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2024-06-11T16:48:30
2024-06-13T02:22:55
2024-06-13T02:22:55
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# What does this PR do ? PR for in framework deployment. Took the commits from this PR: https://github.com/NVIDIA/NeMo/pull/8958 and made a few changes.
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I_kwDOC_bI7s6L2ra8
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CTC Language Finetuning convergence
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[ "Its a very old model, I would recommend you to try with Fast Conformer. \r\n@titu1994 Few notebooks are based on quartznet architecture, we need to update them to use FastConformer! ", "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." ]
2024-06-11T12:56:18
2024-08-10T01:51:39
2024-08-10T01:51:38
NONE
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**Describe the bug** CTC finetuning is not converging I tried to change hyperparameters, but still there is no luck. During training the model started to output empty strings. **Expected behavior** expected to converge for new language (I was simply following the tutorial given). https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/ASR_CTC_Language_Finetuning.ipynb **Environment overview ** - Environment location: conda environment - Method of NeMo install: python -m pip install git+https://github.com/NVIDIA/NeMo.git@main #egg=nemo_toolkit[all] **Environment details** Package Version ----------------------------- -------------- absl-py 2.1.0 accelerated-scan 0.2.0 addict 2.4.0 aiohttp 3.9.5 aiosignal 1.3.1 alabaster 0.7.16 alembic 1.13.1 aniso8601 9.0.1 antlr4-python3-runtime 4.9.3 anyio 4.4.0 argon2-cffi 23.1.0 argon2-cffi-bindings 21.2.0 arrow 1.3.0 asciitree 0.3.3 asteroid-filterbanks 0.4.0 asttokens 2.4.1 async-lru 2.0.4 async-timeout 4.0.3 attrdict 2.0.1 attrs 23.2.0 audioread 3.0.1 Babel 2.15.0 beautifulsoup4 4.12.3 black 24.4.2 bleach 6.1.0 boto3 1.34.113 botocore 1.34.113 braceexpand 0.1.7 causal-conv1d 1.2.2.post1 cdifflib 1.2.6 certifi 2024.2.2 cffi 1.16.0 charset-normalizer 3.3.2 click 8.0.2 clip 0.2.0 cloudpickle 3.0.0 colorama 0.4.6 colorlog 6.8.2 comm 0.2.2 contourpy 1.2.1 cycler 0.12.1 Cython 3.0.10 cytoolz 0.12.3 datasets 2.19.1 debugpy 1.8.1 decorator 5.1.1 decord 0.6.0 defusedxml 0.7.1 diart 0.9.0 diffusers 0.28.0 dill 0.3.8 Distance 0.1.3 docker-pycreds 0.4.0 docopt 0.6.2 docutils 0.21.2 dtw-python 1.4.2 editdistance 0.8.1 einops 0.8.0 einops-exts 0.0.4 exceptiongroup 1.2.0 executing 2.0.1 faiss-cpu 1.8.0 fasteners 0.19 fastjsonschema 2.19.1 fasttext 0.9.2 fiddle 0.3.0 filelock 3.14.0 Flask 2.2.5 Flask-RESTful 0.3.10 fonttools 4.51.0 fqdn 1.5.1 frozenlist 1.4.1 fsspec 2024.3.1 ftfy 6.2.0 future 1.0.0 g2p-en 2.1.0 gdown 5.2.0 gitdb 4.0.11 GitPython 3.1.43 graphviz 0.20.3 greenlet 3.0.3 grpcio 1.64.0 h11 0.14.0 h5py 3.11.0 httpcore 1.0.5 httpx 0.27.0 huggingface-hub 0.23.3 hydra-core 1.3.2 HyperPyYAML 1.2.2 idna 3.7 ijson 3.2.3 imageio 2.34.1 imagesize 1.4.1 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[NeMo-UX] Adding support for mcore distributed optimizer
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[ "@marcromeyn can you fix the DCO? thanks." ]
2024-06-11T10:37:55
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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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[ "we released 1B model but not the large model here: https://huggingface.co/spaces/nvidia/parakeet-tdt_ctc-1.1b ", "Thanks, I have seen this. I was interested to know about Large-FastConformer(~120M) model trained with this config:\r\nhttps://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/fastconformer/long_fastconformer/fast-conformer-long_ctc_bpe.yaml" ]
2024-06-11T07:48:11
2024-07-31T18:19:27
2024-07-31T18:19:27
NONE
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Hi NeMo team, Do you plan to release En Large FastConformer-Long-CTC-BPE model trained with Local Attention and Global token?
null
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Draft: Add LoRA test with sequence parallelism
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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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Prevents people from getting unexpected syntax errors when they install on a python version too old.
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Conflict between bf16-mixed Precision Setting and MegatronHalfPrecisionPlugin in MegatronGPT Training
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[ "@moutasemalakkad \r\n\r\nHi, I've just faced same issue.\r\n- work on Docker image ; nvcr.io/nvidia/nemo:24.03.framework\r\n\r\nHere is my solution : insert `cfg.trainer.precision = None` above this line.\r\nhttps://github.com/NVIDIA/NeMo/blob/ebba8b14263ca513c4453fcde0472785c19f46c1/examples/nlp/language_modeling/megatron_gpt_continue_training.py#L167\r\n\r\nThis solution is inspired from this PR https://github.com/NVIDIA/NeMo/pull/8908/\r\n\r\nIt should solve the conflict.\r\n\r\n", "Thanks! That also did not work, the work around was to set the plugins to an empty list\r\n \r\ntrainer = Trainer(plugins=[], strategy=strategy, **cfg.trainer, callbacks=callbacks) \r\n", "Hi, the contrinue_pretraining script was [consolidated](https://github.com/NVIDIA/NeMo/pull/9413) with the pretraining one [megatron_gpt_pretraining.py](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/megatron_gpt_pretraining.py), so you may want to try this. I think you should not face the issue any more.\r\n\r\nThanks." ]
2024-06-10T18:08:26
2024-07-25T22:50:52
2024-07-25T22:50:52
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**Title: Conflict between precision settings and MegatronHalfPrecisionPlugin in MegatronGPT training** **Describe the bug** When attempting to continue training the MegatronGPT model, I encountered a conflict between `precision=bf16-mixed` and the `MegatronHalfPrecisionPlugin`. This results in a `ValueError` indicating that both `precision=bf16-mixed` and the `MegatronHalfPrecisionPlugin` were received and only one should be chosen. **Steps/Code to reproduce bug** 1. Set up the environment as described below. 2. Use the following configuration and code snippet to initiate training. **Configuration:** ``` DATA='{train:[1.0,training_data_indexed/train_text_document], validation:[training_data_indexed/val_text_document], test:[training_data_indexed/test_text_document]}' !torchrun --nproc_per_node=1 /opt/NeMo/examples/nlp/language_modeling/megatron_gpt_continue_training.py \ model.data.data_prefix="$DATA" \ name=megatron_gpt_ \ exp_manager.name=megatron_gpt_1 \ restore_from_path='/workspace/new_nemo_out/new_megatron_gpt_model.nemo' \ trainer.devices=1 \ trainer.num_nodes=1 \ trainer.precision=16-mixed \ trainer.val_check_interval=300 \ trainer.max_steps=1200 \ model.megatron_amp_O2=False \ model.tensor_model_parallel_size=1 \ model.pipeline_model_parallel_size=1 \ model.micro_batch_size=1 \ model.global_batch_size=1 \ ++model.use_flash_attention=False \ ++model.seq_len_interpolation_factor=null ``` **Error Message:** ``` [NeMo W 2024-06-09 00:24:29 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/pytorch_lightning/_graveyard/precision.py:49: The `MixedPrecisionPlugin` is deprecated. Use `pytorch_lightning.plugins.precision.MixedPrecision` instead. [NeMo W 2024-06-09 00:24:29 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/lightning_fabric/connector.py:563: `precision=bf16` is supported for historical reasons but its usage is discouraged. Please set your precision to bf16-mixed instead! Error executing job with overrides: [] Traceback (most recent call last): File "/opt/NeMo/examples/nlp/language_modeling/megatron_gpt_continue_training.py", line 167, in main trainer = Trainer(plugins=plugins, strategy=strategy, **cfg.trainer, callbacks=callbacks) File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/argparse.py", line 70, in insert_env_defaults return fn(self, **kwargs) File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 401, in __init__ self._accelerator_connector = _AcceleratorConnector( File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py", line 134, in __init__ self._check_config_and_set_final_flags( File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py", line 271, in _check_config_and_set_final_flags raise ValueError( ValueError: Received both `precision=bf16-mixed` and `plugins=<nemo.collections.nlp.parts.nlp_overrides.MegatronHalfPrecisionPlugin object at 0x7fed4a8568c0>`. Choose one. Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace. ``` **Expected behavior** The training should proceed without conflicts between precision settings and plugins. **Environment overview (please complete the following information)** - **Environment location:** Docker **Environment details** - **OS version:** Ubuntu 20.04 - **PyTorch version:** 1.13.1 - **Python version:** 3.10 **Additional context** - **GPU model:** NVIDIA A100 - **NVIDIA Driver version:** 470.57.02 - **CUDA version:** 11.4
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RAM memory leaks for EncDecCTCModelBPE at inference
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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 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.", "Try to run the model undel \n```\nwith torch.no_grad()\n```\n" ]
2024-06-10T17:09:07
2024-10-26T20:45:01
2024-08-22T01:52:59
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Numbers suggests that `EncDecCTCModelBPE` has RAM memory leaks. I'm running inference with `batch_size=1` on a directory with 10k small audio files with total duration of around 9.5 hours. Does any body know where this excessive RAM usage comes from? Here is a sample script: ``` import time from pathlib import Path import psutil import torch from nemo.collections.asr.models import EncDecCTCModelBPE torch.set_num_threads(1) proc_usage = psutil.Process() cur_usage_rss = proc_usage.memory_info().rss / 1024 / 1024 print(f"Initial rss usage:{cur_usage_rss}") dir_name = "/mnt/data/subset_10k" audio_paths = Path(dir_name) def find_leaks(): model = EncDecCTCModelBPE.from_pretrained(model_name="stt_en_conformer_ctc_medium") res_file_path = f"memory_usage3_start_{time.time()}.txt" fd = open(res_file_path, "w") files = audio_paths.glob("*.wav") for idx, a_path in enumerate(files): res = model.transcribe(str(a_path), verbose=False) if idx % 100 == 0: cur_usage_rss = proc_usage.memory_info().rss / 1024 / 1024 msg = f"{idx: <10} rss usage:{cur_usage_rss}" print(msg) print(msg, file=fd) fd.close() if __name__ == "__main__": find_leaks() ``` And a sample output: ``` 0 rss usage:1433.66796875 100 rss usage:1561.16796875 200 rss usage:1584.16796875 300 rss usage:1596.66796875 400 rss usage:1607.16796875 500 rss usage:1629.9375 600 rss usage:1636.4375 700 rss usage:1660.2890625 800 rss usage:1660.2890625 900 rss usage:1661.2890625 1000 rss usage:1672.7890625 1100 rss usage:1680.7890625 1200 rss usage:1688.7890625 1300 rss usage:1695.7890625 1400 rss usage:1697.2890625 1500 rss usage:1706.2890625 1600 rss usage:1718.09765625 1700 rss usage:1718.09765625 1800 rss usage:1721.09765625 1900 rss usage:1725.09765625 2000 rss usage:1738.59765625 ``` **Expected behavior** Constant memory consumption over time. **Environment overview (please complete the following information)** - Environment location: Bare-metal - Method of NeMo install: `python -m pip install git+https://github.com/NVIDIA/NeMo.git@{2.0.0rc0}#egg=nemo_toolkit[all]` **Environment details** - OS version: Ubuntu 22.04, kernel 6.5.0-35-generic - PyTorch version: 2.2.2 - Python version: 3.10.12
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query tp_comm_disable_qkv via getattr
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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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Use TensorRT-LLM native parameter names in nemo.export module
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# What does this PR do ? A small renaming PR to use native TensorRT-LLM parameter names: * `max_input_len` instead of `max_input_token` * `max_output_len` instead of `max_output_token` For `export` and `forward` methods in `TensorRTLLM` class it is still possible to use old names but a deprecation warning is issued. **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**: - [x] Refactor - [ ] 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 ? Allow users to specifying **alpha** for INT8 SmoothQuant method. For example, setting it to 0.8 improves Llama3 (8b, 70b) MMLU accuracy results compared to default alpha=1.0. ``` | MODEL | ALPHA | MMLU | |------------|---------|--------| | LLAMA3-70B | 0 | 0.004 | | LLAMA3-70B | 0.25 | 0.116 | | LLAMA3-70B | 0.5 | 0.516 | | LLAMA3-70B | 0.7 | 0.765 | | LLAMA3-70B | 0.75 | 0.77 | | LLAMA3-70B | 0.8 | 0.772 | | LLAMA3-70B | 0.85 | 0.766 | | LLAMA3-70B | 0.9 | 0.75 | | LLAMA3-70B | 1 | 0.728 | | LLAMA3-8B | 0 | 0.062 | | LLAMA3-8B | 0.25 | 0.227 | | LLAMA3-8B | 0.5 | 0.379 | | LLAMA3-8B | 0.7 | 0.617 | | LLAMA3-8B | 0.75 | 0.624 | | LLAMA3-8B | 0.8 | 0.625 | | LLAMA3-8B | 0.85 | 0.62 | | LLAMA3-8B | 0.9 | 0.619 | | LLAMA3-8B | 0.95 | 0.612 | | LLAMA3-8B | 1 | 0.594 | ``` **Collection**: NLP # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python python megatron_quantization.py \ ... quantization.algorithm=int8_sq \ quantization.alpha=0.5 \ .... ``` # 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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# What does this PR do ? This PR generalizes FP8 extra state wrapping for all tensors. **Collection**: nlp # 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
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2024-06-09T23:05:12
2024-06-11T17:54:15
2024-06-11T17:54:14
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# What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # GitHub Actions CI The Jenkins CI system has been replaced by GitHub Actions self-hosted runners. The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR. To re-run CI remove and add the label again. To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run". # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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[ "This 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." ]
2024-06-09T14:39:38
2024-08-15T13:20:27
2024-08-15T13:20:27
CONTRIBUTOR
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**Describe the bug** Some scripts still throw errors similar to #7166: ``` transcribe_speech.py 104 <module> @dataclass dataclasses.py 1232 dataclass return wrap(cls) dataclasses.py 1222 wrap return _process_class(cls, init, repr, eq, order, unsafe_hash, dataclasses.py 958 _process_class cls_fields.append(_get_field(cls, name, type, kw_only)) dataclasses.py 815 _get_field raise ValueError(f'mutable default {type(f.default)} for field ' ValueError: mutable default <class 'nemo.collections.asr.modules.conformer_encoder.ConformerChangeConfig'> for field conformer is not allowed: use default_factory ``` **Steps/Code to reproduce bug** `python NeMo-1.23.0/examples/asr/transcribe_speech.py` **Expected behavior** Run without error. **Environment overview (please complete the following information)** - Environment location: Bare-metal. - Method of NeMo install: `pip install nemo_toolkit['asr']`. **Environment details** - OS version: Linux DESKTOP-4S0973M 5.15.146.1-microsoft-standard-WSL2 - PyTorch version: 2.3.0 - Python version: 3.11.9
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Fix: Ensure start_index is converted to a CPU-based NumPy array
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2024-06-08T15:24:17
2024-06-30T01:52:15
2024-06-30T01:52:14
NONE
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# What does this PR do ? Ensures the `start_index` is correctly converted to a CPU-based NumPy array to maintain compatibility with operations requiring NumPy arrays. **Collection**: `RNNT Decoding` # Changelog - Updated the calculation of `start_index` to include `.cpu().numpy()` conversion in the `AbstractRNNTDecoding` class. # 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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Getting `TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.`
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2024-06-08T14:14:32
2024-06-08T14:50:05
2024-06-08T14:49:52
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I am getting `TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.` when transcribing using a transducer model by setting `return_hypotheses=True` and `compute_timestamps=True` in decoding config. If I remove `compute_timestamps=True`, I don't get this error but the char and word level timestamp information won't be included. **Steps/Code to reproduce bug** ```python config = nemo_asr.models.ASRModel.restore_from(model_path, return_config=True) decoding_config = config.decoding decoding_config.strategy = "greedy_batch" with open_dict(decoding_config): decoding_config.preserve_alignments = True decoding_config.fused_batch_size = -1 # temporarily stop fused batch during inference. decoding_config.compute_timestamps = True model.change_decoding_strategy(decoding_config) hypothesis = model.transcribe(audio=sample_audio, batch_size=1, return_hypotheses=True)[0][0] ``` **Error** ```bash File [~/NeMo/nemo/collections/asr/parts/submodules/rnnt_decoding.py:851], in AbstractRNNTDecoding._compute_offsets(hypothesis, token_repetitions, rnnt_token) 849 # Construct the start and end indices brackets 850 end_indices = np.asarray(token_repetitions).cumsum() --> 851 start_indices = np.concatenate(([start_index], end_indices[:-1])) . . . File [~/miniconda3/envs/base/lib/python3.11/site-packages/torch/_tensor.py:1062], in Tensor.__array__(self, dtype) 1060 return handle_torch_function(Tensor.__array__, (self,), self, dtype=dtype) 1061 if dtype is None: -> 1062 return self.numpy() 1063 else: 1064 return self.numpy().astype(dtype, copy=False) TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first. ``` **Environment overview (please complete the following information)** - Environment location: AWS - Method of NeMo install: from source **Environment details** - OS version: Ubuntu 22.04 - PyTorch version: 2.2.1 - Python version: 3.11
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Fix README
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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 option to merge distributed optimizer buckets
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[ "@ericharper \r\nCan you please review and merge?" ]
2024-06-07T23:38:42
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# What does this PR do ? Adds an option to merge distributed optimizer buckets with GPT. By default each bucket corresponds to a Transformer layer (without interleaved pipeline parallelism) or to virtual pipeline parallel stage (with interleaved pipeline parallelism) **Collection**: NLP # Changelog - Add option to merge distributed optimizer buckets # Usage Run GPT, e.g. with the config at https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml. Enable the distributed optimizer with `model.optim.name=distributed_fused_adam` and merge buckets with `model.distributed_adam_bucket_merge_size=<N>`. For example, if training GPT without pipeline parallelism, `model.distributed_adam_bucket_merge_size=2` will cause each distopt bucket to contain the weights of two Transformer layers. # 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**: - [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. Pinging @erhoo82. # Additional Information
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Consolidate gpt continue training script into pretraining script
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[ "Can we add github CI test for this and then also test the regular GPT pretraining, with resuming, is the same before and after this PR?", "We should also add docs for this, the readme here is a good start.", "sure i will add some tests. the gpt resume behavior is not changed in my local tests", "@ericharper as I work on CI. I did some local tests \r\n![image](https://github.com/NVIDIA/NeMo/assets/54727607/87434f33-aaa7-4f6a-aac4-086948815a4a)\r\n" ]
2024-06-07T20:12:56
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# What does this PR do ? ## Continual Learning with Pretrained Checkpoints Continual learning enables large language models (LLMs) to acquire new skills and remain current with the swiftly advancing landscape of human knowledge. This guide details how to engage in continual learning using existing pretrained checkpoints with the `examples/nlp/language_modeling/megatron_gpt_pretraining.py` script. This process is applicable to various models including Llama 1/2/3, Gemma, Mistral, Mixtral, and others. ### Configuration for Continual Learning To enable continual learning, modify the `model` configuration in your script as follows: ```yaml model: # Use the following settings to specify the source for continual learning: restore_from_path: null # Set this to a .nemo file path to restore only the model weights. restore_from_ckpt: null # Set this to a checkpoint path to restore both model weights and optimizer states. ``` - **restore_from_path**: Use this when you want to restore only the model weights from a `.nemo` file. - **restore_from_ckpt**: Use this to restore both model weights and optimizer states from a PyTorch checkpoint. ### Adjusting Training Configurations Upon engaging in continual learning, it is possible and often beneficial to modify various training configurations: - **Model Parallelism**: Depending on the computational resources available, you may adjust the model's parallelism settings. - **Data Blend**: You can change the dataset or modify how data is blended during training to better suit the new training objectives. - **Learning Rate Scheduler**: Adjusting the learning rate schedule can help optimize training for the new conditions. Refer to the *pretrain* section of the documentation for details on configuring these parameters. ### Important Notes on Continual Learning - **State Reset**: When engaging in continual learning, all states including model parallelism configuration, dataset, learning rate scheduler, and randomness are reset, except for the states loaded from the checkpoint. - **Resuming Training**: If training is interrupted (e.g., due to a system failure) and you wish to resume the continual learning process, ensure that the restore flags (`restore_from_path` or `restore_from_ckpt`) are set back to `null`. This prevents the system from resetting the states again, which could lead to incorrect results. **Collection**: [nlp] # 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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ci: Enrich notifications
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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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cherry pick of #9266
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# What does this PR do ? cherry pick of #9266 PR. **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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FSDP reduce_scatter can not overlap with compute
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2024-06-07T07:25:59
2024-08-10T01:51:40
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I use nemo 24.03 and i find FSDP reduce_scatter can not overlap with compute <img width="838" alt="截屏2024-06-07 15 24 28" src="https://github.com/NVIDIA/NeMo/assets/25609661/0aa82472-88a5-4445-aa0c-0c8d8d41bb32"> ```[tasklist] ### Tasks ```
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Enable encoder adapters for Canary and MultiTaskAED models
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# What does this PR do ? Enable multi task AED models to add adapter support to its encoders (speech encoder and transformer encoder) and decoder (transformer decoder). Also add new API to automatically swap out original modules with adapter compatible modules. **Collection**: [ASR] # Changelog - Add support for adapter module mixin to AED multitask model - [CORE] Add support for `replace_adapter_compatible_modules()` which automatically swaps out registered original module class with the adapter class. It also updates config so save_to() and restore_from() will automatically pick current class. - Support mha adapter and linear adapter for both speech encoder and transformer encoder + decoder modules - Refactor AdapterModuleMixin to separate adapter type assertion from `add_adapter` - Refactor ASRAdapterModelMixin to separate the logic of what model is parsed - now its determined by a simple string list of module names - Add tests for aed multi task model with adapter support # Usage ```python from nemo.collections.asr.models import ASRModel from nemo.collections.asr.parts.submodules.adapters import TransformerEncoderMultiHeadAttentionAdapterConfig from nemo.collections.common.parts.adapter_modules import LinearAdapterConfig model = ASRModel.from_pretrained("nvidia/canary-1b") # New API to automatically swap out all original modules in model with adapter compatible modules via registry model.replace_adapter_compatible_modules() # Tiny Attention Adapters adapter_cfg = TransformerEncoderMultiHeadAttentionAdapterConfig( hidden_size=1024, num_attention_heads=1, proj_dim=8, ) # OR # Linear Adapters adapter_cfg = LinearAdapterConfig( in_features=1024, dim=8, ) # Load adapter weights and freeze all other weights # Adapter targets can be "", "encoder", "transf_encoder" and "transf_decoder". "" refers to encoder by default. # Verify supported adapter target modules using `model.adapter_module_names`. model.add_adapter("encoder:my_adapter", adapter_cfg) model.freeze() model.unfreeze_enabled_adapters() # Train model ... ``` # GitHub Actions CI The Jenkins CI system has been replaced by GitHub Actions self-hosted runners. The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR. To re-run CI remove and add the label again. To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run". # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [x] Did you write any new necessary tests? - [x] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR.
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Fix bug with GPT distopt buckets when interleaved pipelining is enabled
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# What does this PR do ? When training GPT with interleaved pipelining and the distributed optimizer, we want each distopt bucket to correspond to a pipeline stage to get the best communication overlapping. However, https://github.com/NVIDIA/NeMo/pull/8221 has a bug where each distopt bucket corresponds to a Transformer layer. **Collection**: NLP # Changelog - GPT configures distopt buckets to correspond to pipeline stages when interleaved pipelining are enabled # Usage Run GPT, e.g. with the config at https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml. Enable the distributed optimizer with `model.optim.name=distributed_fused_adam`. # 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 * Bug introduced in https://github.com/NVIDIA/NeMo/pull/8221.
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[Nemo CICD] timeouts fix
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# What does this PR do ? Adds T5 TTS model **Collection**: tts, nlp # Changelog - Adds T5 TTS model # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ```
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Update to work using Llama 3
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2024-06-06T18:14:40
2024-06-18T17:10:43
2024-06-18T17:10:43
NONE
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# What does this PR do ? fix the script to convert llama3 from nemo to hf format. ![image](https://github.com/NVIDIA/NeMo/assets/27715376/ab24e8b4-95cf-4e31-bb56-c60a6516cf6d) # Changelog - Stop using LlamaTokenizer and start using AutoTokenizer from Transformers. # Usage ```python python3 NeMo/scripts/checkpoint_converters/convert_llama_nemo_to_hf.py \ --input_name_or_path llama3-8b.nemo \ --output_path llama3-8b-hf/pytorch_model.bin \ --hf_input_path Meta-Llama-3-8B \ --hf_output_path llama3-8b-hf \ --input_tokenizer Meta-Llama-3-8B \ --hf_output_tokenizer llama3-8b-hf ``` you must except something like this when the script finishes: ![image](https://github.com/NVIDIA/NeMo/assets/27715376/1093b660-4b8a-460e-b6b2-9065c55ba2de) # 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 * there is an issue in hf related but is not the same script https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/discussions/37
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fix operator precedence
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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." ]
2024-06-06T16:32:51
2024-06-24T14:00:16
2024-06-24T14:00:15
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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 - [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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9,402
TRT-LLM 0.10 Update
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[ "@oyilmaz-nvidia could you please tell how to install the NeMo in a way that doesn't bring up all the dependencies? I don't see `requirements_export.txt` anymore.", "I cannot find `use_inflight_batching`, `enable_context_fmha` parameters of the `export()` method. What is the new way of setting them?", "@PeganovAnton I'll create a release branch for you.\r\n\r\nSo for the `use_inflight_batching`, `enable_context_fmha`, we no longer get those parameters since trt-llm also changed it. So, as long as the gemm plugin is enabled, remove padding is also true, inflight batching is enabled underneath. Basically, the default parameters should enable the IFB automatically." ]
2024-06-06T15:54:23
2024-06-13T02:22:34
2024-06-13T02:22:33
COLLABORATOR
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# What does this PR do ? Update the trt-llm export code to the version 0.10
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[NeMo-UX] Removing default_path from ModelConnector
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2024-06-06T15:21:56
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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] Adding file-lock to Connector
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ci: Fix `L2_Segmentation_Tool_Parallel_ctc_segmentation_test_L2_Eng_C…
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Nemo_toolkit 2.0.0.rc0 installation failure
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[ "Thanks for the information, we're taking a look at whether we can make it an optional dependency. ", "I just tried installing in a clean conda env and it worked for me: \r\n```\r\nconda create env --name test_r2.0.0rc0 python==3.10.12\r\nconda activate test_r2.0.0rc0\r\nconda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia\r\npip install Cython\r\npip install packaging\r\npip install --pre nemo_toolkit[all]\r\n```\r\nCould you try it?\r\n\r\nOr if you use the container `nvcr.io/nvidia/pytorch:24.02-py3` just \r\n```\r\npip install --pre nemo_toolkit[all]\r\n```\r\nshould work.", "Thank you so much for your help\r\n\r\nOn Fri, 7 Jun, 2024, 11:31 Eric Harper, ***@***.***> wrote:\r\n\r\n> I just tried installing in a clean conda env and it worked for me:\r\n>\r\n> conda create env --name test_r2.0.0rc0 python==3.10.12\r\n> conda activate test_r2.0.0rc0\r\n> conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia\r\n> pip install Cython\r\n> pip install packaging\r\n> pip install --pre nemo_toolkit[all]\r\n>\r\n> Could you try it?\r\n>\r\n> Or if you use the container nvcr.io/nvidia/pytorch:24.02-py3 just\r\n>\r\n> pip install --pre nemo_toolkit[all]\r\n>\r\n> should work.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/NVIDIA/NeMo/issues/9397#issuecomment-2154140932>, or\r\n> unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/BI7V4KOCJ4NR4FU2HARAF43ZGFEFHAVCNFSM6AAAAABI4MMH36VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDCNJUGE2DAOJTGI>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> ***@***.***>\r\n>\r\n\r\n-- \r\nThe information in this message may be proprietary and/or confidential and \r\nprotected from disclosure. If the reader of this message is not the \r\nintended recipient, you are hereby notified that any dissemination, \r\ndistribution, or copying of this communication is strictly prohibited. If \r\nyou have received this communication in error, please notify ATMECS and \r\ndelete it from your computer.\r\n\r\n", "Please let us know if this resolves your issue.", "ollecting nemo_toolkit[all]\r\n Downloading https://files.pythonhosted.org/packages/cd/94/1031a231453146b5ec302a6c2282dc90c2e18844deeee2ff867900b90368/nemo_toolkit-2.0.0rc0-py3-none-any.whl (3.6MB)\r\n 100% |████████████████████████████████| 3.6MB 5.4MB/s eta 0:00:01\r\nCollecting ruamel.yaml (from nemo_toolkit[all])\r\n Downloading https://files.pythonhosted.org/packages/4b/5d/678d7d15071816cb9a5e015fcd090ddc59a6a195ce8965363313e2044595/ruamel.yaml-0.18.3-py3-none-any.whl (114kB)\r\n 100% |████████████████████████████████| 122kB 12.2MB/s ta 0:00:01\r\nCollecting numpy>=1.22 (from nemo_toolkit[all])\r\n Could not find a version that satisfies the requirement numpy>=1.22 (from nemo_toolkit[all]) (from versions: 1.3.0, 1.4.1, 1.5.0, 1.5.1, 1.6.0, 1.6.1, 1.6.2, 1.7.0, 1.7.1, 1.7.2, 1.8.0, 1.8.1, 1.8.2, 1.9.0, 1.9.1, 1.9.2, 1.9.3, 1.10.0.post2, 1.10.1, 1.10.2, 1.10.4, 1.11.0, 1.11.1, 1.11.2, 1.11.3, 1.12.0, 1.12.1, 1.13.0, 1.13.1, 1.13.3, 1.14.0, 1.14.1, 1.14.2, 1.14.3, 1.14.4, 1.14.5, 1.14.6, 1.15.0, 1.15.1, 1.15.2, 1.15.3, 1.15.4, 1.16.0, 1.16.1, 1.16.2, 1.16.3, 1.16.4, 1.16.5, 1.16.6, 1.17.0, 1.17.1, 1.17.2, 1.17.3, 1.17.4, 1.17.5, 1.18.0, 1.18.1, 1.18.2, 1.18.3, 1.18.4, 1.18.5, 1.19.0, 1.19.1, 1.19.2, 1.19.3, 1.19.4, 1.19.5)\r\nNo matching distribution found for numpy>=1.22 (from nemo_toolkit[all]\r\nThis is the error i got now after runnig the command \r\nThe numpy i installed in my system in 1.11.3 version .\r\ncan you please help me to resolve this issue\r\n", "i tried installing nemo-toolkit with the command pip install nemo-toolkit[all]==2.0.0rc0. It shows the nemo version and only asr collections and tts collections are importing,nlp collections are not importing. This is the error i got \r\nImportError Traceback (most recent call last)\r\n/tmp/ipykernel_53908/447078856.py in <module>\r\n----> 1 import nemo.collections.nlp as nemo_nlp\r\n\r\n~/.local/lib/python3.10/site-packages/nemo/collections/nlp/__init__.py in <module>\r\n 13 # limitations under the License.\r\n 14 \r\n---> 15 from nemo.collections.nlp import data, losses, models, modules\r\n 16 from nemo.package_info import __version__\r\n 17 \r\n\r\n~/.local/lib/python3.10/site-packages/nemo/collections/nlp/data/__init__.py in <module>\r\n 40 BertTokenClassificationInferDataset,\r\n 41 )\r\n---> 42 from nemo.collections.nlp.data.zero_shot_intent_recognition.zero_shot_intent_dataset import (\r\n 43 ZeroShotIntentDataset,\r\n 44 ZeroShotIntentInferenceDataset,\r\n\r\n~/.local/lib/python3.10/site-packages/nemo/collections/nlp/data/zero_shot_intent_recognition/__init__.py in <module>\r\n 14 \r\n 15 \r\n---> 16 from nemo.collections.nlp.data.zero_shot_intent_recognition.zero_shot_intent_dataset import (\r\n 17 ZeroShotIntentInferenceDataset,\r\n 18 calc_class_weights_from_dataloader,\r\n\r\n~/.local/lib/python3.10/site-packages/nemo/collections/nlp/data/zero_shot_intent_recognition/zero_shot_intent_dataset.py in <module>\r\n 28 from nemo.collections.nlp.data.glue_benchmark.data_processors import InputExample\r\n 29 from nemo.collections.nlp.data.glue_benchmark.glue_benchmark_dataset import GLUEDataset\r\n---> 30 from nemo.collections.nlp.parts.utils_funcs import tensor2list\r\n 31 from nemo.core.neural_types import CategoricalValuesType, ChannelType, MaskType, NeuralType\r\n 32 from nemo.utils import logging\r\n\r\n~/.local/lib/python3.10/site-packages/nemo/collections/nlp/parts/__init__.py in <module>\r\n 15 \r\n 16 from nemo.collections.nlp.parts.megatron_lr_schedulers import CosineAnnealingExp\r\n---> 17 from nemo.collections.nlp.parts.utils_funcs import list2str, tensor2list\r\n\r\n~/.local/lib/python3.10/site-packages/nemo/collections/nlp/parts/utils_funcs.py in <module>\r\n 35 from torch import Tensor\r\n 36 \r\n---> 37 from nemo.collections.nlp.modules.common.megatron.utils import erf_gelu\r\n 38 from nemo.collections.nlp.modules.common.megatron.utils import openai_gelu as openai_gelu_func\r\n 39 from nemo.collections.nlp.modules.common.megatron.utils import squared_relu\r\n\r\n~/.local/lib/python3.10/site-packages/nemo/collections/nlp/modules/__init__.py in <module>\r\n 14 \r\n 15 \r\n---> 16 from nemo.collections.nlp.modules.common import (\r\n 17 AlbertEncoder,\r\n 18 BertEncoder,\r\n\r\n~/.local/lib/python3.10/site-packages/nemo/collections/nlp/modules/common/__init__.py in <module>\r\n 16 \r\n 17 from nemo.collections.nlp.modules.common.bert_module import BertModule\r\n---> 18 from nemo.collections.nlp.modules.common.huggingface import (\r\n 19 AlbertEncoder,\r\n 20 BertEncoder,\r\n\r\n~/.local/lib/python3.10/site-packages/nemo/collections/nlp/modules/common/huggingface/__init__.py in <module>\r\n 17 from nemo.collections.nlp.modules.common.huggingface.camembert import CamembertEncoder\r\n 18 from nemo.collections.nlp.modules.common.huggingface.distilbert import DistilBertEncoder\r\n---> 19 from nemo.collections.nlp.modules.common.huggingface.huggingface_utils import (\r\n 20 get_huggingface_lm_model,\r\n 21 get_huggingface_pretrained_lm_models_list,\r\n\r\n~/.local/lib/python3.10/site-packages/nemo/collections/nlp/modules/common/huggingface/huggingface_utils.py in <module>\r\n 16 from typing import List, Optional\r\n 17 \r\n---> 18 from transformers import (\r\n 19 ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,\r\n 20 BERT_PRETRAINED_MODEL_ARCHIVE_LIST,\r\n\r\nImportError: cannot import name 'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST' from 'transformers' (/home/subramanyam/.local/lib/python3.10/site-packages/transformers/__init__.py)\r\n\r\nI upgrade transformers version to 4.41.2 but it does not importing the nlp collections of nemo-toolkit.\r\nPlease help me to resolve this issue also\r\n\r\n\r\n", "You need to reduce your HF transformers version, not increase it. 4.40.0 is the supported version for 2.0 rc0", "ok i will try and install it\r\n", "ImportError: cannot import name 'packaging' from 'pkg_resources' (/home/subramanyam/.local/lib/python3.10/site-packages/pkg_resources/__init__.py)\r\nnow i got this error i also updated the package version into 24.1 and setup tools version but iam getting the same error", "Please see the readme - you need to install Cython and packaging before installing Nemo ", "Please do not update random libraries, we do not support setup tools 70.x, you'll need to downgrade that first to 69 or lower ", "ok i already installed cython and packaging before installing the nemo toolkit but if i run the command \r\nimport nemo.collections.nlp as nlp\r\nit shows this error\r\nImportError: cannot import name 'packaging' from 'pkg_resources' (/home/subramanyam/.local/lib/python3.10/site-packages/pkg_resources/__init__.py)\r\niam using jupyter notebook", "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.", "This issue was closed because it has been inactive for 7 days since being marked as stale." ]
2024-06-06T10:00:36
2024-08-10T01:51:42
2024-08-10T01:51:41
NONE
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I have tried to install nemo_toolkit==2.0.0.rc0 but it shows error: RuntimeError: causal_conv1d is only supported on CUDA 11.6 and above. Note: make sure nvcc has a supported version by running nvcc -V. torch.__version__ = 2.3.0+cu121 [end of output] note: This error originates from a subprocess, and is likely not a problem with pip. error: metadata-generation-failed × Encountered error while generating package metadata. ╰─> See above for output. note: This is an issue with the package mentioned above, not pip. hint: See above for details. can anyone help me to resolve this issue??
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PR_kwDOC_bI7s5xoAez
9,396
LoRA for MoE Layer
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2024-06-06T02:53:32
2024-06-11T19:55:02
2024-06-11T19:55:01
COLLABORATOR
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# What does this PR do ? Add LoRA for MoE layer (e.g. Mixtral model). This does not necessarily improve the convergence of LoRA on Mixtral. However, it is important for QLoRA to achieve the best memory savings **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * The following flag now works for MoE models: ```bash model.peft.lora_tuning.target_modules=[all] ``` # 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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9,395
video input 'image_aspect_ratio=pad' not work
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2024-06-06T00:20:58
2024-07-06T05:30:41
2024-07-06T05:30:41
CONTRIBUTOR
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**Describe the bug** Hi, Please check the this [line](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/multimodal/data/neva/neva_dataset.py#L980) and this [line](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/multimodal/parts/utils.py#L527) The input to the `expand2square` is expected to be an image object but now in the video preprocessing part, it's a list of images so it will raise issue.
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9,394
Why isn't FSDP supported by DistributedCheckpointIO?
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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.", "I'm also interested in this.", "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." ]
2024-06-05T22:56:05
2024-08-16T01:51:39
2024-08-16T01:51:38
NONE
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Hi team, Thank you for your efforts in adding async_save support to DistributedCheckpointIO. I am currently testing async_save with DistributedCheckpointIO and found that it only supports [non-FSDP MCore models](https://github.com/NVIDIA/NeMo/blob/ae06138a5012375e39a93b512d76b5fbc4d8c229/nemo/collections/nlp/parts/megatron_trainer_builder.py#L145). I have reviewed the source code, including [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), but I am unclear on why FSDP is not supported by DistributedCheckpointIO. Could the team provide more information regarding this limitation? Thank you.
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Update README.rst
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2024-06-05T19:09:01
2024-06-05T22:50:25
2024-06-05T22:13:18
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Revised content per https://gitlab-master.nvidia.com/nemo-framework-tme/documentation/-/issues/25. Also removed reference to NIMs in LLMs and MMs Deployment and Optimization. It should be NVIDIA NeMo Microservices and not NIM. Removed nemo:24.03.framework and nemo:24.01.speech in Docker Containers section and replaced with 24.05 . Please verify all changes. # 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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PeFT fix for distOpt
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[ "This is now ready; Tested DDP, TP=2, PP=2 apex & distOpt.\r\nThere was a small bug in get_model_module_list that was also fixed.", "CI passed: https://github.com/NVIDIA/NeMo/actions/runs/9422621053" ]
2024-06-05T18:54:02
2024-06-10T01:47:22
2024-06-10T01:47:21
MEMBER
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Riva and k2 ASR WFST decoding (2)
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[ "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR 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.", "@GNroy, can you please provide the instructions for testing this PR? \r\nA model + test set + LM/text to build LM with the sequence of required operations to build graphs/start decoding (both k2 and Riva).\r\nIt may also be worth providing such information for users (make a section in the documentation).", "Please, also fix CodeQL suggestions - most of them seems to be valuable (e.g., `'except' clause does nothing but pass and there is no explanatory comment.`, `This 'lambda' is just a simple wrapper around a callable object. Use that object directly.`, `Variable wfst_path is not used.` etc.)" ]
2024-06-05T16:51:17
2024-08-22T17:04:17
2024-08-21T12:46:20
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# What does this PR do ? Adds Riva and k2 WFST decoding options for ASR inference. **Collection**: [ASR] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # 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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Force diarizer to use CUDA if cuda is available and if device=None.
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# What does this PR do ? This PR changes clustering_diarizer.py code to use CUDA if cuda is available and if device=None. **Collection**: asr # Changelog ` if speaker_model is not None: if self._cfg.device is None and torch.cuda.is_available(): self._speaker_model = speaker_model.to(torch.device('cuda')) else: self._speaker_model = speaker_model ` # Usage Use diarizaiton examples ``` Run the entire speaker diarization pipeline: VAD, clustering diarizer for initializing clustering then Multi-scale Diarization Decoder (MSDD). python multiscale_diar_decoder_infer.py --config-path='../conf/inference' --config-name='diar_infer_telephonic.yaml' \ diarizer.vad.model_path=<NeMo VAD model path> \ diarizer.msdd_model.model_path=<NeMo MSDD model path> \ diarizer.oracle_vad=False \ diarizer.manifest_filepath=<test_manifest> \ diarizer.out_dir=<test_temp_dir> \ ``` # GitHub Actions CI The Jenkins CI system has been replaced by GitHub Actions self-hosted runners. The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR. To re-run CI remove and add the label again. To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run". # Before your PR is "Ready for review" **Pre checks**: - [x ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [x] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in NeMo ASR
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[NeMo-UX] Removing un-used ModelConfig class
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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 support for new mcore ds features
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[ "@dimapihtar - can you add\r\ndrop_last_partial_validation_sequence = True and add_extra_token_to_sequence = True to a github actions test?", "> @dimapihtar - can you add drop_last_partial_validation_sequence = True and add_extra_token_to_sequence = True to a github actions test?\r\n\r\n@ShriyaPalsamudram we have these values as True by default so I think all the tests are running in this way.", "One necessary consistency test will be to measure validation eval between legacy and mcore code paths, with all options turned on to eval on the entire validation dataset\r\n\r\nYou'll have to make sure that the loss and ppl computation is able to handle partial batches and partial sequences. NeMo already does this at least for the legacy code path, but you'll want to make sure there aren't any breaking changes." ]
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# What does this PR do ? Adds new mcore dataset features to NeMo. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # 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] Integrating mcore's DistributedDataParallel into MegatronStrategy
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# What does this PR do ? Fixes Sample input order needed for export via torchscript. **Collection**: [TTS] # Changelog Fix sample rate order for torchscript export # 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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**Describe the bug** Setting `model.data.splits_string` to `1000,0,0` results in `TypeError: object of type 'NoneType' has no len()` **Steps/Code to reproduce bug** 1. Follow the steps in the [GPT model training tutorial](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/nlp/nemo_megatron/gpt/gpt_training.html) 2. Change `model.data.splits_string=\'1000,0,0\'` 3. Run **Expected behavior** I should be able to perform pretraining without having to: 1. Provide validation dataset 2. Provide test dataset 3. Run validation **Environment overview (please complete the following information)** N/A. I was able to run the tutorial when `splits_string=\'980,10,10\'`. **Additional context** Traceback: ``` Traceback (most recent call last): File "/data/projects/11003281/multi-node/cache/source_files/nemo/NeMo/examples/nlp/language_modeling/megatron_gpt_pretraining.py", line 42, in main trainer.fit(model) File "/data/projects/11003281/multi-node/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 544, in fit call._call_and_handle_interrupt( File "/data/projects/11003281/multi-node/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 43, in _call_and_handle_interrupt return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs) File "/data/projects/11003281/multi-node/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 105, in launch return function(*args, **kwargs) File "/data/projects/11003281/multi-node/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 580, in _fit_impl self._run(model, ckpt_path=ckpt_path) File "/data/projects/11003281/multi-node/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 949, in _run call._call_setup_hook(self) # allow user to set up LightningModule in accelerator environment File "/data/projects/11003281/multi-node/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 94, in _call_setup_hook _call_lightning_module_hook(trainer, "setup", stage=fn) File "/data/projects/11003281/multi-node/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 157, in _call_lightning_module_hook output = fn(*args, **kwargs) File "/data/projects/11003281/multi-node/cache/source_files/nemo/NeMo/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 1619, in setup self.setup_validation_data(self.cfg.data) File "/data/projects/11003281/multi-node/cache/source_files/nemo/NeMo/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 1645, in setup_validation_data f'Setting up validation dataloader with len(len(self._validation_ds)): {len(self._validation_ds)} and consumed samples: {consumed_samples}' TypeError: object of type 'NoneType' has no len() ```
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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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[ "Look good to me :+1:\r\n\r\nCan you apply a similar `sharded_state_dict` delay here? https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/parts/nlp_overrides.py#L947", "@mikolajblaz \r\n> Look good to me 👍\r\n> \r\n> Can you apply a similar `sharded_state_dict` delay here? https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/parts/nlp_overrides.py#L947\r\n\r\nDone, thanks for review.\r\n", "@ericharper or @mikolajblaz\r\n\r\nCould you add the `run CICD` label?" ]
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# What does this PR do ? Add a one line overview of what this PR aims to accomplish. Move loading sharded state dict after initialize parallel state. Addresses https://github.com/NVIDIA/NeMo/issues/8460 Notes: - see the original issue for more details - unsure if this is the correct approach, or if there are downstream impacts for this change - This fix also resolves issues when running SFT using NeMo-Aligner (https://github.com/NVIDIA/NeMo-Aligner/blob/main/examples/nlp/gpt/train_gpt_sft.py) loading from a distributed ckpt directly instead of legacy or .nemo ``` Traceback (most recent call last): File "train_sft.py", line 64, in main ptl_model, updated_cfg = load_megatron_model(GPTSFTModel, cfg, trainer, additional_keys=additional_keys) File "/workspace/NeMo/nemo/collections/nlp/models/nlp_model.py", line 397, in load_from_checkpoint checkpoint = dist_checkpointing.load(sharded_state_dict=checkpoint, checkpoint_dir=checkpoint_dir) File "/workspace/Megatron-LM/megatron/core/dist_checkpointing/serialization.py", line 133, in load validate_sharding_integrity(nested_values(sharded_state_dict)) File "/workspace/Megatron-LM/megatron/core/dist_checkpointing/serialization.py", line 437, in validate_sharding_integrity _validate_sharding_for_key(shardings) File "/workspace/Megatron-LM/megatron/core/dist_checkpointing/serialization.py", line 475, in _validate_sharding_for_key raise CheckpointingException(f'Invalid access pattern for {rank_sharding[0][1]}') megatron.core.dist_checkpointing.core.CheckpointingException: Invalid access pattern for ShardedTensor(key='model.embedding.word_embeddings.weight') ``` **Collection**: [Note which collection this PR will affect] nlp/language_modeling # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below For the nemo aligner SFT use case: 1. change the implementation of nemo_aligner.models.nlp.gpt.gpt_sft_model.GPTSFTModel like so ```python from omegaconf import DictConfig from pytorch_lightning.trainer.trainer import Trainer from nemo_aligner.models.nlp.gpt.gpt_sft_model import GPTSFTModel from torch.nn.modules.module import Module class _GPTSFTModel(GPTSFTModel): """ Megatron GPT Supervised Fine-Tuning, support load from mcore distributed ckpt. """ def __init__(self, cfg: DictConfig, trainer: Trainer): super().__init__(cfg, trainer=trainer) def load_state_dict(self, state_dict, strict: bool = True): if self.use_peft and self.setup_complete: super().load_state_dict(state_dict, strict=strict) else: if self.mcore_gpt and not self.use_fsdp: # mcore distributed checkpoint # we defer loading the state_dict until the class has been initialized return Module.load_state_dict(self, state_dict, strict=False) else: super().load_state_dict(state_dict, strict=strict) ``` 2. Load the checkpoint overriding the files like so ```python from fsspec.implementations.local import LocalFileSystem def load_from_checkpoint_dir(cfg, trainer, modify_confg_fn, checkpoint_logger=None): cls = _GPTSFTModel # from above app_state = AppState() if cfg.model.tensor_model_parallel_size > 1 or cfg.model.pipeline_model_parallel_size > 1: app_state.model_parallel_size = ( cfg.model.tensor_model_parallel_size * cfg.model.pipeline_model_parallel_size * cfg.get("expert_model_parallel_size", 1) ) app_state.tensor_model_parallel_size = cfg.model.tensor_model_parallel_size app_state.pipeline_model_parallel_size = cfg.model.pipeline_model_parallel_size app_state.expert_model_parallel_size = cfg.get("expert_model_parallel_size", 1) ( app_state.tensor_model_parallel_rank, app_state.pipeline_model_parallel_rank, app_state.expert_model_parallel_rank, app_state.model_parallel_size, app_state.data_parallel_size, app_state.pipeline_model_parallel_split_rank, app_state.virtual_pipeline_model_parallel_rank, ) = fake_initialize_model_parallel( world_size=app_state.model_parallel_size, rank=trainer.global_rank, tensor_model_parallel_size_=cfg.model.tensor_model_parallel_size, pipeline_model_parallel_size_=cfg.model.pipeline_model_parallel_size, pipeline_model_parallel_split_rank_=cfg.model.get("pipeline_model_parallel_split_rank", None), expert_model_parallel_size_=cfg.get("expert_model_parallel_size", 1), ) checkpoint_path = os.path.join( cfg.model.pretrained_checkpoint.checkpoint_dir, cfg.model.pretrained_checkpoint.checkpoint_name, ) if not os.path.isdir(checkpoint_path): # legacy checkpoint needs model parallel rank injection checkpoint_path = inject_model_parallel_rank(checkpoint_path) fs = LocalFileSystem() with fs.open(cfg.model.pretrained_checkpoint.hparams_file, "r") as fin: hparams_file = OmegaConf.load(fin) gpt_cfg = modify_confg_fn(hparams_file.cfg, cfg, add_cfg_to_tree=True) with tempfile.NamedTemporaryFile(suffix=".yaml") as f: OmegaConf.save(config=gpt_cfg, f=f.name) model = cls.load_from_checkpoint(checkpoint_path=checkpoint_path, trainer=trainer, hparams_file=f.name) return model, gpt_cfg ``` 3. in the config, resume the training from a dist ckpt dir: ``` model: pretrained_checkpoint: checkpoint_dir: /experiments/example_gpt_model_A/checkpoints/ checkpoint_name: megatron_gpt--val_loss=1.23-step=5000-consumed_samples=100000.0 hparams_file: /hparams.yaml ``` * this hparams file, had to manually create as by default hparams.yaml is not saved with dist ckpt format last I checked. It seems like the behavior is to only create and store with .nemo or the legacy pp tp partition format with the PTL callback. Unrelated, but it might be nice to also save the hparams.yaml file when saving a distributed ckpt (not with .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**: - [ ] 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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vLLM Export Support
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# What does this PR do ? Implements export of NeMo checkpoints to vLLM 0.5.0 and deployment of vLLM engines on the Triton server. **Collection**: NLP # Changelog - Added `nemo.export.vllm` package that implements the export and deployment functionality, primarily with the `nemo.export.vllm.Exporter` class. - Added script `scripts/deploy/nlp/deploy_triton_vllm.py` to use the new Exporter. - Removed the shortcut `nemo.export.TensorRTLLM` to avoid initializing TRT-LLM when anything is accessed in the `nemo.export` package, use `nemo.export.tensorrt_llm.TensorRTLLM` instead. - Moved the `sentencepiece_tokenizer.py` module from `nemo.export.tensorrt_llm.nemo` into `nemo.export` for the same reason. # Supported Models - LLAMA2 - Mistral - Mixtral (should work but I couldn't test it due to GPU OOM) - Gemma - StarCoder2 # Usage ```sh deploy_triton_vllm.py --nemo_checkpoint <path/to/checkpoint.nemo> --model_type llama --triton_model_name LLAMA --num_gpus 1 ``` See `nemo/export/vllm/exporter.py` for inline documentation on the Exporter class. # PR Type - [X] New Feature # Known issues - Currently, vLLM and TRT-LLM cannot be installed in the same container because they require conflicting versions of various packages. Also, installing vLLM breaks something in the `transformer_engine` module, requiring it to be uninstalled. - Fine-tuning is not supported currently, neither P-tuning nor LoRA
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Force diarizer to use CUDA if cuda is available and if device=None.
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# What does this PR do ? This PR changes clustering_diarizer.py code to use CUDA if cuda is available and if device=None. **Collection**: asr # Changelog ` if speaker_model is not None: if self._cfg.device is None and torch.cuda.is_available(): self._speaker_model = speaker_model.to(torch.device('cuda')) else: self._speaker_model = speaker_model ` # Usage Use diarizaiton examples ``` Run the entire speaker diarization pipeline: VAD, clustering diarizer for initializing clustering then Multi-scale Diarization Decoder (MSDD). python multiscale_diar_decoder_infer.py --config-path='../conf/inference' --config-name='diar_infer_telephonic.yaml' \ diarizer.vad.model_path=<NeMo VAD model path> \ diarizer.msdd_model.model_path=<NeMo MSDD model path> \ diarizer.oracle_vad=False \ diarizer.manifest_filepath=<test_manifest> \ diarizer.out_dir=<test_temp_dir> \ ``` # GitHub Actions CI The Jenkins CI system has been replaced by GitHub Actions self-hosted runners. The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR. To re-run CI remove and add the label again. To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run". # Before your PR is "Ready for review" **Pre checks**: - [x ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [x] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in NeMo ASR
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Fix null/None truncation field with extra generating tokens
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2024-06-04T22:55:51
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# What does this PR do ? When truncation field is None, we will truncate our inputs from right or left. But we need to leave spaces for generating tokens. **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 ? This PR is revision of the old PR https://github.com/NVIDIA/NeMo/pull/9182. TitaNet model was updated but some previous configurations were not. This PR provides support for prevevious msdd configuration versions. **Collection**: SD # Changelog - Catch the `_target_` code in newer titanet versions, which is not available in previous versions. # 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
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# What does this PR do ? This PR is revision of the old PR https://github.com/NVIDIA/NeMo/pull/9182. TitaNet model was updated but some previous configurations were not. This PR provides support for prevevious msdd configuration versions. **Collection**: SD # Changelog - Catch the `_target_` code in newer titanet versions, which is not available in previous versions. # 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
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# What does this PR do ? fp16 precision fix. Fixes this error when using mcore=True & precision is fp16: `RuntimeError: expected scalar type Half but found Float` **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 reset learning rate functionality
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[ "- Use case 1: `reset_lr=True, reset_lr_steps=False`\r\nWe want to reset_lr to init_lr (0 or optim.lr) in order to train it on another data and don't care about learning rate's max number of steps.\r\n\r\n- Use case 2: `reset_lr=True, reset_lr_steps=False` \r\nWe want to reset_lr and we also want to reach min_lr by the end of the training by using the same trainer.max_steps as for previous run of this checkpoint. So in this case we want to update lr's max_steps and decay_steps in the next way:\r\n`max_steps = max_steps - consumed_steps,\r\ndecay_steps = decay_steps - consumed_steps`\r\nwhere `conusmed_steps` is the number of steps already completed at the checkpoint.\r\nExample:\r\n![image](https://github.com/NVIDIA/NeMo/assets/37850217/90387f47-f29e-40d4-ac6f-790bb0a265ec)\r\n\r\n" ]
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# What does this PR do ? Adds reset learning rate functionality. **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 GreedyBatchedCTCInfer regression from GreedyCTCInfer. (#9347)
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* Fix GreedyBatchedCTCInfer regression from GreedyCTCInfer. decoder_lengths is allowed to be on CPU even when decoder_output is on GPU. This matches the behavior of GreedyCTCInfer. Even though that behavior is unintentional, there is code depending on that behavior, including our jupyter notebooks. Signed-off-by: Daniel Galvez <[email protected]> * Apply isort and black reformatting Signed-off-by: titu1994 <[email protected]> --------- Signed-off-by: Daniel Galvez <[email protected]> Signed-off-by: titu1994 <[email protected]> Co-authored-by: Somshubra Majumdar <[email protected]> Co-authored-by: titu1994 <[email protected]> Co-authored-by: Nithin Rao <[email protected]> (cherry picked from commit aed9d071c700080b3eb024e8a5d7f091f20f0183) # What does this PR do ? Information is here - https://github.com/NVIDIA/NeMo/pull/9347 # 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**: - [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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fix typo infer_seq_lenght -> infer_seq_length
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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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Fix conv1d package in r2.0.0rc0
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Fix conv1d package in r2.0.0rc0
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Edits for syntax, missing words in tutorials
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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.", "These are grammar, etc. fixes, this would be a pretty quick merge.", "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.", "Hey folks, think this wouldnt be much work to merge, just textual edits.", "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]
2024-06-03T19:43:07
2024-09-01T02:03:24
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# What does this PR do ? This PR makes a few edits for syntax and missing words in the NeMo tutorials. **tutorials**
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move AED chunked infer script
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# What does this PR do ? move AED chunked infer script **Collection**: [asr]
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Fix prompt formatter's defaults=None case in multi-task model
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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 Gemma conversion script
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# What does this PR do ? Update and fix Gemma conversion script # 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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[Nemo CICD] Remove unnecessary commented out code
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2024-06-03T16:52:46
2024-06-03T16:53:40
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Remove unnecessary commented out code
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9,363
Citrinet Training: Sentences are cut during prediction
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[ "Just saw this. Citrinet is a CTC model, did you check If your audio after 8x down sampling was shorter than the text transcript tokens in subword count ? That's often the reason for dropped words, especially because German transcripts are usually verbose and contain long words.\r\n\r\nId suggest trying a fast conformer transducer instead, 105M model should match the perf and memory of Citrinet 512 quite easily. " ]
2024-06-03T09:55:25
2024-06-11T08:39:35
2024-06-11T08:35:52
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**Describe the bug** Currently I train a citrinet-512 Model. I copied the config from the example configs in here and didnt change them (https://github.com/NVIDIA/NeMo/tree/main/examples/asr/conf/citrinet). After detecting the issue, I also used another config of hugging face where someone finetuned a citrinet model (https://huggingface.co/neongeckocom/stt_de_citrinet_512_gamma_0_25). Both configs lead to the same problem. A lot of sentences are cut randomly during prediction like e.g. ``` [NeMo I 2024-06-02 22:59:01 wer_bpe:302] reference:so ließ es sich nicht ändern er blieb oberleutnant und um so lieber weil ihm herr bantes sein gewesener vormund längst den winzigen rest seines väterlichen erbteils ausgehändigt hatte und dieses längst schon zu allen heiden ausgewandert war [NeMo I 2024-06-02 22:59:01 wer_bpe:303] predicted:so ließ es sich nicht ändern er blieb oberleutnant und um so lieber weil ihm herr bantes sein gewesener vormund längst winzigen rest seines väterlichen erbteils ausgehändigt hatte [NeMo I 2024-06-02 22:59:03 wer_bpe:302] reference:nicht mehr seine was ist denn aus ihr geworden die war so alt und gebrechlich dass sie schließlich zusammenkrachte schon vor einer guten reihe von jahren [NeMo I 2024-06-02 22:59:03 wer_bpe:303] predicted:nicht mehr seine was ist denn aus ihr geworden die war so alt und gebrechlich dass sie schließlich zu seinem ``` This happens right after a few epochs (2 epochs) and doesnt vanish even after 90 epochs. Its not a dataset specific issue, but occurs randomly for several datasets. It does not happen for every sample but for a relevant share of the data. It happens for training and evaluation. I tried to figure out what the problem relates to or if any parameter could solve it, but couldnt detect where this issue comes from. **Steps/Code to reproduce bug** Here is the config used: ``` name: &name "Citrinet-512 training" model: sample_rate: &sample_rate 16000 log_prediction: true train_ds: manifest_filepath: "/train_cleaned.json" sample_rate: 16000 batch_size: 32 trim_silence: false max_duration: 16.0 min_duration: 1.0 shuffle: true use_start_end_token: false num_workers: 8 pin_memory: true is_tarred: false tarred_audio_filepaths: null shuffle_n: 2048 bucketing_strategy: 'synced_randomized' bucketing_batch_size: null validation_ds: manifest_filepath: "/dev_cleaned.json" sample_rate: 16000 batch_size: 32 shuffle: false use_start_end_token: false num_workers: 8 pin_memory: true test_ds: manifest_filepath: "/test_cleaned.json" sample_rate: 16000 batch_size: 32 shuffle: false use_start_end_token: false num_workers: 8 pin_memory: true model_defaults: repeat: 5 dropout: 0.1 separable: true se: true se_context_size: -1 kernel_size_factor: 1 filters: 512 enc_final: 640 tokenizer: dir: "tokenizer_spe_unigram_v1024" # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe) type: "bpe" # Can be either bpe or wpe preprocessor: _target_: 'nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor' sample_rate: 16000 normalize: 'per_feature' window_size: 0.025 window_stride: 0.01 window: 'hann' features: &n_mels 80 n_fft: 512 frame_splicing: 1 dither: 1e-05 pad_to: 16 stft_conv: false spec_augment: _target_: 'nemo.collections.asr.modules.SpectrogramAugmentation' freq_masks: 2 time_masks: 5 freq_width: 27 time_width: 0.05 encoder: _target_: nemo.collections.asr.modules.ConvASREncoder feat_in: *n_mels activation: relu conv_mask: true jasper: - filters: 512 repeat: 1 kernel: [5] stride: [1] dilation: [1] dropout: 0.0 residual: false separable: ${model.model_defaults.separable} se: ${model.model_defaults.se} se_context_size: ${model.model_defaults.se_context_size} - filters: 512 repeat: ${model.model_defaults.repeat} kernel: [11] stride: [2] dilation: [1] dropout: ${model.model_defaults.dropout} residual: true separable: ${model.model_defaults.separable} se: ${model.model_defaults.se} se_context_size: ${model.model_defaults.se_context_size} stride_last: true residual_mode: "stride_add" kernel_size_factor: ${model.model_defaults.kernel_size_factor} - filters: 512 repeat: ${model.model_defaults.repeat} kernel: [13] stride: [1] dilation: [1] dropout: ${model.model_defaults.dropout} residual: true separable: ${model.model_defaults.separable} se: ${model.model_defaults.se} se_context_size: ${model.model_defaults.se_context_size} kernel_size_factor: ${model.model_defaults.kernel_size_factor} - filters: 512 repeat: ${model.model_defaults.repeat} kernel: [15] stride: [1] dilation: [1] dropout: ${model.model_defaults.dropout} residual: true separable: ${model.model_defaults.separable} se: ${model.model_defaults.se} se_context_size: ${model.model_defaults.se_context_size} kernel_size_factor: ${model.model_defaults.kernel_size_factor} - filters: 512 repeat: ${model.model_defaults.repeat} kernel: [17] stride: [1] dilation: [1] dropout: ${model.model_defaults.dropout} residual: true separable: ${model.model_defaults.separable} se: ${model.model_defaults.se} se_context_size: ${model.model_defaults.se_context_size} kernel_size_factor: ${model.model_defaults.kernel_size_factor} - filters: 512 repeat: ${model.model_defaults.repeat} kernel: [19] stride: [1] dilation: [1] dropout: ${model.model_defaults.dropout} residual: true separable: ${model.model_defaults.separable} se: ${model.model_defaults.se} se_context_size: ${model.model_defaults.se_context_size} kernel_size_factor: ${model.model_defaults.kernel_size_factor} - filters: 512 repeat: ${model.model_defaults.repeat} kernel: [21] stride: [1] dilation: [1] dropout: ${model.model_defaults.dropout} residual: true separable: ${model.model_defaults.separable} se: ${model.model_defaults.se} se_context_size: ${model.model_defaults.se_context_size} kernel_size_factor: ${model.model_defaults.kernel_size_factor} - filters: 512 repeat: ${model.model_defaults.repeat} kernel: [13] stride: [2] # *stride dilation: [1] dropout: ${model.model_defaults.dropout} residual: true separable: ${model.model_defaults.separable} se: ${model.model_defaults.se} se_context_size: ${model.model_defaults.se_context_size} stride_last: true residual_mode: "stride_add" kernel_size_factor: ${model.model_defaults.kernel_size_factor} - filters: 512 repeat: ${model.model_defaults.repeat} kernel: [15] stride: [1] dilation: [1] dropout: ${model.model_defaults.dropout} residual: true separable: ${model.model_defaults.separable} se: ${model.model_defaults.se} se_context_size: ${model.model_defaults.se_context_size} kernel_size_factor: ${model.model_defaults.kernel_size_factor} - filters: 512 repeat: ${model.model_defaults.repeat} kernel: [17] stride: [1] dilation: [1] dropout: ${model.model_defaults.dropout} residual: true separable: ${model.model_defaults.separable} se: ${model.model_defaults.se} se_context_size: ${model.model_defaults.se_context_size} kernel_size_factor: ${model.model_defaults.kernel_size_factor} - filters: 512 repeat: ${model.model_defaults.repeat} kernel: [19] stride: [1] dilation: [1] dropout: ${model.model_defaults.dropout} residual: true separable: ${model.model_defaults.separable} se: ${model.model_defaults.se} se_context_size: ${model.model_defaults.se_context_size} kernel_size_factor: ${model.model_defaults.kernel_size_factor} - filters: 512 repeat: ${model.model_defaults.repeat} kernel: [21] stride: [1] dilation: [1] dropout: ${model.model_defaults.dropout} residual: true separable: ${model.model_defaults.separable} se: ${model.model_defaults.se} se_context_size: ${model.model_defaults.se_context_size} kernel_size_factor: ${model.model_defaults.kernel_size_factor} - filters: 512 repeat: ${model.model_defaults.repeat} kernel: [23] stride: [1] dilation: [1] dropout: ${model.model_defaults.dropout} residual: true separable: ${model.model_defaults.separable} se: ${model.model_defaults.se} se_context_size: ${model.model_defaults.se_context_size} kernel_size_factor: ${model.model_defaults.kernel_size_factor} - filters: 512 repeat: ${model.model_defaults.repeat} kernel: [25] stride: [1] dilation: [1] dropout: ${model.model_defaults.dropout} residual: true separable: ${model.model_defaults.separable} se: ${model.model_defaults.se} se_context_size: ${model.model_defaults.se_context_size} kernel_size_factor: ${model.model_defaults.kernel_size_factor} - filters: 512 repeat: ${model.model_defaults.repeat} kernel: [25] stride: [2] # stride dilation: [1] dropout: ${model.model_defaults.dropout} residual: true separable: ${model.model_defaults.separable} se: ${model.model_defaults.se} se_context_size: ${model.model_defaults.se_context_size} stride_last: true residual_mode: "stride_add" kernel_size_factor: ${model.model_defaults.kernel_size_factor} - filters: 512 repeat: ${model.model_defaults.repeat} kernel: [27] stride: [1] dilation: [1] dropout: ${model.model_defaults.dropout} residual: true separable: ${model.model_defaults.separable} se: ${model.model_defaults.se} se_context_size: ${model.model_defaults.se_context_size} kernel_size_factor: ${model.model_defaults.kernel_size_factor} - filters: 512 repeat: ${model.model_defaults.repeat} kernel: [29] stride: [1] dilation: [1] dropout: ${model.model_defaults.dropout} residual: true separable: ${model.model_defaults.separable} se: ${model.model_defaults.se} se_context_size: ${model.model_defaults.se_context_size} kernel_size_factor: ${model.model_defaults.kernel_size_factor} - filters: 512 repeat: ${model.model_defaults.repeat} kernel: [31] stride: [1] dilation: [1] dropout: ${model.model_defaults.dropout} residual: true separable: ${model.model_defaults.separable} se: ${model.model_defaults.se} se_context_size: ${model.model_defaults.se_context_size} kernel_size_factor: ${model.model_defaults.kernel_size_factor} - filters: 512 repeat: ${model.model_defaults.repeat} kernel: [33] stride: [1] dilation: [1] dropout: ${model.model_defaults.dropout} residual: true separable: ${model.model_defaults.separable} se: ${model.model_defaults.se} se_context_size: ${model.model_defaults.se_context_size} kernel_size_factor: ${model.model_defaults.kernel_size_factor} - filters: 512 repeat: ${model.model_defaults.repeat} kernel: [35] stride: [1] dilation: [1] dropout: ${model.model_defaults.dropout} residual: true separable: ${model.model_defaults.separable} se: ${model.model_defaults.se} se_context_size: ${model.model_defaults.se_context_size} kernel_size_factor: ${model.model_defaults.kernel_size_factor} - filters: 512 repeat: ${model.model_defaults.repeat} kernel: [37] stride: [1] dilation: [1] dropout: ${model.model_defaults.dropout} residual: true separable: ${model.model_defaults.separable} se: ${model.model_defaults.se} se_context_size: ${model.model_defaults.se_context_size} kernel_size_factor: ${model.model_defaults.kernel_size_factor} - filters: 512 repeat: ${model.model_defaults.repeat} kernel: [39] stride: [1] dilation: [1] dropout: ${model.model_defaults.dropout} residual: true separable: ${model.model_defaults.separable} se: ${model.model_defaults.se} se_context_size: ${model.model_defaults.se_context_size} kernel_size_factor: ${model.model_defaults.kernel_size_factor} - filters: ${model.model_defaults.enc_final} repeat: 1 kernel: [41] stride: [1] dilation: [1] dropout: 0.0 residual: false separable: ${model.model_defaults.separable} se: ${model.model_defaults.se} se_context_size: ${model.model_defaults.se_context_size} kernel_size_factor: ${model.model_defaults.kernel_size_factor} decoder: _target_: 'nemo.collections.asr.modules.ConvASRDecoder' feat_in: 640 num_classes: 1024 vocabulary: [] optim: name: 'novograd' lr: 0.005 betas: [0.8, 0.25] weight_decay: 0.0001 sched: name: 'CosineAnnealing' warmup_steps: null warmup_ratio: 0.1 min_lr: 1e-05 last_epoch: -1 target: 'nemo.collections.asr.models.ctc_bpe_models.EncDecCTCModelBPE' nemo_version: '1.12.0' decoding: strategy: 'greedy' preserve_alignments: null compute_timestamps: null word_seperator: ' ' ctc_timestamp_type: 'all' batch_dim_index: 0 greedy: preserve_alignments: false compute_timestamps: false trainer: devices: 2 # number of gpus max_epochs: 100 max_steps: -1 # computed at runtime if not set num_nodes: 1 accelerator: gpu strategy: auto accumulate_grad_batches: 1 enable_checkpointing: True # Provided by exp_manager enable_progress_bar: True logger: false # Provided by exp_manager log_every_n_steps: 50 # Interval of logging. val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations check_val_every_n_epoch: 1 precision: 32 sync_batchnorm: false benchmark: false # needs to be false for models with variable-length speech input as it slows down training exp_manager: exp_dir: null name: *name create_tensorboard_logger: false create_checkpoint_callback: false create_mlflow_logger: true mlflow_logger_kwargs: experiment_name: "training-citrinet-512" tracking_uri: [removed, here would be the tracking uri] checkpoint_callback_params: monitor: "val_wer" mode: "min" save_top_k: 3 always_save_nemo: True #not tested yet, found this in a nemo repo create_wandb_logger: false wandb_logger_kwargs: name: null project: null entity: null resume_if_exists: false resume_ignore_no_checkpoint: false ``` **Expected behavior** I expect the training to predict the sentences correctly. For a lot of the sentences it works, for some the predictions are just cut even though the rest was welll detected. I believe this affects the loss and the WER and hence its hard to judge how good the model is actually. **Environment overview (please complete the following information)** On Azure I set up an environment and trained multi-gpu. NeMo is pip installed via nemo_toolkit==1.21.0 **Environment details** tensorflow-2.8-cuda11 python=3.8 torch=2.3.0 **Additional context**
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9,362
Add MPU rank specifiers for EP & DDP
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[ "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]
2024-06-03T08:46:42
2024-08-01T16:36:27
2024-07-04T01:48:49
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# What does this PR do ? I introduced in mcore a config logger, which when enabled dumps to disk configuration options at entry points of mcore. The logger uses the rank as part of the logs filepath & as a result we need a way to specify these when the distributed environment has not yet been setup. **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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9,361
fix wpe tokenizer function
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# What does this PR do ? Correcting WPE tokenizer ids_to_text function which now handles punctuation marks and spaces. **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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ImportError: cannot import name '_library_root_logger' from 'apex' (unknown location)
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2024-06-03T07:13:25
2024-06-03T19:35:30
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Error : Traceback (most recent call last): File "/opt/NeMo/scripts/checkpoint_converters/convert_llama_hf_to_nemo.py", line 312, in <module> convert(args) File "/opt/NeMo/scripts/checkpoint_converters/convert_llama_hf_to_nemo.py", line 288, in convert model = load_state_dict_helper(MegatronGPTModel, nemo_config, trainer, checkpoint['state_dict']) File "/opt/NeMo/nemo/collections/nlp/parts/utils_funcs.py", line 217, in load_state_dict_helper model = cls(cfg, trainer) File "/opt/NeMo/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 287, in __init__ super().__init__(cfg, trainer=trainer, no_lm_init=True) File "/opt/NeMo/nemo/collections/nlp/models/language_modeling/megatron_base_model.py", line 191, in __init__ initialize_model_parallel_for_nemo( File "/opt/NeMo/nemo/collections/nlp/modules/common/megatron/megatron_init.py", line 159, in initialize_model_parallel_for_nemo set_logging_level(apex_transformer_log_level) File "/opt/apex/apex/transformer/log_util.py", line 16, in set_logging_level from apex import _library_root_logger ImportError: cannot import name '_library_root_logger' from 'apex' (unknown location) command : python /opt/NeMo/scripts/checkpoint_converters/convert_llama_hf_to_nemo.py --input_name_or_path=./llama2-7b-hf/ --output_path=llama2-7b.nemo Can anyone help me solve this
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Add tutorial for Llama-3-8B lora training and deployment
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[ "@chrisalexiuk-nvidia @vinhngx could you please review?", "![image](https://github.com/NVIDIA/NeMo/assets/161380339/35fbb40e-d479-4bc9-a356-8eef581930d2)\r\n\r\nThere's some formatting mismatch here for the `Fig Text`.\r\n\r\n", "Adding @nealvaidya for any review comments covering the NIM part", "<img width=\"156\" alt=\"Screenshot 2024-06-05 at 5 02 21 PM\" src=\"https://github.com/NVIDIA/NeMo/assets/161380339/fe4af46d-6477-4249-870c-ffda6838c50c\">\r\n\r\nFirst notebook workin' like a charm.", "Approved comments" ]
2024-06-03T04:37:32
2024-06-07T17:35:57
2024-06-07T17:10:06
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* Adds a notebook for Llama-3-8b LoRA PEFT with NeMo FW * Adds a notebook for sending multi-LoRA inference request to NVIDIA NIM * Adds README that includes instructions fore context and set up
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CI test for RAG Pipeline
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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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Error(s): ConfidenceConfig.__init__() got an unexpected keyword argument 'measure_cfg'
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[ "@nithinraok @titu1994 ", "Please update to NeMo 1.23.0. ", "> Please update to NeMo 1.23.0.\r\n@titu1994 \r\n\r\nTraceback (most recent call last):\r\n File \"/home/ma-user/work/exp/speech_to_text_eval.py\", line 71, in <module>\r\n import transcribe_speech\r\n File \"/home/ma-user/work/exp/transcribe_speech.py\", line 111, in <module>\r\n class TranscriptionConfig:\r\n File \"/home/ma-user/work/exp/transcribe_speech.py\", line 159, in TranscriptionConfig\r\n fused_batch_size=-1, greedy=GreedyBatchedRNNTInferConfig(use_cuda_graph_decoder=True)\r\nTypeError: GreedyBatchedRNNTInferConfig.__init__() got an unexpected keyword argument 'use_cuda_graph_decoder'\r\n\r\n\r\n**model_config:**\r\n\r\n```\r\ntarget: nemo.collections.asr.models.ctc_bpe_models.EncDecCTCModelBPE\r\nnemo_version: 1.21.0\r\ndecoding:\r\n strategy: greedy\r\n preserve_alignments: null\r\n compute_timestamps: null\r\n word_seperator: ' '\r\n ctc_timestamp_type: all\r\n batch_dim_index: 0\r\n greedy:\r\n preserve_alignments: false\r\n compute_timestamps: false\r\n preserve_frame_confidence: false\r\n confidence_measure_cfg:\r\n name: entropy\r\n entropy_type: tsallis\r\n alpha: 0.33\r\n entropy_norm: exp\r\n temperature: DEPRECATED\r\n confidence_method_cfg: DEPRECATED\r\n beam:\r\n beam_size: 4\r\n search_type: default\r\n preserve_alignments: false\r\n compute_timestamps: false\r\n return_best_hypothesis: true\r\n beam_alpha: 1.0\r\n beam_beta: 0.0\r\n kenlm_path: null\r\n flashlight_cfg:\r\n lexicon_path: null\r\n boost_path: null\r\n beam_size_token: 16\r\n beam_threshold: 20.0\r\n unk_weight: -.inf\r\n sil_weight: 0.0\r\n pyctcdecode_cfg:\r\n beam_prune_logp: -10.0\r\n token_min_logp: -5.0\r\n prune_history: false\r\n hotwords: null\r\n hotword_weight: 10.0\r\n confidence_cfg:\r\n preserve_frame_confidence: false\r\n preserve_token_confidence: false\r\n preserve_word_confidence: false\r\n exclude_blank: true\r\n aggregation: min\r\n measure_cfg:\r\n name: entropy\r\n entropy_type: tsallis\r\n alpha: 0.33\r\n entropy_norm: exp\r\n temperature: DEPRECATED\r\n method_cfg: DEPRECATED\r\n temperature: 1.0\r\n\r\n```", "@titu1994 @nithinraok your help kindly ", "You're using the script from main, that won't work. Switch branch to r1.23 and then use that script ", "> You're using the script from main, that won't work. Switch branch to r1.23 and then use that script\r\n\r\n**Still facing this error:** TypeError: ConfidenceConfig.__init__() got an unexpected keyword argument 'measure_cfg'\r\n\r\nWhat I see is that, r1.23.0 is also using 'method_cfg', but my trained model using 'measure_cfg'\r\n\r\n@titu1994 ", "Delete the entire \"confidence_cfg\" part using following \r\n\r\n```pythob\r\ncfg = ASRModel.restore_from(filepath, return_config=True)\r\n\r\ndel cfg.decoding.confidence_cfg\r\n\r\nModel = ASRModel.restore_from(filepath, override_config_path=cfg)\r\n\r\nModel.save_to(new_filepath)\r\n\r\n```", "> Delete the entire \"confidence_cfg\" part using following\r\n> \r\n> ```\r\n> cfg = ASRModel.restore_from(filepath, return_config=True)\r\n> \r\n> del cfg.decoding.confidence_cfg\r\n> \r\n> Model = ASRModel.restore_from(filepath, override_config_path=cfg)\r\n> \r\n> Model.save_to(new_filepath)\r\n> ```\r\n\r\nThis error occurs:\r\n\r\nTarget class: nemo.collections.asr.models.ctc_bpe_models.EncDecCTCModelBPE\r\n Error(s): nemo.collections.asr.parts.utils.asr_confidence_utils.ConfidenceMethodConfig() argument after ** must be a mapping, not str\r\n Traceback (most recent call last):\r\n File \"/home/ma-user/work/exp/nemo/core/classes/common.py\", line 508, in from_config_dict\r\n instance = imported_cls(cfg=config, trainer=trainer)\r\n File \"/home/ma-user/work/exp/nemo/collections/asr/models/ctc_bpe_models.py\", line 75, in __init__\r\n super().__init__(cfg=cfg, trainer=trainer)\r\n File \"/home/ma-user/work/exp/nemo/collections/asr/models/ctc_models.py\", line 102, in __init__\r\n self.decoding = CTCDecoding(self.cfg.decoding, vocabulary=OmegaConf.to_container(self.decoder.vocabulary))\r\n File \"/home/ma-user/work/exp/nemo/collections/asr/parts/submodules/ctc_decoding.py\", line 1032, in __init__\r\n super().__init__(decoding_cfg=decoding_cfg, blank_id=blank_id)\r\n File \"/home/ma-user/work/exp/nemo/collections/asr/parts/submodules/ctc_decoding.py\", line 235, in __init__\r\n self._init_confidence(self.cfg.get('confidence_cfg', None))\r\n File \"/home/ma-user/work/exp/nemo/collections/asr/parts/utils/asr_confidence_utils.py\", line 384, in _init_confidence\r\n else OmegaConf.structured(ConfidenceMethodConfig(**confidence_method_cfg))\r\n TypeError: nemo.collections.asr.parts.utils.asr_confidence_utils.ConfidenceMethodConfig() argument after ** must be a mapping, not str\r\n \r\nCode:\r\n```\r\nimport nemo.collections.asr as nemo_asr\r\n\r\nfilepath= \"Conformer-CTC-BPE-multilang.nemo\"\r\n\r\nasr_model = nemo_asr.models.EncDecCTCModelBPE\r\ncfg = asr_model.restore_from(filepath, return_config=True)\r\n\r\ndel cfg.decoding.confidence_cfg\r\n\r\nModel = asr_model.restore_from(filepath, override_config_path=cfg)\r\n\r\nModel.save_to(\"./modified/Conformer-CTC-BPE-multilang.nemo\")\r\n```\r\n@titu1994 ", "Oh I see, also delete in a similar way the cfg.decoding.greedy.confidence_measure_cfg", "Still..\r\nTraceback (most recent call last):\r\n File \"/home/ma-user/work/exp/del.py\", line 11, in <module>\r\n Model = asr_model.restore_from(filepath, override_config_path=cfg)\r\n File \"/home/ma-user/work/exp/nemo/core/classes/modelPT.py\", line 449, in restore_from\r\n instance = cls._save_restore_connector.restore_from(\r\n File \"/home/ma-user/work/exp/nemo/core/connectors/save_restore_connector.py\", line 241, in restore_from\r\n loaded_params = self.load_config_and_state_dict(\r\n File \"/home/ma-user/work/exp/nemo/core/connectors/save_restore_connector.py\", line 164, in load_config_and_state_dict\r\n instance = calling_cls.from_config_dict(config=conf, trainer=trainer)\r\n File \"/home/ma-user/work/exp/nemo/core/classes/common.py\", line 518, in from_config_dict\r\n raise e\r\n File \"/home/ma-user/work/exp/nemo/core/classes/common.py\", line 510, in from_config_dict\r\n instance = cls(cfg=config, trainer=trainer)\r\n File \"/home/ma-user/work/exp/nemo/collections/asr/models/ctc_bpe_models.py\", line 73, in __init__\r\n super().__init__(cfg=cfg, trainer=trainer)\r\n File \"/home/ma-user/work/exp/nemo/collections/asr/models/ctc_models.py\", line 97, in __init__\r\n self.decoding = CTCDecoding(self.cfg.decoding, vocabulary=OmegaConf.to_container(self.decoder.vocabulary))\r\n File \"/home/ma-user/work/exp/nemo/collections/asr/parts/submodules/ctc_decoding.py\", line 935, in __init__\r\n super().__init__(decoding_cfg=decoding_cfg, blank_id=blank_id)\r\n File \"/home/ma-user/work/exp/nemo/collections/asr/parts/submodules/ctc_decoding.py\", line 193, in __init__\r\n self._init_confidence(self.cfg.get('confidence_cfg', None))\r\n File \"/home/ma-user/work/exp/nemo/collections/asr/parts/utils/asr_confidence_utils.py\", line 370, in _init_confidence\r\n else OmegaConf.structured(ConfidenceMethodConfig(**confidence_method_cfg))\r\nTypeError: nemo.collections.asr.parts.utils.asr_confidence_utils.ConfidenceMethodConfig() argument after ** must be a mapping, not str\r\n\r\nI think you’ve solved the issue here https://github.com/NVIDIA/NeMo/pull/6810, but in which branch?\r\n\r\n@titu1994 ", "I had the same problem because I trained the model using nemo-toolkit[all]==1.21.0 and then load the models on latest version. I solved it by reinstall \"nemo-toolkit[all]==1.21.0\". It worked ", "Can you print you full config after you delete the two items ? If that doesn't work there's not much to be done. Also share your script of how you're updating the config ", "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." ]
2024-06-02T08:39:14
2024-07-12T01:49:12
2024-07-12T01:49:11
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How to solve the issue of 'measure_cfg' and 'method_cfg'? When loading the model using the text_to_speech_eval.py script the model instantiation fails: [NeMo E 2024-06-02 11:21:20 common:529] Model instantiation failed! Target class: nemo.collections.asr.models.ctc_bpe_models.EncDecCTCModelBPE Error(s): ConfidenceConfig.__init__() got an unexpected keyword argument 'measure_cfg' Traceback (most recent call last): File "/home/ma-user/work/exp/nemo/core/classes/common.py", line 508, in from_config_dict instance = imported_cls(cfg=config, trainer=trainer) File "/home/ma-user/work/exp/nemo/collections/asr/models/ctc_bpe_models.py", line 75, in __init__ super().__init__(cfg=cfg, trainer=trainer) File "/home/ma-user/work/exp/nemo/collections/asr/models/ctc_models.py", line 102, in __init__ self.decoding = CTCDecoding(self.cfg.decoding, vocabulary=OmegaConf.to_container(self.decoder.vocabulary)) File "/home/ma-user/work/exp/nemo/collections/asr/parts/submodules/ctc_decoding.py", line 1032, in __init__ super().__init__(decoding_cfg=decoding_cfg, blank_id=blank_id) File "/home/ma-user/work/exp/nemo/collections/asr/parts/submodules/ctc_decoding.py", line 235, in __init__ self._init_confidence(self.cfg.get('confidence_cfg', None)) File "/home/ma-user/work/exp/nemo/collections/asr/parts/utils/asr_confidence_utils.py", line 352, in _init_confidence ConfidenceConfig() if confidence_cfg is None else ConfidenceConfig(**confidence_cfg) TypeError: ConfidenceConfig.__init__() got an unexpected keyword argument 'measure_cfg' Model trained with NeMo version 1.21.0
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bugfix if using mcore distOpt with sft
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# What does this PR do ? skip entering if statement's block that's intended only for apex distOpt if using mcore distOpt. if the mcore distOpt is used it will set self.with_distributed_adam to true. This is something we can refactor to improve readability/maintainability. **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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support null/None truncation field
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2024-05-31T16:56:53
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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 - [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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Chat template support for megatron_gpt_eval.py
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[ "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]
2024-05-31T03:33:30
2024-07-04T18:51:44
2024-07-04T18:51:42
MEMBER
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# What does this PR do ? Adds support for chat-templated inputs (e.g. with -instruct models) to the eval script. **Collection**: [Note which collection this PR will affect] NLP # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # GitHub Actions CI The Jenkins CI system has been replaced by GitHub Actions self-hosted runners. The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR. To re-run CI remove and add the label again. To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run". # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] 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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Re-org export code
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2024-05-31T03:22:17
2024-06-25T08:02:48
2024-06-06T17:05:34
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# What does this PR do ? Re-org the export code to make it more step by step. In the new code, there will be 3 main parts including the reading the nemo ckpt, converting nemo ckpt to trt-llm ckpt, and building the trt-llm engine. Once this PR is completed, then the converting nemo ckpt to trt-llm ckpt, and building the trt-llm engine parts will be moved to the mcore.
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[Fault tolerance] Heartbeat detection
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[ "Hi @maanug-nv I was wondering how can I use the Heartbeat detection and Fault Tolerance features of nemo. Im using the latest NeMo image (`nvcr.io/nvidia/nemo:24.07`) and it's not included. Also I couldn't find any `ptl_resiliency ` nor `gwe_resiliency_pkg` package.\r\n\r\nAre there any installation instructions for this features? \r\n\r\nThanks,\r\n\r\nToni ", "Hi @TJ-Solergibert , unfortunately the resiliency package has not made it into the container yet. I think we initially thought it would be a smoother process so this PR was already merged. I currently expect that the package will be in the 24.09 container. \r\n\r\nSorry for the confusion. " ]
2024-05-31T00:29:09
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# What does this PR do ? Enables heartbeat detection, terminate and restart of training. **Collection**: [Note which collection this PR will affect] # Changelog - New PTL callback for sending heartbeats and calculating timeout interval # Usage See changes to `docs/source/exp_manager.rst` # 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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Revert "Fix GreedyBatchedCTCInfer regression from GreedyCTCInfer."
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Reverts NVIDIA/NeMo#9347
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Fix GreedyBatchedCTCInfer regression from GreedyCTCInfer. (#9347)
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* Fix GreedyBatchedCTCInfer regression from GreedyCTCInfer. decoder_lengths is allowed to be on CPU even when decoder_output is on GPU. This matches the behavior of GreedyCTCInfer. Even though that behavior is unintentional, there is code depending on that behavior, including our jupyter notebooks. Signed-off-by: Daniel Galvez <[email protected]> * Apply isort and black reformatting Signed-off-by: titu1994 <[email protected]> --------- Signed-off-by: Daniel Galvez <[email protected]> Signed-off-by: titu1994 <[email protected]> Co-authored-by: Somshubra Majumdar <[email protected]> Co-authored-by: titu1994 <[email protected]> Co-authored-by: Nithin Rao <[email protected]> (cherry picked from commit aed9d071c700080b3eb024e8a5d7f091f20f0183) # What does this PR do ? Information is here - https://github.com/NVIDIA/NeMo/pull/9347 # 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**: - [x] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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# What does this PR do ? Add a 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 ? This PR brings two updates: * Fix SSL contrastive loss to support loss computation for variable input length rather than computation based on subsampling factor * Add an option to remove bias from Linear and Conv layers in Conformer layers to support scaling for multi billion parameter training [tested currently for 1B parameter using bf16] **Collection**: ASR # Changelog - Updated ssl_loss - Added argument for conformerlayer to optionally remove bias, default is False # 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
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Fix GreedyBatchedCTCInfer regression from GreedyCTCInfer.
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[ "~Fix not going to r2.0.0rc0?~\r\n" ]
2024-05-30T07:00:13
2024-05-30T17:08:24
2024-05-30T17:08:24
COLLABORATOR
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# What does this PR do ? decoder_lengths is allowed to be on CPU even when decoder_output is on GPU. This matches the behavior of GreedyCTCInfer. Even though that behavior is unintentional, there is code depending on that behavior, including our jupyter notebooks. # Testing I tested in the following way. I ran: ``` pytest -k test_batched_decoding_ tests/collections/asr/decoding/test_ctc_decoding.py CUDA_VISIBLE_DEVICES="" pytest -k test_batched_decoding_ tests/collections/asr/decoding/test_ctc_decoding.py ``` CUDA_VISIBLE_DEVICES="" ensures that the test runs when on CPU-only machines, as well as on machines with GPU. My previous PR related to this failed to test on GPU, which caused the crash we see today when we run with lengths on CPU but logprobs on GPU. I also manually ran examples/notebooks/asr/ASR_with_Subword_Tokenization.ipynb and examples/notebooks/asr/ASR_with_NeMo.ipynb manually. These run to completion. I had to comment out the part that install NeMo from pypi, which would overwrite my local installation. **Collection**: ASR **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 NVBug 4647443 and 4647231
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9,346
why we reverse the RGB channels in video processor
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[ "This has been fixed by https://github.com/NVIDIA/NeMo/pull/9245 " ]
2024-05-30T04:34:21
2024-06-04T22:35:57
2024-06-04T22:35:57
CONTRIBUTOR
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**Describe the bug** Hi developers, I noticed we reverse the video frame channels [here](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/multimodal/data/neva/neva_dataset.py#L103) and [here](https://github.com/NVIDIA/NeMo/blob/deb613adc7b7ad0a540f5cc1f0bc5032ddb345ff/nemo/collections/multimodal/parts/utils.py#L471). ``` frames = [Image.fromarray(vr[i].asnumpy()[:, :, ::-1]).convert('RGB') for i in indices] ``` Here are the images that without reversing vs reversing: Normal image: ![img1](https://github.com/NVIDIA/NeMo/assets/6286804/2b41d859-dfb3-43c2-8cc7-aaa2c3937c96) Reverse image: ![img2](https://github.com/NVIDIA/NeMo/assets/6286804/44200c0d-e6f4-48b5-a773-687d75221652) In some other project like [LITA](https://github.com/NVlabs/LITA/blob/56a23db03197beab45b28f0971129eadc1a6db03/lita/utils.py#L58) and [video LLaVA](https://github.com/PKU-YuanGroup/Video-LLaVA/blob/c5d2b7250f0a082f733974cfa40d4b09b97b88f5/videollava/model/multimodal_encoder/languagebind/video/processing_video.py#L89) They simply do the below: ``` decord.bridge.set_bridge("torch") video_reader = VideoReader(uri=video_path) idx = np.round(np.linspace(0, len(video_reader) - 1, num_frames)).astype(int) frames = video_reader.get_batch(idx) frames = processor.preprocess(frames, return_tensors='pt')['pixel_values'] ``` If we use opencv, we probably need to convert BGR2RGB. But now we are using `decord`, not sure if it's still the case.
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update mcoreddp call
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[ "Draft because it's pending mcore update on GH", "Thank you @timmoon10 for the fast review, will update the PR." ]
2024-05-29T22:53:18
2024-06-25T08:01:13
2024-06-25T08:01:12
MEMBER
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# What does this PR do ? Updates Mcore DDP call to match https://github.com/NVIDIA/Megatron-LM/commit/02871b4df8c69fac687ab6676c4246e936ce92d0 **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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Skip sequence_parallel allreduce when using Mcore DistOpt
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# What does this PR do ? Mcore distOpt handles layernorm all-reduce, so we need to skip it in NeMo side (MegatronGPTModel) **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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Re-enable cuda graphs in training modes.
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"global" capture mode was sporadically crashing because of pinning host memory in other threads spawned by the data loader when num_workers > 0. This would cause the ASR_dev_run_Speech_To_Text_HF_Finetuning CI/CD test to fail sporadically (maybe 1 out of 5 times). # What does this PR do ? Fixes the crash by using "thread_local" stream capture instead of "global" stream capture. **Collection**: ASR # Usage Cuda graphs will now be used by default for inference in both training and inference scripts, following the previous behavior. # 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". **PR Type**: - [ ] New Feature - [ X ] Bugfix - [ ] Documentation Note that I tested this by applying the following diff: ``` modified examples/asr/conf/asr_finetune/speech_to_text_hf_finetune.yaml @@ -134,6 +134,14 @@ model: warmup_steps: 5000 warmup_ratio: null min_lr: 5e-6 + decoding: + strategy: "greedy_batch" + + # greedy strategy config + greedy: + max_symbols: 5 + loop_labels: false + use_cuda_graph_decoder: true trainer: devices: -1 # number of GPUs, -1 would use all available GPUs modified examples/asr/speech_to_text_finetune.py @@ -212,6 +212,12 @@ def main(cfg): if hasattr(cfg.model, 'spec_augment') and cfg.model.spec_augment is not None: asr_model.spec_augment = ASRModel.from_config_dict(cfg.model.spec_augment) + if hasattr(asr_model, 'change_decoding_strategy') and hasattr(asr_model, 'decoding'): + # This is not robust to all model types. + # import ipdb; ipdb.set_trace() + decoding_cfg = cfg.model.decoding + asr_model.change_decoding_strategy(decoding_cfg) + trainer.fit(asr_model) ``` Then I ran this script: ``` for i in $(seq 1 100); do python examples/asr/speech_to_text_finetune.py \ --config-path="conf/asr_finetune" --config-name="speech_to_text_hf_finetune" \ ~model.train_ds.hf_data_cfg \ model.train_ds.num_workers=1 \ model.train_ds.batch_size=2 model.validation_ds.batch_size=2 \ model.train_ds.streaming=true \ +model.train_ds.hf_data_cfg.path="librispeech_asr" \ +model.train_ds.hf_data_cfg.name=null \ +model.train_ds.hf_data_cfg.split="test.clean" \ +model.train_ds.hf_data_cfg.streaming=true \ ~model.validation_ds.hf_data_cfg \ model.validation_ds.streaming=true \ +model.validation_ds.hf_data_cfg.path="librispeech_asr" \ +model.validation_ds.hf_data_cfg.name=null \ +model.validation_ds.hf_data_cfg.split="test.clean" \ +model.validation_ds.hf_data_cfg.streaming=true \ ~model.test_ds \ init_from_pretrained_model="stt_en_fastconformer_transducer_large" \ model.tokenizer.update_tokenizer=False \ model.optim.sched.warmup_steps=0 \ +model.optim.sched.max_steps=3 \ trainer.max_epochs=null \ trainer.devices=1 \ trainer.accelerator="gpu" \ +trainer.fast_dev_run=True \ model.decoding.greedy.loop_labels=True \ exp_manager.exp_dir=examples/asr/speech_finetuning_results done for i in $(seq 1 100); do python examples/asr/speech_to_text_finetune.py \ --config-path="conf/asr_finetune" --config-name="speech_to_text_hf_finetune" \ ~model.train_ds.hf_data_cfg \ model.train_ds.num_workers=1 \ model.train_ds.batch_size=2 model.validation_ds.batch_size=2 \ model.train_ds.streaming=true \ +model.train_ds.hf_data_cfg.path="librispeech_asr" \ +model.train_ds.hf_data_cfg.name=null \ +model.train_ds.hf_data_cfg.split="test.clean" \ +model.train_ds.hf_data_cfg.streaming=true \ ~model.validation_ds.hf_data_cfg \ model.validation_ds.streaming=true \ +model.validation_ds.hf_data_cfg.path="librispeech_asr" \ +model.validation_ds.hf_data_cfg.name=null \ +model.validation_ds.hf_data_cfg.split="test.clean" \ +model.validation_ds.hf_data_cfg.streaming=true \ ~model.test_ds \ init_from_pretrained_model="stt_en_fastconformer_transducer_large" \ model.tokenizer.update_tokenizer=False \ model.optim.sched.warmup_steps=0 \ +model.optim.sched.max_steps=3 \ trainer.max_epochs=null \ trainer.devices=1 \ trainer.accelerator="gpu" \ +trainer.fast_dev_run=True \ model.decoding.greedy.loop_labels=False \ exp_manager.exp_dir=examples/asr/speech_finetuning_results done ``` Basically, I needed to add a way for speech_to_text_finetune.py to be able to modify the decoding algorithm, so I could test both the loop frames and loop labels code paths. I do not include this code in the PR, since it is not robust to all model types (e.g., AED). Since I run 100 times for each algorithm, we can be pretty sure that this fixes the problem.
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add deprecation note for nmt
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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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Fix Peft Weights Loading in NeVA
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# What does this PR do ? For megatron amp o2, the assert needs to happen before the key name update. **Collection**: [multimodal] # 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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QLoRA
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# What does this PR do ? Add QLoRA ## Current Results ### Convergence Llama 7b, Squad, packed seq 2k, LoRA on all layers gbs4, 2e-4, 2k iterations `exact_match 89.199 f1 94.791 rougeL 94.400 total 6064.000` Normal LoRA (qkv only) `exact_match 88.819 f1 94.415 rougeL 94.084 total 6064.000` ### Memory Usage Llama 13b, Alpaca, seq len 512, LoRA on all layers, gbs256 Peak memory reserved (GB): | Batch Size | LoRA | QLoRA | % Reduction | |--------|--------|--------|--------| | 2 | 37.5 | 18.3 | 51% | | 4 | 46.7 | 27.4 | 41% | | 8 | 65.4 | 46.4 | 29% | ### Performance Llama 13b, Alpaca, seq len 512, LoRA on all layers, gbs256 Global batch step timing (s): | Batch Size | LoRA | QLoRA | % Increase | |--------|--------|--------|--------| | 2 | 2.7 | 6.7 | 148% | | 4 | 2.3 | 4.4 | 91% | | 8 | 2.2 | 3.2 | 46% | **Collection**: NLP # Changelog - Add specific line by line info of high level changes in this PR. # Usage Set this flag in `megatron_gpt_finetuning_config.yaml` to enable QLoRA ```bash model.peft.peft_scheme=qlora ``` To get the maximum memory savings, also set these flags ```bash ++model.dist_ckpt_load_on_device=False # load checkpoint on CPU ++model.use_cpu_initialization=True # initialize model on CPU ++model.peft.lora_tuning.target_modules=[all] # quantize all linear layers in the model ``` # GitHub Actions CI The Jenkins CI system has been replaced by GitHub Actions self-hosted runners. The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR. To re-run CI remove and add the label again. To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run". # Before your PR is "Ready for review" **Pre checks**: - [ ] 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 CICD] update flaky test
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[Nemo CICD] update flaky test
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Re-enable cuda graphs in training modes.
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[ "@titu1994 yes, that is the only change, and I am very confident in it. I can elaborate if you want.", "Well, I also had to undo Vladimir's previous commit that turns cuda graphs off by default, except in transcribe_speech.py and transcribe_speech_parallel.py. This commit's changes: [bb26e9846f](https://github.com/NVIDIA/NeMo/commit/bb26e9846f)", "@artbataev good point. I completely missed TDT. Done. I'm not sure how to test that one, but I suspect that the change is low risk anyway." ]
2024-05-29T15:47:29
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"global" capture mode was sporadically crashing because of pinning host memory in other threads spawned by the data loader when num_workers > 0. This would cause the ASR_dev_run_Speech_To_Text_HF_Finetuning CI/CD test to fail sporadically (maybe 1 out of 5 times). # What does this PR do ? Fixes the crash by using "thread_local" stream capture instead of "global" stream capture. **Collection**: ASR # Usage Cuda graphs will now be used by default for inference in both training and inference scripts, following the previous behavior. # 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". **PR Type**: - [ ] New Feature - [ X ] Bugfix - [ ] Documentation Note that I tested this by applying the following diff: ``` modified examples/asr/conf/asr_finetune/speech_to_text_hf_finetune.yaml @@ -134,6 +134,14 @@ model: warmup_steps: 5000 warmup_ratio: null min_lr: 5e-6 + decoding: + strategy: "greedy_batch" + + # greedy strategy config + greedy: + max_symbols: 5 + loop_labels: false + use_cuda_graph_decoder: true trainer: devices: -1 # number of GPUs, -1 would use all available GPUs modified examples/asr/speech_to_text_finetune.py @@ -212,6 +212,12 @@ def main(cfg): if hasattr(cfg.model, 'spec_augment') and cfg.model.spec_augment is not None: asr_model.spec_augment = ASRModel.from_config_dict(cfg.model.spec_augment) + if hasattr(asr_model, 'change_decoding_strategy') and hasattr(asr_model, 'decoding'): + # This is not robust to all model types. + # import ipdb; ipdb.set_trace() + decoding_cfg = cfg.model.decoding + asr_model.change_decoding_strategy(decoding_cfg) + trainer.fit(asr_model) ``` Then I ran this script: ``` for i in $(seq 1 100); do python examples/asr/speech_to_text_finetune.py \ --config-path="conf/asr_finetune" --config-name="speech_to_text_hf_finetune" \ ~model.train_ds.hf_data_cfg \ model.train_ds.num_workers=1 \ model.train_ds.batch_size=2 model.validation_ds.batch_size=2 \ model.train_ds.streaming=true \ +model.train_ds.hf_data_cfg.path="librispeech_asr" \ +model.train_ds.hf_data_cfg.name=null \ +model.train_ds.hf_data_cfg.split="test.clean" \ +model.train_ds.hf_data_cfg.streaming=true \ ~model.validation_ds.hf_data_cfg \ model.validation_ds.streaming=true \ +model.validation_ds.hf_data_cfg.path="librispeech_asr" \ +model.validation_ds.hf_data_cfg.name=null \ +model.validation_ds.hf_data_cfg.split="test.clean" \ +model.validation_ds.hf_data_cfg.streaming=true \ ~model.test_ds \ init_from_pretrained_model="stt_en_fastconformer_transducer_large" \ model.tokenizer.update_tokenizer=False \ model.optim.sched.warmup_steps=0 \ +model.optim.sched.max_steps=3 \ trainer.max_epochs=null \ trainer.devices=1 \ trainer.accelerator="gpu" \ +trainer.fast_dev_run=True \ model.decoding.greedy.loop_labels=True \ exp_manager.exp_dir=examples/asr/speech_finetuning_results done for i in $(seq 1 100); do python examples/asr/speech_to_text_finetune.py \ --config-path="conf/asr_finetune" --config-name="speech_to_text_hf_finetune" \ ~model.train_ds.hf_data_cfg \ model.train_ds.num_workers=1 \ model.train_ds.batch_size=2 model.validation_ds.batch_size=2 \ model.train_ds.streaming=true \ +model.train_ds.hf_data_cfg.path="librispeech_asr" \ +model.train_ds.hf_data_cfg.name=null \ +model.train_ds.hf_data_cfg.split="test.clean" \ +model.train_ds.hf_data_cfg.streaming=true \ ~model.validation_ds.hf_data_cfg \ model.validation_ds.streaming=true \ +model.validation_ds.hf_data_cfg.path="librispeech_asr" \ +model.validation_ds.hf_data_cfg.name=null \ +model.validation_ds.hf_data_cfg.split="test.clean" \ +model.validation_ds.hf_data_cfg.streaming=true \ ~model.test_ds \ init_from_pretrained_model="stt_en_fastconformer_transducer_large" \ model.tokenizer.update_tokenizer=False \ model.optim.sched.warmup_steps=0 \ +model.optim.sched.max_steps=3 \ trainer.max_epochs=null \ trainer.devices=1 \ trainer.accelerator="gpu" \ +trainer.fast_dev_run=True \ model.decoding.greedy.loop_labels=False \ exp_manager.exp_dir=examples/asr/speech_finetuning_results done ``` Basically, I needed to add a way for speech_to_text_finetune.py to be able to modify the decoding algorithm, so I could test both the loop frames and loop labels code paths. I do not include this code in the PR, since it is not robust to all model types (e.g., AED). Since I run 100 times for each algorithm, we can be pretty sure that this fixes the problem.
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TitaNet Batch Verify Speaker
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2024-05-29T14:46:20
2024-07-05T19:39:57
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# What does this PR do ? Adds possibility to verify speakers via TitaNet with batch inference. **Collection**: [ASR] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python import nemo.collections.asr as nemo_asr speaker_model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(model_name='titanet_large') audio_pairs = [ (/path/to/audio_0_0, /path/to/audio_0_1), (/path/to/audio_1_0, /path/to/audio_1_1), (/path/to/audio_2_0, /path/to/audio_2_1), (/path/to/audio_3_0, /path/to/audio_3_1) ] decision = speaker_model.verify_speakers_batch(audio_pairs, batch_size=16, device='cuda') ``` # 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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Unify code paths for building TRTLLM engines for nemo & qnemo checkpoints
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2024-05-29T09:59:55
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# What does this PR do ? Unify code paths for building TRTLLM engines for nemo & qnemo checkpoints. **Collection**: NLP # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # GitHub Actions CI The Jenkins CI system has been replaced by GitHub Actions self-hosted runners. The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR. To re-run CI remove and add the label again. To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run". # Before your PR is "Ready for review" **Pre checks**: - [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] Refactor - [ ] 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 FSDP gradient calculation with orig params
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2024-05-29T08:52:41
2024-05-30T05:45:56
2024-05-30T05:45:56
CONTRIBUTOR
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As observed by some users in #8487, FSDP causes differences in loss. This is because gradients are not correctly calculated when `fsdp_use_orig_params=True`. Currently, any non-`FlatParameter` is treated as being unsharded, when in reality they may be sharded when `use_orig_params=True`. This leads to a double reduction of sharded gradients. We thus simply adjust the parameters that are reduced manually. This requires use of a private variable in the FSDP module. Alternatively, the value of `self.kwargs['ignored_states']` in `nlp_overrides.py` could be set as an attribute on the model to avoid this private variable access. Thanks to @ofivite for suggesting that `use_orig_params=True` could be the cause of the issue, which greatly helped with analysis. # What does this PR do ? Fix FSDP gradient calculation when `fsdp_use_orig_params=True`. **Collection**: nlp # 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 ## 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 #8487 (issue)
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Nvidia GPU is Not accessing in my Docker Container
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[ "Similar issue: https://github.com/NVIDIA/NeMo/issues/9268 ", "My issue got solved instead of pulling nvcr.io/nvidia/nemo:24.03.01.framework pull nvcr.io/nvidia/nemo:dev.framework" ]
2024-05-29T07:41:13
2024-05-30T12:29:44
2024-05-30T12:28:36
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Error: ``` ERROR: The NVIDIA Driver is present, but CUDA failed to initialize. GPU functionality will not be available. [[ System has unsupported display driver / cuda driver combination (error 803) ]] ``` Command: ``` docker run --gpus device=0 --shm-size=2g --net=host --ulimit memlock=-1 --rm -it -v ${PWD}:/workspace -w /workspace -v ${PWD}/results:/results nvcr.io/nvidia/nemo:24.03.01.framework bash ``` ![image](https://github.com/NVIDIA/NeMo/assets/116936023/86a68453-4596-47ca-b183-5c138bf4236e) My Docker version: Docker version 26.1.3, build b72abbb My GPU: NVIDIA RTX A6000 Nvidia-smi (non docker) ![image](https://github.com/NVIDIA/NeMo/assets/116936023/9f79917b-1676-4fcc-8214-87cfb7e3bd5c) Nvidia-smi(docker container) ![image](https://github.com/NVIDIA/NeMo/assets/116936023/c8d4608d-6f02-42a4-9072-55026b0601c9) ---
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[Nemo CICD] Comment out flaky tests
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2024-05-28T22:17:02
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[Nemo CICD] Comment out flaky tests
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HF_TOKEN param for the scripts
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[ "Hi, I wonder why passing HF-TOKEN via CLI is required instead of passing it via an environment variable? Can you please clarify for future readers? 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.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]
2024-05-28T20:17:02
2024-06-21T01:47:22
2024-06-21T01:47:21
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# What does this PR do ? Adds HF TOKEN to the scripts
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Call teardown even when something fails.
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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.", "Commenting to kill the stale label.\r\n\r\nThis really just needs a reviewer. Would be great to know if this is the desired solution; I think I'm seeing the same thing in another model and would replicate this approach there, if so.", "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]
2024-05-28T18:32:28
2024-07-10T01:50:02
2024-07-10T01:50:01
CONTRIBUTOR
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# What does this PR do ? Avoid tons of terminal spam such as: ``` [rank0]:[W528 18:14:19.086678488 ProcessGroupNCCL.cpp:1122] WARNING: process group has NOT been destroyed before it is being destructed. On normal program exit, the application should call destroy_process_group to ensure that any pending NCCL data transfers have finished in this process. In rare cases this process can exit before this point and block the progress of another member of the process group. This constraint has always been present, but this warning has only been added since PyTorch 2.4 ``` when an error occurs, which can obscure the real error. **Collection**: Stable diffusion # Changelog - stable diffusion: Clean up process groups after an error occurs. # 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? - - Are any necessary for this? - [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] Bugfix ## 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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