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fix the error:
ValueError: "max_utts" parameter is not supported for tarred datasets
Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
asr
# Changelog
- Add specific line by line info of high level changes in this PR.
replaced raise error with warning
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
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# Before your PR is "Ready for review"
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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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**Collection**: [Note which collection this PR will affect]
# Changelog
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```python
# Add a code snippet demonstrating how to use this
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
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```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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 issue was closed because it has been inactive for 7 days since being marked as stale."
] | 2024-09-06T11:52:09 | 2024-10-15T01:59:32 | 2024-10-15T01:59:31 | NONE | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
Could not find a ressource to convert the .nemo mamba2 hybrid model to safestensors.
**Describe the solution you'd like**
A conversion script that allows converting to .safetensor format.
**Describe alternatives you've considered**
-I've seen that quantizing the model using [Quantized Checkpoints](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/checkpoints/intro.html) outputs a .qnemo format that has safetensor format for each rank but it's still not supporting mamba and it's dedicated only for quantization not the for full precision.
-Another alternative : a script converting to .bin format and convert to safetensors using [Huggingface Converter](https://huggingface.co/spaces/safetensors/convert)
**Additional context**
-None
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"I ended up also failing with the above Colab notebook \n```Requirement already satisfied: wget in /usr/local/lib/python3.10/dist-packages (3.2)\nReading package lists... Done\nBuilding dependency tree... Done\nReading state information... Done\nlibsndfile1 is already the newest version (1.0.31-2ubuntu0.1).\nffmpeg... | 2024-09-06T07:37:54 | 2024-12-10T02:25:42 | 2024-09-22T22:11:44 | NONE | {
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A clear and concise description of what the bug is.
Starting the first cell return the following error:
```
Building wheels for collected packages: nemo_toolkit, causal-conv1d
Building wheel for nemo_toolkit (pyproject.toml) ... done
Created wheel for nemo_toolkit: filename=nemo_toolkit-2.0.0rc0-py3-none-any.whl size=3638332 sha256=eb71e169d2af77bef4cdfa4b889524b9890d1b75456bb8d2ffb4ab2ec5ba50a0
Stored in directory: /tmp/pip-ephem-wheel-cache-iivwnj4n/wheels/0e/bb/ab/27e77a2c5e6f5b7f87468678c238256a315ec40fd5fb4deeae
error: subprocess-exited-with-error
× python setup.py bdist_wheel did not run successfully.
│ exit code: 1
╰─> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
Building wheel for causal-conv1d (setup.py) ... error
ERROR: Failed building wheel for causal-conv1d
Running setup.py clean for causal-conv1d
Successfully built nemo_toolkit
Failed to build causal-conv1d
ERROR: ERROR: Failed to build installable wheels for some pyproject.toml based projects (causal-conv1d)
```
**Steps/Code to reproduce bug**
- go to the google collab page
- connect a T4 GPU
- start first cell
**Environment overview (please complete the following information)**
- Environment location:Google Collab | {
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} | # What does this PR do ?
Release tags should start with `v` and not with `r`
**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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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 ?
fix topk 1 -> 2
**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
```
# GitHub Actions CI
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To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
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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.
## 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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This PR adds support of defining custom lightning profilers
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
To use `PyTorchProfiler` after 100 warm-up steps a user can define trainer.profiler as follows:
```
trainer:
profiler:
_target_: pytorch_lightning.profilers.PyTorchProfiler
schedule:
_target_: torch.profiler.schedule
skip_first: 100
wait: 5
warmup: 1
active: 3
repeat: 2
```
# GitHub Actions CI
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The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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?
- [x] Did you add or update any necessary documentation?
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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?
@MaximumEntropy @ericharper @ekmb @yzhang123 @VahidooX @vladgets @malay-nagda @mikolajblaz @yaoyu-33 @akoumpa
Anyone in the community is free to review the PR once the checks have passed.
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Add the interface and communication hooks for MCORE Distributed Optimizer and data-parallel communications
# Changelog
- Change the knob name from `overlap_param_sync` to `overlap_param_gather`
- Change the knob name from `delay_param_gather` to `align_param_gather`
- Add `overlap_param_gather_with_optimizer_step` for the overlap of the first param all-gather chunk with optim.step
- Add the interface to enable FP8 parameter all-gather (`fp8_param_gather`)
# Usage
Use the above knobs to enable according features
# GitHub Actions CI
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The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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.
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📝 Please remember the following to-do's before merge:
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🙏 Please merge this PR only if the CI workflow completed successfully. | {
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**Collection**: [Note which collection this PR will affect]
# Changelog
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```python
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To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
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# Usage
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```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
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To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
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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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- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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**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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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.
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} | # What does this PR do ?
Update MoE configs for perf
**Collection**: [llm]
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```python
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"Please add some doc-strings to all functions + some tests like outlined in the [ADD-RECIPE.md](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/llm/recipes/ADD-RECIPE.md)",
"@marcromeyn I looked through current unit test. Seems like `test_trainer_parallelism_options` is included in `test_trainer` in `te... | 2024-09-05T06:04:04 | 2024-09-24T22:57:41 | 2024-09-24T22:57:41 | 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
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# Usage
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## Who can review?
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"This is closed because FSDP2 will be supported in megatron core. A separate PR #10545 is created to use the megatron code path (instead of directly from torch)."
] | 2024-09-05T04:11:31 | 2024-09-19T20:17:46 | 2024-09-19T20:15:02 | COLLABORATOR | {
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If you haven't finished some of the above items you can still open "Draft" PR.
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"Can you please show how you solve this problem, thx! I'm encountering this same problem.\n\nFileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmp6d5snvot/model_weights/model.decoder.layers.self_attention.core_attention._extra_state/shard_0_32.pt'\n",
"@wittysam hello have you solved the problem im fa... | 2024-09-05T02:54:41 | 2024-11-28T06:37:27 | 2024-09-18T10:03:14 | NONE | {
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Continual training model: llama3-8b.nemo
if mcore_gpt set True，
why this files are need, how to find this tmp files?
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📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
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# What does this PR do ?
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```python
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
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 ?
1) make nemo2-ckpts self-contained https://github.com/NVIDIA/NeMo/pull/10351/files#diff-5fed9dcc17cd5128af7ed4f9228d37c1dcaa22bf052620cc0a59d196770d3acaR109 and https://github.com/NVIDIA/NeMo/pull/10351/files#diff-9d41499746a3f28bb941592bb19445c90a0baa974ee59c0c5c8d78eb6ec1db5bR31
2) fix a bug in tokenizer IO https://github.com/NVIDIA/NeMo/pull/10351/files#diff-f9d7e4a01855072a41a91e2a37c117f1a90d933aba0258036eeaabd130fc2b5bL240
3) add tokenizer restoration when loading from nemo2-ckpts https://github.com/NVIDIA/NeMo/pull/10351/files#diff-6cc360fb87bae4067befd052372e537754bdf079589dd10818307a1ba5778735R93
llama2, llama3 confirmed to work.
**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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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
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https://api.github.com/repos/NVIDIA/NeMo/issues/10350 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/10350/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/10350/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/10350/events | https://github.com/NVIDIA/NeMo/pull/10350 | 2,506,320,273 | PR_kwDOC_bI7s56crGS | 10,350 | Add project section to pyproject.toml | {
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"hey @pstjohn, adding support for `uv` should be fine, but this PR has 1300 and 8 changed file - I guess your auto-formatter ran across the whole repo. While such a refactoring PR will most likely come in future - would you mind separating the actual Pyproject.toml change out of this PR? \r\n\r\nLet's handle refact... | 2024-09-04T21:14:59 | 2024-09-10T17:43:37 | 2024-09-10T17:43:37 | CONTRIBUTOR | {
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} | # What does this PR do ?
In bionemo we're exploring using [`uv`](https://docs.astral.sh/uv/) to handle pinning pypi dependencies in our docker builds. For this to work, we'll need to modernize some of the packaging for NeMo and mcore by adding `project` sections to their pyproject.toml files.
**Collection**: all
# Changelog
- Adds a [package] section to the pyproject.toml file.
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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?
- [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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"thanks for the explanations!"
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} | # What does this PR do ?
Remove the `apply_query_key_layer_scaling` in the GPT model configs.
This arg causes annoying issues with `NVTE_APPLY_QK_LAYER_SCALING`.
QK scaling is supposed to used for stability reason for certain models using the formula: `Q * K^T by 1 / layer-number`.
When using TE for attention blocks, This scaling is triggered only when ALL the following criteria is met:
- Training with FP16 precision
- Disable FlashAttention and Disable FusedAttention
For all other cases, the scaling will NOT be used regardless of the flag value is True or False.
Remove this flag in all GPT models as it pretty much have no effect to all the models (since we rarely use fp16, and rarely not use FA).
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# Changelog
- Add specific line by line info of high level changes in this PR.
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* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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# Changelog
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```
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# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
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# GitHub Actions CI
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# Before your PR is "Ready for review"
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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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Refactor parsing flags for TensorRTLLM exporter
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# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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.
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* Related to # (issue)
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# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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.
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This PR removes SSL masking from `forward`, and it adds `forward_for_eval` which keeps validation masking and can be used in SSL pretraining.
**Collection**: audio
# Changelog
- Remove `encoded = self.ssl_pretrain_masking` from `forward`
- Added `forward_for_eval`, which keeps masking
- Changed `evaluation_step` to use `forward_for_eval`
# Usage
N/A
# GitHub Actions CI
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# GitHub Actions CI
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To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
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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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**Collection**: [multimodal]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
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To run CI on an untrusted fork, a NeMo user with write access must first 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?
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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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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.
# 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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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.
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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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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.
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* Related to # (issue)
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📝 Please remember the following to-do's before merge:
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"url": "https://api.github.com/repos/NVIDIA/NeMo/pulls/10334",
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"merged_at": "2024-09-03T19:13:46"
} | # What does this PR do ?
Change flags in `export_to_trt_llm.py` according to [Update TRTLLM 0.12 #10215](https://github.com/NVIDIA/NeMo/pull/10215).
**Collection**: nlp
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
**Pre checks**:
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# Additional Information
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"Hi, today I changed the other two audios by using the same code and it worked. The duration of these two audios are 29.131020408163266s and 17.04204081632653s respectively. And the duration of the failed one is 29.861043083900228s. All of these three audios are WAV format. I don't know what happened, could anyone ... | 2024-09-02T10:53:29 | 2024-09-03T03:38:27 | 2024-09-03T03:38:26 | NONE | {
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} | null | null | null | Hi, all. These days I want to use 'canary-1b' to try ASR function, but when I followed this[ tutorial](https://developer.nvidia.com/blog/new-standard-for-speech-recognition-and-translation-from-the-nvidia-nemo-canary-model/), I always got **AssertionError: Expected MonoCut**. Here is the whole output.
(base) root@autodl-container-02954198e9-e04d5d74:~/autodl-tmp/canary-1b-test# python version_test.py
[NeMo I 2024-09-02 18:34:48 mixins:196] _setup_tokenizer: detected an aggregate tokenizer
[NeMo I 2024-09-02 18:34:48 mixins:330] Tokenizer SentencePieceTokenizer initialized with 32 tokens
[NeMo I 2024-09-02 18:34:48 mixins:330] Tokenizer SentencePieceTokenizer initialized with 1024 tokens
[NeMo I 2024-09-02 18:34:48 mixins:330] Tokenizer SentencePieceTokenizer initialized with 1024 tokens
[NeMo I 2024-09-02 18:34:48 mixins:330] Tokenizer SentencePieceTokenizer initialized with 1024 tokens
[NeMo I 2024-09-02 18:34:48 mixins:330] Tokenizer SentencePieceTokenizer initialized with 1024 tokens
[NeMo I 2024-09-02 18:34:48 aggregate_tokenizer:72] Aggregate vocab size: 4128
[NeMo W 2024-09-02 18:34:49 modelPT:165] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.
Train config :
tarred_audio_filepaths: null
manifest_filepath: null
sample_rate: 16000
shuffle: true
batch_size: null
num_workers: 8
use_lhotse: true
max_duration: 40
pin_memory: true
use_bucketing: false
bucket_duration_bins: null
num_buckets: 1
text_field: answer
lang_field: target_lang
batch_duration: 360
quadratic_duration: 15
bucket_buffer_size: 20000
shuffle_buffer_size: 10000
[NeMo W 2024-09-02 18:34:49 modelPT:172] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s).
Validation config :
manifest_filepath: null
sample_rate: 16000
batch_size: 8
shuffle: false
num_workers: 0
pin_memory: true
tarred_audio_filepaths: null
use_lhotse: true
text_field: answer
lang_field: target_lang
use_bucketing: false
[NeMo W 2024-09-02 18:34:49 modelPT:178] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).
Test config :
manifest_filepath: null
sample_rate: 16000
batch_size: 32
shuffle: false
num_workers: 0
pin_memory: true
tarred_audio_filepaths: null
use_lhotse: true
text_field: answer
lang_field: target_lang
use_bucketing: false
[NeMo I 2024-09-02 18:34:49 features:289] PADDING: 0
[NeMo W 2024-09-02 18:35:06 nemo_logging:349] /root/miniconda3/lib/python3.10/site-packages/nemo/core/connectors/save_restore_connector.py:571: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(model_weights, map_location='cpu')
[NeMo I 2024-09-02 18:35:13 save_restore_connector:249] Model EncDecMultiTaskModel was successfully restored from /root/.cache/huggingface/hub/models--nvidia--canary-1b/snapshots/dd32c0c709e2bfc79f583e16b9df4b3a160f7e86/canary-1b.nemo.
Traceback (most recent call last):
File "/root/autodl-tmp/canary-1b-test/version_test.py", line 3, in <module>
transcript = canary_model.transcribe(audio=["segment_0.wav"])
File "/root/miniconda3/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
return func(*args, **kwargs)
TypeError: EncDecMultiTaskModel.transcribe() got an unexpected keyword argument 'audio'
(base) root@autodl-container-02954198e9-e04d5d74:~/autodl-tmp/canary-1b-test# python version_test.py
[NeMo I 2024-09-02 18:36:14 mixins:196] _setup_tokenizer: detected an aggregate tokenizer
[NeMo I 2024-09-02 18:36:14 mixins:330] Tokenizer SentencePieceTokenizer initialized with 32 tokens
[NeMo I 2024-09-02 18:36:14 mixins:330] Tokenizer SentencePieceTokenizer initialized with 1024 tokens
[NeMo I 2024-09-02 18:36:14 mixins:330] Tokenizer SentencePieceTokenizer initialized with 1024 tokens
[NeMo I 2024-09-02 18:36:14 mixins:330] Tokenizer SentencePieceTokenizer initialized with 1024 tokens
[NeMo I 2024-09-02 18:36:14 mixins:330] Tokenizer SentencePieceTokenizer initialized with 1024 tokens
[NeMo I 2024-09-02 18:36:14 aggregate_tokenizer:72] Aggregate vocab size: 4128
[NeMo W 2024-09-02 18:36:15 modelPT:165] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.
Train config :
tarred_audio_filepaths: null
manifest_filepath: null
sample_rate: 16000
shuffle: true
batch_size: null
num_workers: 8
use_lhotse: true
max_duration: 40
pin_memory: true
use_bucketing: false
bucket_duration_bins: null
num_buckets: 1
text_field: answer
lang_field: target_lang
batch_duration: 360
quadratic_duration: 15
bucket_buffer_size: 20000
shuffle_buffer_size: 10000
[NeMo W 2024-09-02 18:36:15 modelPT:172] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s).
Validation config :
manifest_filepath: null
sample_rate: 16000
batch_size: 8
shuffle: false
num_workers: 0
pin_memory: true
tarred_audio_filepaths: null
use_lhotse: true
text_field: answer
lang_field: target_lang
use_bucketing: false
[NeMo W 2024-09-02 18:36:15 modelPT:178] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).
Test config :
manifest_filepath: null
sample_rate: 16000
batch_size: 32
shuffle: false
num_workers: 0
pin_memory: true
tarred_audio_filepaths: null
use_lhotse: true
text_field: answer
lang_field: target_lang
use_bucketing: false
[NeMo I 2024-09-02 18:36:15 features:289] PADDING: 0
[NeMo W 2024-09-02 18:36:31 nemo_logging:349] /root/miniconda3/lib/python3.10/site-packages/nemo/core/connectors/save_restore_connector.py:571: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(model_weights, map_location='cpu')
[NeMo I 2024-09-02 18:36:37 save_restore_connector:249] Model EncDecMultiTaskModel was successfully restored from /root/.cache/huggingface/hub/models--nvidia--canary-1b/snapshots/dd32c0c709e2bfc79f583e16b9df4b3a160f7e86/canary-1b.nemo.
Transcribing: 0it [00:00, ?it/s]
Traceback (most recent call last):
File "/root/autodl-tmp/canary-1b-test/version_test.py", line 3, in <module>
transcript = canary_model.transcribe(["segment_0.wav"])
File "/root/miniconda3/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
return func(*args, **kwargs)
File "/root/miniconda3/lib/python3.10/site-packages/nemo/collections/asr/models/aed_multitask_models.py", line 472, in transcribe
for test_batch in tqdm(temporary_datalayer, desc="Transcribing", disable=not verbose):
File "/root/miniconda3/lib/python3.10/site-packages/tqdm/std.py", line 1181, in __iter__
for obj in iterable:
File "/root/miniconda3/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 630, in __next__
data = self._next_data()
File "/root/miniconda3/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1344, in _next_data
return self._process_data(data)
File "/root/miniconda3/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1370, in _process_data
data.reraise()
File "/root/miniconda3/lib/python3.10/site-packages/torch/_utils.py", line 706, in reraise
raise exception
AssertionError: Caught AssertionError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/root/miniconda3/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py", line 309, in _worker_loop
data = fetcher.fetch(index) # type: ignore[possibly-undefined]
File "/root/miniconda3/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 54, in fetch
data = self.dataset[possibly_batched_index]
File "/root/miniconda3/lib/python3.10/site-packages/nemo/collections/asr/data/audio_to_text_lhotse_prompted.py", line 55, in __getitem__
tokens = self.prompt_format_fn(cuts, self.tokenizer)
File "/root/miniconda3/lib/python3.10/site-packages/nemo/collections/asr/data/audio_to_text_lhotse_prompted.py", line 127, in canary
assert isinstance(cut, MonoCut), "Expected MonoCut."
AssertionError: Expected MonoCut.
and here is my code:
`from nemo.collections.asr.models import EncDecMultiTaskModel
canary_model = EncDecMultiTaskModel.from_pretrained('nvidia/canary-1b')
transcript = canary_model.transcribe(["segment_0.wav"])
print(transcript)` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/10332 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/10332/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/10332/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/10332/events | https://github.com/NVIDIA/NeMo/issues/10332 | 2,500,464,213 | I_kwDOC_bI7s6VCg5V | 10,332 | Unusually high initial loss during continual pre-training of the Gemma2-2B model. | {
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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."
] | 2024-09-02T09:27:14 | 2024-10-15T01:59:34 | 2024-10-15T01:59:34 | NONE | {
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} | null | null | null | Could someone please share the details on how to properly set up the Gemma2 pre-training? Many thanks in advance! :)
The SFT scripts are performing well (training loss < 3), but the pre-training loss is extremely high (training loss > 20).
### Experiments
The following experiments are running with the same docker `nvcr.io/nvidia/nemo:24.05.llama3.1` and the same dataset `databricks-dolly-15k`.
**SFT training:**
Following the [official document](https://docs.nvidia.com/nemo-framework/user-guide/latest/llms/gemma/sft.html), I set up the SFT training.
[A] gemma2_2b_sft_bos_rc2:
-codebase: https://github.com/NVIDIA/NeMo/tree/r2.0.0rc1
-data: databricks-dolly-15k/train.jsonl
-add bos: Yes
[B]gemma2_2b_sft_0830_main:
-codebase: https://github.com/NVIDIA/NeMo/tree/1ce9089143b0136523cb08bb37941a35c9b08307
-data: databricks-dolly-15k/train.jsonl
-add bos: Yes
**Continual pre-training**
I didn't find the pre-training recipe for gemma. Thus I just preprocess the dataset with following cmd.
```
python ${NEMO_CODE_PATH}/scripts/nlp_language_modeling/preprocess_data_for_megatron.py \
--input ${DATASET_PATH}/sample.jsonl \
--json-keys text \
--tokenizer-library huggingface \
--dataset-impl mmap \
--tokenizer-type ${MODEL_PATH}/gemma2_2b \
--output-prefix ${DATASET_PATH}/sample_gemma2_preprocessed/sample \
--append-eod \
--workers=1
```
[C]gemma2_2b_sft_pretraining
-codebase: https://github.com/NVIDIA/NeMo/tree/1ce9089143b0136523cb08bb37941a35c9b08307
-data: databricks-dolly-15k/train.jsonl
-add bos: No
[D]gemma2_2b_sft_pretraining_bos
-codebase: https://github.com/NVIDIA/NeMo/tree/1ce9089143b0136523cb08bb37941a35c9b08307
-data: databricks-dolly-15k/train.jsonl
-add bos: Yes
I modify [preprocess_data_for_megatron.py](https://github.com/NVIDIA/NeMo/blob/1ce9089143b0136523cb08bb37941a35c9b08307/scripts/nlp_language_modeling/preprocess_data_for_megatron.py#L178) as following to append the BOS token.
```
for sentence in Encoder.splitter.tokenize(text):
sentence_ids = Encoder.tokenizer.text_to_ids(sentence)
sentence_ids = [Encoder.tokenizer.bos_id] + sentence_ids
if len(sentence_ids) > 0:
doc_ids.append(sentence_ids)
if len(doc_ids) > 0 and self.args.append_eod:
doc_ids[-1].append(Encoder.tokenizer.eos_id)
ids[key] = doc_ids
```
### Observations
(1) SFT tuning is generally stable, but varies depending on changes in the codebase. [A] and [B] use the same data and Docker environment, yet they show different loss curves.
(2) plays a crucial role, as highlighted by @Emperorizzis in [comments](https://github.com/NVIDIA/NeMo/pull/9672#issuecomment-2225972135). This was further explored in detail by [https://unsloth.ai/blog/gemma-bugs](https://unsloth.ai/blog/gemma-bugs).
### Looking for help
Could the official maintainers provide a stable version tag for the codebase and detailed guidance on how to run NeMo for pre-training the Gemma2 model?
Thank you for your outstanding contributions to the amazing NeMo framework!
_Originally posted by @longxudou in https://github.com/NVIDIA/NeMo/issues/9672#issuecomment-2324228927_
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https://api.github.com/repos/NVIDIA/NeMo/issues/10330 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/10330/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/10330/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/10330/events | https://github.com/NVIDIA/NeMo/issues/10330 | 2,499,247,809 | I_kwDOC_bI7s6U937B | 10,330 | Nemo ASR: TypeError: ConfidenceConfig.__init__() got an unexpected keyword argument 'tdt_include_duration' | {
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"I got the same bug ",
"When I comment 339-342 lines in \n.local/lib/python3.10/site-packages/nemo/collections/asr/parts/utils/asr_confidence_utils.py\nthe script works.\n```\n # OmegaConf.structured ensures that post_init check is always executed\n #confidence_cfg = OmegaConf.structured(\n #... | 2024-09-01T06:05:04 | 2024-09-23T11:39:26 | 2024-09-23T11:39:26 | NONE | {
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} | null | null | null | Dear Team,
I met a strange issue.
After training of new model I am unable to rut it on other machines.
I am getting next error, meanwhile I can run it in the original machine without problem.
Could you advise me? I failed to find any useful information in Internet.
Thank you in advance!
```
[NeMo E 2024-09-01 06:45:53 common:517] Model instantiation failed!
Target class: nemo.collections.asr.models.ctc_bpe_models.EncDecCTCModelBPE
Error(s): ConfidenceConfig.__init__() got an unexpected keyword argument 'tdt_include_duration'
Traceback (most recent call last):
File "/home/deep/.local/lib/python3.10/site-packages/nemo/core/classes/common.py", line 496, in from_config_dict
instance = imported_cls(cfg=config, trainer=trainer)
File "/home/deep/.local/lib/python3.10/site-packages/nemo/collections/asr/models/ctc_bpe_models.py", line 73, in __init__
super().__init__(cfg=cfg, trainer=trainer)
File "/home/deep/.local/lib/python3.10/site-packages/nemo/collections/asr/models/ctc_models.py", line 97, in __init__
self.decoding = CTCDecoding(self.cfg.decoding, vocabulary=OmegaConf.to_container(self.decoder.vocabulary))
File "/home/deep/.local/lib/python3.10/site-packages/nemo/collections/asr/parts/submodules/ctc_decoding.py", line 935, in __init__
super().__init__(decoding_cfg=decoding_cfg, blank_id=blank_id)
File "/home/deep/.local/lib/python3.10/site-packages/nemo/collections/asr/parts/submodules/ctc_decoding.py", line 193, in __init__
self._init_confidence(self.cfg.get('confidence_cfg', None))
File "/home/deep/.local/lib/python3.10/site-packages/nemo/collections/asr/parts/utils/asr_confidence_utils.py", line 339, in _init_confidence
ConfidenceConfig() if confidence_cfg is None else ConfidenceConfig(**confidence_cfg)
TypeError: ConfidenceConfig.__init__() got an unexpected keyword argument 'tdt_include_duration'
Traceback (most recent call last):
File "/maroc/nemoasr/eval.py", line 27, in <module>
asr_model = nemo_asr.models.EncDecCTCModel.restore_from(restore_path="checkpoints/ASR-Char-Model-Language-hy.nemo",map_location="cpu")
File "/home/deep/.local/lib/python3.10/site-packages/nemo/core/classes/modelPT.py", line 449, in restore_from
instance = cls._save_restore_connector.restore_from(
File "/home/deep/.local/lib/python3.10/site-packages/nemo/core/connectors/save_restore_connector.py", line 241, in restore_from
loaded_params = self.load_config_and_state_dict(
File "/home/deep/.local/lib/python3.10/site-packages/nemo/core/connectors/save_restore_connector.py", line 164, in load_config_and_state_dict
instance = calling_cls.from_config_dict(config=conf, trainer=trainer)
File "/home/deep/.local/lib/python3.10/site-packages/nemo/core/classes/common.py", line 518, in from_config_dict
raise e
File "/home/deep/.local/lib/python3.10/site-packages/nemo/core/classes/common.py", line 510, in from_config_dict
instance = cls(cfg=config, trainer=trainer)
File "/home/deep/.local/lib/python3.10/site-packages/nemo/collections/asr/models/ctc_models.py", line 97, in __init__
self.decoding = CTCDecoding(self.cfg.decoding, vocabulary=OmegaConf.to_container(self.decoder.vocabulary))
File "/home/deep/.local/lib/python3.10/site-packages/nemo/collections/asr/parts/submodules/ctc_decoding.py", line 935, in __init__
super().__init__(decoding_cfg=decoding_cfg, blank_id=blank_id)
File "/home/deep/.local/lib/python3.10/site-packages/nemo/collections/asr/parts/submodules/ctc_decoding.py", line 193, in __init__
self._init_confidence(self.cfg.get('confidence_cfg', None))
File "/home/deep/.local/lib/python3.10/site-packages/nemo/collections/asr/parts/utils/asr_confidence_utils.py", line 339, in _init_confidence
ConfidenceConfig() if confidence_cfg is None else ConfidenceConfig(**confidence_cfg)
TypeError: ConfidenceConfig.__init__() got an unexpected keyword argument 'tdt_include_duration'
```
I am using very plain code
```
import nemo.collections.asr as nemo_asr
wave_file = "test.wav"
#asr_model = nemo_asr.models.EncDecCTCModel.load_from_checkpoint("checkpoints/ASR-Char-Model-Language-hy--val_wer=0.0292-epoch=4.ckpt",map_location="cpu")
asr_model = nemo_asr.models.EncDecCTCModel.restore_from(restore_path="checkpoints/ASR-Char-Model-Language-hy.nemo",map_location="cpu")
print(asr_model.transcribe(wave_file,batch_size=8))
``` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/10329 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/10329/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/10329/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/10329/events | https://github.com/NVIDIA/NeMo/pull/10329 | 2,499,108,334 | PR_kwDOC_bI7s56EORo | 10,329 | Bump `Dockerfile.ci` (2024-09-01) | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/10328 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/10328/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/10328/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/10328/events | https://github.com/NVIDIA/NeMo/issues/10328 | 2,498,815,603 | I_kwDOC_bI7s6U8OZz | 10,328 | Error in converting LLaMA3.1 nemo checkpoint into HF | {
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"Sorry, I have not tried with the newest dev container. I will give it a try and I will close the issue if it gets resolved.",
"Yeah it works in the newest container."
] | 2024-08-31T12:11:09 | 2024-09-01T16:42:02 | 2024-09-01T16:42:01 | NONE | {
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} | null | null | null | The same as #10013 but in the other direction, that is, from `.nemo` to HF. It happens even in the 24.07 container.
**Describe the bug**
I'm trying to convert LLama3.1-8b nemo checkpoint into huggingface checkpoint using convert_llama_nemo_to_hf.py script. It's giving ``ValueError: `rope_scaling` must be a dictionary with two fields, `type` and `factor`, got {'factor': 8.0, 'low_freq_factor': 1.0, 'high_freq_factor': 4.0, 'original_max_position_embeddings': 8192, 'rope_type': 'llama3'}``
Find the complete trace below :
``` [rank0]: Traceback (most recent call last):
[rank0]: File "/opt/NeMo/scripts/checkpoint_converters/convert_llama_nemo_to_hf.py", line 257, in <module>
[rank0]: replace_hf_weights_and_tokenizer(
[rank0]: File "/opt/NeMo/scripts/checkpoint_converters/convert_llama_nemo_to_hf.py", line 231, in replace_hf_weights_and_tokenizer
[rank0]: model = AutoModelForCausalLM.from_pretrained(input_hf_path, local_files_only=True, torch_dtype=dtype,)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py", line 523, in from_pretrained
[rank0]: config, kwargs = AutoConfig.from_pretrained(
[rank0]: File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py", line 952, in from_pretrained
[rank0]: return config_class.from_dict(config_dict, **unused_kwargs)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 761, in from_dict
[rank0]: config = cls(**config_dict)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/transformers/models/llama/configuration_llama.py", line 161, in __init__
[rank0]: self._rope_scaling_validation()
[rank0]: File "/usr/local/lib/python3.10/dist-packages/transformers/models/llama/configuration_llama.py", line 181, in
_rope_scaling_validation
[rank0]: raise ValueError(
[rank0]: ValueError: `rope_scaling` must be a dictionary with two fields, `type` and `factor`, got
{'factor': 8.0, 'low_freq_factor': 1.0, 'high_freq_factor': 4.0, 'original_max_position_embeddings': 8192, 'rope_type': 'llama3'}
```
**Steps/Code to reproduce bug**
`python <path_to_NeMo>/scripts/checkpoint_converters/convert_llama_hf_to_nemo.py --<arguments>`
**Expected behavior**
Successful conversion of llama3.1-8b nemo checkpoints into hf checkpoint.
**Environment overview**
I'm using nvcr.io/nvidia/nemo:24.07 container (have tried 24.05.llama3.1 as well).
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"can we add some test for async ckpt and make sure unfinished checkpoints are not present once finished?",
"> can we add some test for async ckpt and make sure unfinished checkpoints are not present once finished?\r\n\r\nYeah for sure, but I would like to address it in a separate PR since there's no easy way to a... | 2024-08-31T00:27:05 | 2024-09-04T17:26:12 | 2024-09-04T17:26:11 | 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
```
# GitHub Actions 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?
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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**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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# Before your PR is "Ready for review"
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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.
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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
```
# GitHub Actions CI
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The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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
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"Will model weights be open after merge?",
"> Will model weights be open after merge?\r\n\r\n@sphmel Yes we will opensource the checkpoints as well, after PR is merged",
"@stevehuang52 PR is merged! Are checkpoints, and any usage guide available now? I can not find in docs, and this repo",
"Hi @sphmel, the do... | 2024-08-30T21:01:25 | 2024-12-16T15:10:11 | 2024-10-04T21:25:37 | COLLABORATOR | {
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} | # What does this PR do ?
Add NEST SSL to main
# Change Log
- Add NEST model, dataset and related modules.
- Add support for assigning dataset names via setting `validation_ds.name=[val1,val2]` in hydra (`model_utils.py`).
- Add support for validating with speaker verification metric (EER) in `label_models.py`, `audio_to_label.py`
- misc fixes and improvements
**Collection**: [asr]
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"@BoxiangW McoreDistributedOptimizer is a NeMo class not an Mcore class, and will hold whatever optimizer mcore returns as an attribute, would there b... | 2024-08-30T19:53:46 | 2024-08-31T00:26:18 | 2024-08-31T00:26:17 | MEMBER | {
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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
```
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If you haven't finished some of the above items you can still open "Draft" PR.
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Removes dependency on Apex when using virtual pipeline parallel
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
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**PR Type**:
- [ ] New Feature
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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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Fixes Device assertion error with transducer models when using partial_transcribe_audio
**Collection**: ASR
# Changelog
- move cpu only for logprobs case.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
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To re-run CI remove and add the label again.
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# Before your PR is "Ready for review"
**Pre checks**:
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- [x] Bugfix
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If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Enabling user to pass arguments in [nemo_export.py](https://github.com/NVIDIA/NeMo/blob/main/tests/export/nemo_export.py) to `TensorRT-LLM.export` as the build API is constantly changing and besides some parameters needs to be set on per-model basis.
**Collection**: NLP
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
```python
python tests/export/nemo_export.py \
--model_name=$MODEL_NAME \
...
--trt_llm_export_kwargs='{"reduce_fusion": false, "gemm_plugin": "auto"}'
```
Default value or specyfing `--trt_llm_export_kwargs='{}'` results in empty kwargs.
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
## Who can review?
Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
# Additional Information
* Related to # (issue)
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} | # What does this PR do ?
The same changes as https://github.com/NVIDIA/NeMo/pull/10288, but different target branch.
**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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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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"OMG I solved this. I installed triton again ([followed this answer](https://github.com/triton-lang/triton/issues/1057#issuecomment-2211657873)) then tried \"pip install nemo-toolkit==1.23.0\" again. I'm so sorry for bothering you for such a stupid question (T__T ). "
] | 2024-08-30T07:05:08 | 2024-08-30T07:20:07 | 2024-08-30T07:20:06 | NONE | {
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} | null | null | null | Hi, I am trying to use canary-1b. As following the installing steps, I installed nemo and nemo-toolkit successfully. But when I try to import EncDecMultiTaskModel, it shows cannot find reference "EncDecMultiTaskModel" in "__init__.py". Then I printed the packages version, it shows the nemo version is 6.0.3 and nemo-toolkit version is 1.21.0. Therefore, I tried upgrading nemo-toolkit, but after I used "pip install nemo-toolkit==1.23.0", it shows
"ERROR: Could not find a version that satisfies the requirement triton (from nemo-toolkit) (from versions: none)
ERROR: No matching distribution found for triton
"
Could you please help me to figure it out what happened and what should I do to deal with this problem? Thank you sooooo much!! | {
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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.",
"This PR is stale because it has been open for 14 days with no activity.... | 2024-08-30T05:34:25 | 2025-01-01T02:03:44 | 2025-01-01T02:03:43 | NONE | {
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} | # What does this PR do ?
- Voicebox implementation & training
- Speech editing & zero-shot TTS with Voicebox
- Evaluate RealEdit generated by Voicebox
**Collection**: tts
# Changelog
- Add voicebox model in `nemo/collections/tts/models/voicebox.py`
- Create `scripts/voicebox_edit/test_edit.py` for speech editing & zero-shot TTS
- Create `tutorials/tts/Voicebox_MFA.ipynb` for training data preparation tutorial with Montreal Forced Aligner
- Create `tutorials/tts/Voicebox_Training.ipynb` for training usage tutorial
- Create `tutorials/tts/Voicebox_Edit.ipynb` for speech editing & zero-shot TTS usage tutorial
# Usage
Checkout the following files:
- `tutorials/tts/Voicebox_MFA.ipynb` for training data preparation tutorial with Montreal Forced Aligner
- `tutorials/tts/Voicebox_Training.ipynb` for training usage tutorial
- `tutorials/tts/Voicebox_Edit.ipynb` for speech editing & zero-shot TTS usage tutorial
# GitHub Actions CI
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To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".
# Before your PR is "Ready for review"
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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?
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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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📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
🙏 Please merge this PR only if the CI workflow completed successfully. | {
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"Seeming this repo is not very active, \nMy case is Llama 3 8b Instruct\nI'm just change the learning rate to at small as possible (e.g 1e-8) and set the global batch size to 256 in-order to help the training progress stable.\nHope this will help.",
"This issue is stale because it has been open for 30 days with n... | 2024-08-29T22:29:38 | 2024-10-25T01:59:32 | 2024-10-25T01:59:32 | NONE | {
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} | null | null | null | **Describe the bug**
I am trying to finetune the OPT-13b model and getting nan loss at step=4 with following configuration.
PP=4
TP=4
MBS=4
Batch size=128
Running model less than or equal to 7b parameters with above configuration works fine.
What could be the issue and How can I fix it?
A clear and concise description of what the bug is.
**Steps/Code to reproduce bug**
Following llama SFT training guide.
https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/llama2sft.html
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)] Docker nemo:24.03.01
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
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- OS version
- PyTorch version
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**Additional context**
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**Collection**: [Note which collection this PR will affect]
# Changelog
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**PR Type**:
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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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# Before your PR is "Ready for review"
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- [ ] 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.
# Additional Information
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# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
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- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
- [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
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- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation
If you haven't finished some of the above items you can still open "Draft" PR.
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Anyone in the community is free to review the PR once the checks have passed.
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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# Usage
* You can potentially add a usage example below
```python
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# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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 adds experimental support for utilizing [Megatron-Energon](https://github.com/NVIDIA/Megatron-Energon) as a dataloader for NeVA training.
Co-authored:
@xuanzic
**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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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 ?
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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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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"I can reproduce the problem, working on fixing it",
"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.",
"@artbataev any news on this?",
"@itzsimpl I'm still investigating, seeing multiple issues with CUDA graphs. \nPlea... | 2024-08-29T14:50:33 | 2024-12-23T01:59:49 | 2024-12-23T01:59:48 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
Running a basic fastconformer hybrid recipe fails with image `nemo:24.07` and newer; more specifically, the reported RNNT WER numbers are all over the place, whereas CTC WER numbers decrease normally. Tested with a known good setup and images `nemo:24.01.01.framework`, `nemo:24.05.01`, `nemo:24.07` and `nemo:dev`, running with 8 GPUs on two machine types, a DGX-H100 and a no-name system with 8x A100 80GB PCIe. The drivers are 550.90.07.
Since the checkpoints are saved with RNNT WER this messes up training completely.
Up to `nemo:24.05.01` the (RNNT) WER and CTC WER charts are
With `nemo:24.07` and `nemo:dev` the (RNNT) WER and CTC WER charts are
The charts for `training_batch_wer` and `training_batch_ctc_wer` show no such anomaly. To me this all points to the newly introduced Cuda graphs. In the logs of `nemo:24.07` I have noticed messages that Cuda graphs get **Disabled** during training but **Enabled** during validation, even though the driver does not support cuda toolkit 12.6.
```
Epoch 0: 0%| | 0/443 [00:00<?, ?it/s] [NeMo I 2024-08-28 19:28:28 optional_cuda_graphs:53] Disabled CUDA graphs for module <class 'nemo.collections.asr.models.hybrid_rnnt_ctc_bpe_models.EncDecHybridRNNTCTCBPEModel'>.decoding.decoding
[NeMo I 2024-08-28 19:28:28 optional_cuda_graphs:53] Disabled CUDA graphs for module <class 'nemo.collections.asr.metrics.wer.WER'>joint._wer.decoding.decoding
[NeMo W 2024-08-28 19:37:18 rnnt_loop_labels_computer:270] No conditional node support for Cuda.
Cuda graphs with while loops are disabled, decoding speed will be slower
Reason: Driver supports cuda toolkit version 12.4, but the driver needs to support at least 12,6. Please update your cuda driver.
Validation: | | 0/? [00:00<?, ?it/s][A[NeMo I 2024-08-28 19:37:18 optional_cuda_graphs:79] Enabled CUDA graphs for module <class 'nemo.collections.asr.models.hybrid_rnnt_ctc_bpe_models.EncDecHybridRNNTCTCBPEModel'>.decoding.decoding
[NeMo I 2024-08-28 19:37:18 optional_cuda_graphs:79] Enabled CUDA graphs for module <class 'nemo.collections.asr.metrics.wer.WER'>joint._wer.decoding.decoding
```
**Steps/Code to reproduce bug**
Run a basic training with the fastconformer hybrid recipe.
**Expected behavior**
Training progresses normally as it did up to `nemo:24.05.01`.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Slurm]
- Method of NeMo install: [pip install or from source]. nemo image `nemo:24.01.01.framework`, `nemo:24.05.01`, `nemo:24.07` and `nemo:dev`
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
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"Where is the model checkpoint?",
"> Where is the model checkpoint?\r\n\r\nThe checkpoint is currently undergoing legal review. We will provide an ETA when we are closer to finishing our publication review process.",
"[🤖]: Hi @Edresson 👋,\n\nWe wanted to let you know that a [CICD pipeline](https://github.com/... | 2024-08-29T14:13:33 | 2024-10-10T15:02:29 | 2024-10-10T15:02:27 | COLLABORATOR | {
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} | # What does this PR do ?
It adds the implementation of our latest 21Hz Audio Codec called "Low-frame rate Speech Codec"
**Collection**: [TTS]
# Changelog
- Add a WavLM-based discriminator which improves the performance of the codec in low bitrate scenario
- Add config files to training 22khz NeMo Speech Codec model.
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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?
- [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)
- [x] 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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} | # What does this PR do ?
Quick-fix for pytorch update
**Collection**: nlp
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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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📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
🙏 Please merge this PR only if the CI workflow completed successfully. | {
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} | # What does this PR do ?
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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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 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.",
"The change has passed testing on a H100x8 system.",
"Tested this exam... | 2024-08-28T19:00:16 | 2024-10-08T12:07:00 | 2024-10-08T12:07:00 | CONTRIBUTOR | {
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} | # What does this PR do ?
Add ModelOpt transformer model pruning example for Llama3 model
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
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To re-run CI remove and add the label again.
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# Before your PR is "Ready for review"
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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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Work in progress Neva's update to NeMo 2.0. In this PR we will support NeMo pretrain and fine-tuning as NeMo 1.0.
**Collection**: [multimodal, vlm]
# Changelog
- Add mock data modules
- Re-write lazy dataset modules for conversational llava like dataset
- Rebase the implementation of Model Module of Neva to be based on MCore llava. So we can reuse many parts of the codes and all optimizations from MCore side.
- Add Neva Model Configs for NeMo 2.0 design
- Add Importer code for loading from HF
- Add example script for using NeMo 2.0 to pretrain and infer Neva.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
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To run CI on an untrusted fork, a NeMo user with write access must first 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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**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
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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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
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# Before your PR is "Ready for review"
**Pre checks**:
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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)
- [ ] 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 ?
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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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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- [ ] 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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} | cherry-pick of https://github.com/NVIDIA/NeMo/pull/9884
# 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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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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"Original PR: https://github.com/NVIDIA/NeMo/pull/10177.\r\n@Victor49152 @gautham-kollu @pablo-garay @ko3n1g Please review the changes, thanks!\r\ncc @Dido0o0",
"Hi @ko3n1g, how can we move this forward and merge it?",
"> Hi @ko3n1g, how can we move this forward and merge it?\r\n\r\nThanks for the reminder, I m... | 2024-08-28T15:50:09 | 2024-09-02T01:21:28 | 2024-09-02T01:21:11 | CONTRIBUTOR | {
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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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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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"The changes is good to me. Let me know if you are ready to merge",
"Please help check if internal tests are ok. If no more changes are needed, please help merge. Thanks!"
] | 2024-08-28T15:08:47 | 2024-08-28T21:15:06 | 2024-08-28T21:15:06 | CONTRIBUTOR | {
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} | # What does this PR do ?
A simple script working as an example and test script to do multimodal model export and inference
**Collection**: NA
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```bash
python3 /opt/NeMo/scripts/export/export_mm_to_trtllm.py \
--modality=audio \
--output_dir=/ws/salm \
--visual_checkpoint_path=/ws/NeMo/examples/multimodal/speech_llm/export/output/speechllm_fc_llama2_7b_perception \
--llm_checkpoint_path=/ws/NeMo/examples/multimodal/speech_llm/export/output/speechllm_fc_llama2_7b_llm_merged.nemo \
--vision_max_batch_size=16 \
--model_type="salm" \
--llm_model_type="llama" \
--delete_existing_files \
--input_text="Q: what's the transcription of the audio? A:" \
--input_media=/ws/data/test-clean-processed/7729-102255-0009.wav
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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)
- [ ] 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 ?
Fix import of CTCDecodingConfig
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation
# Additional Information
* Related to #8745
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https://api.github.com/repos/NVIDIA/NeMo/issues/10285 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/10285/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/10285/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/10285/events | https://github.com/NVIDIA/NeMo/issues/10285 | 2,492,148,238 | I_kwDOC_bI7s6UiyoO | 10,285 | [rank1]: AttributeError: 'NoneType' object has no attribute 'get' (finetuning Mamba Hybrid) | {
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... | closed | false | null | [] | null | [
"Encounter the same problem here, with the latest main branch. \nIt works well with https://github.com/NVIDIA/NeMo/tree/r2.0.0rc1 version.",
"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 becau... | 2024-08-28T13:52:51 | 2024-10-11T01:58:15 | 2024-10-11T01:58:15 | NONE | {
"total": 0,
"completed": 0,
"percent_completed": 0
} | null | null | null | **Describe the bug**
As described in the title, error when launching the fine tuning script in [Here](https://github.com/NVIDIA/NeMo/blob/main/tutorials/llm/mamba/mamba.rst#run-fine-tuning)
**Steps/Code to reproduce bug**
Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
```bash
Epoch 0: : 0%| | 0/700 [00:00<?]Error executing job with overrides: ['trainer.devices=2', 'trainer.precision=bf16', 'trainer.accelerator=gpu', 'trainer.log_every_n_steps=1', 'trainer.val_check_interval=100', 'trainer.limit_val_batches=50', '+trainer.num_sanity_val_steps=0', '+trainer.accumulate_grad_batches=1', 'trainer.max_steps=700', 'trainer.gradient_clip_val=1.0', 'exp_manager.exp_dir=/workspace/checkpoints/finetuned', 'exp_manager.resume_if_exists=True', 'exp_manager.create_checkpoint_callback=True', 'exp_manager.create_wandb_logger=False', '++model.hidden_dropout=0.1', '++model.attention_dropout=0.1', 'model.tensor_model_parallel_size=1', 'model.sequence_parallel=False', 'model.peft.peft_scheme=none', 'model.megatron_amp_O2=True', 'model.encoder_seq_length=2048', 'model.data.validation_ds.pad_to_max_length=True', 'model.data.train_ds.pad_to_max_length=True', 'model.optim.name=distributed_fused_adam', 'model.data.train_ds.max_seq_length=2048', 'model.data.validation_ds.max_seq_length=2048', 'model.micro_batch_size=4', 'model.global_batch_size=128', '++data.micro_batch_size=4', '++data.global_batch_size=128', 'model.restore_from_path=/workspace/checkpoints/mamba-7b.nemo', 'model.data.train_ds.file_names=[/workspace/nemo/NeMo/databricks-dolly-15k/training.jsonl]', 'model.data.validation_ds.file_names=[/workspace/nemo/NeMo/databricks-dolly-15k/validation.jsonl]', 'model.optim.lr=5e-6', 'model.optim.sched.min_lr=1e-7']
[rank0]: Traceback (most recent call last):
[rank0]: File "/opt/NeMo/examples/nlp/language_modeling/tuning/megatron_mamba_finetuning.py", line 60, in <module>
[rank0]: main()
[rank0]: File "/workspace/nemo/NeMo/nemo/core/config/hydra_runner.py", line 129, in wrapper
[rank0]: _run_hydra(
[rank0]: File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 394, in _run_hydra
[rank0]: _run_app(
[rank0]: File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 457, in _run_app
[rank0]: run_and_report(
[rank0]: File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 223, in run_and_report
[rank0]: raise ex
[rank0]: File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 220, in run_and_report
[rank0]: return func()
[rank0]: File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 458, in <lambda>
[rank0]: lambda: hydra.run(
[rank0]: File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/hydra.py", line 132, in run
[rank0]: _ = ret.return_value
[rank0]: File "/usr/local/lib/python3.10/dist-packages/hydra/core/utils.py", line 260, in return_value
[rank0]: raise self._return_value
[rank0]: File "/usr/local/lib/python3.10/dist-packages/hydra/core/utils.py", line 186, in run_job
[rank0]: ret.return_value = task_function(task_cfg)
[rank0]: File "/opt/NeMo/examples/nlp/language_modeling/tuning/megatron_mamba_finetuning.py", line 56, in main
[rank0]: trainer.fit(model)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 543, in fit
[rank0]: call._call_and_handle_interrupt(
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py", line 43, in _call_and_handle_interrupt
[rank0]: return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 105, in launch
[rank0]: return function(*args, **kwargs)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 579, in _fit_impl
[rank0]: self._run(model, ckpt_path=ckpt_path)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 986, in _run
[rank0]: results = self._run_stage()
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 1030, in _run_stage
[rank0]: self.fit_loop.run()
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/fit_loop.py", line 205, in run
[rank0]: self.advance()
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/fit_loop.py", line 363, in advance
[rank0]: self.epoch_loop.run(self._data_fetcher)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/training_epoch_loop.py", line 140, in run
[rank0]: self.advance(data_fetcher)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/training_epoch_loop.py", line 250, in advance
[rank0]: batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/optimization/automatic.py", line 190, in run
[rank0]: self._optimizer_step(batch_idx, closure)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/optimization/automatic.py", line 268, in _optimizer_step
[rank0]: call._call_lightning_module_hook(
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py", line 159, in _call_lightning_module_hook
[rank0]: output = fn(*args, **kwargs)
[rank0]: File "/workspace/nemo/NeMo/nemo/collections/nlp/models/language_modeling/megatron_base_model.py", line 1297, in optimizer_step
[rank0]: super().optimizer_step(*args, **kwargs)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/core/module.py", line 1308, in optimizer_step
[rank0]: optimizer.step(closure=optimizer_closure)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/core/optimizer.py", line 153, in step
[rank0]: step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/strategies/ddp.py", line 270, in optimizer_step
[rank0]: optimizer_output = super().optimizer_step(optimizer, closure, model, **kwargs)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/strategies/strategy.py", line 238, in optimizer_step
[rank0]: return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/plugins/precision/amp.py", line 74, in optimizer_step
[rank0]: return super().optimizer_step(optimizer, model=model, closure=closure, **kwargs)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/plugins/precision/precision.py", line 122, in optimizer_step
[rank0]: return optimizer.step(closure=closure, **kwargs)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py", line 75, in wrapper
[rank0]: return wrapped(*args, **kwargs)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/torch/optim/optimizer.py", line 391, in wrapper
[rank0]: out = func(*args, **kwargs)
[rank0]: File "/opt/apex/apex/contrib/optimizers/distributed_fused_adam.py", line 2292, in step
[rank0]: loss = closure()
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/plugins/precision/precision.py", line 108, in _wrap_closure
[rank0]: closure_result = closure()
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/optimization/automatic.py", line 144, in __call__
[rank0]: self._result = self.closure(*args, **kwargs)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
[rank0]: return func(*args, **kwargs)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/optimization/automatic.py", line 129, in closure
[rank0]: step_output = self._step_fn()
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/optimization/automatic.py", line 317, in _training_step
[rank0]: training_step_output = call._call_strategy_hook(trainer, "training_step", *kwargs.values())
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py", line 311, in _call_strategy_hook
[rank0]: output = fn(*args, **kwargs)
[rank0]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/strategies/strategy.py", line 390, in training_step
[rank0]: return self.lightning_module.training_step(*args, **kwargs)
[rank0]: File "/workspace/nemo/NeMo/nemo/utils/model_utils.py", line 434, in wrap_training_step
[rank0]: output_dict = wrapped(*args, **kwargs)
[rank0]: File "/workspace/nemo/NeMo/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 860, in training_step
[rank0]: loss_mean = self.training_step_fwd_bwd_step_call(dataloader_iter, forward_only=False)
[rank0]: File "/workspace/nemo/NeMo/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 781, in training_step_fwd_bwd_step_call
[rank0]: loss_mean = self.fwd_bwd_step(dataloader_iter, forward_only)
[rank0]: File "/workspace/nemo/NeMo/nemo/collections/nlp/models/language_modeling/megatron_gpt_sft_model.py", line 365, in fwd_bwd_step
[rank0]: data_iter = get_iterator_k_split(batch, get_num_microbatches(), self.enforce_divisible_batch)
[rank0]: File "/opt/apex/apex/transformer/pipeline_parallel/utils.py", line 93, in get_num_microbatches
[rank0]: return _GLOBAL_NUM_MICROBATCHES_CALCULATOR.get()
[rank0]: AttributeError: 'NoneType' object has no attribute 'get'
Error executing job with overrides: ['trainer.devices=2', 'trainer.precision=bf16', 'trainer.accelerator=gpu', 'trainer.log_every_n_steps=1', 'trainer.val_check_interval=100', 'trainer.limit_val_batches=50', '+trainer.num_sanity_val_steps=0', '+trainer.accumulate_grad_batches=1', 'trainer.max_steps=700', 'trainer.gradient_clip_val=1.0', 'exp_manager.exp_dir=/workspace/checkpoints/finetuned', 'exp_manager.resume_if_exists=True', 'exp_manager.create_checkpoint_callback=True', 'exp_manager.create_wandb_logger=False', '++model.hidden_dropout=0.1', '++model.attention_dropout=0.1', 'model.tensor_model_parallel_size=1', 'model.sequence_parallel=False', 'model.peft.peft_scheme=none', 'model.megatron_amp_O2=True', 'model.encoder_seq_length=2048', 'model.data.validation_ds.pad_to_max_length=True', 'model.data.train_ds.pad_to_max_length=True', 'model.optim.name=distributed_fused_adam', 'model.data.train_ds.max_seq_length=2048', 'model.data.validation_ds.max_seq_length=2048', 'model.micro_batch_size=4', 'model.global_batch_size=128', '++data.micro_batch_size=4', '++data.global_batch_size=128', 'model.restore_from_path=/workspace/checkpoints/mamba-7b.nemo', 'model.data.train_ds.file_names=[/workspace/nemo/NeMo/databricks-dolly-15k/training.jsonl]', 'model.data.validation_ds.file_names=[/workspace/nemo/NeMo/databricks-dolly-15k/validation.jsonl]', 'model.optim.lr=5e-6', 'model.optim.sched.min_lr=1e-7']
[rank1]: Traceback (most recent call last):
[rank1]: File "/opt/NeMo/examples/nlp/language_modeling/tuning/megatron_mamba_finetuning.py", line 60, in <module>
[rank1]: main()
[rank1]: File "/workspace/nemo/NeMo/nemo/core/config/hydra_runner.py", line 129, in wrapper
[rank1]: _run_hydra(
[rank1]: File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 394, in _run_hydra
[rank1]: _run_app(
[rank1]: File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 457, in _run_app
[rank1]: run_and_report(
[rank1]: File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 223, in run_and_report
[rank1]: raise ex
[rank1]: File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 220, in run_and_report
[rank1]: return func()
[rank1]: File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 458, in <lambda>
[rank1]: lambda: hydra.run(
[rank1]: File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/hydra.py", line 132, in run
[rank1]: _ = ret.return_value
[rank1]: File "/usr/local/lib/python3.10/dist-packages/hydra/core/utils.py", line 260, in return_value
[rank1]: raise self._return_value
[rank1]: File "/usr/local/lib/python3.10/dist-packages/hydra/core/utils.py", line 186, in run_job
[rank1]: ret.return_value = task_function(task_cfg)
[rank1]: File "/opt/NeMo/examples/nlp/language_modeling/tuning/megatron_mamba_finetuning.py", line 56, in main
[rank1]: trainer.fit(model)
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 543, in fit
[rank1]: call._call_and_handle_interrupt(
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py", line 43, in _call_and_handle_interrupt
[rank1]: return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 105, in launch
[rank1]: return function(*args, **kwargs)
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 579, in _fit_impl
[rank1]: self._run(model, ckpt_path=ckpt_path)
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 986, in _run
[rank1]: results = self._run_stage()
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 1030, in _run_stage
[rank1]: self.fit_loop.run()
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/fit_loop.py", line 205, in run
[rank1]: self.advance()
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/fit_loop.py", line 363, in advance
[rank1]: self.epoch_loop.run(self._data_fetcher)
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/training_epoch_loop.py", line 140, in run
[rank1]: self.advance(data_fetcher)
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/training_epoch_loop.py", line 250, in advance
[rank1]: batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/optimization/automatic.py", line 190, in run
[rank1]: self._optimizer_step(batch_idx, closure)
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/optimization/automatic.py", line 268, in _optimizer_step
[rank1]: call._call_lightning_module_hook(
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py", line 159, in _call_lightning_module_hook
[rank1]: output = fn(*args, **kwargs)
[rank1]: File "/workspace/nemo/NeMo/nemo/collections/nlp/models/language_modeling/megatron_base_model.py", line 1297, in optimizer_step
[rank1]: super().optimizer_step(*args, **kwargs)
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/core/module.py", line 1308, in optimizer_step
[rank1]: optimizer.step(closure=optimizer_closure)
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/core/optimizer.py", line 153, in step
[rank1]: step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs)
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/strategies/ddp.py", line 270, in optimizer_step
[rank1]: optimizer_output = super().optimizer_step(optimizer, closure, model, **kwargs)
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/strategies/strategy.py", line 238, in optimizer_step
[rank1]: return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/plugins/precision/amp.py", line 74, in optimizer_step
[rank1]: return super().optimizer_step(optimizer, model=model, closure=closure, **kwargs)
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/plugins/precision/precision.py", line 122, in optimizer_step
[rank1]: return optimizer.step(closure=closure, **kwargs)
[rank1]: File "/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py", line 75, in wrapper
[rank1]: return wrapped(*args, **kwargs)
[rank1]: File "/usr/local/lib/python3.10/dist-packages/torch/optim/optimizer.py", line 391, in wrapper
[rank1]: out = func(*args, **kwargs)
[rank1]: File "/opt/apex/apex/contrib/optimizers/distributed_fused_adam.py", line 2292, in step
[rank1]: loss = closure()
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/plugins/precision/precision.py", line 108, in _wrap_closure
[rank1]: closure_result = closure()
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/optimization/automatic.py", line 144, in __call__
[rank1]: self._result = self.closure(*args, **kwargs)
[rank1]: File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
[rank1]: return func(*args, **kwargs)
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/optimization/automatic.py", line 129, in closure
[rank1]: step_output = self._step_fn()
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/optimization/automatic.py", line 317, in _training_step
[rank1]: training_step_output = call._call_strategy_hook(trainer, "training_step", *kwargs.values())
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py", line 311, in _call_strategy_hook
[rank1]: output = fn(*args, **kwargs)
[rank1]: File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/strategies/strategy.py", line 390, in training_step
[rank1]: return self.lightning_module.training_step(*args, **kwargs)
[rank1]: File "/workspace/nemo/NeMo/nemo/utils/model_utils.py", line 434, in wrap_training_step
[rank1]: output_dict = wrapped(*args, **kwargs)
[rank1]: File "/workspace/nemo/NeMo/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 860, in training_step
[rank1]: loss_mean = self.training_step_fwd_bwd_step_call(dataloader_iter, forward_only=False)
[rank1]: File "/workspace/nemo/NeMo/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 781, in training_step_fwd_bwd_step_call
[rank1]: loss_mean = self.fwd_bwd_step(dataloader_iter, forward_only)
[rank1]: File "/workspace/nemo/NeMo/nemo/collections/nlp/models/language_modeling/megatron_gpt_sft_model.py", line 365, in fwd_bwd_step
[rank1]: data_iter = get_iterator_k_split(batch, get_num_microbatches(), self.enforce_divisible_batch)
[rank1]: File "/opt/apex/apex/transformer/pipeline_parallel/utils.py", line 93, in get_num_microbatches
[rank1]: return _GLOBAL_NUM_MICROBATCHES_CALCULATOR.get()
[rank1]: AttributeError: 'NoneType' object has no attribute 'get'
Epoch 0: : 0%| | 0/700 [00:09<?]
[2024-08-28 13:45:50,925] torch.distributed.elastic.multiprocessing.api: [ERROR] failed (exitcode: 1) local_rank: 0 (pid: 5090) of binary: /usr/bin/python
Traceback (most recent call last):
File "/usr/local/bin/torchrun", line 33, in <module>
sys.exit(load_entry_point('torch==2.3.0a0+40ec155e58.nv24.3', 'console_scripts', 'torchrun')())
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 347, in wrapper
return f(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/run.py", line 834, in main
run(args)
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/run.py", line 825, in run
elastic_launch(
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/launcher/api.py", line 137, in __call__
return launch_agent(self._config, self._entrypoint, list(args))
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/launcher/api.py", line 271, in launch_agent
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
/opt/NeMo/examples/nlp/language_modeling/tuning/megatron_mamba_finetuning.py FAILED
------------------------------------------------------------
Failures:
[1]:
time : 2024-08-28_13:45:50
host : fs-api-66209ad2-6222-4e62-bbbf-e3de667aab8e
rank : 1 (local_rank: 1)
exitcode : 1 (pid: 5091)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
time : 2024-08-28_13:45:50
host : fs-api-66209ad2-6222-4e62-bbbf-e3de667aab8e
rank : 0 (local_rank: 0)
exitcode : 1 (pid: 5090)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
```
**Expected behavior**
Expected to start fine tuning directly before the error appears.
**Environment overview (please complete the following information)**
- Environment location: Docker, VM Cloud
- Method of NeMo install: Docker Image : nvcr.io/nvidia/nemo:24.07
- If method of install is [Docker], provide `docker pull` & `docker run` commands used :
```bash
docker run --gpus all --shm-size=80g --net=host --ulimit memlock=-1 --rm -it \
-v /ephemeral/navinenv/work/megatron:/workspace/megatron \
-v /ephemeral/navinenv/work/data:/workspace/dataset/data \
-v /ephemeral/navinenv/work/outfix:/workspace/dataset/outfix \
-v /ephemeral/navinenv/work/tok:/workspace/dataset/tok \
-v /ephemeral/navinenv/work/checkpoints:/workspace/checkpoints \
-v /ephemeral/navinenv/work/nemo:/workspace/nemo \
-v /ephemeral/navinenv/work/tmp:/tmp \
nvcr.io/nvidia/nemo:24.07
````
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
GPUS : 2*A100 80 GB
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https://api.github.com/repos/NVIDIA/NeMo/issues/10284 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/10284/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/10284/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/10284/events | https://github.com/NVIDIA/NeMo/pull/10284 | 2,492,122,979 | PR_kwDOC_bI7s55tnFu | 10,284 | Fix import path for CTCDecodingConfig | {
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} | # What does this PR do ?
Fixes the import of CTCDecodingConfig which is located in ctc_decoding
**Collection**: [Note which collection this PR will affect]
# Changelog
- Fixed import
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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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https://api.github.com/repos/NVIDIA/NeMo/issues/10283 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/10283/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/10283/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/10283/events | https://github.com/NVIDIA/NeMo/issues/10283 | 2,491,671,555 | I_kwDOC_bI7s6Ug-QD | 10,283 | NeMo/tutorials/speaker_tasks/ASR_with_SpeakerDiarization needs confidence estimation | {
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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.",
"Hi. Neither NeMo ASR models nor NeMo diarization natively support confidenc... | 2024-08-28T10:18:15 | 2024-10-10T22:54:05 | 2024-10-06T02:02:37 | NONE | {
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} | null | null | null | While performing offline ASR_with_SpeakerDiarization
the function at
nemo>collections>asr>parts>utils>diarization_utils.py `
def convert_word_dict_seq_to_ctm(
word_dict_seq_list: List[Dict[str, float]], uniq_id: str = 'null', decimals: int = 3
) -> Tuple[List[str], str]:
Convert word_dict_seq_list into a list containing transcription in CTM format.
Args:
word_dict_seq_list (list):
List containing words and corresponding word timestamps in dictionary format.
Example:
>>> word_dict_seq_list = \
>>> [{'word': 'right', 'start_time': 0.0, 'end_time': 0.34, 'speaker': 'speaker_0'},
{'word': 'and', 'start_time': 0.64, 'end_time': 0.81, 'speaker': 'speaker_1'},
...],
Returns:
ctm_lines_list (list):
List containing the hypothesis transcript in CTM format.
Example:
>>> ctm_lines_list= ["my_audio_01 speaker_0 0.0 0.34 right 0",
my_audio_01 speaker_0 0.64 0.81 and 0",
ctm_lines = []
confidence = 0
for word_dict in word_dict_seq_list:
spk = word_dict['speaker']
stt = word_dict['start_time']
dur = round(word_dict['end_time'] - word_dict['start_time'], decimals)
word = word_dict['word']
ctm_line_str = f"{uniq_id} {spk} {stt} {dur} {word} {confidence}"
ctm_lines.append(ctm_line_str)
return ctm_lines
`
considers confidence as zero is it possible to use confidence estimation to return real confidence for each word
Thank you
kind Regards | {
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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.",
"Hello, I have also encountered this bug. I am referring to the documentation here: [NeMo Framework PEFT with Llama2, Mixtral-8x7B, and Nemotron 4 340B](https://docs.nvidia... | 2024-08-28T02:58:44 | 2024-12-02T02:06:16 | 2024-12-02T02:06:15 | NONE | {
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} | null | null | null | **Describe the bug**
I was training to run sft based on Mixtral-8x7B-instruct model with tensor parallel size=4 (sequence parallel=True) and LoRA (target modules =[all]).
It reports that the output dims of original module and the corresponding lora adapter module is not matched so they cannot be added together.
**Steps/Code to reproduce bug**
I used the recommended docker nvcr.io/nvidia/nemo:24.07 and my scripts is as follow:
```shell
export CUDA_VISIBLE_DEVICES="4,5,6,7"
nemo_ckpt_path=/path/to/Mixtral-8x7B-instruct-v0.1-nemo
train_data='/path/to/train.json'
valid_data='/path/to/valid.json'
output='/data/results/mixtral-8x7b-instruct-sft'
python /opt/NeMo-Aligner/examples/nlp/gpt/train_gpt_sft.py \
trainer.precision=bf16 \
trainer.num_nodes=1 \
trainer.devices=4 \
trainer.sft.max_steps=-1 \
trainer.sft.limit_val_batches=40 \
trainer.sft.val_check_interval=1000 \
trainer.sft.max_epochs=2 \
model.tensor_model_parallel_size=4 \
model.sequence_parallel=True \
model.use_flash_attention=True \
model.encoder_seq_length=1024 \
model.megatron_amp_O2=False \
model.restore_from_path=$nemo_ckpt_path \
model.optim.lr=5e-6 \
model.optim.name=fused_adam\
model.answer_only_loss=True \
model.data.num_workers=0 \
model.data.train_ds.micro_batch_size=1 \
model.data.train_ds.global_batch_size=128 \
model.data.train_ds.file_path=$train_data \
model.data.validation_ds.micro_batch_size=1 \
model.data.validation_ds.global_batch_size=128 \
model.data.validation_ds.file_path=$valid_data \
model.peft.peft_scheme=lora \
model.peft.lora_tuning.target_modules=[attention_dense,mlp_fc1,mlp_fc2] \
model.peft.lora_tuning.adapter_dim=128 \
exp_manager.create_wandb_logger=False \
exp_manager.explicit_log_dir=$output \
exp_manager.wandb_logger_kwargs.project=sft_run \
exp_manager.wandb_logger_kwargs.name=ruozhiba_sft_run \
exp_manager.checkpoint_callback_params.save_nemo_on_train_end=True \
exp_manager.resume_if_exists=True \
exp_manager.resume_ignore_no_checkpoint=True \
exp_manager.create_checkpoint_callback=True \
exp_manager.checkpoint_callback_params.monitor=validation_loss
```
And it runs into error like:
```shell
...
File "/opt/NeMo-Aligner/nemo_aligner/algorithms/supervised.py", line 145, in train_single_step
loss_mean, metrics = self.model.get_loss_and_metrics(batch=batch, forward_only=False)
File "/opt/NeMo-Aligner/nemo_aligner/models/nlp/gpt/gpt_sft_model.py", line 93, in get_loss_and_metrics
losses_reduced = fwd_bwd_function(
File "/opt/megatron-lm/megatron/core/pipeline_parallel/schedules.py", line 439, in forward_backward_no_pipelining
output_tensor, num_tokens = forward_step(
File "/opt/megatron-lm/megatron/core/pipeline_parallel/schedules.py", line 264, in forward_step
output_tensor, loss_func = forward_step_func(data_iterator, model)
File "/opt/NeMo/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 1273, in fwd_output_and_loss_func
output_tensor = model(**forward_args)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "/opt/megatron-lm/megatron/core/models/gpt/gpt_model.py", line 191, in forward
hidden_states = self.decoder(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "/opt/megatron-lm/megatron/core/transformer/transformer_block.py", line 411, in forward
hidden_states, context = layer(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "/opt/megatron-lm/megatron/core/transformer/transformer_layer.py", line 178, in forward
attention_output_with_bias = self.self_attention(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "/opt/NeMo/nemo/collections/nlp/modules/common/megatron/adapters/mcore_mixins.py", line 202, in forward
query, key, value = self.get_query_key_value_tensors(hidden_states, key_value_states)
File "/opt/NeMo/nemo/collections/nlp/modules/common/megatron/adapters/mcore_mixins.py", line 117, in get_query_key_value_tensors
mixed_qkv = mixed_qkv + lora_mixed_qkv
RuntimeError: The size of tensor a (400) must match the size of tensor b (1600) at non-singleton dimension 0
```
And I try to fix this by modify the following two .py files:
/opt/NeMo/nemo/collections/nlp/modules/common/megatron/adapters/mcore_mixins.py
```patch
--- mcore_mixins.py 2024-08-28 02:51:12.000000000 +0000
+++ /opt/NeMo/nemo/collections/nlp/modules/common/megatron/adapters/mcore_mixins.py 2024-08-28 02:51:13.952291250 +0000
@@ -379,7 +379,6 @@
if lora_fc2_adapter and self.adapter_cfg[AdapterName.LORA_4HtoH_ADAPTER]['enabled']:
lora_output = lora_fc2_adapter(intermediate_parallel)
elif lora_moe_fc2_adapter and self.adapter_cfg[AdapterName.LORA_MOE_4HtoH_ADAPTER]['enabled']:
- lora_moe_fc2_adapter.expert_adapters[expert_idx].input_is_parallel=False
lora_output = lora_moe_fc2_adapter(intermediate_parallel, expert_idx)
output = output + lora_output
```
/opt/NeMo/nemo/collections/nlp/modules/common/megatron/adapters/parallel_adapters.py
```patch
--- parallel_adapters.py 2024-08-28 02:52:03.000000000 +0000
+++ /opt/NeMo/nemo/collections/nlp/modules/common/megatron/adapters/parallel_adapters.py 2024-08-28 02:52:04.712871536 +0000
@@ -291,7 +291,7 @@
if self.norm_position == 'pre':
x = self.layer_norm(x)
- if self._sequence_parallel and not self.input_is_parallel and self.norm_position=='pre':
+ if self._sequence_parallel and not self.input_is_parallel:
# for attention_qkv and linear_fc1
# layernorm before lora is impacted by sequence parallel,
# hence seq dim need to be gathered right before lora linear layers
```
After the modifications, I can run sft with LoRA and tensor and sequence parallel, but I am not sure it runs correctly. Hope you guys can provide elegant solutions for it.
**Expected behavior**
LoRA can be used with tensor and sequence parallel.
**Environment overview (please complete the following information)**
nvcr.io/nvidia/nemo:24.07
docker run
```shell
HERE=$(pwd -P)
user=`whoami`
uid=`id -u`
gid=`id -g`
docker run \
-v /dev/shm:/dev/shm\
-v /data:/data \
-e USER=$user -e UID=$uid -e GID=$gid \
-v $HERE:/home/dir/ \
-w /home/dir/ \
--security-opt \
seccomp=unconfined \
-it \
--rm \
--network=host \
--gpus all \
nvcr.io/nvidia/nemo:24.07
```
**Environment details**
I used the default environment of the nemo docker
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} | # What does this PR do ?
Adds option to selectively load part of the `TrainerContext`. So for eg, if you want to just load the model's config, you can do `load_context("path/to/io.json", subpath="model.config")`.
# Changelog
- Add specific line by line info of high level changes in this PR.
# Usage
* You can potentially add a usage example below
```python
# Add a code snippet demonstrating how to use this
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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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} | 🚀 PR to Bump `Dockerfile.ci`.
📝 Please remember the following to-do's before merge:
- [x] Verify the presubmit CI
🙏 Please merge this PR only if the CI workflow completed successfully. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/10277 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/10277/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/10277/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/10277/events | https://github.com/NVIDIA/NeMo/issues/10277 | 2,490,522,689 | I_kwDOC_bI7s6UclxB | 10,277 | fix where the transcription is saved please | {
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"This issue was closed because it has been inactive for 7 days since being marked as stale."
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} | null | null | null | In your transcribe_text.py script you need to allow a user to save the .json file that contains the transcription wherever they want, but at least in the directory in which the script is run...Currently, it saves it to some temp directory.
```
# After this line:
logging.info(f"Finished writing predictions to {output_filename}!")
# Add the following code to copy the output file to the current directory
try:
current_dir = os.getcwd()
new_output_path = os.path.join(current_dir, "nemo_transcription.json")
shutil.copyfile(output_filename, new_output_path)
logging.info(f"Transcriptions also saved to {new_output_path}")
except Exception as e:
logging.error(f"Failed to copy the transcription file to the current directory: {e}")
``` | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/10276 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/10276/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/10276/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/10276/events | https://github.com/NVIDIA/NeMo/issues/10276 | 2,490,513,486 | I_kwDOC_bI7s6UcjhO | 10,276 | be prepared when a user selects a file that isn't strictly mono and other file extensions as well | {
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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.",
"You can choose the channel with https://github.com/NVIDIA/NeMo/blob/c9c4bee... | 2024-08-27T23:04:51 | 2024-10-10T21:07:37 | 2024-10-04T01:58:51 | NONE | {
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} | null | null | null | In your ```transcribe_utils.py``` script you need to be prepared when audio files are in stereo or other channels as well.
You can use ```pydub``` but I prefer to use ```soundfile```. Either way, you need to be able to handle a wide variety of file formats and channels and gracefully convert them - even temporarily in a temp file - so that the program doesn't stop working altogether. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/10275 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/10275/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/10275/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/10275/events | https://github.com/NVIDIA/NeMo/issues/10275 | 2,490,469,926 | I_kwDOC_bI7s6UcY4m | 10,275 | fix exp_manager.py to work on Windows | {
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} | null | null | null | In the ```exp_manager.py``` script, you need to change the line that reads
```rank_termination_signal: signal.Signals = signal.SIGKILL```
to...
```rank_termination_signal: signal.Signals = signal.SIGTERM if os.name == 'nt' else signal.SIGKILL```
That way it will work on Windows.
You're welcome. | {
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"Thank you @ko3n1g, I think I'll need a few tries to get my test working correctly, so I made this a draft. Apologies for the trouble, will let you know once it's ready.",
"[🤖]: Hi @akoumpa 👋,\n\nI just wanted to let you know that, you know, a [CICD pipeline](https://github.com/NVIDIA/NeMo/actions/runs/11064116... | 2024-08-27T21:10:59 | 2024-09-27T07:20:41 | 2024-09-27T07:20:40 | MEMBER | {
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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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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
```
# GitHub Actions CI
The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.
The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first 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.
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* Related to # (issue)
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"It's been removed already from HF utils in NeMo. "
] | 2024-08-27T20:52:59 | 2024-09-07T02:21:56 | 2024-09-07T02:21:56 | NONE | {
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} | null | null | null | Your usage of ModelFilter and possibly "DatasetFilter" from the ```huggingface_hub``` library is out of date. Apparently those were removed in this here:
https://github.com/huggingface/huggingface_hub/commit/bb38f9422cb88ba02694c87fb5979cc48ecef8a0#diff-1b045ed2e860a78218b3501b6b8df1f5609c493c1e2fa16769486fb020f7c164
I noticed this when trying to implement your transcription script that you say on the huggingface repository that I can use.
You're welcome. | {
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