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[Audio] Metric with Squim objective and MOS
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# What does this PR do ? Add a `Metric` object for calculating Squim MOS. **Collection**: audio # Changelog - Added a metric wrapper for Squim objective and MOS models - Added unit tests - Fixed a bug in `transcribe_utils` # Usage For example, in model config: ```python ... model: ... metrics: val: squim_mos: # Squim MOS _target_: nemo.collections.audio.metrics.SquimMOSMetric fs: ${model.sample_rate} squim_stoi: # Squim STOI _target_: nemo.collections.audio.metrics.SquimObjectiveMetric metric: stoi fs: ${model.sample_rate} ... ``` # GitHub Actions CI The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.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**: - [x] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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TorchAudio installation workaround for incorrect `PYTORCH_VERSION` variable
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# What does this PR do ? Fixed TorchAudio installation for `nvcr.io/nvidia/pytorch:24.03-py3` container and other environments, where `PYTORCH_VERSION` env variable contains incorrect PyTorch version (real PyTorch version differs). Should fix https://nvbugs/4738248 **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**: - [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? - [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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[NeMo-UX] Minor bug fix when TE/Apex not installed
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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 FSDP for NeMo 2.0
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[ "Hey, I noticed that in NeMo 1.X, FSDP support is really not well-integrated across the code base. Many scripts do not work with it because they only implement `NLPDDPStrategy`, checkpoint conversion was a huge undertaking to get working (PR pending), and some feature flags straight-up [don't work](https://github.com/NVIDIA/NeMo/issues/9531) or [had bugs](https://github.com/NVIDIA/NeMo/pull/9335).\r\n\r\nI'm super happy that FSDP is supported at all, having it integrated is really nice and for 7B models, performance with default settings was surprisingly higher than Megatron's 4D parallelism. However, I would really wish for and encourage more proper support in NeMo 2.X; it would be amazing if all NeMo workflows – not just training – just worked out of the box. :)\r\n\r\nLet me know how to properly help in this endeavor, too, please. I've had the disheartening experience that PRs randomly get ignored, so I don't want to spend time improving the code base if it'll go wasted in the end." ]
2024-07-15T23:02:47
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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 - Made a strategies/ direcotory to hold all strategies (e.g. MegatronStrategy & FSDPStrategy) - Extracted common logic shared by different strategies into helper functions (utils.py) - Wrote FSDPStrategy which uses FSDP1 to replace MegatronStrategy if users want to run FSDP # 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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Skip quantization if model is already quantized (QAT resuming)
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2024-07-15T22:19:28
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# What does this PR do ? When doing QAT for a big model, we often need to resume training. This PR adds a check if model restored from the resume checkpoint is already quantized or not, and if quantized, it skips this step. **Collection**: N/A # Changelog - Skip quantization if model is already quantized (QAT resuming) # Usage - N/A # 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? - [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 - [ ] Bugfix - [ ] Documentation - [x] Minor improvement 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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Update path to Llama model in fine-tuning notebook
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# What does this PR do ? The path to the model used during the evaluation stage should be updated to reflect the new NGC model registry download location. **Collection**: LLMs # Changelog - The original path was leftover from the previous code where the model was downloaded from HuggingFace and converted to `.nemo`. # 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.
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Update starthere docs text
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# What does this PR do ? 1-line update to docs **Collection**: Docs # Changelog - 1-line update to docs # Usage N/A # 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) - [N/A] Did you write any new necessary tests? - [x] Did you add or update any necessary documentation? - [N/A] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [x] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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ci: Bump MCore tag
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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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🚀 PR to Bump `Dockerfile.ci`. 📝 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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🚀 PR to Bump `Dockerfile.ci`. 📝 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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🚀 PR to Bump `Dockerfile.ci`. 📝 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 ? Manually copy changes from: https://github.com/NVIDIA/NeMo/pull/9679/files **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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TorchAudio installation workaround for incorrect `PYTORCH_VERSION` variable
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2024-07-15T20:40:44
2024-07-16T00:14:16
2024-07-16T00:14:15
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# What does this PR do ? Fixed TorchAudio installation for `nvcr.io/nvidia/pytorch:24.03-py3` container and other environments, where `PYTORCH_VERSION` env variable contains incorrect PyTorch version (real PyTorch version differs). Should fix https://nvbugs/4738248 **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**: - [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? - [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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Log TFLOPs per sec per GPU
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# What does this PR do ? Log TFLOPs per sec per GPU (logs to stdout and tensorboard logger) **Collection**: 'common' # Changelog Supported models- Nemotron, GPT3, Llama2, Llama3, Mixtral Supported GPUs- H200, H100, A100 ** Tested on H100 for supported models ** Llama2 PEFT (LoRA and SFT) support coming soon # Usage ```python flops_measurement_callback = FLOPsMeasurementCallback(cfg) lightning_trainer.callbacks.append(flops_measurement_callback) ``` # 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**: - [x] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to
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Add Lita, Vila and Vita TRTLLM export
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CONTRIBUTOR
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# What does this PR do ? 1) modify the neva_model to remove hardcoded 'vision_encoder" name. 2) add trtllm export support for lita, vila and vita .nemo model. 3) add run example for running lita, vila and vita inference. 4) add triton deployment for these models. **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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[NeMo-UX] Make TE and Apex dependencies optional
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I wanted to train a multitask model, like canary. needed more information on how to build the tokenizer, and data manifest file.
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[ "@pzelasko @krishnacpuvvada provide necessary documentation for the same. ", "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." ]
2024-07-15T07:10:42
2024-09-09T01:57:02
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**I wanted to train a multitask model, like canary. needed more information on how to build the tokenizer, and data manifest file.** **A clear and concise description of what the problem is.** Due to lack of information on how to build a canary like model, a tutorial, some refrence to build the tokenizer and manifest file, inorder to train such model. **Describe alternatives you've considered** I followed the information available at huggingface model card. but its insufficient. @titu1994 @nithinraok @VahidooX please guide me
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Unable to reproduce cache aware streaming results for Conformer that were there for Fastconformer.
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[ "duplicate of https://github.com/NVIDIA/NeMo/issues/9521 " ]
2024-07-15T07:06:18
2024-08-01T13:21:35
2024-08-01T13:21:35
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I trained Conformer-CTC using cache aware method. but unable to reproduce the results that were there with "Fastconformer cache aware streaming". By using Conformer-CTC cache aware method i get partial sub-words, words combined together like ("hello world" ==> herld) , in streaming output. the same was the case when i deployed it in a RIVA pipeline. but when i use the "transcribe" function i get proper transcription for the audio file. @titu1994 @VahidooX can you please enlighten on this. When to use and how to use streaming models in .nemo format and in RIVA pipeline. My question has some relevance with these issues [https://github.com/https://github.com/NVIDIA/NeMo/discussions/7010] [https://github.com/https://github.com/NVIDIA/NeMo/discussions/5284]
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CPU memory keeps increasing in every step during training LLM with Nemo framework?
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[ "I realize that CPU memory leaked in every training step but I don't know why. I tried to change batch_size, num_workker, log_every_n_steps but nothing works. Anyone has an idea? @dimapihtar ", "Hi, could you try to include these params to your run?\r\n`model.use_cpu_initialization=False`\r\n`model.cpu_offloading=False`", "I added those 2 params to my script but the behavior is still the same @dimapihtar ", "> I added those 2 params to my script but the behavior is still the same @dimapihtar\r\n\r\nI tried to run job with exactly the same config as you attached but I don't have this issue. Could you try to use newer 24.05 container? `nvcr.io/nvidia/nemo:24.05`\r\n", "> I tried to run job with exactly the same config as you attached but I don't have this issue. Could you try to use newer 24.05 container? `nvcr.io/nvidia/nemo:24.05`\r\n\r\nThat's weird! I tried on 2 nemo version: nvcr.io/nvidia/nemo:24.03.framework and nvcr.io/nvidia/nemo:24.05.01 but the issue remains the same. I will try nvcr.io/nvidia/nemo:24.05 but I don't think it will be different", "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.", "Did you run with `torchrun`? try to comment `mp.set_start_method(\"spawn\", force=True)` in the megatron_gpt_finetuning.py" ]
2024-07-15T01:41:18
2024-09-06T07:25:25
2024-08-26T01:52:46
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**Describe the bug** Im training Llama3-8b with Nemo framework on 4 A100 80GB GPU. During training, my CPU RAM keeps increasing in every step and it consumed in buff/cache memory. My server has 2TB CPU RAM and it reached 100% with just 20 steps (my epoch has total 900 steps). Edit: Both my shared and cache memory increase continuously. ![image](https://github.com/user-attachments/assets/8425d4d0-bca4-467f-ba23-6a33515088eb) Soon my training task will collap. **Steps/Code to reproduce bug** Here is my training script (https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/llama2sft.html): ``` torchrun --nproc_per_node=4 \ /opt/NeMo/examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py \ trainer.devices=4 \ trainer.num_nodes=1 \ trainer.val_check_interval=50 \ trainer.max_epochs=1 \ trainer.max_steps=900 \ model.restore_from_path="my_model.nemo" \ model.micro_batch_size=1 \ model.global_batch_size=256 \ model.tensor_model_parallel_size=4 \ model.pipeline_model_parallel_size=1 \ model.megatron_amp_O2=True \ model.sequence_parallel=True \ model.activations_checkpoint_granularity=selective \ model.activations_checkpoint_method=uniform \ model.optim.name=distributed_fused_adam \ model.optim.lr=5e-6 \ model.answer_only_loss=True \ model.peft.peft_scheme=none \ model.data.train_ds.file_names="my_train_data.jsonl" \ model.data.validation_ds.file_names="my_val_data.jsonl" \ model.data.test_ds.file_names="my_test_data.jsonl" \ model.data.train_ds.concat_sampling_probabilities="[1]" \ model.data.train_ds.max_seq_length=8192 \ model.data.validation_ds.max_seq_length=8192 \ model.data.train_ds.micro_batch_size=1 \ model.data.train_ds.global_batch_size=256 \ model.data.validation_ds.micro_batch_size=1 \ model.data.validation_ds.global_batch_size=32 \ model.data.test_ds.micro_batch_size=1 \ model.data.test_ds.global_batch_size=32 \ +model.data.num_workers=4 \ model.data.validation_ds.metric.name=loss \ model.data.test_ds.metric.name=loss \ exp_manager.create_wandb_logger=False \ exp_manager.explicit_log_dir=results_v2 \ exp_manager.resume_if_exists=False \ exp_manager.resume_ignore_no_checkpoint=True \ exp_manager.create_checkpoint_callback=True \ exp_manager.checkpoint_callback_params.monitor=step \ exp_manager.checkpoint_callback_params.mode='max' \ exp_manager.checkpoint_callback_params.save_top_k=4 \ exp_manager.checkpoint_callback_params.save_best_model=True \ exp_manager.checkpoint_callback_params.save_nemo_on_train_end=True ``` **Environment overview** Environment location: Nemo framework: nvcr.io/nvidia/nemo:24.03.01.framework
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Add option to convert PyTriton response to OpenAI format
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# What does this PR do ? 1. Adds option to convert responses from PyTriton server to OpenAI compatible format. 2. Also adds `triton_request_timeout` and `openai_format_response` as user defined args which are then written to a json file along with `triton IP` and `triton port` to be accessed by `rest_model_api.py` (Deletes the existing `nemo/deploy/service/config.json` favor of these changes) **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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Fix missing parallelisms
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[ "fixed base branch and target branch", "I'd like to add this to main, but instead of manual copy use a function to copy all config attributes that end with _paralle_size. Made https://github.com/NVIDIA/NeMo/compare/main...akoumparouli/copy_parallel_size for this purpose. " ]
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# What does this PR do ? CP and EP config values were not being passed to model parallel initialization methods. **Collection**: [llm] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # GitHub 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) - [x] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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# What does this PR do ? 1-line update to docs **Collection**: Docs # Changelog - 1-line update to docs # Usage N/A # 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) - [N/A] Did you write any new necessary tests? - [x] Did you add or update any necessary documentation? - [N/A] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [x] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Remove mask if use fusion mask
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[ "I don't want to set default to `False` since there are models not using FA or mask fusion. " ]
2024-07-12T20:52:27
2024-07-15T16:28:03
2024-07-15T16:28:01
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# What does this PR do ? Set create_attention_mask=False when we have mask in built-in modules like FlashAttention. **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**: - [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 - [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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9,722
Set TE flag in legacy -> mcore conversion script
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[ "#9585 was merged to the main branch two weeks ago so the release branch should already have the commit?", "@cuichenx You're right, I'm actually surprised github said we could make this MR. When I look at the `0ec7e90` commit history for that conversion script it actually doesn't even show this commit. So the git history on the conversion script looks okay:\r\n\r\n![image](https://github.com/user-attachments/assets/71985909-127c-4e2c-8162-66477dc5d37c)\r\n" ]
2024-07-12T16:59:54
2024-07-16T22:32:48
2024-07-12T17:05:01
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Same as #9585, but into release branch
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refactor: notebook branch release
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2024-07-12T16:22:23
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[NeMo-UX] Adding recipes
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[ "> Can we rename the directory `models` to `recipes`?\r\n> \r\n> Otherwise it's confusing why there's a mistral.py under both `collections/llm/gpt/model` and `collections/llm/models`. I don't have any strong preference as long as one of the two directories changes name.\r\n\r\nDone" ]
2024-07-12T13:50:22
2024-07-23T16:14:03
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# What does this PR do ? Adding a flag to run `_sample_output` in `Quantizer` on demand (defaults to `True`) as this step often hangs for a few minutes at (see [here](https://github.com/NVIDIA/NeMo/actions/runs/9895230553/job/27334855354)): ``` [NeMo I 2024-07-11 17:03:57 quantizer:182] Generating sample output for the model... Loading extension modelopt_cuda_ext... ``` and this is to disable sample query in Github CI. **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 - [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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# 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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Allows non-strict load with distributed checkpoints
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CONTRIBUTOR
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# What does this PR do ? With distributed checkpoints, the mismatches between the runtime model and checkpoint model manifest during `dist_checkpoint.load` (not during `model.load_state_dict` as with regular checkpoints). This PR adds a flag that allows to adjust load strictness (e.g. ignore unexpected keys). This PR relies on MCore feature that is not merged yet (merge ETA 5th July): https://gitlab-master.nvidia.com/ADLR/megatron-lm/-/merge_requests/1628 **Collection**: NLP # Changelog - Add `model.dist_ckpt_load_strictness` flag to control dist ckpt load strictness. The most useful value is `log_all` which warns about all mismatches but performs the checkpoint load for a matching state dict subset. # 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**: - [x] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Resiliency features update
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# What does this PR do ? Changes for the updated version of resiliency features: - Fault Tolerance - Straggler Detection **Collection**: [Note which collection this PR will affect] # Changelog - Updated/clarified documentation - Removed duplicated "straggler detection" section from the documentation - Updated straggler detection unit test - Do not pass logger instance to the straggler det. callback - Exposed `safety_factor` FT param - Pass `exp_dir` to the FT callback # 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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[DRAFT] Lita refactor
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# What does this PR do ? Refactor LITA part **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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refactor: Uniform BRANCH for notebooks
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Not work even use the official docker when multiple GPU training LLM
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even I use 24.05.01, still stuck in this status ![image](https://github.com/user-attachments/assets/2676e533-2570-49d5-a1e7-41755d1f92c3) use another sft script which refer by official document is also not work by this error https://docs.nvidia.com/nemo-framework/user-guide/latest/modelalignment/sft.html#step-2-sft-training Traceback (most recent call last): File "/opt/NeMo-Aligner/examples/nlp/gpt/train_gpt_sft.py", line 243, in main() File "/opt/NeMo/nemo/core/config/hydra_runner.py", line 129, in wrapper _run_hydra( File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 394, in _run_hydra _run_app( File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 457, in _run_app run_and_report( File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 223, in run_and_report raise ex File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 220, in run_and_report return func() File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/utils.py", line 458, in lambda: hydra.run( File "/usr/local/lib/python3.10/dist-packages/hydra/_internal/hydra.py", line 132, in run _ = ret.return_value File "/usr/local/lib/python3.10/dist-packages/hydra/core/utils.py", line 260, in return_value raise self._return_value File "/usr/local/lib/python3.10/dist-packages/hydra/core/utils.py", line 186, in run_job ret.return_value = task_function(task_cfg) File "/opt/NeMo-Aligner/examples/nlp/gpt/train_gpt_sft.py", line 129, in main ptl_model, updated_cfg = load_from_nemo( File "/opt/NeMo-Aligner/nemo_aligner/utils/utils.py", line 98, in load_from_nemo model = cls.restore_from( File "/opt/NeMo/nemo/collections/nlp/models/nlp_model.py", line 465, in restore_from return super().restore_from( File "/opt/NeMo/nemo/core/classes/modelPT.py", line 464, in restore_from instance = cls._save_restore_connector.restore_from( File "/opt/NeMo-Aligner/nemo_aligner/utils/utils.py", line 51, in restore_from return super().restore_from(*args, **kwargs) File "/opt/NeMo/nemo/collections/nlp/parts/nlp_overrides.py", line 1172, in restore_from checkpoint = checkpoint_io.load_checkpoint(tmp_model_weights_dir, sharded_state_dict=checkpoint) File "/opt/NeMo/nemo/utils/callbacks/dist_ckpt_io.py", line 78, in load_checkpoint return dist_checkpointing.load( File "/opt/megatron-lm/megatron/core/dist_checkpointing/serialization.py", line 133, in load validate_sharding_integrity(nested_values(sharded_state_dict)) File "/opt/megatron-lm/megatron/core/dist_checkpointing/serialization.py", line 425, in validate_sharding_integrity torch.distributed.all_gather_object(all_sharding, sharding) File "/usr/local/lib/python3.10/dist-packages/torch/distributed/c10d_logger.py", line 75, in wrapper return func(*args, *kwargs) File "/usr/local/lib/python3.10/dist-packages/torch/distributed/distributed_c10d.py", line 2310, in all_gather_object all_gather(object_size_list, local_size, group=group) File "/usr/local/lib/python3.10/dist-packages/torch/distributed/c10d_logger.py", line 75, in wrapper return func(args, kwargs) File "/usr/local/lib/python3.10/dist-packages/torch/distributed/distributed_c10d.py", line 2724, in all_gather work = default_pg.allgather([tensor_list], [tensor]) torch.distributed.DistBackendError: NCCL error in: /opt/pytorch/pytorch/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1961, internal error - please report this issue to the NCCL developers, NCCL version 2.19.3 ncclInternalError: Internal check failed. Last error: Attribute busid of node nic not found
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[ "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days." ]
2024-07-12T05:54:50
2024-08-19T16:39:14
2024-08-19T16:39:12
CONTRIBUTOR
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# What does this PR do ? Duplicate of #9587 for release branch Also includes transformers version update from #9606 **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**: - [X] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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[NeMo-UX] Match nemo 1's default behavior for drop_last and pad_samples_to_global_batch_size
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2024-07-12T00:44:13
2024-07-16T16:27:07
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COLLABORATOR
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This PR updates the default behavior of NeMo-UX's dataloader to match NeMo 1's. See https://github.com/NVIDIA/NeMo/blob/d4d03857f432a9248e6d58ecea12b17870e112eb/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py#L1552-L1579. # 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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chore: Pin branch in notebooks
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2024-07-11T22:02:26
2024-07-26T08:40:49
2024-07-26T08:40:49
CONTRIBUTOR
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# What does this PR do ? Integrates TRT-LLM v0.11 to nemo export module.
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add documentation for reset_lr feature
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# What does this PR do ? Adds a doc file for reset_lr feature. **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 dummy vision and text transformer config (assumed mcore to be false)
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# What does this PR do ? Add parameters to CLIP model loading for SD training. **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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Update the test checking for cooperative kernels in conditional nodes.
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Now we conditionally xfail only when a cuda driver version less than 12.5 is installed. CUDA 12.5 fixes this issue. Before it, cooperative kernels could not be used within the body of a conditional node. Those internal can look at internal nvbug 4452698.
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chore: Pin branch in notebooks
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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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Question: Which decoder are we supposed to use on parakeet-tdt_ctc-1.1b model?
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[ "That is expected, currently we don't support getting timestamps from tdt/rnnt decoder, so if you are looking for timestamps you could get through CTC, however performance with CTC decoder is a bit degraded compared to tdt decoder but shouldn't be worse. \r\n\r\nI have created this space that gets timestamps from CTC and combines with output from TDT, It might help for your use case. https://huggingface.co/spaces/nvidia/parakeet-tdt_ctc-1.1b", "Thanks for your answer, @nithinraok . Is getting timestamps from tdt decoding something that will be released later? ", "Unfortunately, no plans for it yet! " ]
2024-07-11T15:01:36
2024-08-01T15:01:23
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The default seems to be rnnt but it fails if we specify compute_timestamps=True in decoding config. If I switch the decoder on this model to ctc, it works and also provides timestamps, but i see worse performace in terms of WERs.
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[NeMo-UX] Fix imports so local configuration of runs works again
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Updating num_weights check in unit_test of RETRO
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Due to underlying changes in MLM mcore RETRO, the total number of parameters slightly changed. We update the `assert num_weights ` accordingly in unit test for RETRO. # 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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[Nemo-UX] Including all trainable-params in a PEFT-checkpoint
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2024-07-11T12:56:30
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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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[NeMo-UX] Fix imports so local configuration of runs works again
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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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NeVa::forward - remove device syncs (torch.where) and vectorize over batch dimensions
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[ "@yaoyu-33 , could you please have another look and let the workflow be run?", "FWIW, I ran our little test (a `neva_pretrain.py` invocation) with `NEMO_TESTING=1` and things ran fine there.", "@yaoyu-33 , @ericharper , could you please run the workflow again? Thank you!", "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.", "bump to kill 'stale'.\r\ni checked that i have access to an internal cluster today, will check convergence soon", "Thanks you, @tfogal ! FWIW, the very last CI run turned out to be green", "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.", "Is it possible to move it forward? Commenting to remove the current stale status.", "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.", "> Remove stale label or comment or update or this will be closed in 7 days.\r\n\r\ncomment", "@yaoyu-33 , could you please enable the workflow so that we could merge it once the tests are green?", "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.", "\"\r\nWait it out\r\nGonna wait it out\r\nWait it out (be patient)\r\n\" The Patient, Tool", "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.", "Is there an interest in reviving this PR? I assume it is rather low impact with perf benefits assuming the tests are green. And green they were the last time CIs were run." ]
2024-07-11T12:34:07
2024-11-30T08:52:30
2024-11-28T02:04:04
NONE
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The following patch modifies the forward of the NeVa model (the `replace_media_embeddings` method) by introducing the following improvements: - Elimination of `torch.where` with a single argument. That call introduces device synchronizations when run on GPUs. - Media index selection manipulation in the construction of `padded_media_indices` can be done in a single vectorized call. - Reduction in the overall number of kernel launches. - Elimination of calls to `all`. This also causes device syncs. We can leave it, or run it in debug mode only. It is best to have a dedicated test instead of that assert, imho.
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add auto configurator to NeMo
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[ "General comment: NeMo uses google-style docstrings so please use that format.", "This is already a big PR which in this particular case is OK I feel like since it's a port of functionality that exists in the launcher now. But it seems like it's not rebased correctly and therefore it contains many un-related changes. Can this be fixed @dimapihtar ?", "> This is already a big PR which in this particular case is OK I feel like since it's a port of functionality that exists in the launcher now. But it seems like it's not rebased correctly and therefore it contains many un-related changes. Can this be fixed @dimapihtar ?\r\n\r\n@marcromeyn @ko3n1g \r\nno worries about that. It was my try to rebase PR to main branch but it still has a lot of conflicts so I've already created new one based on main branch. I keep this PR opened in order to not to miss @marcromeyn comments. I'll close this PR a bit later.\r\n\r\nNew PR:\r\nhttps://github.com/NVIDIA/NeMo/pull/10270" ]
2024-07-11T11:59:34
2024-09-10T07:55:53
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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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# What does this PR do ? Adds two workflows: * "NeMo Code freeze" * "NeMo Code release" # "NeMo Code freeze" Started via workflow dispatch. Input is the next version number. Does the following: * Reads $CURRENT_VERSION from main, check out new branch `r$CURRENT_VERSION` and pushes to origin and opens PR. Comments in that PR serve as platform for documenting changelog * Bumps `package_info.py` with $NEXT_VERSION and pushes to `main` # "NeMo Code release" Started via comment to PR. Creates GH release/tag, builds and pushes the wheel, closes PR # Additional Information This automation requires a couple of secrets like Twine credentials or Personal Access Tokens. We an environment `main` and couple these secrets to it such that they are only exposed when running on that protected branch. Secrets are secure as only code-owners permit changes to `main` and on other branches the secrets are not interpolated.
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Add KV-Cache for MegatronLMEncoderDecoderModel
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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 @Kurokabe,\r\nThank you for pointing out. Indeed, T5 inference is not supporting KV_cache yet. \r\nWe have plans to support it in the future. However, as of now, we are having other priorities.\r\n \r\n" ]
2024-07-11T10:27:21
2024-08-22T20:42:00
2024-08-22T01:52:52
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**Is your feature request related to a problem? Please describe.** I have been using the MegatronLMEncoderDecoderModel which seems to be the closest implementation for my need (encoder-decoder model), but when generating results (with the `decode` method), the generation becomes slower as it generates more tokens. My guess is that KV-Cache is not used for this model. After a lot of digging, I found that the MegatronGPTModel uses for inference `set_inference_key_value_memory` and `inference_max_sequence_len`. I have tried to set `set_inference_key_value_memory=True` during the first decoding iteration, then to False, as well as providing only 1 token at a time, similar to `GPTModelTextGenerationStrategy` https://github.com/NVIDIA/NeMo/blob/900ca0b8bac3d038b9854bd498967b0525aaeb35/nemo/collections/nlp/modules/common/text_generation_strategy.py#L312-L327 , but I am faced with many issues, and the usage isn't straightforward. To notice the generation speed decrease, you can wrap the for loop with tqdm: ```python from tqdm.auto import tqdm def decode( self, tokens_enc, enc_mask, num_tokens_to_generate, encoder_input=None, tokenizer=None, enc_output=None, enc_output_attn_mask=None, ignore_ids=[], bos_id=None, # If bos=None, will use tokenizer.bos_id unless explicitly set to something else. predicted_tokens_dec=None, batch_data=None, sampling_method: str = "greedy-search", sampling_kwargs: dict = {}, ): ... for i in tqdm(range(num_tokens_to_generate)): ``` **Describe the solution you'd like** It would be nice if KV-Cache could be used for the MegatronLMEncoderDecoderModel, or provide a snippet of code to use the `decode` method without any slowdown during the generation. **Describe alternatives you've considered** `MegatronGPTModel` seems to uses the KV-Cache, but is a decoder only model. Since `MegatronGPTModel` uses internally (to the lower level) a `ParallelTransformer`, like the `MegatronTransformerEncoderModule` and `MegatronTransformerDecoderModule`, a parameter could be specified to `MegatronGPTModel` to enable cross attention inside it to be used like a decoder, with an encoder output specified ```python def forward( self, input_ids, position_ids, attention_mask, enc_output=None, # Add this input loss_mask=None, labels=None, token_type_ids=None, layer_past=None, get_key_value=False, forward_method_parallel_output=None, encoder_input=None, set_inference_key_value_memory=False, inference_max_sequence_len=None, checkpoint_activations_all_layers=None, ): lm_output = self.language_model( input_ids, position_ids, attention_mask, encoder_output=encoder_output, # Add this layer_past=layer_past, get_key_value=get_key_value, encoder_input=encoder_input, set_inference_key_value_memory=set_inference_key_value_memory, inference_max_sequence_len=inference_max_sequence_len, checkpoint_activations_all_layers=checkpoint_activations_all_layers, ) ``` So that GPT model can be used with CrossAttention like [HuggingFace GPT2 model](https://github.com/huggingface/transformers/blob/6c1d0b069de22d7ed8aa83f733c25045eea0585d/src/transformers/models/gpt2/modeling_gpt2.py#L1091), because KV-Cache is implemented in `MegatronGPTModel.generate`
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RuntimeError: Error(s) in loading state_dict for MegaMolBARTModel after ANY fine tuning
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[ "I have also run 'pytest test_megamolbart_triton.py' pointing that to my 'fine tuned' .nemo file and it gives me the same.\r\n\r\n======================================================================= short test summary info =======================================================================\r\nERROR test_megamolbart_triton.py::test_seq_to_embedding_triton - RuntimeError: Error(s) in loading state_dict for MegaMolBARTModel:\r\nERROR test_megamolbart_triton.py::test_seq_to_hidden_triton - RuntimeError: Error(s) in loading state_dict for MegaMolBARTModel:\r\nERROR test_megamolbart_triton.py::test_hidden_to_seqs_triton - RuntimeError: Error(s) in loading state_dict for MegaMolBARTModel:\r\nERROR test_megamolbart_triton.py::test_samplings_triton - RuntimeError: Error(s) in loading state_dict for MegaMolBARTModel:\r\nERROR test_megamolbart_triton.py::test_seq_to_embedding_direct - RuntimeError: Error(s) in loading state_dict for MegaMolBARTModel:\r\nERROR test_megamolbart_triton.py::test_seq_to_hidden_direct - RuntimeError: Error(s) in loading state_dict for MegaMolBARTModel:\r\nERROR test_megamolbart_triton.py::test_hidden_to_seqs_direct - RuntimeError: Error(s) in loading state_dict for MegaMolBARTModel:\r\nERROR test_megamolbart_triton.py::test_samplings_direct - RuntimeError: Error(s) in loading state_dict for MegaMolBARTModel:\r\n========================================================================= 8 errors in 27.01s ", "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." ]
2024-07-11T05:27:52
2024-08-19T01:52:43
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**Describe the bug** I can successfully run my inference code on the default megamolbart.nemo, but as soon as I run any kind of fine tuning on it then I get the error RuntimeError: Error(s) in loading state_dict for MegaMolBARTModel. I've attached code, config and output for both finetune and infer stages. My desired outcome is to use the finetuning process to add in some of my own data, then call the inference code to get the SMILES string and the prediction. **Steps/Code to reproduce bug** **1. FineTune - code, config and output** Actually I get the same issue if I take trainer.fit(model) out of the code as well. My Fine Tune Code (company1.6_finetune_donothing.py.txt) just loads the default megamolbart.nemo using finetune_config.yaml.txt (doesn't alter anything if restore_from_path is filled in or not). The Result (company1.6_finetune_donothing_RESULT.txt) seems fine. [company1.6_finetune_donothing.py.txt](https://github.com/user-attachments/files/16171757/company1.6_finetune_donothing.py.txt) [finetune_config.yaml.txt](https://github.com/user-attachments/files/16171759/finetune_config.yaml.txt) [company1.6_finetune_donothing_RESULT.txt](https://github.com/user-attachments/files/16171760/company1.6_finetune_donothing_RESULT.txt) **2. Infer - code, config and output** I then run my infer code (company1.6_infer.py.txt) with the default infer.yaml file (infer.yaml.txt) with just the restore_from_path pointing to the file created in Step 1. NOTE: as can be seen in the 'Expected behavior' section, if I use the default megamolbart.nemo file then it all works as expected. [company1.6_infer.py.txt](https://github.com/user-attachments/files/16171785/company1.6_infer.py.txt) [infer.yaml.txt](https://github.com/user-attachments/files/16171786/infer.yaml.txt) [company1.6_infer_RESULT.txt](https://github.com/user-attachments/files/16171788/company1.6_infer_RESULT.txt) **TLDR;** This is my error: [NeMo I 2024-07-11 01:57:35 regex_tokenizer:254] Loading regex from file = /workspace/bionemo/tokenizers/molecule/megamolbart/vocab/megamolbart.model [NeMo I 2024-07-11 01:57:35 megatron_base_model:315] Padded vocab_size: 640, original vocab_size: 523, dummy tokens: 117. [NeMo W 2024-07-11 01:57:35 megatron_lm_encoder_decoder_model:240] Could not find encoder or decoder in config. This is probably because of restoring an old checkpoint. Copying shared model configs to encoder and decoder configs. [NeMo W 2024-07-11 01:57:35 megatron_lm_encoder_decoder_model:206] bias_gelu_fusion is deprecated. Please use bias_activation_fusion instead. [NeMo W 2024-07-11 01:57:35 megatron_lm_encoder_decoder_model:206] bias_gelu_fusion is deprecated. Please use bias_activation_fusion instead. Traceback (most recent call last): File "/workspace/bionemo/examples/molecule/megamolbart/company1.6_infer.py", line 59, in <module> inferer = load_model_for_inference(cfg, interactive=True) File "/workspace/bionemo/bionemo/triton/utils.py", line 238, in load_model_for_inference model = infer_class(cfg, interactive=interactive, **kwargs) File "/workspace/bionemo/bionemo/model/molecule/infer.py", line 40, in __init__ super().__init__( File "/workspace/bionemo/bionemo/model/core/infer.py", line 468, in __init__ super().__init__( File "/workspace/bionemo/bionemo/model/core/infer.py", line 146, in __init__ self.model = self.load_model(cfg, model=model, restore_path=restore_path, strict=strict_restore_from_path) File "/workspace/bionemo/bionemo/model/core/infer.py", line 206, in load_model model = restore_model( File "/workspace/bionemo/bionemo/model/utils.py", line 363, in restore_model model = model_cls.restore_from( File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/nlp_model.py", line 465, in restore_from return super().restore_from( File "/usr/local/lib/python3.10/dist-packages/nemo/core/classes/modelPT.py", line 442, in restore_from instance = cls._save_restore_connector.restore_from( File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/parts/nlp_overrides.py", line 751, in restore_from super().load_instance_with_state_dict(instance, state_dict, strict) File "/usr/local/lib/python3.10/dist-packages/nemo/core/connectors/save_restore_connector.py", line 203, in load_instance_with_state_dict instance.load_state_dict(state_dict, strict=strict) File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/nlp_model.py", line 447, in load_state_dict results = super(NLPModel, self).load_state_dict(state_dict, strict=strict) File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 2152, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for MegaMolBARTModel: Missing key(s) in state_dict: "enc_dec_model.encoder_embedding.word_embeddings.weight", "enc_dec_model.encoder_embedding.position_embeddings.weight", "enc_dec_model.decoder_embedding.word_embeddings.weight", "enc_dec_model.decoder_embedding.position_embeddings.weight", "enc_dec_model.enc_dec_model.encoder.model.layers.0.input_layernorm.weight", "enc_dec_model.enc_dec_model.encoder.model.layers.0.input_layernorm.bias", "enc_dec_model.enc_dec_model.encoder.model.layers.0.self_attention.query_key_value.weight", "enc_dec_model.enc_dec_model.encoder.model.layers.0.self_attention.query_key_value.bias", "enc_dec_model.enc_dec_model.encoder.model.layers.0.self_attention.dense.weight", **Expected behavior** Run the Inference code using the default downloadable megamolbart.nemo and it works fine as can be seen here: [company1.6_infer_RESULT_DEFAULT_NEMO.txt](https://github.com/user-attachments/files/16171714/company1.6_infer_RESULT_DEFAULT_NEMO.txt) aka we can get simple things like Reconstructed SMILES: from the system. **Environment overview (please complete the following information)** - Environment location: Azure - Method of NeMo install: Docker - If method of install is [Docker], provide `docker pull` & `docker run` commands used: docker run -it --rm --gpus all -v /home/azureuser/company_info:/workspace/bionemo/company_info nvcr.io/nvidia/clara/bionemo-framework:1.3 bash also tried docker run -it --rm --gpus all -v /home/azureuser/company_info:/workspace/bionemo/company_info nvcr.io/nvidia/clara/bionemo-framework:1.6 bash **Environment details** NVIDIA docker image is used **Additional context** Azure T4 GPU
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9,684
Cherry pick: LITA Integration
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2024-07-11T05:10:57
2024-07-11T23:18:09
2024-07-11T23:18:08
CONTRIBUTOR
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# What does this PR do ? To cherry pick https://github.com/NVIDIA/NeMo/pull/9578 LITA integration into 2.0.0rc1. That PR was already merged. See details there. Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] NLP and 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 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**: - [X] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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[NeMo-UX] Added Megatron loss function and LR scheduler
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2024-07-11T05:03:08
2024-08-13T01:52:46
2024-08-13T01:52:46
COLLABORATOR
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# What does this PR do ? The PR adds Megatron loss function and LR scheduler for GPT pretrain. Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] LLM # Changelog - Parametrized GPT model loss function - Added `MegatronAnnealing` learning rate and weight decay scheduler - Added `MegatronMaskedTokenLossReduction` loss function # 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**: - [ x] 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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9,682
Speeds up copying of necessary artifact files with SaveRestoreConnector
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[ "I'm looking into the test failures. Very bizarre since running the complete suite locally hangs and I cannot reproduce the failures by running the failed cases individually", "I figured out the test failure issue. It turned out in my unit test I `os.chdir` into a temporary directory and don't `os.chdir` back into the CWD. This causes failures for tests in different scopes. So I wasn't seeing the issue when running just the failed tests by themselves, but instead saw it only when running the entire suite together." ]
2024-07-11T03:13:08
2024-07-17T18:42:06
2024-07-17T18:42:04
COLLABORATOR
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# What does this PR do ? Previously, the SaveRestoreConnector would copy and untar entire checkpoints just to copy out a tokenizer. For models in the >100GB, this led to timeouts since only rank=0 did this work, while other ranks moved on and waited at an all-gather barrier (observed NCCL timeout at 10min). Revision of #9299 that keeps most of the core logic surrounding tempdir creation
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# What does this PR do ? Fix few issues and docs for neva and clip in r2.0.0rc1 **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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Adding support for mcore T5 Eval - SFT - PEFT
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Fix for `train.controlnet.controlnet_v1_5_1node_100steps`
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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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Enabling bias_dropout_add_fused with no bias term
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2024-07-10T21:18:49
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# What does this PR do ? Specific LLM/VLM hang when using specific PP settings and with the bias term is set to false hang. By enabling a fused dropout_add_fused we should fix this problem as well as speed up training of LLM/VLM that do not have the bias term **Collection**: nlp # Changelog - Add specific line by line info of high level changes in this PR. - nemo/collections/nlp/modules/common/megatron/transformer.py - Enabling the bias_dropout_add_fusion to be used for both bias and non bias enabled - nemo/collections/nlp/modules/common/megatron/fused_bias_dropout_add.py - Check whether the bias term is there and choose relevant fused function # 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**: - [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [N/A] Did you write any new necessary tests? - [ X] Did you add or update any necessary documentation? - [N/A] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [N/A] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [N/A] 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 #6396
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Release `2.0.0rc1`
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🚀 PR to release NeMo `2.0.0rc1`. 📝 Please remember the following to-do's before merge: - [ ] Fill-in the comment `Highlights` - [ ] Review the comment `Detailed Changelogs` 🚨 Please also keep in mind to _not_ delete the headings of the task commits. They are required by the post-merge automation. 🙏 Please merge this PR only if the CI workflow completed successfully.
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[NeMo-UX] Make 'load_directly_on_device' configurable
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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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Util for measuring MFU?
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[ "We will add support for this in this repo: computing Model Flops Utilization. \r\nOur team has this task in the backlog and will be working on it.", "@dementrock I'll be adding this support this week. Any particular model(s) that you are looking at? \r\n@ericharper Could you assign this to me?", "@ericharper can you help review and merge this PR- #9735?", "@malay-nagda thanks a lot! Support for the common models like llama / mistral would be sufficient for us for now", "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." ]
2024-07-10T18:48:02
2024-08-23T23:08:59
2024-08-23T23:08:59
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We'd like to track model flops utilization (MFU) during training. Is there any pointers for how to do this or would it be possible to add support for this within NeMo?
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Gemma 2
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[ "Hi! Thank you for your work!\r\n\r\nWhen continue pretrain the model using your updated gemma-related code, I found that _**the initial loss is around 9.x**_, while _**hf was about 2.x**_. Are there still some differences that are not aligned?\r\n", "> Hi! Thank you for your work!\r\n> \r\n> When continue pretrain the model using your updated gemma-related code, I found that _**the initial loss is around 9.x**_, while _**hf was about 2.x**_. Are there still some differences that are not aligned?\r\n\r\nHi @Emperorizzis, thanks for your interest! It should work the same as HF but there could be a bug somewhere. Do you mind sharing your config and/or run command? ", "> > Hi! Thank you for your work!\r\n> > When continue pretrain the model using your updated gemma-related code, I found that _**the initial loss is around 9.x**_, while _**hf was about 2.x**_. Are there still some differences that are not aligned?\r\n> \r\n> Hi @Emperorizzis, thanks for your interest! It should work the same as HF but there could be a bug somewhere. Do you mind sharing your config and/or run command?\r\n\r\n Hi @cuichenx, I used your modeling file, as well as the native megatron (f3a3020031f384ddafd9b7e9f3a587798c0aea21) for training (with a few additional arguments). Below are my megatron arguments configuration during training:\r\n\r\n```\r\n------------------------ arguments ------------------------\r\n accumulate_allreduce_grads_in_fp32 .............. True\r\n adam_beta1 ...................................... 0.9\r\n adam_beta2 ...................................... 0.95\r\n adam_eps ........................................ 1e-08\r\n adaptive_seq_len ................................ False\r\n add_bias_attn_fc ................................ True\r\n add_bias_linear ................................. False\r\n add_bias_linear_fc .............................. True\r\n add_position_embedding .......................... True\r\n add_qkv_bias .................................... False\r\n adlr_autoresume ................................. False\r\n adlr_autoresume_interval ........................ 1000\r\n apply_layernorm_1p .............................. True\r\n apply_query_key_layer_scaling ................... False\r\n apply_residual_connection_post_layernorm ........ False\r\n apply_rope_fusion ............................... False\r\n async_tensor_model_parallel_allreduce ........... False\r\n attention_dropout ............................... 0.0\r\n attention_head_type ............................. None\r\n attention_softmax_in_fp32 ....................... False\r\n auto_detect_ckpt_format ......................... False\r\n barrier_with_L1_time ............................ True\r\n bert_binary_head ................................ True\r\n bert_embedder_type .............................. megatron\r\n bert_load ....................................... None\r\n bf16 ............................................ True\r\n bias_dropout_fusion ............................. True\r\n bias_gelu_fusion ................................ False\r\n bias_swiglu_fusion .............................. True\r\n biencoder_projection_dim ........................ 0\r\n biencoder_shared_query_context_model ............ False\r\n block_data_path ................................. None\r\n check_for_nan_in_loss_and_grad .................. True\r\n ckpt_fully_parallel_save ........................ False\r\n ckpt_step ....................................... None\r\n classes_fraction ................................ 1.0\r\n clip_grad ....................................... 1.0\r\n clone_scatter_output_in_embedding ............... True\r\n consumed_train_samples .......................... 0\r\n consumed_valid_samples .......................... 0\r\n context_parallel_size ........................... 1\r\n create_attention_mask_in_dataloader ............. True\r\n cvcuda_image_processing ......................... False\r\n data_cache_path ................................. None\r\n data_dir ........................................ None\r\n data_impl ....................................... mmap\r\n data_parallel_random_init ....................... False\r\n data_parallel_size .............................. 2\r\n data_path ....................................... ['/basedir/mcore_data/gemma2/testing/testing']\r\n data_per_class_fraction ......................... 1.0\r\n data_sharding ................................... True\r\n dataloader_type ................................. single\r\n dataset ......................................... None\r\n decoder_num_layers .............................. None\r\n decoder_seq_length .............................. None\r\n decoupled_lr .................................... None\r\n decoupled_min_lr ................................ None\r\n delay_grad_reduce ............................... True\r\n delay_param_gather .............................. False\r\n dino_bottleneck_size ............................ 256\r\n dino_freeze_last_layer .......................... 1\r\n dino_head_hidden_size ........................... 2048\r\n dino_local_crops_number ......................... 10\r\n dino_local_img_size ............................. 96\r\n dino_norm_last_layer ............................ False\r\n dino_teacher_temp ............................... 0.07\r\n dino_warmup_teacher_temp ........................ 0.04\r\n dino_warmup_teacher_temp_epochs ................. 30\r\n dist_ckpt_format ................................ torch_dist\r\n distribute_saved_activations .................... False\r\n distributed_backend ............................. nccl\r\n distributed_timeout_minutes ..................... 60\r\n dummy_load ...................................... \r\n embed_layernorm ................................. False\r\n embedding_path .................................. None\r\n empty_unused_memory_level ....................... 0\r\n enable_one_logger ............................... False\r\n enable_parallel_output .......................... True\r\n enable_shared_expert ............................ False\r\n encoder_num_layers .............................. 42\r\n encoder_seq_length .............................. 64\r\n end_weight_decay ................................ 0.1\r\n eod_mask_loss ................................... False\r\n epochs .......................................... None\r\n eval_dev ........................................ False\r\n eval_fp32 ....................................... False\r\n eval_interval ................................... 1000000000\r\n eval_iters ...................................... 1\r\n evidence_data_path .............................. None\r\n exit_duration_in_mins ........................... None\r\n exit_interval ................................... None\r\n exit_on_missing_checkpoint ...................... False\r\n exit_signal_handler ............................. False\r\n expert_interval ................................. 2\r\n expert_model_parallel_size ...................... 1\r\n expert_tensor_parallelism ....................... False\r\n extra_vocab_size ................................ 0\r\n ffn_hidden_size ................................. 14336\r\n finetune ........................................ False\r\n fp16 ............................................ False\r\n fp16_lm_cross_entropy ........................... False\r\n fp32_residual_connection ........................ False\r\n fp8 ............................................. None\r\n fp8_amax_compute_algo ........................... most_recent\r\n fp8_amax_history_len ............................ 1\r\n fp8_interval .................................... 1\r\n fp8_margin ...................................... 0\r\n fp8_wgrad ....................................... True\r\n freeze_clip_vision_tower ........................ False\r\n freeze_llm ...................................... False\r\n generation_length ............................... None\r\n global_batch_size ............................... 8\r\n glu_activation .................................. None\r\n gradient_accumulation_fusion .................... True\r\n group_query_attention ........................... True\r\n head_lr_mult .................................... 1.0\r\n hidden_dropout .................................. 0.0\r\n hidden_size ..................................... 3584\r\n hysteresis ...................................... 2\r\n ict_head_size ................................... None\r\n ict_load ........................................ None\r\n image_aspect_ratio .............................. square\r\n image_folder .................................... \r\n image_size ...................................... None\r\n img_h ........................................... 224\r\n img_w ........................................... 224\r\n indexer_batch_size .............................. 128\r\n indexer_log_interval ............................ 1000\r\n inference_batch_times_seqlen_threshold .......... 512\r\n init_method_std ................................. 0.02\r\n init_method_xavier_uniform ...................... False\r\n initial_loss_scale .............................. 4294967296\r\n input_len ....................................... 1\r\n intermediate_size ............................... None\r\n iter_per_epoch .................................. 1250\r\n keep_last ....................................... False\r\n kv_channels ..................................... 256\r\n lazy_mpu_init ................................... None\r\n load ............................................ /basedir/mcore_models/gemma-2-9b-to-mcore-tp4-pp1-te-nemo\r\n local_rank ...................................... None\r\n log_batch_size_to_tensorboard ................... False\r\n log_interval .................................... 1\r\n log_learning_rate_to_tensorboard ................ True\r\n log_loss_scale_to_tensorboard ................... True\r\n log_memory_to_tensorboard ....................... False\r\n log_num_zeros_in_grad ........................... False\r\n log_params_norm ................................. False\r\n log_progress .................................... False\r\n log_throughput .................................. True\r\n log_timers_to_tensorboard ....................... False\r\n log_validation_ppl_to_tensorboard ............... False\r\n log_world_size_to_tensorboard ................... False\r\n loss_scale ...................................... None\r\n loss_scale_window ............................... 1000\r\n lr .............................................. 3e-05\r\n lr_decay_iters .................................. 1953125\r\n lr_decay_samples ................................ None\r\n lr_decay_style .................................. cosine\r\n lr_warmup_fraction .............................. None\r\n lr_warmup_init .................................. 0.0\r\n lr_warmup_iters ................................. 0\r\n lr_warmup_samples ............................... 0\r\n make_vocab_size_divisible_by .................... 128\r\n manual_gc ....................................... False\r\n manual_gc_eval .................................. True\r\n manual_gc_interval .............................. 0\r\n mask_factor ..................................... 1.0\r\n mask_prob ....................................... 0.15\r\n mask_type ....................................... random\r\n masked_softmax_fusion ........................... False\r\n max_padding_length .............................. None\r\n max_position_embeddings ......................... 64\r\n max_tokens_to_oom ............................... 12000\r\n merge_file ...................................... None\r\n micro_batch_size ................................ 1\r\n min_loss_scale .................................. 1.0\r\n min_lr .......................................... 3e-06\r\n mm_projector_type ............................... None\r\n mm_use_im_patch_token ........................... False\r\n mm_use_im_start_end ............................. False\r\n mm_vision_select_layer .......................... None\r\n mmap_bin_files .................................. True\r\n mock_data ....................................... False\r\n moe ............................................. False\r\n moe_aux_loss_coeff .............................. 0.0\r\n moe_eval_capacity_factor ........................ 1.0\r\n moe_expert_parallel_size ........................ None\r\n moe_ffn_hidden_size ............................. None\r\n moe_grouped_gemm ................................ False\r\n moe_input_feature_slicing ....................... False\r\n moe_input_jitter_eps ............................ None\r\n moe_loss_coeff .................................. 0.01\r\n moe_min_capacity ................................ 4\r\n moe_per_layer_logging ........................... False\r\n moe_router_load_balancing_type .................. aux_loss\r\n moe_router_topk ................................. 2\r\n moe_token_dispatcher_type ....................... allgather\r\n moe_token_dropping .............................. False\r\n moe_topk ........................................ 1\r\n moe_train_capacity_factor ....................... 1.0\r\n moe_z_loss_coeff ................................ None\r\n n_head_kv ....................................... None\r\n nccl_communicator_config_path ................... None\r\n no_load_optim ................................... None\r\n no_load_rng ..................................... None\r\n no_persist_layer_norm ........................... False\r\n no_save_optim ................................... None\r\n no_save_rng ..................................... None\r\n norm_epsilon .................................... 1e-06\r\n normalization ................................... RMSNorm\r\n num_attention_heads ............................. 16\r\n num_channels .................................... 3\r\n num_classes ..................................... 1000\r\n num_experts ..................................... None\r\n num_fewshot ..................................... None\r\n num_layers ...................................... 42\r\n num_layers_per_virtual_pipeline_stage ........... None\r\n num_query_groups ................................ 8\r\n num_workers ..................................... 8\r\n one_logger_entity ............................... hwinf_dcm\r\n one_logger_project .............................. e2e-tracking\r\n one_logger_run_name ............................. None\r\n onnx_safe ....................................... None\r\n openai_gelu ..................................... False\r\n optimizer ....................................... adam\r\n out_seq_length .................................. 1024\r\n output_bert_embeddings .......................... False\r\n overlap_grad_reduce ............................. False\r\n overlap_p2p_comm ................................ False\r\n overlap_param_gather ............................ False\r\n override_opt_param_scheduler .................... False\r\n params_dtype .................................... torch.bfloat16\r\n patch_dim ....................................... 16\r\n patch_size ...................................... None\r\n patch_tokenizer_type ............................ Gemma2Tokenizer\r\n perform_initialization .......................... True\r\n pipeline_model_parallel_size .................... 1\r\n pipeline_model_parallel_split_rank .............. None\r\n position_embedding_type ......................... rope\r\n position_encoding_2d ............................ False\r\n pretrained_checkpoint ........................... None\r\n profile ......................................... False\r\n profile_ranks ................................... [0]\r\n profile_step_end ................................ 12\r\n profile_step_start .............................. 10\r\n qk_layernorm .................................... False\r\n query_in_block_prob ............................. 0.1\r\n rampup_batch_size ............................... None\r\n rank ............................................ 0\r\n recompute_granularity ........................... None\r\n recompute_method ................................ uniform\r\n recompute_num_layers ............................ None\r\n repetition_penalty .............................. 1.1\r\n reset_attention_mask ............................ False\r\n reset_position_ids .............................. False\r\n retriever_report_topk_accuracies ................ []\r\n retriever_score_scaling ......................... False\r\n retriever_seq_length ............................ 256\r\n retro_add_retriever ............................. False\r\n retro_attention_gate ............................ 1\r\n retro_cyclic_train_iters ........................ None\r\n retro_encoder_attention_dropout ................. 0.1\r\n retro_encoder_hidden_dropout .................... 0.1\r\n retro_encoder_layers ............................ 2\r\n retro_num_neighbors ............................. 2\r\n retro_num_retrieved_chunks ...................... 2\r\n retro_project_dir ............................... None\r\n retro_verify_neighbor_count ..................... True\r\n rotary_base ..................................... 10000\r\n rotary_interleaved .............................. False\r\n rotary_percent .................................. 1.0\r\n rotary_scale_factor ............................. 1\r\n rotary_seq_len_interpolation_factor ............. None\r\n router_type ..................................... topk\r\n sample_rate ..................................... 1.0\r\n save ............................................ /basedir/output_mcore_models/gemma-2-9b-tp4-pp1-for-test\r\n save_interval ................................... 1000000000\r\n scatter_gather_tensors_in_pipeline .............. True\r\n seed ............................................ 1234\r\n seq_length ...................................... 64\r\n sequence_parallel ............................... True\r\n sgd_momentum .................................... 0.9\r\n shared_moe_ffn_hidden_size ...................... None\r\n short_seq_prob .................................. 0.1\r\n skip_train ...................................... False\r\n sliding_window .................................. None\r\n source_seq_len .................................. None\r\n spec ............................................ None\r\n split ........................................... 100,0,0\r\n squared_relu .................................... False\r\n standalone_embedding_stage ...................... False\r\n start_weight_decay .............................. 0.1\r\n swiglu .......................................... False\r\n swin_backbone_type .............................. tiny\r\n target_seq_len .................................. None\r\n task_list ....................................... all\r\n temperature ..................................... 1.0\r\n tensor_model_parallel_size ...................... 4\r\n tensorboard_dir ................................. /basedir/testing/tensorboard\r\n tensorboard_log_interval ........................ 1\r\n tensorboard_queue_size .......................... 1000\r\n test_data_path .................................. None\r\n test_mode ....................................... False\r\n text_generate_gt_file ........................... \r\n text_generate_input_file ........................ \r\n text_generate_output_file ....................... \r\n time ............................................ False\r\n timing_log_level ................................ 0\r\n timing_log_option ............................... minmax\r\n titles_data_path ................................ None\r\n tokenizer_model ................................. /basedir/hf_models/gemma-2-9b\r\n tokenizer_type .................................. NullTokenizer\r\n top_k ........................................... 0\r\n top_p ........................................... 0.0\r\n tp_comm_bulk_dgrad .............................. True\r\n tp_comm_bulk_wgrad .............................. True\r\n tp_comm_overlap ................................. False\r\n tp_comm_overlap_ag .............................. True\r\n tp_comm_overlap_cfg ............................. None\r\n tp_comm_overlap_rs .............................. True\r\n tp_comm_split_ag ................................ True\r\n tp_comm_split_rs ................................ True\r\n train_data ...................................... None\r\n train_data_path ................................. None\r\n train_iters ..................................... 1953125\r\n train_samples ................................... None\r\n transformer_impl ................................ transformer_engine\r\n transformer_pipeline_model_parallel_size ........ 1\r\n transformer_timers .............................. False\r\n transformer_type ................................ megatron\r\n tune_mm_mlp_adapter ............................. False\r\n untie_embeddings_and_output_weights ............. False\r\n use_alibi_mask .................................. False\r\n use_checkpoint_args ............................. False\r\n use_checkpoint_opt_param_scheduler .............. False\r\n use_cpu_initialization .......................... None\r\n use_dist_ckpt ................................... False\r\n use_distributed_optimizer ....................... True\r\n use_flash_attn .................................. False\r\n use_llama2_rotary_position_embeddings ........... False\r\n use_mcore_models ................................ True\r\n use_mistral_rotary_position_embeddings .......... False\r\n use_normhead .................................... False\r\n use_one_sent_docs ............................... False\r\n use_ring_exchange_p2p ........................... False\r\n use_rotary_position_embeddings .................. False\r\n use_tutel ....................................... False\r\n valid_data ...................................... None\r\n valid_data_path ................................. None\r\n variable_seq_lengths ............................ False\r\n verbosity ....................................... INFO\r\n version ......................................... plain\r\n virtual_pipeline_model_parallel_size ............ None\r\n vision_backbone_type ............................ vit\r\n vision_pretraining .............................. False\r\n vision_pretraining_type ......................... classify\r\n vision_tower .................................... \r\n vocab_extra_ids ................................. 0\r\n vocab_file ...................................... None\r\n vocab_size ...................................... -1\r\n wandb_exp_name .................................. \r\n wandb_project ................................... \r\n wandb_save_dir .................................. \r\n weight_decay .................................... 0.1\r\n weight_decay_incr_style ......................... constant\r\n world_size ...................................... 8\r\n yaml_cfg ........................................ None\r\n z_loss_weight ................................... 0.0\r\n-------------------- end of arguments ---------------------\r\n```\r\n\r\nAnd below are the losses of first 5 steps:\r\n```\r\n... lm loss: 8.237988E+00 | loss scale: 1.0 | grad norm: 354.350 ...\r\n... lm loss: 4.174296E+00 | loss scale: 1.0 | grad norm: 68.632 ...\r\n... lm loss: 4.116720E+00 | loss scale: 1.0 | grad norm: 43.852 ...\r\n... lm loss: 3.366329E+00 | loss scale: 1.0 | grad norm: 28.838 ...\r\n... lm loss: 3.328519E+00 | loss scale: 1.0 | grad norm: 26.727 ...\r\n```\r\n\r\nThe data should be fine; I sampled 1 million articles from enwiki and tokenized them using the tokenizer from gemma2-9b.\r\n", "@Emperorizzis I verified inference and finetuning performance with the code in nemo framework, and the accuracy looked okay.\r\nMaybe you can double check the new components are included in your implementation? These are 1) logit soft capping, 2) alternating SWA, 3) post attn/mlp layernorm, 4) custom scale in attention", "Hi! Thank you for your response! After our testing, we found that there **seems to be two bugs** :\r\n\r\n**1) Sliding Window**\r\n\r\n```python\r\ndef get_swa(seq_q, seq_kv, w):\r\n \"\"\"Create the equivalent attention mask fro SWA in [seq_q, seq_kv] shape\"\"\"\r\n m = torch.ones(seq_q, seq_kv, dtype=torch.bool, device=\"cuda\")\r\n ### original\r\n # mu = torch.triu(m, diagonal=seq_kv - seq_q - w[0])\r\n ### after modified\r\n mu = torch.triu(m, diagonal=seq_kv - seq_q - w[0] + 1)\r\n ml = torch.tril(mu, diagonal=seq_kv - seq_q + w[1])\r\n ml = ~ml\r\n return ml\r\n```\r\n\r\nFor example:\r\n\r\n```text\r\n【example】\r\nseq_len=4\r\nsliding_window=2\r\n\r\nget_swa(4, 4, (2, 0))\r\n\r\noriginal answer (×)\r\ntensor([[False, True, True, True],\r\n [False, False, True, True],\r\n [ False, False, False, True],\r\n [ True, False, False, False]])\r\n\r\nafter modified (aligned with hf, √):\r\ntensor([[False, True, True, True],\r\n [False, False, True, True],\r\n [ True, False, False, True],\r\n [ True, True, False, False]])\r\n```\r\n\r\n**2) The odd and even layers of the sliding window are reversed**\r\n\r\nhttps://github.com/huggingface/transformers/commit/a695c18649fc6ab4b1fb1d9c8cfa9258c5908e2a\r\n\r\n```text\r\n... ...\r\n\r\nself.layer_number = max(1, layer_number)\r\n\r\n# self.window_size = None\r\n# if self.layer_number % 2 == 0:\r\n# self.window_size = config.window_size\r\nself.sliding_window_size = None\r\n### original\r\n# if self.layer_number % 2 == 0:\r\n### after modified\r\nif self.layer_number % 2 != 0:\r\n self.sliding_window_size = (config.sliding_window_size, 0)\r\n\r\n... ...\r\n```\r\n\r\nAnd we also found that **adding or not adding \"\\<bos>\" token when continue pretraining Gemma Base model has a significant impact on the initial loss** (possibly a difference of **up to double**).\r\nAdditionally, we have discovered that the Gemma Base model is **highly sensitive** to the data and the length of the data.\r\n\r\n**Hope this finding can help other people.**\r\n\r\nThank you again for your work!", "@Emperorizzis @cuichenx I meet the same problem (high initial loss) for continual pre-training gemma2-2B model.\r\nCould you share more details on how to correctly set up the pre-training? Many thanks for that :)\r\n\r\nSFT scripts works well (training loss < 3), but pertaining loss (training loss > 20) is extremely high. \r\n\r\n### Experiments\r\nThe following experiments are running with the same docker `nvcr.io/nvidia/nemo:24.05.llama3.1` and the same dataset `databricks-dolly-15k`.\r\n\r\n![image](https://github.com/user-attachments/assets/94d4e204-dd74-426e-adf5-4636b7513d20)\r\n\r\n**SFT training:**\r\nFollowing the [official document](https://docs.nvidia.com/nemo-framework/user-guide/latest/llms/gemma/sft.html), I set up the SFT training.\r\n[A] gemma2_2b_sft_bos_rc2:\r\n-codebase: https://github.com/NVIDIA/NeMo/tree/r2.0.0rc1\r\n-data: databricks-dolly-15k/train.jsonl\r\n-add bos: Yes\r\n\r\n[B]gemma2_2b_sft_0830_main: \r\n-codebase: https://github.com/NVIDIA/NeMo/tree/1ce9089143b0136523cb08bb37941a35c9b08307\r\n-data: databricks-dolly-15k/train.jsonl\r\n-add bos: Yes\r\n\r\n**Continual pre-training**\r\nI didn't find the pre-training recipe for gemma. Thus I just preprocess the dataset with following cmd.\r\n```\r\npython ${NEMO_CODE_PATH}/scripts/nlp_language_modeling/preprocess_data_for_megatron.py \\\r\n --input ${DATASET_PATH}/sample.jsonl \\\r\n --json-keys text \\\r\n --tokenizer-library huggingface \\\r\n --dataset-impl mmap \\\r\n --tokenizer-type ${MODEL_PATH}/gemma2_2b \\\r\n --output-prefix ${DATASET_PATH}/sample_gemma2_preprocessed/sample \\\r\n --append-eod \\\r\n --workers=1\r\n```\r\n[C]gemma2_2b_sft_pretraining\r\n-codebase: https://github.com/NVIDIA/NeMo/tree/1ce9089143b0136523cb08bb37941a35c9b08307\r\n-data: databricks-dolly-15k/train.jsonl\r\n-add bos: No\r\n\r\n[D]gemma2_2b_sft_pretraining_bos\r\n-codebase: https://github.com/NVIDIA/NeMo/tree/1ce9089143b0136523cb08bb37941a35c9b08307\r\n-data: databricks-dolly-15k/train.jsonl\r\n-add bos: Yes\r\nI 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.\r\n```\r\n for sentence in Encoder.splitter.tokenize(text):\r\n sentence_ids = Encoder.tokenizer.text_to_ids(sentence)\r\n sentence_ids = [Encoder.tokenizer.bos_id] + sentence_ids\r\n if len(sentence_ids) > 0:\r\n doc_ids.append(sentence_ids)\r\n if len(doc_ids) > 0 and self.args.append_eod:\r\n doc_ids[-1].append(Encoder.tokenizer.eos_id)\r\n ids[key] = doc_ids\r\n```\r\n\r\n### Observations\r\n(1) SFT tuning is normal, but also differs with the codebase changes. => [A] and [B] adopts the same data and docker, but receives different loss curve.\r\n(2) BOS matters a lot as @Emperorizzis said. It got further studied by [https://unsloth.ai/blog/gemma-bugs](https://unsloth.ai/blog/gemma-bugs).\r\n\r\n\r\n### Looking for help\r\nCould @cuichenx provide a stable codebase version tag and the necessary guidance on how to run the NeMo for Gemma2 model for both pre-training? Thanks for your great work! ", "Thanks for reporting these issues! I will look into them this week." ]
2024-07-10T18:36:34
2024-09-04T01:06:46
2024-07-12T05:54:25
COLLABORATOR
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# What does this PR do ? Duplicate of #9587 for release branch Also includes transformers version update from #9606 **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**: - [X] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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PR_kwDOC_bI7s50_2cD
9,671
enables default data step in megatron parallel to operate on a wider variety of tensors - second try
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[ "@cuichenx , it sounded like @ashors1 wanted you to review and approve this PR? Let me know if someone else is better. While @marcromeyn is out, who should we have review and help us get things we need on the BioNeMo2 side into NeMo2 in general?", "> @cuichenx , it sounded like @ashors1 wanted you to review and approve this PR? Let me know if someone else is better. While @marcromeyn is out, who should we have review and help us get things we need on the BioNeMo2 side into NeMo2 in general?\r\n\r\nI think it generally depends on the PR, but I can approve this one as well given that Marc approved the original attempt previously. We just need the CI to pass; then we should be able to merge" ]
2024-07-10T17:25:22
2024-07-15T16:12:49
2024-07-15T16:12:49
CONTRIBUTOR
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# What does this PR do ? Add more flexibility to the default data step inside megatron parallel. # 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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Canary Adapters tutorial
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2024-07-10T17:23:29
2024-07-16T19:03:42
2024-07-16T19:03:40
COLLABORATOR
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# What does this PR do ? Adds a tutorial for Canary Adapters **Collection**: [ASR] # Changelog - Add tutorial for canary adapters - Adds Neural Module support for preserving freeze and unfreeze partially # 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) - [x] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] New Feature - [ ] Bugfix - [ ] Documentation
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9,669
Parametrize FPS group
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# What does this PR do ? When doing a parallel save, exchange metadata in a whole world instead of a DP group. For yet unknown reason `all_gather_object` crashes with `group != world` **Collection**: NLP # Changelog - Parametrize metadata exchange group for parallel save - Turn on parallel save by default # Usage * By default metadata will be exchanged within the whole world. Set `model.dist_ckpt_parallel_save_within_dp=True` to exchange within DP. * Set `model.dist_ckpt_parallel_save=False` to turn off parallel save ```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. # Additional Information * Related to # (issue)
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Fix the serialization of partial functions in nemo 2.0
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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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Update llama-3 PEFT notebook to download model from NGC
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[ "I am providing additional copyedits to the file, ReadMe.rst, herein. I was not able to provide inline edits because part of the file was read-only.\r\n\r\n**Line 4 fix punctuation**\r\n\r\n`Llama 3 <https://blogs.nvidia.com/blog/meta-llama3-inference-acceleration/>`_ is an open-source large language model by Meta that delivers state-of-the-art performance on popular industry benchmarks.\r\n\r\n**Line 6 fix punctuation**\r\n\r\n`Low-Rank Adaptation (LoRA) <https://arxiv.org/pdf/2106.09685>`__ has emerged as a popular Parameter-Efficient Fine-Tuning (PEFT) technique that tunes a very small number of additional parameters as compared to full fine-tuning, thereby reducing the compute required.\r\n\r\n**Line 19 revise for clarity**\r\n\r\nNIM enables seamless deployment of multiple LoRA adapters (referred to as ‘multi-LoRA’) on the same base model. It dynamically loads adapter weights based on incoming requests at runtime. This flexibility allows handling inputs from various tasks or use cases without requiring a unique model for each individual scenario. For further details, consult the NIM documentation for LLMs.\r\n\r\n**Line 24 delete the following sentence, it is sufficient to retain the bullets**\r\n\r\nIn order to proceed, ensure that you have met the following requirements:\r\n\r\n**Line 103/109 fix punctuation**\r\n\r\n2. Prepare the LoRA model store. \r\n\r\n**Line 110/116 fix grammar**\r\n\r\nTo ensure the model store is organized as expected, create a folder named llama3-8b-pubmed-qa and move your .nemo checkpoint there.\r\n\r\n**Line 122/128 revise for clarity**\r\n\r\nEnsure that the LoRA model store directory follows this structure: the model name should be a sub-folder containing the .nemo file.\r\n\r\n**Line 138/144 fix punctuation**\r\n\r\n3. Set up NIM. \r\n\r\n**Line 170/176 fix grammar**\r\n\r\nThe first time you run the command, it will download the model and cache it in $NIM_CACHE_PATH so subsequent deployments are even faster. There are several options to configure NIM other than the ones listed above. You can find a full list in the NIM configuration documentation.\r\n\r\n**Line 173/179 fix punctuation**\r\n\r\n4. Start the notebook.\r\n\r\n**Line 175/181 fix grammar**\r\n\r\nFrom another terminal, follow the same instructions as the previous notebook to launch Jupyter Lab, and then navigate to this notebook.\r\n\r\n**Line 178/185 revise for clarity**\r\n\r\nYou can use the same NeMo Framework docker container in which Jupyter Lab was previously installed." ]
2024-07-10T08:49:58
2024-07-10T23:10:46
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* Updates the llama-3 PEFT tutorial to download the model from NVIDIA NGC to skip conversion
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Revert "enables default data step in megatron parallel to operate on a wider variety of tensors"
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2024-07-10T08:42:49
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Reverts NVIDIA/NeMo#9641
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[TTS] Add VietnameseCharsTokenizer
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2024-07-26T08:24:49
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Signed-off-by: Tu [[email protected]](mailto:[email protected]) # What does this PR do ? Add a Vietnamese language tokenizer for TTS training **Collection**: [TTS] # Changelog - Add VietnameseCharsTokenizer - Add unit tests for Vietnamese # Usage ```python from nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers import VietnameseCharsTokenizer text = "Xin chào các bạn." tokenizer = VietnameseCharsTokenizer( pad_with_space=True, ) tokens = tokenizer(text) graphemes = tokenizer.decode(tokens) graphemes = graphemes.replace('|', '') print(tokens) # xin chào các bạn. print(graphemes) ``` # 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? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] New Feature - [ ] Bugfix - [ ] Documentation
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Add sequence packing and proper attention masking support for LLM pretraining?
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[ "Based on DM, this should be already supported in gpt dataset https://github.com/NVIDIA/Megatron-LM/blob/c7a1f82d761577e6ca0338d3521eac82f2aa0904/megatron/core/datasets/gpt_dataset.py#L189\r\nclosing" ]
2024-07-09T23:44:22
2024-07-10T04:01:40
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Right now when performing LLM pretraining, different documents are just naively concatenated without proper masking. This can become problematic for model training with longer context window, as noted in e.g. https://arxiv.org/abs/2402.08268. Llama 3 blog post also mentioned they applied proper masking e.g. see https://ai.meta.com/blog/meta-llama-3/. Seems like https://github.com/NVIDIA/NeMo/pull/7945 added support for packed sequence with proper masking for SFT. Would it be possible to make pretraining support this too?
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llama CI fix
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2024-07-09T21:43:17
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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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Fixing import error fior llama-index (RAG pipeline)
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[NeMo-UX] Fix pipeline parallel bug
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CLIP and Neva Fix for NeMo 2.0.0rc
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# What does this PR do ? bug fix for neva and clip **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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When should mcore_gpt: True be used?
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2024-07-09T18:23:02
2024-07-15T04:14:24
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This flag is used in some of the launch files like https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml. I know this flag causes different implementation of the model to be used, but can their differences and recommendations for when to use one vs another be explained in more detail in the doc?
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In framework export
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[NeMo-UX] Make 'load_directly_on_device' configurable
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Making TDT models support all-positive durations (previously duration must contain 0)
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[ "@hainan-xv Something is wrong with tests in this branch (not related to TDT). Please, update to main and toggle `Run CICD` label", "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.", "@hainan-xv This still an IP?", "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.", "Addressed review comments. @artbataev @tbartley94 ", "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 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.", "@hainan-xv let's get this merged if there's nothing you want to add", "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 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.", "<!-- pylint-output-strict-mode-false -->\n\nbeep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please try helping us to improve the code base.\n\n---\n\nYour code was analyzed with PyLint. The following annotations have been identified:\n\n```\n************* Module nemo.collections.asr.parts.numba.rnnt_loss.utils.cuda_utils.gpu_rnnt_kernel\nnemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt_kernel.py:745:0: C0301: Line too long (121/119) (line-too-long)\nnemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt_kernel.py:856:0: C0301: Line too long (134/119) (line-too-long)\nnemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt_kernel.py:913:0: C0301: Line too long (124/119) (line-too-long)\nnemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt_kernel.py:980:0: C0301: Line too long (125/119) (line-too-long)\nnemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt_kernel.py:995:0: C0301: Line too long (123/119) (line-too-long)\nnemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt_kernel.py:1183:0: C0301: Line too long (128/119) (line-too-long)\nnemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt_kernel.py:1189:0: C0301: Line too long (145/119) (line-too-long)\nnemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt_kernel.py:1194:0: C0301: Line too long (132/119) (line-too-long)\nnemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt_kernel.py:1208:0: C0301: Line too long (142/119) (line-too-long)\nnemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt_kernel.py:1383:0: C0301: Line too long (144/119) (line-too-long)\nnemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt_kernel.py:1394:0: C0301: Line too long (162/119) (line-too-long)\nnemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt_kernel.py:1410:0: C0301: Line too long (167/119) (line-too-long)\nnemo/collections/asr/parts/numba/rnnt_loss/utils/cuda_utils/gpu_rnnt_kernel.py:68:0: C0116: Missing function or method docstring (missing-function-docstring)\n\n-----------------------------------\nYour code has been rated at 9.64/10\n```\n\n---\n\nThank you for improving NeMo's documentation!" ]
2024-07-09T16:50:32
2024-11-20T19:56:45
2024-11-20T19:56:45
COLLABORATOR
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# What does this PR do ? This PR makes TDT models support all-positive durations (previously duration must contain 0). **Collection**: [ASR] # Changelog - the kernal code for computing TDT loss is changed. - tests are added to test the correctness of new code and also ensures it's back-compatible. # Usage N/A # 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**: - [x] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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[ "@ericharper @yaoyu-33 @pablo-garay this is good to go? " ]
2024-07-09T16:21:57
2024-07-11T23:19:11
2024-07-11T23:19:11
CONTRIBUTOR
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* add slurm files to .gitignore * add differentiable decode to SDXL VAE * Optionally return predicted noise during the single step sampling process * also change `get_gamma` as a new function to use inside other functions which may interact with sampling (e.g. draft+) * debugging sdunet converter script * Added SD/SDXL conversion script from HF to NeMo * added 'from_nemo' config for VAE * tmp commit, please make changes (oci is super slow, cannot even run vim) * new inference yaml works * add logging to autoencoder * !(dont squash) Added enabling support for LinearWrapper for SDLoRA * added samples_per_batch and fsdp arguments to SDXL inference * added extra optionally wrapper to FSDP * remove unncessary comments * remove unnecessary comments * Apply isort and black reformatting --------- # 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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2024-07-09T16:10:28
2024-07-09T16:13:22
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CONTRIBUTOR
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# What does this PR do ? This PR adds additional functionality to SDXL class of models to support Draft+, and: bug fix for adapter control in LoRA (LinearParallelAdapter) conversion script for SDXL from huggingface weights to nemo (existing converter script doesnt work) additional inference config file to load converted hf weights added FSDP strategy for multimodal models function to return adapter config, similar to function that returns the adapter itself # 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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Fixing import error fior llama-index (RAG pipeline)
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[Nemo-UX] Including all trainable-params in a PEFT-checkpoint
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Object shard /models/Nemotron-4-340B-Reward/model_weights/model.rm_head._extra_state/shard_0_1.pt not found
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**Describe the bug** Instruct model seems to work as expected, but I am experiencing issues with the Reward model. https://huggingface.co/nvidia/Nemotron-4-340B-Reward/discussions/3 Tested containers: nemo_24_01_framework and nemo_24_03_01_framework Resource config: #SBATCH --nodes=2 #SBATCH --ntasks-per-node=8 #SBATCH --gpus-per-node=8 Attempted resolution: Repeated downloading of the Nemotron-4-340B-Reward model Related error: https://github.com/NVIDIA/NeMo/issues/8785 A clear and concise description of what the bug is. NeMo reports that files are missing from the Nemotron-4-340B-Reward model. **Steps/Code to reproduce bug** Run the example code (https://huggingface.co/nvidia/Nemotron-4-340B-Reward) with one of the following containers: nemo_24_01_framework and nemo_24_03_01_framework **Expected behavior** A clear and concise description of what you expected to happen. I expect the job to complete and not produce an error. **Environment overview (please complete the following information)** Local H100 DGX cluster, using NGC containers,
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Parametrize FPS group
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2024-07-09T11:27:59
2024-07-10T15:00:51
2024-07-10T15:00:49
COLLABORATOR
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# What does this PR do ? When doing a parallel save, exchange metadata in a whole world instead of a DP group. For yet unknown reason `all_gather_object` crashes with `group != world` **Collection**: NLP # Changelog - Parametrize metadata exchange group for parallel save - Turn on parallel save by default # Usage * By default metadata will be exchanged within the whole world. Set `model.dist_ckpt_parallel_save_within_dp=True` to exchange within DP. * Set `model.dist_ckpt_parallel_save=False` to turn off parallel save ```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. # Additional Information * Related to # (issue)
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