
],
"mask": "user",
"type": None,
}
"""
import json
import argparse
from tqdm import tqdm
parser = argparse.ArgumentParser(description='Convert OpenAI chat format to NeMo chat format')
parser.add_argument('input_file', type=str, help='Input file(s) in OpenAI chat format')
parser.add_argument('--output_file', type=str, default=None, help='Output file in NeMo chat format')
args = parser.parse_args()
def convert_openai_to_nemo_chat_format(openai_messages, mask_role='user'):
nemo_instance = {}
assert len(openai_messages) > 0, "OpenAI instance must have at least one conversation"
if openai_messages[0]['role'] == 'system':
nemo_instance['system'] = openai_messages[0]['content']
openai_messages = openai_messages[1:]
else:
nemo_instance['system'] = None
nemo_instance['conversations'] = []
nemo_instance['mask'] = mask_role
# https://github.com/xingyaoww/NeMo/blob/main/nemo/collections/nlp/data/language_modeling/megatron/gpt_sft_chat_dataset.py#L203-L206
nemo_instance['type'] = None
for message in openai_messages:
if message['role'] == 'user':
nemo_instance['conversations'].append({'from': 'user', 'value': message['content'], 'label': None})
elif message['role'] == 'assistant':
nemo_instance['conversations'].append({'from': 'assistant', 'value': message['content'], 'label': None})
else:
raise ValueError(f"Unknown role: {message['role']}")
return nemo_instance
input_files = args.input_file.split(',')
n_total = 0
with open(args.output_file, 'w') as fout:
for input_file in tqdm(input_files, desc='Converting files'):
assert input_file.endswith('.jsonl'), "Input file must be in jsonl format"
with open(input_file, 'r') as fin:
for line in tqdm(fin, desc=f'Converting {input_file}'):
openai_instance = json.loads(line)
assert 'conversations' in openai_instance, "OpenAI instance must have 'conversations' key"
nemo_instance = convert_openai_to_nemo_chat_format(openai_instance['conversations'])
fout.write(json.dumps(nemo_instance) + '\n')
n_total += 1
print(f"Converted {input_file} to NeMo chat format and saved to {args.output_file}")
print(f"Converted {n_total} instances to NeMo chat format and saved to {args.output_file}")
```
Then you can script to convert such JSONL (`data/datasets/codeact-mixture.nemo.jsonl`) to NeMO format:
```bash
#!/bin/bash
FILEPATH=data/datasets/codeact-mixture.nemo.jsonl
NEMO_MODEL=data/models/nemo/mistral-7b-base.nemo
MAX_SEQ_LEN=16384
OUTPUT_DIR=data/datasets_packed/codeact_mixture_mistral_7b_16k
# final 4409 examples
mkdir -p $OUTPUT_DIR
# make all paths absolute
FILEPATH=$(realpath $FILEPATH)
NEMO_MODEL=$(realpath $NEMO_MODEL)
OUTPUT_DIR=$(realpath $OUTPUT_DIR)
export PYTHONPATH=$(pwd)/NeMo:$(pwd)/Megatron-LM:$PYTHONPATH
pushd NeMo/
python scripts/nlp_language_modeling/prepare_packed_ft_chat_dataset.py \
model.data.train_ds.file_names=[$FILEPATH] \
model.data.train_ds.max_seq_length=$MAX_SEQ_LEN \
model.restore_from_path=$NEMO_MODEL \
+output_dir=$OUTPUT_DIR \
+pack_sizes=[$MAX_SEQ_LEN] \
+seed=42 \
+model.data.chat=True \
'+model.data.chat_prompt_tokens.system_turn_start="<|im_start|>"' \
'+model.data.chat_prompt_tokens.system_role="system"' \
'+model.data.chat_prompt_tokens.turn_start="<|im_start|>"' \
'+model.data.chat_prompt_tokens.label_start="<|im_start|>"' \
'+model.data.chat_prompt_tokens.end_of_turn="<|im_end|>\n"' \
'+model.data.chat_prompt_tokens.end_of_name="\n"' \
'+model.data.chat_prompt_tokens.add_special_tokens=["<|im_start|>", "<|im_end|>"]'
```
## Config File for Fine-tuning
```yaml
# Modified from NeMo/examples/nlp/language_modeling/tuning/conf/megatron_gpt_sft.yaml
name: megatron_mistral_sft_codeact
trainer:
devices: 4
accelerator: gpu
num_nodes: 2
precision: bf16
logger: False # logger provided by exp_manager
enable_checkpointing: False
use_distributed_sampler: False
# max_epochs: 5 # will be override by max_steps
max_steps: ?? # 645 steps per epoch, 5 epochs, 3225 examples, 32 bsz, 3225/32=100.78125
# consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches
log_every_n_steps: 1 # frequency with which training steps are logged
# If is an int n > 1, will run val every n training steps, if a float 0.0 - 1.0 will run val every epoch fraction, e.g. 0.25 will run val every quarter epoch
# val_check_interval: 10
limit_val_batches: 0
gradient_clip_val: 1.0
exp_manager:
explicit_log_dir: null
exp_dir: ???
name: ${name}
create_wandb_logger: True
wandb_logger_kwargs:
project: nemo-sft
name: nemo-mistral-sft-codeact
resume_if_exists: True
resume_ignore_no_checkpoint: True
create_checkpoint_callback: True
checkpoint_callback_params:
save_nemo_on_train_end: True
every_n_train_steps: ??
every_n_epochs: null
filename: 'ckpt-step_{step}-{consumed_samples}'
model_parallel_size: ${model.tensor_model_parallel_size}
model:
seed: 42
tensor_model_parallel_size: 8 # intra-layer model parallelism
pipeline_model_parallel_size: 1 # inter-layer model parallelism
global_batch_size: ??
micro_batch_size: 1
restore_from_path: ??? # Path to an existing p-tuned/prompt tuned .nemo model you wish to add new tasks to or run inference with
resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc.
save_nemo_on_validation_end: True # Saves an inference ready .nemo file every time a checkpoint is saved during training.
sync_batch_comm: False
## Copied from Baichuan2
mcore_gpt: True
gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory)
# Megatron O2-style half-precision
megatron_amp_O2: True # Enable O2-level automatic mixed precision using main parameters # False!!!
# grad_allreduce_chunk_size_mb: 125
overlap_p2p_comm: True # Overlap p2p communication with computes. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1
batch_p2p_comm: True # Batch consecutive inter-peer send/recv operations. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1
# Fusion
grad_div_ar_fusion: True # Fuse grad division into torch.distributed.all_reduce. Only used with O2 and no pipeline parallelism..
gradient_accumulation_fusion: False # Fuse weight gradient accumulation to GEMMs. Only used with pipeline parallelism and O2.
bias_activation_fusion: False # Use a kernel that fuses the bias addition from weight matrices with the subsequent activation function.
bias_dropout_add_fusion: False # Use a kernel that fuses the bias addition, dropout and residual connection addition.
masked_softmax_fusion: True # Use a kernel that fuses the attention softmax with it's mask.
get_attention_mask_from_fusion: True # When using fused softmax it will create the attention mask so we won't copy it to the pipeline stages.
## Sequence Parallelism
# Makes tensor parallelism more memory efficient for LLMs (20B+) by parallelizing layer norms and dropout sequentially
# See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details.
sequence_parallel: True
## Activation Checkpoint
activations_checkpoint_granularity: full # null # 'selective' or 'full'
activations_checkpoint_method: uniform # 'uniform', 'block', not used with 'selective'
# 'uniform' divides the total number of transformer layers and checkpoints the input activation
# of each chunk at the specified granularity
# 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity
activations_checkpoint_num_layers: 1 # not used with 'selective'
activations_checkpoint_layers_per_pipeline: null
answer_only_loss: True
hidden_dropout: 0.0
attention_dropout: 0.0
ffn_dropout: 0.0
# FSDP
fsdp: False # Enable training with torch FSDP.
fsdp_sharding_strategy: 'full' # Method to shard model states. Available options are 'full', 'hybrid', and 'grad'.
fsdp_grad_reduce_dtype: 'fp32' # Gradient reduction data type.
fsdp_sharded_checkpoint: False # Store and load FSDP shared checkpoint.
fsdp_use_orig_params: False # Set to True to use FSDP for specific peft scheme.
peft:
peft_scheme: null # "adapter" # can be either adapter,ia3, or ptuning
restore_from_path: null
data:
train_ds:
packed_sequence: True
# Example of how to specify paths to multiple datasets
# file_names:
# - /path/to/squad.jsonl
# - /path/to/mnli.jsonl
# - /path/to/boolq.jsonl
# Example of how each dataset is formatted
# {'input': 'John von Neumann\nVon Neumann made fundamental contributions .... Q: What did the math of artificial viscosity do?', 'output': 'smoothed the shock transition without sacrificing basic physics'}
file_names: ??? # Path to a list of JSONL files corresponding to the source data.
global_batch_size: ${model.global_batch_size}
micro_batch_size: ${model.micro_batch_size}
shuffle: True
num_workers: 8
memmap_workers: null
pin_memory: True
max_seq_length: ??
min_seq_length: 1
pad_to_max_length: True
drop_last: True
# Example of how to specify concat_sampling_probabilities
# concat_sampling_probabilities:
# - 0.5
# - 0.25
# - 0.25
concat_sampling_probabilities: null # When providing a list of datasets, this arg defines the sampling probabilities from each dataset when strategy='random'
validation_ds:
file_names: null # Path to a list of JSONL files corresponding to the source data. Data format is identical to train_ds.
names: null # Names of the corresponding datasets used to log metrics.
metric:
name: "loss" # Name of the evaluation metric to use. Options: ['exact_string_match', 'loss', 'rouge', 'token_f1']
average: null # Average the metric over the dataset. Options: ['macro', 'micro']. Works only for 'F1', 'accuracy' etc. Refer to torchmetrics for metrics where this is supported.
num_classes: null
optim:
name: distributed_fused_adam
bucket_cap_mb: 60
overlap_grad_sync: True
overlap_param_sync: True
contiguous_grad_buffer: True
grad_sync_dtype: bf16
lr: 1e-5
weight_decay: 0.01
betas:
- 0.9
- 0.98
sched:
name: CosineAnnealing
# scheduler config override
warmup_ratio: 0.1 # Warmup steps will be 10% of the training steps.
min_lr: 1e-6
```
Example script for training:
```bash
#!/bin/bash
export NCCL_DEBUG=INFO
export PYTHONFAULTHANDLER=1
NEMO_MODEL=data/models/nemo/mistral-7b-base.nemo
MAX_SEQ_LEN=16384
PACKED_DATA_FILEPATH=data/datasets_packed/codeact_mixture_mistral_7b_16k/packed_16384_seed42.npy
NUM_EXAMPLES=4409
EXP_ID=Mistral-7B-16k-sft-codeact
CONFIG_PATH=scripts/train/configs/$EXP_ID.yaml
OUTPUT_DIR=data/ckpts/$EXP_ID
mkdir -p $OUTPUT_DIR
GLOBAL_BATCH_SIZE=32
N_EPOCHS=5
echo "GLOBAL_BATCH_SIZE: $GLOBAL_BATCH_SIZE"
STEPS_PER_EPOCH=$((NUM_EXAMPLES / GLOBAL_BATCH_SIZE))
echo "STEPS_PER_EPOCH: $STEPS_PER_EPOCH"
MAX_STEPS=$((NUM_EXAMPLES * N_EPOCHS / GLOBAL_BATCH_SIZE))
echo "MAX_STEPS: $MAX_STEPS"
# make all paths absolute
OUTPUT_DIR=$(realpath $OUTPUT_DIR)
NEMO_MODEL=$(realpath $NEMO_MODEL)
PACKED_DATA_FILEPATH=$(realpath $PACKED_DATA_FILEPATH)
CONFIG_PATH=$(realpath $CONFIG_PATH)
CONFIG_DIR=$(dirname $CONFIG_PATH)
CONFIG_NAME=$(basename $CONFIG_PATH .yaml)
echo "NEMO_MODEL: $NEMO_MODEL"
echo "OUTPUT_DIR: $OUTPUT_DIR"
echo "CONFIG_NAME: $CONFIG_NAME"
echo "CONFIG_DIR: $CONFIG_DIR"
export PYTHONPATH=$(pwd)/NeMo:$(pwd)/Megatron-LM:$PYTHONPATH
# export WANDB_DISABLED=True
export CUDA_DEVICE_MAX_CONNECTIONS=1
pushd NeMo/
# https://docs.nvidia.com/nemo-framework/user-guide/latest/modelalignment/sft.html#step-2-sft-training
python examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py \
--config-path $CONFIG_DIR \
--config-name $CONFIG_NAME \
trainer.max_steps=$MAX_STEPS \
exp_manager.exp_dir=$OUTPUT_DIR \
exp_manager.checkpoint_callback_params.every_n_train_steps=$STEPS_PER_EPOCH \
model.restore_from_path=$NEMO_MODEL \
model.global_batch_size=$GLOBAL_BATCH_SIZE \
model.data.train_ds.max_seq_length=$MAX_SEQ_LEN \
model.data.train_ds.file_names=[$PACKED_DATA_FILEPATH] \
'model.data.train_ds.concat_sampling_probabilities=[1.0]' \
```
# Jenkins CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
There's no need to comment `jenkins` on the PR to trigger Jenkins CI.
The GitHub Actions CI will run automatically when the PR is opened.
To run CI on an untrusted fork, a NeMo user with write access must click "Approve and run".
# 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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"This PR was closed because it has been inactive for 7 days since being marked as stale."
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
There's no need to comment `jenkins` on the PR to trigger Jenkins CI.
The GitHub Actions CI will run automatically when the PR is opened.
To run CI on an untrusted fork, a NeMo user with write access must click "Approve and run".
# 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
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## Who can review?
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https://api.github.com/repos/NVIDIA/NeMo/issues/9007 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/9007/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/9007/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/9007/events | https://github.com/NVIDIA/NeMo/pull/9007 | 2,257,675,625 | PR_kwDOC_bI7s5taXoA | 9,007 | [ASR] Support for transcription of multi-channel audio for AED models | {
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} | # What does this PR do ?
Currently, AED models do not use channel selector.
If the input manifest is pointing to multi-channel audio files, transcription will fail with
```
AssertionError: Expected MonoCut.
```
This PR adds support for
1) setting `channel_selector` to an integer value, e.g., `0/1` to always select the first/second channel in the input file
2) setting `channel_selector` to a string value to use a field from the input manifest to select the channel
**Collection**: ASR
# Changelog
- Propagate `channel_selector` in `_setup_transcribe_dataloader`
- Added map with `_select_channel` in `nemo/collections/common/data/lhotse/dataloader.py`
# Usage
Example of use, assuming Canary model is loaded
```python
channel_selector = 0 # use the first channel
channel_selector = 1 # use the second channel
channel_selector = 'reference_channel' # use 'reference_channel' field from the manifest file
predicted_text = canary_model.transcribe(
mc_manifest, # manifest with multi-channel audio
batch_size=1, # batch size to run the inference with
channel_selector=channel_selector, # channel
)
```
Similarly, it can be used with `transcribe_speech.py`
```shell
CHANNEL_SELECTOR=0
CHANNEL_SELECTOR=1
CHANNEL_SELECTOR=reference_channel # use 'reference_channel' field from the manifest file
python ${NEMO_ROOT}/examples/asr/transcribe_speech.py \
pretrained_name=nvidia/canary-1b \
dataset_manifest=./mc_manifest.json \
channel_selector=${CHANNEL_SELECTOR}
```
## Toy Example:
Scripts & toy dataset:
- [pr_example_01.tar.gz](https://github.com/NVIDIA/NeMo/files/15069660/pr_example_01.tar.gz)
Run as
```shell
# test transcribe_speech.py
bash test_transcribe.sh 0
bash test_transcribe.sh 1
bash test_transcribe.sh reference_channel
# test python API
python test_transcribe.py --channel-selector 0
python test_transcribe.py --channel-selector 1
python test_transcribe.py --channel-selector reference_channel
```
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} | # What does this PR do ?
Fix all current doc-building errors and most warnings. I have not fixed all warnings in the interest of making the PR not too large, and have tried to avoid making some changes that might conflict with @yaoyu-33 's current work on improving the doc structure.
FAO @titu1994 : please look over the changes to `docs/source/conf.py`. I removed the duplicate "sphinxcontrib.bibtex" extension. I have also commented out the "sphinx.ext.autosectionlabel" extension as that was causing a lot of "duplicate label" warnings.
**Collection**: Docs
# 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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**PR Type**:
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- [x] Bugfix
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## Who can review?
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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.",
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} | # What does this PR do ?
Change microbatch calculator implementation into mcore. Related to MCore # 24
**Collection**: [Note which collection this PR will affect]
# Jenkins CI
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There's no need to comment `jenkins` on the PR to trigger Jenkins CI.
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- [ ] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
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} | # What does this PR do ?
make nemo lora compatible with HF ("canonical") lora.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
There's no need to comment `jenkins` on the PR to trigger Jenkins CI.
The GitHub Actions CI will run automatically when the PR is opened.
To run CI on an untrusted fork, a NeMo user with write access must click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
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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**:
- [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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"Tested most of the models and getting this gibberish output from only the nemotron 22B base one \r\n\r\n```\r\n--- Output: [['is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is in the best way to get the best price for your car.\\n\\nHow to sell your car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car'], ['is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is the best way to get to the hotel from the airport?\\n\\nThe hotel is located 1 km from the airport, which means that there is is is is is is is is is is is is is is is is is is is is is is is is is is'], ['is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is is the best way to get to the hotel from the airport?\\n\\nThe hotel is located 1 km from the airport.\\n\\nWhat is the best way way way way way way way way way way way way way way way way way way way way way way way way way']]\r\n```\r\n\r\nCan you guys please take a look at this?",
"jenkins"
] | 2024-04-22T20:20:20 | 2024-05-13T23:14:34 | 2024-05-13T23:14:34 | 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
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
There's no need to comment `jenkins` on the PR to trigger Jenkins CI.
The GitHub Actions CI will run automatically when the PR is opened.
To run CI on an untrusted fork, a NeMo user with write access must click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] 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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"updated here: https://github.com/NVIDIA/NeMo/pull/9010"
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} | # What does this PR do ?
Update requirements file
**Collection**: [nlp]
# Changelog
- Change mcore version to greater or equal
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
There's no need to comment `jenkins` on the PR to trigger Jenkins CI.
The GitHub Actions CI will run automatically when the PR is opened.
To run CI on an untrusted fork, a NeMo user with write access must click "Approve and run".
# 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)
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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
* Related to # (issue)
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"Thanks @erastorgueva-nv, for some reason I thought it's required by sphinx. It should look much better indeed!",
"Docs LGTM"
] | 2024-04-22T19:01:46 | 2024-04-26T21:10:04 | 2024-04-26T21:10:04 | COLLABORATOR | {
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} | # What does this PR do ?
* Comprehensive documentation about random seed configuration, behavior, and effects on dataloading.
* Backward-compatible changes to random seed configuration in training.
* Allows more precise control over randomness; this work was triggered by the need to support mixed data-parallel (DP) and tensor-parallel (TP) training. Before this PR, it wasn't possible to configure the dataloader to yield identical data on identical DP ranks but different TP ranks, causing deadlocks. Now it's possible but the user has to be careful about manually setting different random seeds on training continuations (this is documented).
* Unit tests to ensure the desired behavior in DDP setups.
**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 how to use this
```
# Jenkins CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
There's no need to comment `jenkins` on the PR to trigger Jenkins CI.
The GitHub Actions CI will run automatically when the PR is opened.
To run CI on an untrusted fork, a NeMo user with write access must click "Approve and run".
# 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
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"Hi! If you want use NeMo, you can either use the PyTorch container and install NeMo on it, or use a NeMo speech container. There are instructions for both in the README, e.g. [here](https://github.com/NVIDIA/NeMo/tree/main?tab=readme-ov-file#docker-containers). AFAIK, neither type of container contains any of the [tools from this directory](https://github.com/NVIDIA/NeMo/tree/main/tools). However, all of those tools are essentially relatively lightweight Python scripts that use NeMo (and maybe some other dependencies that are mentioned in their requirements files).\r\n\r\nSo if I were you, I would set up one of the Docker containers and make sure NeMo is installed, then git clone the NeMo repository so that you have access to the NFA scripts here: https://github.com/NVIDIA/NeMo/tree/main/tools/nemo_forced_aligner.\r\n\r\nThen you should be able to use NFA inside the container by calling the `<path to your NeMo location>/tools/nemo_forced_aligner/align.py` script.\r\n",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"Hi, marking as closed since I believe the issue is resolved."
] | 2024-04-22T18:03:20 | 2024-05-24T18:27:36 | 2024-05-24T18:27:36 | NONE | {
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I'm working with the Forced aligner and so far everything has been positive!
Now I wanted to use the prebuilt docker images provided by Nvidia, but when exploring the images I don't see any reference to the Forced Aligner script I used on the Google Colab example you provide.
Is this expected? Do I have to use the pytorch:23.10-py3 Docker image and install NeMo from GitHub?
Thanks in advance. | {
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
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There's no need to comment `jenkins` on the PR to trigger Jenkins CI.
The GitHub Actions CI will run automatically when the PR is opened.
To run CI on an untrusted fork, a NeMo user with write access must click "Approve and run".
# 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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- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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Based on https://github.com/NVIDIA/Megatron-LM/commit/e69187bc3679ea5841030a165d587bb48b56ee77, this PR aims to make user-set timeout applicable to all ProcessGroups instead of just default ProcessGroups
**Collection**: [Note which collection this PR will affect]
# Changelog
- During model parallel initialization, it will pass a timeout parameter (which is the existing attribute of `NLPDDPStrategy`) to new ProcessGroup creation.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
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The GitHub Actions CI will run automatically when the PR is opened.
To run CI on an untrusted fork, a NeMo user with write access must click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"@ipmedenn Thanks, it works now. Closing this PR, but need another one to test changes)"
] | 2024-04-22T12:36:04 | 2024-04-22T18:23:03 | 2024-04-22T18:23:03 | COLLABORATOR | {
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
There's no need to comment `jenkins` on the PR to trigger Jenkins CI.
The GitHub Actions CI will run automatically when the PR is opened.
To run CI on an untrusted fork, a NeMo user with write access must click "Approve and run".
# 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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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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Simplify NLPDDPStrategy.save_checkpoint methods by using native PTL mechanism for state dict construction.
**Collection**: NLP
# Changelog
- Create optimizer state dict with `optimizer_state` method
- Create model state in `on_save_checkpoint` method
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"Looks to me an issue with environment. \r\n\r\nPls run with python 3.10 \r\n\r\nFYI: @athitten ",
"This is because I ran it in Jupyter Notebook."
] | 2024-04-21T18:42:02 | 2024-05-24T16:44:01 | 2024-05-24T16:44:00 | NONE | {
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} | null | null | null | **Describe the bug**
Can't train/finetune TitaNet model using two RTX4090.
```
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[22], line 1
----> 1 trainer.fit(speaker_model)
File ~/miniconda3/envs/nemo/lib/python3.12/site-packages/pytorch_lightning/trainer/trainer.py:532, in Trainer.fit(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
530 self.strategy._lightning_module = model
531 _verify_strategy_supports_compile(model, self.strategy)
--> 532 call._call_and_handle_interrupt(
533 self, self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path
534 )
File ~/miniconda3/envs/nemo/lib/python3.12/site-packages/pytorch_lightning/trainer/call.py:42, in _call_and_handle_interrupt(trainer, trainer_fn, *args, **kwargs)
40 try:
41 if trainer.strategy.launcher is not None:
---> 42 return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
43 return trainer_fn(*args, **kwargs)
45 except _TunerExitException:
File ~/miniconda3/envs/nemo/lib/python3.12/site-packages/pytorch_lightning/strategies/launchers/multiprocessing.py:101, in _MultiProcessingLauncher.launch(self, function, trainer, *args, **kwargs)
99 self._check_torchdistx_support()
100 if self._start_method in ("fork", "forkserver"):
--> 101 _check_bad_cuda_fork()
103 # The default cluster environment in Lightning chooses a random free port number
104 # This needs to be done in the main process here before starting processes to ensure each rank will connect
105 # through the same port
106 assert self._strategy.cluster_environment is not None
File ~/miniconda3/envs/nemo/lib/python3.12/site-packages/lightning_fabric/strategies/launchers/multiprocessing.py:192, in _check_bad_cuda_fork()
190 if _IS_INTERACTIVE:
191 message += " You will have to restart the Python kernel."
--> 192 raise RuntimeError(message)
RuntimeError: Lightning can't create new processes if CUDA is already initialized. Did you manually call `torch.cuda.*` functions, have moved the model to the device, or allocated memory on the GPU any other way? Please remove any such calls, or change the selected strategy. You will have to restart the Python kernel.
```
**Steps/Code to reproduce bug**
1. Open https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb
2. Set `config.trainer.devices = 2`
3. Run it
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install:
```bash
conda create --name nemo python==3.12.3
conda activate nemo
conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
conda install pip
conda install conda-forge::cython
pip install nemo_toolkit['asr']
```
**Environment details**
- OS version: Ubuntu 24.04 Beta
- PyTorch version: 2.2.2
- Python version: 3.12.3
**Additional context**
2x RTX4090
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"Met the same timeout issue. The issue is coming from NCCL default timeout value not set effectively when NeMo calls Megatron. If using NCCL backend, the default timeout value is changed to [10 mins](https://pytorch.org/docs/2.3/distributed.html#torch.distributed.init_process_group). That's why you see NCCL timeout as `Timeout(ms)=600000` (600000 milliseconds = 10 mins)\r\n\r\nNeed to make code changes to pass a larger timeout value (e.g. 30 mins)\r\nhttps://github.com/NVIDIA/Megatron-LM/commit/e69187bc3679ea5841030a165d587bb48b56ee77\r\nand also when calling parallel_state.initialize_model_parallel from NeMo\r\nhttps://github.com/NVIDIA/NeMo/blob/v1.23.0/nemo/collections/nlp/parts/nlp_overrides.py#L125\r\n\r\nAlternatively, upgrade to a later version of Megatron after the commit above.",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-04-21T13:17:34 | 2024-07-15T01:52:31 | 2024-07-15T01:52:31 | NONE | {
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} | null | null | null | **Describe the bug**
Context parallel does not work in some cases, such as pretrain llama-34b with 64 A800 GPUs and seqlen>=32768. **But using megatron-lm directly has no problem with the same config**. I want to use the SFT support like sequence packing in Nemo, hope to solve this soon.
**Environment details**
- image: nvcr.io/nvidia/nemo:24.03.01.framework
- 32 x A800
**test cases**
**error msg details**
1. kernel assertion
```sh
ATen/native/cuda/IndexKernel.cu:92: operator(): block: [3,0,0], thread: [64,0,0] Assertion `-sizes[i] <= index && index < sizes[i] && "index out of bounds"` failed.
/opt/pytorch/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [3,0,0], thread: [65,0,0] Assertion `-sizes[i] <= index && index < sizes[i] && "index out of bounds"` failed.
```
2.nccl timeout (tp=2, pp=4, cp=8)
```sh
terminate called after throwing an instance of 'c10::DistBackendError'
what(): [PG 5 Rank 1] NCCL watchdog thread terminated with exception: [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=910, OpType=_ALLGATHER_BASE, NumelIn=16777216, NumelOut=33554432, Timeout(ms)=600000) ran for 600021 milliseconds before timing out.
```
3.nccl timeout (tp=8, pp=1, cp=8)
```sh
[rank7]:[E ProcessGroupNCCL.cpp:574] [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=2, OpType=COALESCED, NumelIn=18446744073709551615, NumelOut=18446744073709551615, Timeout(ms)=600000) ran for 600014 milliseconds before timing out.
[rank7]:[E ProcessGroupNCCL.cpp:574] [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=21161, OpType=SEND, NumelIn=2097152, NumelOut=2097152, Timeout(ms)=600000) ran for 600022 milliseconds before timing out.
[rank7]:[E ProcessGroupNCCL.cpp:574] [Rank 0] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=15334, OpType=_ALLGATHER_BASE, NumelIn=8388608, NumelOut=67108864, Timeout(ms)=600000) ran for 600079 milliseconds before timing out.
```
**pretrain_llama34b_config.yaml**
```yaml
name: megatron_llama_34b
trainer:
devices: 8
num_nodes: 4
accelerator: gpu
precision: bf16
logger: False # logger provided by exp_manager
enable_checkpointing: False
use_distributed_sampler: False
max_epochs: -1 # PTL default. In practice, max_steps will be reached first.
max_steps: 10 # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches
log_every_n_steps: 1
val_check_interval: 10
limit_val_batches: 1
limit_test_batches: 1
accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models
gradient_clip_val: 1.0
benchmark: False
enable_model_summary: False # default PTL callback for this does not support model parallelism, instead we log manually
num_sanity_val_steps: 0
exp_manager:
explicit_log_dir: null
exp_dir: null
name: megatron_llama_34b
create_wandb_logger: False
wandb_logger_kwargs:
project: null
name: null
resume_if_exists: True
resume_ignore_no_checkpoint: True
create_checkpoint_callback: False
checkpoint_callback_params:
monitor: val_loss
save_top_k: 1
mode: min
always_save_nemo: False # saves nemo file during validation, not implemented for model parallel
save_nemo_on_train_end: False # not recommended when training large models on clusters with short time limits
filename: megatron_gpt--{val_loss:.2f}-{step}-{consumed_samples}
model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}}
save_last: false
model:
mcore_gpt: True
# specify micro_batch_size, global_batch_size, and model parallelism
# gradient accumulation will be done automatically based on data_parallel_size
micro_batch_size: 1 # limited by GPU memory
global_batch_size: 1 # will use more micro batches to reach global batch size
tensor_model_parallel_size: 8 # intra-layer model parallelism
pipeline_model_parallel_size: 1 # inter-layer model parallelism
context_parallel_size: 4
virtual_pipeline_model_parallel_size: null # interleaved pipeline
use_flash_attention: True
encoder_seq_length: 32768
max_position_embeddings: ${.encoder_seq_length}
num_layers: 48 # 7b: 32 | 13b: 40 | 70b: 80
hidden_size: 8192 # 7b: 4096 | 13b: 5120 | 70b: 8192
ffn_hidden_size: 22016 # Transformer FFN hidden size. Usually 4 * hidden_size. | 7b: 11008 | 13b: 13824 | 70b: 28672
num_attention_heads: 64 # 7b: 32 | 13b: 40 | 70b: 64
use_scaled_init_method: True # use scaled residuals initialization
hidden_dropout: 0.0 # Dropout probability for hidden state transformer.
attention_dropout: 0.0 # Dropout probability for attention
ffn_dropout: 0.0 # Dropout probability in the feed-forward layer.
kv_channels: null # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null
normalization: 'rmsnorm' # Normalization layer to use. Options are 'layernorm', 'rmsnorm'
layernorm_epsilon: 1e-5
do_layer_norm_weight_decay: False # True means weight decay on all params
make_vocab_size_divisible_by: 256 # Pad the vocab size to be divisible by this value for computation efficiency.
pre_process: True # add embedding
post_process: True # add pooler
persist_layer_norm: True # Use of persistent fused layer norm kernel.
bias: False # Whether to use bias terms in all weight matrices.
activation: 'fast-swiglu' # Options ['gelu', 'geglu', 'swiglu', 'reglu', 'squared-relu', 'fast-geglu', 'fast-swiglu', 'fast-reglu']
headscale: False # Whether to learn extra parameters that scale the output of the each self-attention head.
transformer_block_type: 'pre_ln' # Options ['pre_ln', 'post_ln', 'normformer']
openai_gelu: False # Use OpenAI's GELU instead of the default GeLU
normalize_attention_scores: True # Whether to scale the output Q * K^T by 1 / sqrt(hidden_size_per_head). This arg is provided as a configuration option mostly for compatibility with models that have been weight-converted from HF. You almost always want to se this to True.
attention_type: 'multihead' # Attention type. Options ['multihead']
share_embeddings_and_output_weights: False # Share embedding and output layer weights.
overlap_p2p_comm: False # Overlap p2p communication with computes. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1
batch_p2p_comm: True # Batch consecutive inter-peer send/recv operations. This argument is valid only when `virtual_pipeline_model_parallel_size` is larger than 1
num_query_groups: 8 # Number of query groups for group query attention. If None, normal attention is used. | 7b: 32 | 13b: 40 | 70b: 8
position_embedding_type: 'rope' # Position embedding type. Options ['learned_absolute', 'rope']
rotary_percentage: 1.0 # If using position_embedding_type=rope, then the per head dim is multiplied by this.
init_method_std: 0.02 # Standard deviation of the zero mean normal distribution used for weight initialization.')
apply_query_key_layer_scaling: True # scale Q * K^T by 1 / layer-number.
tokenizer:
library: 'sentencepiece'
type: null
model: /workspace/Models/Original/CodeLlama-34b-hf/tokenizer.model
vocab_file: null
merge_file: null
delimiter: null # only used for tabular tokenizer
sentencepiece_legacy: False # Legacy=True allows you to add special tokens to sentencepiece tokenizers.
# Mixed precision
# native_amp_init_scale: 4294967296 # 2 ** 32
# native_amp_growth_interval: 1000
# hysteresis: 2 # Gradient scale hysteresis
# fp32_residual_connection: False # Move residual connections to fp32
# fp16_lm_cross_entropy: False # Move the cross entropy unreduced loss calculation for lm head to fp16
# Megatron O2-style half-precision
megatron_amp_O2: True # Enable O2-level automatic mixed precision using main parameters
grad_allreduce_chunk_size_mb: 125
# Fusion
grad_div_ar_fusion: True # Fuse grad division into torch.distributed.all_reduce. Only used with O2 and no pipeline parallelism..
gradient_accumulation_fusion: True # Fuse weight gradient accumulation to GEMMs. Only used with pipeline parallelism and O2.
bias_activation_fusion: False # Use a kernel that fuses the bias addition from weight matrices with the subsequent activation function.
bias_dropout_add_fusion: False # Use a kernel that fuses the bias addition, dropout and residual connection addition.
masked_softmax_fusion: False # Use a kernel that fuses the attention softmax with it's mask.
get_attention_mask_from_fusion: True # When using fused softmax it will create the attention mask so we won't copy it to the pipeline stages.
apply_rope_fusion: True # Use a kernel to add rotary positional embeddings. Only used if position_embedding_type=rope
# Miscellaneous
seed: 1234
resume_from_checkpoint: null # manually set the checkpoint file to load from
use_cpu_initialization: False # Init weights on the CPU (slow for large models)
onnx_safe: False # Use work-arounds for known problems with Torch ONNX exporter.
apex_transformer_log_level: 30 # Python logging level displays logs with severity greater than or equal to this
gradient_as_bucket_view: True # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory)
sync_batch_comm: False # Enable stream synchronization after each p2p communication between pipeline stages
## Activation Checkpointing
activations_checkpoint_granularity: null # 'selective' or 'full'
activations_checkpoint_method: null # 'uniform', 'block'
activations_checkpoint_num_layers: null
num_micro_batches_with_partial_activation_checkpoints: null
activations_checkpoint_layers_per_pipeline: null
sequence_parallel: True
## Transformer Engine
transformer_engine: True
fp8: False # enables fp8 in TransformerLayer forward
fp8_e4m3: False # sets fp8_format = recipe.Format.E4M3
fp8_hybrid: True # sets fp8_format = recipe.Format.HYBRID
fp8_margin: 0 # scaling margin
fp8_interval: 1 # scaling update interval
fp8_amax_history_len: 1024 # Number of steps for which amax history is recorded per tensor
fp8_amax_compute_algo: max # 'most_recent' or 'max'. Algorithm for computing amax from history
reduce_amax: True # Perform reduction to sync amax tensors across GPUs after every iteration
use_emha: False # Use fused multi-head attention for large sequence-length. Note this is not yet supported. Please set to False.
data:
data_prefix:
- 1.0
- /workspace/Jianzhou/Datasets/Nemo/pretrain/EVAL_C4_text_document
index_mapping_dir: null # path to save index mapping .npy files, by default will save in the same location as data_prefix
data_impl: mock
splits_string: 900,50,50
seq_length: ${model.encoder_seq_length}
skip_warmup: True
num_workers: 0
dataloader_type: single # cyclic
reset_position_ids: False # Reset position ids after end-of-document token
reset_attention_mask: False # Reset attention mask after end-of-document token
eod_mask_loss: False # Mask loss for the end of document tokens
validation_drop_last: True # Set to false if the last partial validation samples is to be consumed
no_seqlen_plus_one_input_tokens: False # Set to True to disable fetching (sequence length + 1) input tokens, instead get (sequence length) input tokens and mask the last token
pad_samples_to_global_batch_size: False # Set to True if you want to pad the last partial batch with -1's to equal global batch size
shuffle_documents: True # Set to False to disable documents shuffling. Sample index will still be shuffled
# Nsys profiling options
nsys_profile:
enabled: False
start_step: 5 # Global batch to start profiling
end_step: 6 # Global batch to end profiling
ranks: [0] # Global rank IDs to profile
gen_shape: False # Generate model and kernel details including input shapes
optim:
name: distributed_fused_adam
# overlap_grad_sync: True
# overlap_param_sync: True
# contiguous_grad_buffer: True
lr: 2e-4
weight_decay: 0.01
betas:
- 0.9
- 0.98
sched:
name: CosineAnnealing
warmup_steps: 500
constant_steps: 50000
min_lr: 2e-5
```
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"@ethanhe42 \r\ntoo messy to fix the signature issue for the previous pr: https://github.com/NVIDIA/NeMo/pull/8988\r\nSo restart a new one. LOL..\r\n",
"@Slyne could you move doc to `docs/source/multimodal/mllm/video_neva.rst` ?",
"@ethanhe42 Done."
] | 2024-04-19T22:35:39 | 2024-05-06T19:02:27 | 2024-05-06T19:02:27 | CONTRIBUTOR | {
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} | # What does this PR do ?
This PR is a fix for https://github.com/NVIDIA/NeMo/pull/8634.
**Collection**:
No collection would be affected.
We can run the script independently. And nemo dependency is not needed.
# Changelog
- Fix small bugs
- Add doc
# Usage
For internal usage and evaluate the neva result, you can run the below command. Please check the doc to generate your NGC API for fundation model.
```python
API_TOKEN=nvapi-xxx python3 mixtral_eval.py --model-name-list gpt vneva --media-type image --question-file llava-bench-in-the-wild/questions.jsonl --responses-list responses/gpt4v.jsonl responses/vneva.jsonl --answers-dir ./ --context-file llava-bench-in-the-wild/context.jsonl --output ./output.json
```
You can also check the document for how to do benchmark on `llava-bench-in-the-wild`.
Please contact @PannuMuthu for the response files.
# Jenkins CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
There's no need to comment `jenkins` on the PR to trigger Jenkins CI.
The GitHub Actions CI will run automatically when the PR is opened.
To run CI on an untrusted fork, a NeMo user with write access must click "Approve and run".
# 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?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] 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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With EP enabled mcore_gpt is required (previously was checking megatron_amp_O2 whether it was enabled).
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
There's no need to comment `jenkins` on the PR to trigger Jenkins CI.
The GitHub Actions CI will run automatically when the PR is opened.
To run CI on an untrusted fork, a NeMo user with write access must click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.",
"This PR was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-04-19T17:03:02 | 2024-05-13T01:46:15 | 2024-05-13T01:46:14 | CONTRIBUTOR | {
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} | # What does this PR do ?
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} | null | null | null | Hello,
I have tried downloading and using the frame marblenet vad, but it returns an error.
```
wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/nemo/vad_multilingual_frame_marblenet/versions/1.20.0/zip -O vad_multilingual_frame_marblenet_1.20.0.zip
--2024-01-23 16:14:29-- https://api.ngc.nvidia.com/v2/models/nvidia/nemo/vad_multilingual_frame_marblenet/versions/1.20.0/zip
Resolving api.ngc.nvidia.com (api.ngc.nvidia.com)... 54.188.231.142, 34.223.159.108
Connecting to api.ngc.nvidia.com (api.ngc.nvidia.com)|54.188.231.142|:443... connected.
HTTP request sent, awaiting response... 404 Not Found
2024-01-23 16:14:30 ERROR 404: Not Found.
```
Is it still supported?
Best regards,
Gabriel | {
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"> I found 2 problems with the current implementation:\r\n\r\nI think I fixed those problems. Could you please check again?"
] | 2024-04-19T12:17:22 | 2024-04-21T10:39:27 | 2024-04-21T10:39:27 | COLLABORATOR | {
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} | # What does this PR do ?
This PR enables confidence scores computing for TDT models.
It introduces a new `ConfidenceConfig` parameter named `tdt_include_duration` which allows caclulating frame-level confidence for the token duration prediction.
**Collection**: [ASR]
# Changelog
- Add specific line by line info of high level changes in this PR.
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```python
# Add a code snippet demonstrating how to use this
```
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} | # What does this PR do ?
This PR sets the ground for more advanced distributed checkpointing features, like fully parallel save/load or async save which will be configured and implemented as part of DistributedCheckpointIO plugin. It doesn't introduce any new features, just refactors existing behavior.
Note: the correctness of not applying the "skip if exists" saving logic relies on https://github.com/NVIDIA/NeMo/pull/8980 and is required for async saving
**Collection**: NLP
# Changelog
- implement DistributedCheckpointIO plugin for save/load of distributed checkpoints
- simplify checkpoint saving NLPDDPStrategy flow
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
This PR fixes the behavior of encountering an existing distributed checkpoint. Previously it caused saving skip, this PR reuses the PTL versioning mechanism to adjust the checkpoint name accordingly
**Collection**: NLP
# Changelog
- fix the behavior of skipping duplicate checkpoint by reusing PTL versioning mechanism
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
There's no need to comment `jenkins` on the PR to trigger Jenkins CI.
The GitHub Actions CI will run automatically when the PR is opened.
To run CI on an untrusted fork, a NeMo user with write access must click "Approve and run".
# 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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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
There's no need to comment `jenkins` on the PR to trigger Jenkins CI.
The GitHub Actions CI will run automatically when the PR is opened.
To run CI on an untrusted fork, a NeMo user with write access must click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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* Related to # (issue)
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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1) The member paths within tar archive do not contain any absolute paths or path traversal components (like ../..)
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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
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
support Llama3 model conversion.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
```python
python scripts/checkpoint_converters/convert_llama_hf_to_nemo.py --input_name_or_path PATH_TO_HF_FOLDER --output_path converted_llama3_nemo.nemo
python scripts/checkpoint_converters/convert_llama_nemo_to_hf.py --input_name_or_path llama3.nemo --output_path converted_llama3_hf.bin
```
# 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.
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"LGTM",
"@meatybobby Could you please approve the PR on the code changes tab? We need one approval to be able to merge the PR.",
"jenkins"
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} | # What does this PR do ?
Updates gemma export for trt-llm 0.9
**Collection**: None
# Changelog
- Update gemma export for trt-llm 0.9
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
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"It is not possible to set `trainer.max_epochs` when you set `trainer.max_steps`, due to by default `trainer.max_epochs` is ignored.\r\n\r\nTo set `trainer.max_epochs` you must do it without `trainer.max_steps` but by default `trainer.max_steps=20000` is set.\r\n\r\nYou should find a `trainer.max_steps` setting that suits your needs.",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-04-18T15:14:17 | 2024-06-09T01:51:39 | 2024-06-09T01:51:39 | NONE | {
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} | null | null | null | `examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py` ignores `trainer.max_epochs`.
Always refer to `trainer.max_steps` only.
Tested on: `nvcr.io/nvidia/nemo:24.03.framework`
Test case:
`trainer.max_steps=200` and `trainer.max_epochs=1` (187 steps)
- 200 step job finished
`trainer.max_steps=200` and `trainer.max_epochs=5` (935 steps)
- 200 step job finished
Am I missing something? | {
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"jenkins",
"@galv, @titu1994 Please, review the PR\r\n\r\nThe only issue I see for now is that the Frame-Looping algorithm does not have fallback behavior, and setting `loop_labels=false` without `use_cuda_graph_decoder=false` (default - `true`) can cause failures (if not all requirements are met for Frame-Looping+CUDA graphs).\r\n\r\nOn the other hand, this case is not important. Most users will use the Label-Looping algorithm since it is default and produces the same result as the Frame-Looping algorithm.",
"There is something I'm not quite understanding. Is there a reason why you did not set the default value of these values to true? \r\n\r\nhttps://github.com/NVIDIA/NeMo/blob/7e805bc8d0683cc76fa44b6a847611aaaedb9794/nemo/collections/asr/parts/submodules/rnnt_greedy_decoding.py#L592\r\n\r\nand\r\n\r\nhttps://github.com/NVIDIA/NeMo/blob/7e805bc8d0683cc76fa44b6a847611aaaedb9794/nemo/collections/asr/parts/submodules/rnnt_greedy_decoding.py#L2305\r\n\r\nand\r\n\r\nhttps://github.com/NVIDIA/NeMo/blob/7e805bc8d0683cc76fa44b6a847611aaaedb9794/nemo/collections/asr/parts/submodules/rnnt_greedy_decoding.py#L2655\r\n\r\nIt looks like you are turning cuda graphs on by default only when someone runs transcribe_speech.py or transcribe_speech_parallel.py right now.",
"> On the other hand, this case is not important. Most users will use the Label-Looping algorithm since it is default and produces the same result as the Frame-Looping algorithm.\r\n\r\nI agree. That is was my first attempt. It was very educational, but I don't believe that we need to do the work to add a feature to it that won't be used very much.",
"I didn't hit approve yet. The changes seem good to me. But I want to give some time to defer to @titu1994 ",
"> There is something I'm not quite understanding. Is there a reason why you did not set the default value of these values to true?\r\n> \r\n> https://github.com/NVIDIA/NeMo/blob/7e805bc8d0683cc76fa44b6a847611aaaedb9794/nemo/collections/asr/parts/submodules/rnnt_greedy_decoding.py#L592\r\n> \r\n> and\r\n> \r\n> https://github.com/NVIDIA/NeMo/blob/7e805bc8d0683cc76fa44b6a847611aaaedb9794/nemo/collections/asr/parts/submodules/rnnt_greedy_decoding.py#L2305\r\n> \r\n> and\r\n> \r\n> https://github.com/NVIDIA/NeMo/blob/7e805bc8d0683cc76fa44b6a847611aaaedb9794/nemo/collections/asr/parts/submodules/rnnt_greedy_decoding.py#L2655\r\n> \r\n> It looks like you are turning cuda graphs on by default only when someone runs transcribe_speech.py or transcribe_speech_parallel.py right now.\r\n\r\nThis is done intentionally. The problem is that using `True` as a default value here will result in using CUDA graphs when decoding in training mode, which is can result in issues with bucketing (dynamic batch sizes).\r\nI think it is cleaner for now to use CUDA graphs only for transcribing speech.\r\n\r\nSee `training_step` for RNN-T models \r\nhttps://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/models/rnnt_models.py#L663\r\nhttps://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/modules/rnnt.py#L1490\r\nhttps://github.com/NVIDIA/NeMo/blob/main/nemo/collections/asr/metrics/wer.py#L327\r\n\r\n",
"@galv, @titu1994 Please, review once again\r\n\r\nI reworked the PR.\r\n- CUDA graphs enabled by default now in all classes but turned off in the training pipeline\r\n- I also added more tests and fixed some issues with confidence\r\n- Now max_symbols is set to 10 if it is initially None (with an appropriate warning)"
] | 2024-04-18T15:03:05 | 2024-05-03T16:03:17 | 2024-05-03T11:10:15 | COLLABORATOR | {
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"diff_url": "https://github.com/NVIDIA/NeMo/pull/8972.diff",
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"merged_at": "2024-05-03T11:10:15"
} | # What does this PR do ?
- enable CUDA graphs by default for inference (`transcribe_speech.py`, `speech_to_text_eval.py`)
- add fallback behavior with partial CUDA graphs for Label-Looping algorithm implementation (TDT, RNN-T)
- Use Cuda graphs _with while loops_ when all requirements are met (driver 545+, Cuda 12.3+, cuda-python package)
- Use Cuda graphs _without while loops_ (pure PyTorch functionality) in most other cases. The idea: use several separate Cuda graphs for parts of the decoding algorithm (`<graph before outer loop>` -> `while loop (python)` -> `<graph before inner loop>` -> `inner while loop (python)` -> `<graph for inner loop code>` etc.)
On my local machine, FastConformer L, LibriSpeech test-other decoding time, bfloat16, bs=16
| | no graphs | partial graphs | full graph |
| -------------- | --------- | -------------- | ---------- |
| unsorted batch | 34s | 21s | 19s |
| sorted batch | 23s | 15s | 14s |
**Collection**: [ASR]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
```shell
python examples/asr/speech_to_text_eval.py <with rnnt/tdt model>
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [x] New Feature
- [ ] Bugfix
- [ ] Documentation
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/8971 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8971/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8971/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8971/events | https://github.com/NVIDIA/NeMo/issues/8971 | 2,250,870,319 | I_kwDOC_bI7s6GKY4v | 8,971 | Error while exporting to TensorRTLLM format - AttributeError: 'NoneType' object has no attribute 'get' | {
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"@yaoyu-33 @pradeepdev-1995 Can I work on this issue?",
"It seems the **model_config.yaml** file is being created inside temp directory during exporting ,but the created model_config.yml file is empty. that's why the Nontype object is returning\r\nI am using the given image ```nvcr.io/nvidia/nemo:24.01.gemma``` .\r\n@SarthakNikhal ",
"@okuchaiev can you please check on this?",
"I am not using docker directly to run the container (```nvcr.io/nvidia/nemo:24.01.gemma```) on the GPU machine.\r\nInstead, running these in Kubernetes managed cluster(version 1.27.9) in the following way\r\n\r\nDownload the docker image (```nvcr.io/nvidia/nemo:24.01.gemma```).\r\nPush it to our container Registry.\r\ndeploy that image as a jupyter notebook.\r\nwork using that notebook.",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-04-18T14:39:19 | 2024-06-17T01:50:22 | 2024-06-17T01:50:21 | NONE | {
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} | null | null | null | I could finetune the gemma model and merge the adapter with base model by following this official notebook
https://github.com/NVIDIA/GenerativeAIExamples/blob/main/models/Gemma/lora.ipynb
Now Tried the given code for TensorRTLLM model conversion
```
from nemo.export import TensorRTLLM
trt_llm_exporter = TensorRTLLM(model_dir="/llmmodels/gemma_pubmedqa_merged_trt_llm")
trt_llm_exporter.export(nemo_checkpoint_path="/llmmodels/merged_model/gemma_lora_pubmedqa_merged.nemo", model_type="gemma", n_gpus=1)
```
But it is not working and gives this error
I am using the docker container for the fine-tuning task | {
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"Hi, which one do you want to use the mistral-7b or the mixtral-7b? if you want to use the mistral-7b please use convert_mistral_hf_to_nemo.py instead of convert_mixtral_hf_to_nemo.py. Thanks.",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-04-18T14:16:42 | 2024-07-18T20:22:23 | 2024-06-03T01:48:20 | NONE | {
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} | null | null | null | **Describe the bug**
I am trying to run the example run PEFT with Mistral 7B provided in NEVIDI DOCS HUB. When trying to convert Mistral-7B in huggingface format to nemo format. Got the error message
```
python3 /opt/NeMo/scripts/checkpoint_converters/convert_mixtral_hf_to_nemo.py --input_name_or_path=./mistral-7B-hf/ --output_path=mistral.nemo
/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py:124: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.
warnings.warn(
[NeMo I 2024-04-18 09:56:50 convert_mixtral_hf_to_nemo:150] loading checkpoint ./mistral-7B-hf/
Loading checkpoint shards: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2/2 [07:21<00:00, 220.59s/it]
Traceback (most recent call last):
File "/opt/NeMo/scripts/checkpoint_converters/convert_mixtral_hf_to_nemo.py", line 346, in <module>
convert(args)
File "/opt/NeMo/scripts/checkpoint_converters/convert_mixtral_hf_to_nemo.py", line 153, in convert
nemo_config = load_config(model_args, tokenizer.vocab_file)
File "/opt/NeMo/scripts/checkpoint_converters/convert_mixtral_hf_to_nemo.py", line 115, in load_config
nemo_config.num_moe_experts = int(mixtral_config['num_local_experts'])
KeyError: 'num_local_experts'
```
**Steps/Code to reproduce bug**
I am using the ngc container [24.03
](nemo:24.03.framework.sif) . And following the steps listed on this page https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/mistralpeft.html
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https://api.github.com/repos/NVIDIA/NeMo/issues/8969 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/8969/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/8969/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/8969/events | https://github.com/NVIDIA/NeMo/pull/8969 | 2,250,419,163 | PR_kwDOC_bI7s5tCTJQ | 8,969 | Update chatglm converter | {
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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.",
"This PR was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-04-18T11:12:39 | 2024-05-10T01:46:09 | 2024-05-10T01:46:09 | CONTRIBUTOR | {
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} | # What does this PR do ?
Use the `args.input_name_or_path` to set `tokenizer.type` .
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
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# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] 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?
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"The process tokenizer script decides between libraries - spe for sentencepiece, wpe for hugging face wpe. Spe is chosen cause it is possible to have different libraries implement the BPE algorithm - for example we used yttm for a long time which supported BPE for machine translation. \r\n\r\nThe Mixin class one refers to the algorithm and not the library. Here, we support two algorithms, BPE (sentencepiece) and WPE (HF). We could potentially habe supported yttm as a BPE algorithm provider, but chose not too since it's a deprecated library. \r\n\r\nAs of now we have no intention to support Any tokenizer other than sentencepiece for ASR. Even Hugging face WPE is only used for academic purposes, not for production models ",
"> The process tokenizer script decides between libraries - spe for sentencepiece, wpe for hugging face wpe. Spe is chosen cause it is possible to have different libraries implement the BPE algorithm - for example we used yttm for a long time which supported BPE for machine translation.\r\n> \r\n> The Mixin class one refers to the algorithm and not the library. Here, we support two algorithms, BPE (sentencepiece) and WPE (HF). We could potentially habe supported yttm as a BPE algorithm provider, but chose not too since it's a deprecated library.\r\n> \r\n> As of now we have no intention to support Any tokenizer other than sentencepiece for ASR. Even Hugging face WPE is only used for academic purposes, not for production models\r\n\r\nGot it, thank you for your reply!"
] | 2024-04-18T07:35:33 | 2024-04-18T08:18:49 | 2024-04-18T08:18:48 | NONE | {
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and than (nemo/collections/asr/parts/mixins.py):
spe refers to the Google sentencepiece library tokenizer
bpe for SentencePiece tokenizer
Are they same tokenizer ? | {
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"Hi, @Davood-M This request is aimed to fix the language id mismatch issue when conducting inference on any2any model. Could you please review and merge it to a proper branch?",
"jenkins",
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
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# Before your PR is "Ready for review"
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- [ ] 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.
## Who can review?
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"jenkins",
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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} | # What does this PR do ?
linear_qkv could be fused or non-fused modules. Check the number of output from linear_qkv to grab qkv tensor.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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# Additional Information
* Related to # (issue)
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"The CPU-only tests are failing on:\r\n\r\n```\r\n2024-04-25T23:47:10.4949722Z nemo/collections/asr/parts/preprocessing/features.py:466: in forward\r\n2024-04-25T23:47:10.4950460Z x, _, _ = normalize_batch(x, seq_len, normalize_type=self.normalize)\r\n2024-04-25T23:47:10.4951858Z nemo/collections/asr/parts/preprocessing/features.py:67: in normalize_batch\r\n2024-04-25T23:47:10.4952730Z if not torch.cuda.is_current_stream_capturing() and torch.any(seq_len == 1).item():\r\n2024-04-25T23:47:10.4953491Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n2024-04-25T23:47:10.4953904Z \r\n2024-04-25T23:47:10.4954053Z def is_current_stream_capturing():\r\n2024-04-25T23:47:10.4954490Z r\"\"\"\r\n2024-04-25T23:47:10.4955100Z Returns True if CUDA graph capture is underway on the current CUDA stream, False otherwise.\r\n2024-04-25T23:47:10.4955767Z \r\n2024-04-25T23:47:10.4956394Z If a CUDA context does not exist on the current device, returns False without initializing the context.\r\n2024-04-25T23:47:10.4957165Z \"\"\"\r\n2024-04-25T23:47:10.4957517Z > return _cuda_isCurrentStreamCapturing()\r\n2024-04-25T23:47:10.4958284Z E RuntimeError: CUDA error: no CUDA-capable device is detected\r\n2024-04-25T23:47:10.4959247Z E CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\r\n2024-04-25T23:47:10.4960142Z E For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\r\n2024-04-25T23:47:10.4961287Z E Device-side assertions were explicitly omitted for this error check; the error probably arose while initializing the DSA handlers.\r\n2024-04-25T23:47:10.4961983Z \r\n2024-04-25T23:47:10.4962405Z /usr/local/lib/python3.10/dist-packages/torch/cuda/graphs.py:28: RuntimeError\r\n```\r\n\r\n@galv can you add a check to only call CUDA API if it is available? (sth like `if torch.cuda.is_available():` don't remember the name exactly)"
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} | Commit 1:
Do feature normalization in parallel, rather than via a for loop.
At large batch sizes, this becomes a bottleneck, taking about 9 ms at
batch size 16, for example. See issue https://github.com/NVIDIA/NeMo/issues/8948.
Commit 2:
Remove all instances of cudaStreamSynchronize() in the featurizer when doing "per_feature" normalization.
With this change, we can now do stream capture to a cuda graph on the
preprocessor. This is bound to increase performance
significantly. Even at batch size 16, the GPU is idle about 50% of the
time because these kernels finish so fast.
No unit test. I simply ran:
```
python examples/asr/speech_to_text_eval.py pretrained_name=nvidia/parakeet-ctc-1.1b dataset_manifest=/home/dgalvez/scratch/data/test_other.json batch_size=16 output_filename=test_other_decoded.jsonl amp=true amp_dtype=bfloat16 use_cer=false num_workers=1
```
Before my changes:
```
Transcribing: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 184/184 [00:25<00:00, 7.17it/s]
[NeMo I 2024-04-17 14:50:33 transcribe_speech:398] Finished transcribing from manifest file: /home/dgalvez/scratch/data/test_other.json
[NeMo I 2024-04-17 14:50:33 transcribe_speech:403] Writing transcriptions into file: test_other_decoded.jsonl
[NeMo I 2024-04-17 14:50:33 transcribe_speech:421] Finished writing predictions to test_other_decoded.jsonl!
[NeMo I 2024-04-17 14:50:33 transcribe_speech:439] Writing prediction and error rate of each sample to test_other_decoded.jsonl!
[NeMo I 2024-04-17 14:50:33 transcribe_speech:440] {'samples': 2939, 'tokens': 52343, 'wer': 0.037712779168179125, 'ins_rate': 0.004203045297365455, 'del_rate': 0.0022734654108476776, 'sub_rate': 0.031236268459965993}
[NeMo I 2024-04-17 14:50:33 speech_to_text_eval:131] Finished transcribing speech dataset. Computing ASR metrics..
[NeMo I 2024-04-17 14:50:33 speech_to_text_eval:210] Dataset WER/CER 3.77%/1.41%
```
After my changes:
```
Transcribing: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 184/184 [00:25<00:00, 7.18it/s]
[NeMo I 2024-04-17 14:57:37 transcribe_speech:398] Finished transcribing from manifest file: /home/dgalvez/scratch/data/test_other.json
[NeMo I 2024-04-17 14:57:38 transcribe_speech:403] Writing transcriptions into file: test_other_decoded.jsonl
[NeMo I 2024-04-17 14:57:38 transcribe_speech:421] Finished writing predictions to test_other_decoded.jsonl!
[NeMo I 2024-04-17 14:57:38 transcribe_speech:439] Writing prediction and error rate of each sample to test_other_decoded.jsonl!
[NeMo I 2024-04-17 14:57:38 transcribe_speech:440] {'samples': 2939, 'tokens': 52343, 'wer': 0.03761725541142082, 'ins_rate': 0.004222150048717116, 'del_rate': 0.0022352559081443555, 'sub_rate': 0.03115984945455935}
[NeMo I 2024-04-17 14:57:38 speech_to_text_eval:131] Finished transcribing speech dataset. Computing ASR metrics..
[NeMo I 2024-04-17 14:57:38 speech_to_text_eval:210] Dataset WER/CER 3.76%/1.41%
```
You can see that the WER went down by 0.01%, implying a small change took place. I don't think it is a concern, but worth noting nonetheless. | {
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Fix item 3 described in https://github.com/huggingface/transformers/pull/29402
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# Changelog
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Add a one line overview of what this PR aims to accomplish.
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# Changelog
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```python
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# Jenkins CI
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# Before your PR is "Ready for review"
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] | 2024-04-17T22:58:05 | 2024-06-29T01:47:42 | 2024-06-29T01:47:41 | CONTRIBUTOR | {
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} | # What does this PR do ?
We currently only support deploying TRTLLM models in the Triton inference server. This MR adds the necessary support to let us deploy .nemo models to Triton as well.
**Collection**: deploy
# Changelog
- Adds MegatronLLMDeployable class which implements ITritonDeployable
# Usage
* Usage is shown in the new tests/deploy/test_triton_deployable.py
```python
megatron_deployable = MegatronLLMDeployable(args.nemo_checkpoint, args.num_gpus)
nm = DeployPyTriton(
model=megatron_deployable,
triton_model_name=model_name,
triton_model_version=1,
max_batch_size=8,
port=8000,
address="0.0.0.0",
streaming=False,
)
nm.deploy()
nm.run()
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
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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 ?
Enable Sequence Packing and Pipeline Parallel in NeVA
**Collection**: [Multimodal]
# Changelog
- Added Sequence Packing dataset preparation script
- Update Neva Model and dataset to support packed dataset, also add args to turn on packed dataset
- Enable Pipeline parallelism
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Sphinx's autodoc with mocked imports cannot handle `a | b` typing syntax when one of the arguments is a forward reference (used by autodoc to substitute imported classes). Introducing forward references also brakes the type dispatch mechanism in `@singledispatch`. I used the opportunity to fix a few other obvious errors that I saw in the long list of sphinx warnings.
**Collection**: ASR
# Changelog
- Fix for documentation build of ASR collection.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [x] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [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 a config option to control whether the distributed Adam optimizer stores embedding grads in FP32.
**Collection**: NLP
# Changelog
- Add config option for FP32 embedding grads
# Usage
Run GPT, e.g. with the config at https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml.
Enable the distributed optimizer with `model.optim.name=distributed_fused_adam`. FP32 embedding grads are enabled by default and can be disabled with `model.with_fp32_embedding_grads=False`.
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**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.
# Additional Information
* Currently only supported with GPT and T5.
* Support for FP32 embedding grads was recently refactored in https://github.com/NVIDIA/NeMo/pull/8792 and https://github.com/NVIDIA/NeMo/pull/8867. | {
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} | # What does this PR do ?
a2a fix removed tp world size and group from init
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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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.
# 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
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] 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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"\r\n\r\n\r\nAfter verifying the API key, the image still cannot be downloaded successfully！",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-04-17T08:24:19 | 2024-05-25T01:45:30 | 2024-05-25T01:45:30 | NONE | {
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} | null | null | null | **Describe the bug**
When pulling Nemo mirror for training: 23.08.03 image, there is an issue with authentication required.
**Steps/Code to reproduce bug**
**Expected behavior**
How to solve this bug?
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"@adamlin120 thanks for reporting the issue.\r\n\r\nCan you please try https://github.com/NVIDIA/NeMo/compare/main...akoumparouli/update_megatron_gpt_cont_training and let me know if that fixes the issue for you? thanks.",
"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-04-17T01:42:58 | 2024-07-18T20:23:07 | 2024-06-03T01:48:23 | NONE | {
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} | null | null | null | **Describe the bug**
When attempting to train a model using Nemo 2403, a `ValueError` is raised indicating a conflict between the `precision` argument and the `plugins` argument being passed to the Trainer. The error message states that both `precision=bf16-mixed` and `plugins=PipelineMixedPrecisionPlugin` were received and cannot be used together.
```python
Traceback (most recent call last):
File "/opt/NeMo/examples/nlp/language_modeling/megatron_gpt_continue_training.py", line 167, in main
trainer = Trainer(plugins=plugins, strategy=strategy, **cfg.trainer, callbacks=callbacks)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/argparse.py", line 70, in insert_env_defaults
return fn(self, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 401, in __init__
self._accelerator_connector = _AcceleratorConnector(
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py", line 134, in __init__
self._check_config_and_set_final_flags(
File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py", line 271, in _check_config_and_set_final_flags
raise ValueError(
ValueError: Received both `precision=bf16-mixed` and `plugins=<nemo.collections.nlp.parts.nlp_overrides.PipelineMixedPrecisionPlugin object at 0x1551053e0d90>`. Choose one.
```
**Steps/Code to reproduce bug**
1. megatron_gpt_continue_training mixtral 8x22b with Nemo 2403 image.
**Expected behavior**
- This issue seems to be related to [Issue #8848](https://github.com/NVIDIA/NeMo/issues/8848), where a similar error was encountered when converting Mistral/Mixtral models to the Nemo format.
- [PR #8908](https://github.com/NVIDIA/NeMo/pull/8908) partially addressed this issue by removing precision args in the trainer due to a PyTorch Lightning (PTL) update. However, the changes were not applied to the training script. | {
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"Could you put it to a separate pr ? Let's keep prs small / medium sized and focussed so we can debug them if they cause issues later. ",
"Also, don't make the assumption that a GPU will always be there (or that cuda graphs are supported). We can have fast paths for those cases when GPU is available or cudapython 12.3 and above is installed via conda / pip but they aren't guaranteed. ",
"> [don't make the assumption] that cuda graphs are supported\r\n\r\nIt is good practice to avoid synchronizing with the CPU in general, with GPU code. If we do that, we can get cuda graphs \"for free\". No part of that is building in cuda graphs as a requirement to do execution."
] | 2024-04-16T23:30:44 | 2024-04-30T18:10:26 | 2024-04-30T18:10:26 | COLLABORATOR | {
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} | null | null | null | Another issue I discovered while working on #8673 .
Right now, when running Parakeet CTC 1.1B at batch size 32, the preprocessor takes about 10milliseconds (of course, this depends on the lengths of the inputs, but this was with librispeech test other). 90% of the time is taken by feature normalization, and the GPU is hardly active during that time.
<img width="580" alt="image" src="https://github.com/NVIDIA/NeMo/assets/4767568/26ccbafb-5810-437a-9bf4-4f44787aa379">
You can reproduce via running this:
```
nsys profile -c cudaProfilerApi $(which python) examples/asr/speech_to_text_eval.py pretrained_name=nvidia/parakeet-ctc-1.1b dataset_manifest=/home/dgalvez/scratch/data/test_other_sorted_downward.json \
batch_size=$batch_size output_filename=test_other_decoded.jsonl amp=$amp amp_dtype=bfloat16 use_cer=false num_workers=1
```
The culprit is this code that sequentially does a for loop over the batch: https://github.com/NVIDIA/NeMo/blob/468d5b6d369733909524d42b80a514f33bc19263/nemo/collections/asr/parts/preprocessing/features.py#L65-L73
First of all, it's actually copying a single scalar value of seq_len to CPU three times per loop. If you have to do a GPU to CPU copy, please do it just once via `seq_len = seq_len.cpu()`. But the actual kernels being launched are likely to go faster if we just do a single batched operation. Note that the slowness of this sequential for loop operation only increases as you run at larger batch sizes. An extra 10ms of latency during streaming inference is not really acceptable. The person who wrote the code initially probably wasn't sure how to deal with a ragged tensor for doing mean and std reductions, but we can do it straightforwardly with torch.where() and a mask built from seq_len, without ever communicating with the CPU (which is important for cuda stream capture to a graph).
I will either fix this as part of #8673 or make a separate PR, if the change is larger and riskier than I initially think.
You can download the .nsys-rep file I took a screenshot of here:
[report6.nsys-rep.gz](https://github.com/NVIDIA/NeMo/files/15003685/report6.nsys-rep.gz)
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} | # What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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} | # What does this PR do ?
Add a config option to control whether the distributed Adam optimizer stores embedding grads in FP32.
**Collection**: NLP
# Changelog
- Add config option for FP32 embedding grads
# Usage
Run GPT, e.g. with the config at https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml.
Enable the distributed optimizer with `model.optim.name=distributed_fused_adam`. FP32 embedding grads are enabled by default and can be disabled with `model.with_fp32_embedding_grads=False`.
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [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
* Currently only supported with GPT and T5.
* Support for FP32 embedding grads was recently refactored in https://github.com/NVIDIA/NeMo/pull/8792 and https://github.com/NVIDIA/NeMo/pull/8867. | {
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} | # What does this PR do ?
a2a fix removed tp world size and group from init
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | # What does this PR do ?
a2a fix removed tp world size and group from init
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"@mark-myzhao Thanks we will dig further. In the mean time can you try training lora weights using https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py",
"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.",
"I met the same issue. and also I found that merge the lora on 3090 is different from A100, by different I mean the two merged models have different MD5.",
"Can I ask a naive question, what is the temperature are you using to generate the ansewers? I would like understand if it would always generate the same response or you are simulation creativity. ",
"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.",
"Hi @mark-myzhao I'm facing the same issue, did you find a solution?"
] | 2024-04-16T19:17:50 | 2024-08-02T13:55:51 | 2024-07-29T01:52:11 | NONE | {
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} | null | null | null | **Describe the bug**
We noticed that there were some discrepancies in the inference results between the model with loaded LoRA adapter and the model with merged LoRA weights.
**Steps/Code to reproduce bug**
1. Fine-tune a LoRA Model with [train_gpt_sft.py](https://github.com/NVIDIA/NeMo-Aligner/blob/main/examples/nlp/gpt/train_gpt_sft.py) in NeMo-aligner; (in our case, we used the Mistral-7b as the base model)
2. Run inference with LoRA adapter loaded on-the-fly using [megatron_gpt_generate.py](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/tuning/megatron_gpt_generate.py)
3. Merge the LoRA weights using [merge.py](https://github.com/NVIDIA/NeMo/blob/main/scripts/nlp_language_modeling/merge_lora_weights/merge.py)
4. Run inference using the merged weight model using the same `megatron_gpt_generate` script (while skipping loading LoRA ckpt)
During this process, we found:
- The inference results from step 2 and step 4 are different by a lot
- In the LoRA weight merge script, [the validation inference](https://github.com/NVIDIA/NeMo/blob/main/scripts/nlp_language_modeling/merge_lora_weights/merge.py#L262) results were different from both step 2 and step 4
(sample of model responses were pasted in **Additional context** session)
**Expected behavior**
The inference results from merged weight model and model with LoRA adapter should be the same with the same inference config:
```
inference:
greedy: True
add_BOS: True
tokens_to_generate: 1024
all_probs: False
repetition_penalty: 1.2
min_tokens_to_generate: 0
compute_logprob: False
end_strings: ["<|endoftext|>"]
```
**Environment overview (please complete the following information)**
- Environment location: Docker
- Method of NeMo install: install from source
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
- Nvidia PyTorch Docker: https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-24-01.html
- NeMo version: https://github.com/NVIDIA/NeMo/commit/9b64e390b534d4eb5ad7f28502bcfe4c7f0c6c39
- NeMo-Aligner versino: https://github.com/NVIDIA/NeMo-Aligner/commit/ea78731d9fd86e822b0253fca8a10e0e8a4526c9
**Additional context**
Samples of the inference results
- Inference result from Step 2 (Base Mistral + LoRA Adapter loaded on the fly)
```
# Prompt: Compose an engaging travel blog post about a recent trip to Hawaii, highlighting cultural experiences and must-see attractions.
Include photos and thoughtful captions
that convey the beauty and aloha spirit of the islands.\n\nAloha friends,\n\nI recently had the pleasure of visiting the beautiful islands of Hawaii for the first time. As a travel blogger, I was eager to experience the rich history and culture of this trop
ical paradise. My trip was filled with once-in-a-lifetime experiences
...
```
- Inference result from Step 4 (LoRA model with merged weights)
```
# Prompt: Compose an engaging travel blog post about a recent trip to Hawaii, highlighting cultural experiences and must-see attractions.
Here is a draft with key points to share a fun-filled vacation:\n\nAloha!
I recently returned from a fun-filled trip to the big island of Hawaii.
I was so blessed to have a family member visit and Hawaiian cultural immersion
while there.
...
```
- Inference result in Step 3 in the merge.py
```
# Prompt: Compose an engaging travel blog post about a recent trip to Hawaii, highlighting cultural experiences and must-see attractions.
Here is a draft:\n\nAloha! I recently returned from a dream vacation in the beautiful islands of Hawaii.
I had such a rejuvenating and eye-opening experience there. This laid-back and peaceful paradise really is a special place.\n\nI visited both Big Island and Oahu, and it was such a treat to see the lava flows and waterfalls, and the lush green volcanic crater on Kilauea
...
```
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} | # What does this PR do ?
Adding RETRO tests to Action Tests (cicd-main.yml)
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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"@huvunvidia Whats the priority on this ? I am working on a generate function in Mcore, and nemo is also going to switch to use that. So wondering if you want to wait and directly use that. The gnerate function is already done and in review, I hope to get that merged next week. \r\nIt is significantly faster than NeMo (Over several 100% faster.)",
"> @huvunvidia Whats the priority on this ? I am working on a generate function in Mcore, and nemo is also going to switch to use that. So wondering if you want to wait and directly use that. The gnerate function is already done and in review, I hope to get that merged next week. It is significantly faster than NeMo (Over several 100% faster.)\r\n\r\n@shanmugamr1992 Our team schedule to have this in ASAP to move on to the next project. I think it is reasonable to have this PR in for now, and then down the line when the generate function is done and well tested from Mcore side, we will consider updating to use it. ",
"jenkins"
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} | # What does this PR do ?
Adding zero-shot evaluation code for new RETRO model. The evaluation code for legacy model is modified to *_legacy.py
This PR is on top of huvu/mcore_retro, which implements the new RETRO model.
The files this PR works on are:
- examples/nlp/language_modeling/conf/megatron_retro_inference.yaml
- examples/nlp/language_modeling/megatron_retro_eval.py
- examples/nlp/language_modeling/megatron_retro_qatask_eval.py
- examples/nlp/language_modeling/conf/megatron_retro_qatask.yaml
- nemo/collections/nlp/modules/common/text_generation_strategy.py
- nemo/collections/nlp/modules/common/text_generation_utils.py
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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"Thank you @erastorgueva-nv ! \r\nIn NeMo Guardrails we have GPG signing or sign-off (first is preferred over the second, if possible). I thought that NeMo also accepts GPG. ",
"In NeMo GitHub we have only signoff. We can look into gpg later"
] | 2024-04-16T12:55:57 | 2024-04-17T17:19:47 | 2024-04-17T17:19:47 | NONE | {
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} | # What does this PR do ?
Added NeMo Guardrails paper to publications.
**Collection**: NLP
# Changelog
- Added NeMo Guardrails paper to publications.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
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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
- [X] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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"This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.",
"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-04-16T12:52:20 | 2024-05-24T01:46:15 | 2024-05-24T01:46:15 | NONE | {
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} | null | null | null | I am referring the official LORA finetuning notebook
https://github.com/NVIDIA/GenerativeAIExamples/blob/main/models/Gemma/lora.ipynb
Where in this step
```
from nemo.collections.nlp.parts.peft_config import LoraPEFTConfig
my_peft_config = LoraPEFTConfig(model_cfg)
```
How can I specify the required rank(r value),delta and dropout LORA parameters?
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"This issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-04-16T09:14:34 | 2024-05-24T01:46:17 | 2024-05-24T01:46:17 | NONE | {
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Got the following error when using a built docker container from the `main`.
```
[W init.cpp:767] Warning: nvfuser is no longer supported in torch script, use _jit_
set_nvfuser_enabled is deprecated and a no-op (function operator())
```
Looks like it is the issue of newer version of Pytorch?
**Environment overview (please complete the following information)**
Using docker image built from `Dockerfile` in `main`.
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"Hey @timmoon10 thanks for the reminder! I've update the readme!",
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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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} | # What does this PR do ?
Fix `ModuleNotFoundError: No module named 'group_norm_cuda'` error for Apex when building Dockerfile from `main`.
**Collection**: [Note which collection this PR will affect]
# Changelog
Add `group_norm` option when building `apex`.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [X] Did you write any new necessary tests?
- [X] Did you add or update any necessary documentation?
- [X] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [X] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"@xingyaoww did you solve this problem"
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} | null | null | null | **Describe the bug**
When trying to train the model using the latest NeMo code (`32e630220d2c24550a869f43ce618949ccf1a1a5`) with gradient checkpoint and packed sequence, getting the following error:
```
...
File "/scratch/bcsi/xingyao6/nemo-util/Megatron-LM/megatron/core/transformer/tran
sformer_block.py", line 371, in forward
hidden_states = self._checkpointed_forward(
File "/scratch/bcsi/xingyao6/nemo-util/Megatron-LM/megatron/core/transformer/tran
sformer_block.py", line 257, in _checkpointed_forward
hidden_states, context = checkpoint_handler(
File "/scratch/bcsi/xingyao6/nemo-util/Megatron-LM/megatron/core/transformer/tran
sformer_block.py", line 240, in checkpoint_handler
return tensor_parallel.checkpoint(
File "/scratch/bcsi/xingyao6/nemo-util/Megatron-LM/megatron/core/tensor_parallel$
random.py", line 301, in checkpoint return CheckpointFunction.apply(function, distribute_saved_activations, *args)
File "/usr/local/lib/python3.10/dist-packages/torch/autograd/function.py", line $
51, in apply
return super().apply(*args, **kwargs) # type: ignore[misc]
TypeError: save_for_backward can only save variables, but argument 5 is of type PakedSeqParams
```
**Environment overview (please complete the following information)**
Using commit `32e630220d2c24550a869f43ce618949ccf1a1a5` from the `main` branch with a docker environment build from `Dockerfile`.
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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# Changelog
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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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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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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Change PRE_RELEASE to rc0 to be PEP 440 compliant. The name cannot have both rc and beta in it
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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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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Allow passing fp16_lm_cross_entropy to mcore to reduce memory requirements.
*needs* MR in Mcore first.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
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} | # What does this PR do ?
Add SDXL quantization and trt inference pipeline
**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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## Who can review?
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} | # What does this PR do ?
This PR leverages new mcore gpt spece handling. (not yet merged to mcore)
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
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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**:
- [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.
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} | # What does this PR do ?
Minor updates to quantization pipeline.
**Collection**: NLP
# Changelog
- considering `megatron_amp_O2 = True` case
- fixing artifacts handling utils for a legacy .nemo checkpoint (for example, https://huggingface.co/nvidia/GPT-2B-001)
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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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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} | # What does this PR do ?
This PR adds the support for embedding_read_fusion parameter in MCoreGPTModel. This new parameter will be added into Megatron-LM.
**Collection**: [Note which collection this PR will affect]
# Changelog
Add the embedding_read_fusion parameter, which is got from self.cfg.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
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} | null | null | null | **Describe the bug**
I encountered the following error when calling trainer_eval.predict() to test the fine-tuned LLM Model:
```
AttributeError Traceback (most recent call last)
Cell In[33], line 1
----> 1 response = trainer_eval.predict(model_eval, request_dl)
2 for batch in response:
3 for s in batch['sentences']:
File /usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py:864, in Trainer.predict(self, model, dataloaders, datamodule, return_predictions, ckpt_path)
862 self.state.status = TrainerStatus.RUNNING
863 self.predicting = True
--> 864 return call._call_and_handle_interrupt(
865 self, self._predict_impl, model, dataloaders, datamodule, return_predictions, ckpt_path
866 )
File /usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py:44, in _call_and_handle_interrupt(trainer, trainer_fn, *args, **kwargs)
42 if trainer.strategy.launcher is not None:
43 return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
---> 44 return trainer_fn(*args, **kwargs)
46 except _TunerExitException:
47 _call_teardown_hook(trainer)
File /usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py:903, in Trainer._predict_impl(self, model, dataloaders, datamodule, return_predictions, ckpt_path)
899 assert self.state.fn is not None
900 ckpt_path = self._checkpoint_connector._select_ckpt_path(
901 self.state.fn, ckpt_path, model_provided=model_provided, model_connected=self.lightning_module is not None
902 )
--> 903 results = self._run(model, ckpt_path=ckpt_path)
905 assert self.state.stopped
906 self.predicting = False
File /usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py:987, in Trainer._run(self, model, ckpt_path)
982 self._signal_connector.register_signal_handlers()
984 # ----------------------------
985 # RUN THE TRAINER
986 # ----------------------------
--> 987 results = self._run_stage()
989 # ----------------------------
990 # POST-Training CLEAN UP
991 # ----------------------------
992 log.debug(f"{self.__class__.__name__}: trainer tearing down")
File /usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py:1028, in Trainer._run_stage(self)
1026 return self._evaluation_loop.run()
1027 if self.predicting:
-> 1028 return self.predict_loop.run()
1029 if self.training:
1030 with isolate_rng():
File /usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/utilities.py:182, in _no_grad_context.<locals>._decorator(self, *args, **kwargs)
180 context_manager = torch.no_grad
181 with context_manager():
--> 182 return loop_run(self, *args, **kwargs)
File /usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/prediction_loop.py:124, in _PredictionLoop.run(self)
122 self.batch_progress.is_last_batch = data_fetcher.done
123 # run step hooks
--> 124 self._predict_step(batch, batch_idx, dataloader_idx, dataloader_iter)
125 except StopIteration:
126 # this needs to wrap the `*_step` call too (not just `next`) for `dataloader_iter` support
127 break
File /usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/prediction_loop.py:229, in _PredictionLoop._predict_step(self, batch, batch_idx, dataloader_idx, dataloader_iter)
226 assert data_fetcher is not None
228 if not (using_dataloader_iter := isinstance(data_fetcher, _DataLoaderIterDataFetcher)):
--> 229 batch = trainer.precision_plugin.convert_input(batch)
230 batch = trainer.lightning_module._on_before_batch_transfer(batch, dataloader_idx=dataloader_idx)
231 batch = call._call_strategy_hook(trainer, "batch_to_device", batch, dataloader_idx=dataloader_idx)
File /usr/local/lib/python3.10/dist-packages/pytorch_lightning/plugins/precision/fsdp.py:131, in FSDPPrecision.convert_input(self, data)
129 @override
130 def convert_input(self, data: Any) -> Any:
--> 131 return apply_to_collection(data, function=_convert_fp_tensor, dtype=Tensor, dst_type=self._desired_input_dtype)
AttributeError: 'PipelineMixedPrecisionPlugin' object has no attribute '_desired_input_dtype'
```
**Steps/Code to reproduce bug**
I'm following the steps and guides outlined in the [LoRA.ipynb](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/lora.ipynb) notebook example of finetuning an LLM (megatron_gpt). However, I encountered the following error when testing/evaluating the finetuned model by executing the following cell:
```python
response = trainer_eval.predict(model_eval, request_dl)
for batch in response:
for s in batch['sentences']:
print(f"{s}\n\n")
```
**Expected behavior**
getting output prediction from the fine-tuned model.
I also have a question regarding how to merge the adapter with the base model and save them in a single .nemo file for deployment. Could you please assist?
**Environment overview (please complete the following information)**
pulling the nemofw-training:24.03 image container
on A100 GPU
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"jenkins",
"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.",
"This PR was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-04-14T19:24:04 | 2024-05-06T01:45:31 | 2024-05-06T01:45:31 | COLLABORATOR | {
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} | # What does this PR do ?
Adds Riva and k2 WFST decoding options for ASR inference.
if you are reading this, it means the PR is not finished.
What's left is:
1. Documentation
2. Usage example
3. Tests
**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 how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"Readability is important and has utility at iterating at software in\r\ngeneral. A PR doesn’t need a logical change to have utility at a given\r\nimplementation. Can you say more about the bugs this implementation\r\nintroduces?\r\n\r\nOn Sun, Apr 14, 2024 at 3:48 PM Somshubra Majumdar ***@***.***>\r\nwrote:\r\n\r\n> ***@***.**** requested changes on this pull request.\r\n>\r\n> This PR makes no effective logical change, but introduced potential bugs\r\n> by removing docs string, type annotations and even entire optimization\r\n> cases for some reason.\r\n> ------------------------------\r\n>\r\n> In nemo/core/optim/optimizers.py\r\n> <https://github.com/NVIDIA/NeMo/pull/8920#discussion_r1564677379>:\r\n>\r\n> > @@ -1,49 +1,33 @@\r\n> -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.\r\n>\r\n> Revert. You cannot remove the license header\r\n> ------------------------------\r\n>\r\n> In nemo/core/optim/optimizers.py\r\n> <https://github.com/NVIDIA/NeMo/pull/8920#discussion_r1564679098>:\r\n>\r\n> > @@ -54,20 +38,18 @@\r\n> except ModuleNotFoundError:\r\n> HAVE_APEX = False\r\n>\r\n> -HAVE_APEX_DISTRIBUTED_ADAM = False\r\n> +# Check for APEX distributed Adam optimizer\r\n>\r\n> Why remove the bool?\r\n> ------------------------------\r\n>\r\n> In nemo/core/optim/optimizers.py\r\n> <https://github.com/NVIDIA/NeMo/pull/8920#discussion_r1564680625>:\r\n>\r\n> > if hasattr(optimizer_kwargs, 'keys'):\r\n> - # Attempt class path resolution\r\n>\r\n> Why remove docstring ?\r\n> ------------------------------\r\n>\r\n> In nemo/core/optim/optimizers.py\r\n> <https://github.com/NVIDIA/NeMo/pull/8920#discussion_r1564681391>:\r\n>\r\n> > optimizer_kwargs_config = OmegaConf.create(optimizer_kwargs)\r\n> - optimizer_instance = hydra.utils.instantiate(optimizer_kwargs_config) # type: DictConfig\r\n> + optimizer_instance = hydra.utils.instantiate(optimizer_kwargs_config)\r\n>\r\n> What's the need to remove type annotations ?\r\n> ------------------------------\r\n>\r\n> In nemo/core/optim/optimizers.py\r\n> <https://github.com/NVIDIA/NeMo/pull/8920#discussion_r1564682045>:\r\n>\r\n> > if optimizer_kwargs['name'] == 'auto':\r\n> optimizer_params_name = \"{}_params\".format(optimizer_name)\r\n> optimizer_kwargs.pop('name')\r\n> else:\r\n> optimizer_params_name = optimizer_kwargs.pop('name')\r\n>\r\n> - # Override arguments provided in the config yaml file\r\n>\r\n> ++\r\n> ------------------------------\r\n>\r\n> In nemo/core/optim/optimizers.py\r\n> <https://github.com/NVIDIA/NeMo/pull/8920#discussion_r1564682537>:\r\n>\r\n> > else:\r\n> - # If we are provided a partial class instantiation of a Config,\r\n>\r\n> ++\r\n> ------------------------------\r\n>\r\n> In nemo/core/optim/optimizers.py\r\n> <https://github.com/NVIDIA/NeMo/pull/8920#discussion_r1564683803>:\r\n>\r\n> > adam_nondist_optims = (optim.Adam, optim.AdamW)\r\n> if HAVE_APEX:\r\n> adam_nondist_optims += (FusedAdam,)\r\n> if isinstance(optimizer, adam_nondist_optims):\r\n> for group in optimizer.param_groups:\r\n> for p in group['params']:\r\n> state = optimizer.state[p]\r\n> - if len(state) == 0:\r\n>\r\n> ?? Why remove this?\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/NVIDIA/NeMo/pull/8920#pullrequestreview-1999592376>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/BFMUSBKWEOVYH7DWPZFXS6LY5J3ILAVCNFSM6AAAAABGGEFNDWVHI2DSMVQWIX3LMV43YUDVNRWFEZLROVSXG5CSMV3GSZLXHMYTSOJZGU4TEMZXGY>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.",
"This PR was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-04-14T11:26:46 | 2024-05-12T01:47:54 | 2024-05-12T01:47:53 | NONE | {
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Code Structure Optimization:
Consolidated imports to improve readability and reduce clutter. Grouped related code blocks together for better coherence. Error Handling Improvement:
Simplified error handling by raising meaningful exceptions when encountering errors. This enhances code robustness and makes debugging easier. Efficient Data Handling:
Optimized data handling by directly returning default values if the input is None, eliminating unnecessary computations and enhancing code efficiency. Simplified Logic:
Simplified logic by removing redundant conditions and unnecessary checks, resulting in cleaner and more concise code. Optimized Imports:
Removed redundant imports and unused dependencies to streamline the codebase and reduce overhead. Enhanced Readability:
Improved code readability by using descriptive variable names and concise comments where necessary, aiding in code comprehension and maintenance. Consistent Naming Conventions:
Ensured consistent naming conventions throughout the codebase for better clarity and maintainability.
These changes collectively refine the codebase, making it more efficient, readable, and maintainable while preserving its functionality and purpose.
# What does this PR do ?
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**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
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```python
# Add a code snippet demonstrating how to use this
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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.
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# Add a code snippet demonstrating how to use this
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- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
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- [ ] Did you write any new necessary tests?
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
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**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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 ?
Change PRE_RELEASE to rc0 to be PEP 440 compliant. The name cannot have both rc and beta in it
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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"This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.",
"This PR was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-04-13T02:39:33 | 2024-05-05T01:46:05 | 2024-05-05T01:46:05 | CONTRIBUTOR | {
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} | # What does this PR do ?
Update package info to match branch name
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# Jenkins CI
To run Jenkins, a NeMo User with write access must comment `jenkins` on the PR.
# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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