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# What does this PR do ? Move mask and position id generation in GPT synthetic dataset to __init__. They are same for each data loading, so doing it for every data loading is redundant, it also can create big CPU overheads while seq_len is long. Hence, move them to __init__. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # 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 ? Added some new configurations and updated some of the older configuration files for Conformer models. # Changelog - Added configs for cache-aware fastconformer and hybrid ASR. - Added more info on batch size and precision suggestions in the configs. **PR Type**: - [X] New Feature - [ ] Bugfix - [ ] Documentation
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# What does this PR do ? Add Frame-VAD, including related datasets and models for multi-label audio classification. Also, ASR+VAD pipeline is updated to use Frame-VAD, and support for dropping frames instead of masking is added. **Collection**: [ASR] # Changelog - Frame-VAD: - Model: `nemo.collections.asr.models.classification_models.EncDecFrameClassificationModel` - Dataset: - `nemo.collections.asr.datasets.audio_to_label.AudioToMultiLabelDataset` - `nemo.collections.asr.datasets.audio_to_label.TarredAudioToMultiLabelDataset` - `nemo.collections.asr.datasets.audio_to_label_dataset.get_audio_multi_label_dataset` - `nemo.collections.asr.datasets.audio_to_label_dataset.get_tarred_audio_multi_label_dataset` - Utils: `nemo.collections.asr.parts.utils.vad_utils.py` - Example: - `examples/asr/speech_classifications/speech_to_frame_label.py` - `examples/asr/speech_classifications/frame_vad_infer.py` - `examples/asr/conf/marblenet/marblenet_3x2x64_20ms.yaml` - ASR+VAD: - Example: - `examples/asr_vad/speech_to_text_with_frame_vad.py` - Datasets: - `nemo/collections/asr/data/feature_to_text.py` - `nemo/collections/asr/data/feature_to_label.py` # 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? [Todo in next PR] - [ ] Did you add or update any necessary documentation? [Todo in next PR] - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] New Feature - [ ] Bugfix - [ ] Documentation
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# What does this PR do ? This PR adds ramp up batch size support for GPT models. It allows to increase the batch size up to global batch size linearly during training in a given number of samples. **Collection**: NLP # Changelog - Add rampup_batch_size value to the training config. - Input rampup_batch_size to the apex microbatch_calculator. - Add current global_batch_size value to the progress bar when using rampup_batch_size. - Update the apex microbatch_calculator after each training step. # Usage Can be set via config or CLI. **Config:** ```python rampup_batch_size: [8, 4, 10000] # Should be a list of 3 values: [<start_batch_size>, <batch_size_increment>, <rampup_samples>] ``` **CLI:** ```python model.rampup_batch_size=[8,4,10000] ``` # 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 ? fixing the ability to use temp sampling with concat datasets **Collection**: ASR # Changelog In `audio_to_text_dataset.py` check for sampling probabilities only if the sampling technique is random # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # 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 ? Cache inputs (attention mask, loss mask, and position ids) and reuse. This mitigates CPU-side overhead. # 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 ``` # 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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exceptions with empty test / val ds config sections
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# What does this PR do ? Resolves exceptions when trying to train with empty test/ val ds config sections **Collection**: ASR # Changelog In `nemo/utils/model_utils.py` check if ds_values has values in it before trying to assign names to entries within. These names would have become visible in the test / validation sections of experiment manager (e.g. wandb) - but if these sections are emptty, there is no need to try. # Usage * You can potentially add a usage example below ```python # should no longer fail: python asr_transducer/speech_to_text_rnnt_bpe.py --config-path=../conf/contextnet_rnnt/ --config-name=config_rnnt_bpe.yaml model.tokenizer.dir=data/tokenizer/tokenizer_bpe_v1024/ model.tokenizer.type="bpe" model.train_ds.manifest_filepath=data/LibriSpeech/train_clean_100.json model.train_ds.batch_size=32 model.joint.fuse_loss_wer=true model.joint.fused_batch_size=8 model.decoding.greedy.max_symbols=5 ~model.validation_ds +accelerator="gpu" +strategy="ddp" +devices=4 trainer.max_epochs=1 exp_manager.checkpoint_callback_params.monitor="train_loss" " ``` # 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 bug 4067510
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# What does this PR do ? fixes exceptions when running with empty test /val ds sections **Collection**: ASR # Changelog in `/nemo/utils/model_utils.py`, check if len(ds_values) > 0 before trying to assign dataset names # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # 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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Refactor PP conversion + add support for TP only conversion
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# What does this PR do ? Updates the TP PP conversion script to support TP only conversion (back ported from NeMo 1.16) Refactor PP conversion mechanism. **Collection**: [NLP] # Changelog - Refactor components to functions, split apart TP and PP conversion components - Refactor PP conversion to model specific classes for PP conversion support - Backport TP only conversion support (irrespective of whether PP conversion is supported or not) # Usage ```python python megatron_change_num_partitions.py \ --model_file="ckpt/megatron_gpt_model.nemo" \ --target_file="ckpt/megatron_gpt_model_tp2_pp1.nemo" \ --tensor_model_parallel_size=1 \ --target_tensor_model_parallel_size=2 \ --pipeline_model_parallel_size=1 \ --target_pipeline_model_parallel_size=1 \ --tp_conversion_only ``` # 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? - [x] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] New Feature - [ ] Bugfix - [ ] Documentation
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[TTS] FastPitch speaker encoder
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# What does this PR do ? Adds FastPitch speaker encoder with reference audio and reference speaker embedding **Collection**: TTS # Changelog - Adds multi-speaker FastPitch pre-training with 1) Speaker verification model speaker embedding (reference speaker embedding) and 2) Global Style Tokens (reference audio) - `nemo/collections/tts/data/tts_dataset.py` - `nemo/collections/tts/torch/tts_data_types.py` - `nemo/collections/tts/models/fastpitch.py` - `nemo/collections/tts/modules/fastpitch.py` - `nemo/collections/tts/modules/submodules.py` - Modified config and fine-tuning python script - `examples/tts/conf/fastpitch_speaker_adaptation.yaml` - `examples/tts/fastpitch_finetune_adapters.py` # Usage # 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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[TTS] FastPitch adapter fine-tune and conditional layer normalization
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# What does this PR do ? Adds FastPitch pre-training with CLNs and fine-tuning with adapters. **Collection**: TTS # Changelog - Adds multi-speaker FastPitch pre-training with Conditional Layer Normalization - `nemo/collections/tts/modules/fastpitch.py` - `nemo/collections/tts/modules/transformer.py` - `nemo/collections/tts/modules/submodules.py` - Add adapter modules for FastPitch fine-tuning - `nemo/collections/tts/models/fastpitch.py` - `nemo/collections/tts/modules/fastpitch.py` - `nemo/collections/tts/modules/transformer.py` - `nemo/collections/tts/modules/aligner.py` - `nemo/collections/tts/parts/mixins/__init__.py` - `nemo/collections/tts/parts/mixins/fastpitch_adapter_mixins.py` - Add config and fine-tuning python script - `examples/tts/conf/fastpitch_speaker_adaptation.yaml` - `examples/tts/fastpitch_finetune_adapters.py` - Fix aligner `nan` loss bug - `nemo/collections/tts/losses/aligner_loss.py` # Usage # 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 FastConformer config: correct bucketing strategy
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# What does this PR do ? Fix typo in bucketing strategy in FastConformer configs. **Collection**: [ASR] # Changelog - Add specific line by line info of high level changes in this PR. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] 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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[TTS] whitelist broken path fix.
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# What does this PR do ? fixed broken file path. **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 ``` # 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): https://github.com/NVIDIA/NeMo/issues/6408
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update container info in 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 ``` # 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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Is MoE implemented correctly?
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MoE FFNs are supposed to be sharded between GPUs. It seems like the current MoE implementation puts all FFNs on each GPU. See e.g. `https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/modules/common/megatron/mlp.py#L321`. Am I missing something here?
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Fix the GPT SFT datasets loss mask bug
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# What does this PR do ? Currently the mask position is off by 1. The first token in the label is not included in the loss calculation.
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file does not exist in TTS get_data.py
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https://github.com/NVIDIA/NeMo/blob/main/scripts/dataset_processing/tts/ljspeech/get_data.py#L59 does not exist and needs to be updated.
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Config `share_embeddings_and_output_weights` does not work.
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**Describe the bug** The code [here](https://github.com/NVIDIA/NeMo/blob/ecc116c29a14534abfe393f6eee4339b24ca6e57/nemo/collections/nlp/modules/common/megatron/module.py#L262) init the super class [MegatronModule](https://github.com/NVIDIA/NeMo/blob/ecc116c29a14534abfe393f6eee4339b24ca6e57/nemo/collections/nlp/modules/common/megatron/module.py#L46) with default parameter `share_token_embeddings=True`, so the attribute `share_embeddings_and_output_weights` of the input `module` parameter will never be used in `Float16Module`. Is that a bug or designed?
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Update hyperparameter recommendation based on experiments
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# What does this PR do ? Update hyperparameter recommendation based on experiments **Collection**: tools # 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 ``` # 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 semi sorted sampler.
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# What does this PR do ? Helps to speed up ASR models training without quality degradation. **Collection**: ASR, Core # Changelog - Adds a semi sorted batch sampler and its prepare function to a new file (in the future it might be some other samplers or new techniques like dynamic batching): `nemo/collections/asr/parts/utils/asr_batching.py` - Added changes to the data loader preparation function in the following model classes: `nemo/collections/asr/models/ctc_models.py` `nemo/collections/asr/models/rnnt_bpe_models.py` - Adds changes to the scheduler prepare function because the batch sampler is passed to the dataloader as a sampler and we need to parse the batch size from it.  `nemo/core/optim/lr_scheduler.py` - Adds a change to the `__getitem__` dataset method, as the batch sampler becomes an iterator and returns a list of indices instead of a single index. `nemo/collections/asr/data/audio_to_text.py` # Usage The sampler is currently added to the following models: * `nemo.collections.asr.models.EncDecCTCModel` * `nemo.collections.asr.models.EncDecCTCModelBPE` * `nemo.collections.asr.models.EncDecRNNTModel` * `nemo.collections.asr.models.EncDecRNNTBPEModel` Needs just to add one line in config. ```bash ++config.model.train_ds.use_semi_sorted_batching=true ``` and two line if we want to specify randomization factor (see more in additional information). ```bash ++config.model.train_ds.randomization_factor=0.1 ``` # 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 * Semi Sorted Batching, was proposed in "Speed up training with variable length inputs by efficient batching strategies.", Zhenhao Ge et al. (2021). * **Idea**: let's sort the samples by their duration, after adding pseudo noise to them, and then sample the batches from the sorted list. This way we avoid strict sorting and keep some level of randomness. * Experiment for Conformer RNNT BPE: * Experiment setup The config is used from the [example configs](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/conformer/conformer_transducer_bpe.yaml) with overridden parameters of ` trainer.epochs=50`, `trainer.devices=8` after the divergence of the first experiment `model.optim.lr=1.0`. For the train data LibriSpeech clean 100 and train clean 360, as well as for the test data, LibriSpeech test clean was used. For data processing, a BPE with a dictionary size of 512 was used. * first experiment <img src="https://github.com/fedorovgv/asr_ckpt/blob/main/step_1.png?raw=true" width="500" height="250"> <img src="https://github.com/fedorovgv/asr_ckpt/blob/main/wer_1.png?raw=true" width="500" height="250"> * second experiment <img src="https://github.com/fedorovgv/asr_ckpt/blob/main/step_2.png?raw=true" width="500" height="250"> <img src="https://github.com/fedorovgv/asr_ckpt/blob/main/wer_2.png?raw=true" width="500" height="250"> * Interesting, that in the first experiment, the training of the model broke up without ssb. * In the second experiment, after reducing the learning rate (from default 5.0 to 1.0), it can be seen that the ssb model converges without quality degradation to ~4.4 WER in the first 50k steps and at **17.5%** faster in time. * In paper experiments, it was shown that with further training, the quality of training improves compared to the model without sbb for the same number of epochs with decreasing time.
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Add documentation for preemption support
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# What does this PR do ? Add a one line overview of what this PR aims to accomplish. Adds documentation for preemption support. **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 ``` # 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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Logprob for stt_conformer_transducer_large
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The Quartz model uses [logprobs](https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/asr/Offline_ASR.ipynb#scrollTo=ZhWmR7lbvwSm) , which are very helpful in assessing how well speech is spoken. They make it possible to construct a spectrogram, but it is inferior to the model stt_conformer_transducer_large. Is it possible to get log samples or something similar for this model? Maybe I don't know something and there are other ways to get probabilities for tokens in this model?Thanks in advance for the answer
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Process finished with exit code 139 (interrupted by signal 11: SIGSEGV) while trying to import from nemo.collections.asr.models import EncDecCTCModelBPE
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**I get an "Process finished with exit code 139 (interrupted by signal 11: SIGSEGV)" when I trying to import nemo.collections.asr** I would like to use NeMo model, but have an error Process finished with exit code 139 (interrupted by signal 11: SIGSEGV) at the asr importing step. from nemo.collections.asr.models import EncDecCTCModelBPE file_name = 'audio_test.wav' model = EncDecCTCModelBPE.from_pretrained(model_name="stt_en_conformer_ctc_large_ls") transcribed = model.transcribe(paths2audio_files=file_name, batch_size=1, logprobs=False) Can successfully import nemo, collections, but asr causes this error. How can I fix this problem?
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# What does this PR do ? Remove bottleneck code in `perform_clustering` function **Collection**: [ASR] # Changelog - remove `gc.collect()` code since it makes too slow. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # 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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Fix prompt template unescaping
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# What does this PR do ? Unescape the prompt template to allow providing escape sequences in the prompt template from the CLI. **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 ``` # 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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Inconsistent documentation?
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The documentation at [https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/parallelisms.html] states that `NeMo Megatron supports 4 types of parallelisms (can be mixed together arbitraritly)` The documentation at [https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/megatron.html#model-parallelism] states that `Currently, NeMo only supports tensor model parallelism.` This seems inconsistent. What kind of parallelisms are supported for training and eval? Thanks!
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**Describe the bug** With TP=2 ,PP=2 and model.bias=false, GPT Training process hangs during dataloader sanity checking **Steps/Code to reproduce bug** Clone r1.15.0 realese branch and then run the offical GPT pretrain example with my trivial modification ```shell git clone https://github.com/NVIDIA/NeMo.git cd Nemo git branch r1.15.0 bash reinstall.sh cd examples/nlp/language_modeling bash train.sh ``` Below is the content of train.sh: ```shell MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"} MASTER_PORT=29503 NUM_NODES=1 NODE_RANK=0 GPUS_PER_NODE=8 DATA_PREFIX="[1.0,/data/pretrain/openwebtext2/hfbpe_gpt_training_data_text_document]" torchrun --nnodes ${NUM_NODES} --nproc_per_node ${GPUS_PER_NODE} \ --master_addr ${MASTER_ADDR} --master_port ${MASTER_PORT} --node_rank ${NODE_RANK} \ ./megatron_gpt_pretraining.py \ --config-name=megatron_gpt_config.yaml \ trainer.devices=${GPUS_PER_NODE} \ trainer.num_nodes=${NUM_NODES} \ model.data.data_prefix=${DATA_PREFIX} \ model.tensor_model_parallel_size=2 \ model.pipeline_model_parallel_size=2 \ +model.bias=false \ model.bias_activation_fusion=false \ model.bias_dropout_add_fusion=false \ ``` As we can see, the modification here is just: - disable all the bias item of linear layer in GPT model - diabale bias related CUDA kernal fusion, as we do not need bias yet The program hangs during dataloader sanity checking at very early stage of training. It is OK if I turn on the bias and the related kernal fusion, or if I set PP=1 Logically, there should be no relevance between bias and pipeline. So I think it is an unexpected behaviour. **Expected behavior** It is expected that NeMo is able to train very large model without any bias term in pipeline parallel mode, such as LLama-65B. **Environment overview (please complete the following information)** - Environment location: Docker - Method of NeMo install: `bash reinstall.sh` - If method of install is [Docker], provide `docker pull nvcr.io/nvidia/nemo:23.01 ` & `docker run --shm-size 64g --network=host -v ${HOME}:${HOME} -v ${DATAROOT}:/data -it nvcr.io/nvidia/nemo:23.01 bash` commands used ```[tasklist] ```
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Sharded manifests for tarred datasets
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# What does this PR do ? Helps accelerate manifest loading via optional manifest sharding. This should speed up the loading of large manifests and reduce memory footprint - each worker loads just the shards that it needs. **Collection**: ASR # Changelog - Add specific line by line info of high level changes in this PR. # Usage Use the same notation for manifests as for tarred audio files. There should be a 1:1 correspondence between a sharded audio tar file and a manifest file. The defer_setup flag must be set, otherwise torch.distributed will not be initialized during manifest processing and we will not be able to determine the dataloader length across all loaded manifest shards. ``` model.train_ds.tarred_audio_filepaths='audio__OP_0..511_CL_.tar' model.train_ds.manifest_filepath='manifest__OP_0..511_CL_.json' +model.train_ds.shard_manifests=true +model.train_ds.defer_setup=true ``` # when generating the tarred manifest, do: ```python python convert_to_tarred_audio_dataset.py \ --manifest_path=<path to the manifest file> \ --target_dir=<path to output directory> \ --num_shards=<number of tarfiles that will contain the audio> \ --max_duration=<float representing maximum duration of audio samples> \ --min_duration=<float representing minimum duration of audio samples> \ --shuffle --shuffle_seed=1 \ --sort_in_shards \ --shard_manifests \ --workers=-1 # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ X] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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# What does this PR do ? Add a 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 ``` # 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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Move Parallelism usage from Apex -> Megatron Core
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# What does this PR do ? This PR moves model parallelism in NeMo to use Megatron-core instead of Apex. * All models use dataloader_iter in train and eval step (except eval for T5 p-tuning) * All pre-training models that use a constant sequence length sample based on microbatches * All downstream tasks - p-tuning, adapter etc - that depend on dynamic seq len use global batch based sampling We still use Apex for microbatch calculator, some enums/utils [both to be soon shifted to core], LayerNorm/other things with kernel **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 ``` # 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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remove gc bottleneck code
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# What does this PR do ? Remove bottleneck code in `perform_clustering` function **Collection**: [ASR] # Changelog - remove `gc.collect()` code since it makes too slow. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # 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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Framework for PEFT via mixins
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# What does this PR do ? PTuning and Adapters/IA3 have been implemented in diverging styles. Adapters and IA3 uses the elegant adapter_mixins framework. This PR implements P-Tuning via the same adapter_mixins framework. The high level goal is to unify all PEFT methods to use adapter_mixins. This is allow us to the GPTSFTDataset to be used for all future PEFT methods. Currently this PR does not remove the GPTPromptLearingModel functionality, but once adaper-mixin based p-tuning has been tested we can deprecate GPTPromptLearingModel GPTPromptLearningDataset classes. PTuning does not need to flexibility to insert prompt tokens anywhere in the input. This feature has been removed for simplicity. Also added: - mix-n-match PEFT scheme like `adapter_and_ptuning` can be easily extended to do `ia3_and_ptuning` or `lora_and_ptuning` - grad clipping functionality for PEFT training - LoRA adapters added for GPT **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 ``` # 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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When training HiFiGAN, after some epochs, `d_loss`, `g_loss`, etc. become `NaN`
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Hello, I'm training a 44100 HifiGAN model on my own dataset. After some epochs, `d_loss`, `g_loss`, etc. become `NaN`, ![20230407110450](https://user-images.githubusercontent.com/16176151/230533410-2e69084a-686c-4747-a159-a7c179faa9d4.jpg) ![20230407110514](https://user-images.githubusercontent.com/16176151/230533449-7c93218c-f65f-4163-a8bd-e3489a41adfb.jpg) ![20230407110942](https://user-images.githubusercontent.com/16176151/230533478-68da3b74-ead8-48f8-a6ed-e5e558ae01bd.jpg) and `val_loss` increases, ![20230407110538](https://user-images.githubusercontent.com/16176151/230533562-3a20e813-2173-406a-b624-4ecdf2be9076.jpg) audio quality drops dramatically. What seems to be the problem? Thank you!
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Add [Draft][TTS] FastPitch multi-speaker pre-train and adapter fine-tune
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# What does this PR do ? Adds FastPitch speaker adaptation with adapters. **Collection**: TTS # Changelog - Adds multi-speaker FastPitch pre-training with 1) Looked-up speaker embedding 2) Speaker verification model speaker embedding 3) Global Style Tokens 4) Conditional Layer Normalization - `nemo/collections/tts/data/tts_dataset.py` - `nemo/collections/tts/torch/tts_data_types.py` - `nemo/collections/tts/models/fastpitch.py` - `nemo/collections/tts/modules/fastpitch.py` - `nemo/collections/tts/modules/transformer.py` - `nemo/collections/tts/modules/submodules.py` - Add adapter modules for FastPitch fine-tuning - `nemo/collections/tts/models/fastpitch.py` - `nemo/collections/tts/modules/fastpitch.py` - `nemo/collections/tts/modules/transformer.py` - `nemo/collections/tts/modules/aligner.py` - `nemo/collections/tts/parts/mixins/__init__.py` - `nemo/collections/tts/parts/mixins/fastpitch_adapter_mixins.py` - Add config and fine-tuning python script - `examples/tts/conf/fastpitch_speaker_adaptation.yaml` - `examples/tts/fastpitch_finetune_adapters.py` - Add tutorial - `tutorials/tts/FastPitch_SpeakerAdaptation.ipynb` - Add fast text normalization script - `scripts/dataset_processing/tts/add_normalized_text.py` - Fix aligner `nan` loss bug - `nemo/collections/tts/losses/aligner_loss.py` # Usage * Follow `tutorials/tts/FastPitch_SpeakerAdaptation.ipynb` * Pre-train multi-speaker FastPitch * Fine-tune HiFiGAN with mel-spectrograms from pre-trained FastPitch * Fine-tune FastPitch with adapters * Fine-tune HiFiFAN with mel-spectrograms from fine-tuned FastPitch # 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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Add support for Megatron GPT Untied Embd TP PP Change
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# What does this PR do ? Adds support for untied embeddings for Megatron GPT for TP PP change **Collection**: [NLP] # Changelog - Generalize indexing for tied vs untied gpt embeddings # 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
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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 ``` # 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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user selected max_seq_len should be less than model's max_seq_len
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# What does this PR do ? user selected max_seq_len should be less than model's max_seq_len **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 ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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# What does this PR do ? Adds support for T5 Finetuning/Prompt tuning. # 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 ``` # 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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Installation fails for python 3.10
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Executing `python3.10 -m pip install mecab-python3` produces the error ``` Collecting mecab-python3 Using cached mecab-python3-1.0.6.tar.gz (77 kB) Preparing metadata (setup.py) ... done Installing collected packages: mecab-python3 DEPRECATION: mecab-python3 is being installed using the legacy 'setup.py install' method, because it does not have a 'pyproject.toml' and the 'wheel' package is not installed. pip 23.1 will enforce this behaviour change. A possible replacement is to enable the '--use-pep517' option. Discussion can be found at https://github.com/pypa/pip/issues/8559 Running setup.py install for mecab-python3 ... error error: subprocess-exited-with-error × Running setup.py install for mecab-python3 did not run successfully. │ exit code: 1 ╰─> [18 lines of output] /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/setuptools/installer.py:27: SetuptoolsDeprecationWarning: setuptools.installer is deprecated. Requirements should be satisfied by a PEP 517 installer. warnings.warn( WARNING: The wheel package is not available. WARNING: The wheel package is not available. WARNING: The wheel package is not available. WARNING: The wheel package is not available. running install /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/setuptools/command/install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools. warnings.warn( running build running build_py creating build creating build/lib.macosx-10.9-universal2-cpython-310 creating build/lib.macosx-10.9-universal2-cpython-310/MeCab copying src/MeCab/__init__.py -> build/lib.macosx-10.9-universal2-cpython-310/MeCab copying src/MeCab/cli.py -> build/lib.macosx-10.9-universal2-cpython-310/MeCab running build_ext error: [Errno 2] No such file or directory: 'mecab-config' [end of output] note: This error originates from a subprocess, and is likely not a problem with pip. error: legacy-install-failure × Encountered error while trying to install package. ╰─> mecab-python3 note: This is an issue with the package mentioned above, not pip. hint: See above for output from the failure. ``` MacOS Ventural 13.3, M2 processor.
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# What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: ASR # Changelog - Fixed torch warning about creating a tensor from a list of numpy arrays and sped up execution by putting ```audio``` in a list when calling ```np.array()``` instead of when calling ```torch.tensor()``` # 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.
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Onnx export of cache aware conformer fails
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Latest fixes of onnx export of cache aware conformer still fail. PR [ #6311](https://github.com/NVIDIA/NeMo/pull/6311) (This is not yet in nemo 1.17.0, only https://github.com/NVIDIA/NeMo/pull/5837 is with more problems) The model get's exported, but inferencing results in an exception. This can be seen when the onnx model inference results get's verified with the pytorch model as a last step of the export. This fails with: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Reshape node. Name:'Reshape_1530' Status Message: D:\a_work\1\s\onnxruntime\core\providers\cpu\tensor\reshape_helper.h:26 onnxruntime::ReshapeHelper::ReshapeHelper i < input_shape.NumDimensions() was false. The dimension with value zero exceeds the dimension size of the input tensor. (I had to change CUDAExecutionProvider to CPUExecutionProvider) The model was trained with nemo, but I can not share such a model. Also I would want to remark that the dimension 0 and 1 of tensors: cache_last_channel and cache_last_time get transposed at the input and transposed again at the output, while this transposing is not needed and adds overhead.
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# What does this PR do ? Adds opengraph support for NeMo documentation page **Collection**: [Docs] # Changelog - Add opengraph support for NeMo docs # 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
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# What does this PR do ? Refines the suggested training parameters for the Fast Conformer models. **Collection**: ASR # Changelog - In fast-conformer_ctc_bpe.yaml, change min_lr to 1e-4 (from 1e-6) - in fast-conformer_transducer_bpe.yaml, change lr to 5e-3 and min_lr to 5e-4 (from 1e-6) # Usage * Train as usual but you will be able to use checkpoint averaging to improve results ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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# What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # 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 installation and ASR inference instructions for Mac
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# What does this PR do ? Adds installation and inference instructions for Apple Mac. **Collection**: [ASR (docs)] # Changelog - installation instructions - ASR inference instructions # Usage Developing NeMo on Mac devices. # 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? - [x] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [x] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to #6289
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Adapter tuning accepts expanded language model dir
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# What does this PR do ? set model extracted dir such that the save restore connector can load expanded base models. This is required to load large base models so that we can avoid the untaring step during training. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # 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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Make possible to control tqdm progress bar in ASR models
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# What does this PR do ? Now it's possible to control display of progress bar when calling `transcribe` method of ASR models. **Collection**: ASR # Changelog - `transcribe` method of ASR models now have `verbose` param, which allows to control display of tqdm progress bar # Usage ```python import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained("nvidia/stt_en_conformer_transducer_xlarge") # Will display progress bar by default asr_model.transcribe(['2086-149220-0033.wav']) # Won't display progress bar when verbose is set to False asr_model.transcribe(['2086-149220-0033.wav'], verbose=False) ``` # 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 ## 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 None
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Update wfst_text_normalization.rst
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Add Hungarian (incoming in NeMo-text-processing) # 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 ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [X] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Bugfix for BF16 grad reductions with distopt (#6340)
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Cheery-pick from `main` to `r1.17.0_pt_23.04` * Debug distopt support for BF16 grad reductions * Dump and load FP32 main params * Style tweaks --------- # 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 ``` # 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 conformer configs for hat model
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# What does this PR do ? Add conformer char and bpe configs for hat model (https://arxiv.org/abs/2003.07705) **Collection**: [ASR] # 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
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[Experimental] Update black to 23.x
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Update script for ngram rnnt and hat beam search decoding
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# What does this PR do ? Update previous script for ngram RNNT/HAT beam search decoding by analogy with CTC ngram decoding. **Collection**: [ASR] # Changelog - Add option to return `return_encoder_embiddings` for `rnnt_mode.transcribe()` method (analogy of logits for CTC) - Update `eval_beamsearch_ngram_transducer.py` by analogy with `eval_beamsearch_ngram.py` - Add RNNT/HAT ngram decoding description to docs # 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
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ASR_with_Subword_Tokenization.ipynb's guide on process_asr_text_tokenizer script is broken
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**Describe the bug** When following guide to Build a Customize Tokenizer, process_asr_text_tokenizer.py does not create intended tokenizer folder with required content. Unable to pre-trained on any models that use BPE vocab since no one can pass anything to the new_tokenizer_dir argument now. **Steps/Code to reproduce bug** Try to run below command as taught by the tutorial, see if it works for you. `!python /content/drive/MyDrive/Colab\sNotebooks/AI6127\sNLP/GroupProject/Data/process_asr_text_tokenizer.py \ --manifest="/content/drive/MyDrive/Colab Notebooks/AI6127 NLP/GroupProject/Data/manifest_train.json" \ --data_root="/content/drive/MyDrive/Colab Notebooks/AI6127 NLP/GroupProject/Data" \ --vocab_size=32 \ --tokenizer="spe" \ --no_lower_case \ --log` **Expected behavior** ![image](https://user-images.githubusercontent.com/1837762/230006481-47a2e2e2-f0bf-4d95-8eae-2267bad5f565.png) **Environment overview (please complete the following information)** Colab Pro + **Environment details** NA **Additional context** NA
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[NeMo Megatron] Cleanup configs to infer the models TP PP config automatically
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# What does this PR do ? **Collection**: [NLP] # Changelog - Update configs to set tp pp and pp rank all to -1 - Check and update -1 to the model config results # 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? - [x] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] New Feature - [ ] Bugfix - [ ] Documentation
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Word boosting for Flashlight decoder
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# What does this PR do ? Adds word boosting to the Flashlight decoder in Nemo to mirror this capability in Riva For more info see here: https://docs.nvidia.com/deeplearning/riva/user-guide/docs/tutorials/asr-python-advanced-wordboosting.html And here: https://docs.nvidia.com/deeplearning/riva/user-guide/docs/asr/asr-customizing.html#word-boosting **Collection**: ASR # Changelog - Added word boosting to collections/asr/modules/flashlight_decoder.py - Added new config line item to flashlight_cfg in ctc_beam_decoding.py (I noticed that flashlight_decoder.py had Windows EOLs, so I changed them to Unix, but unfortunately this makes git show as if I had changed the entire file which isn't the case, sorry about this) # Usage * In order to boost certain words, you need to create a flat file which is tab separated. The format of this file is: ``` <word>\t<boost score> ``` For example, here is a valid format boost file on disk, not that it is a single tab separated file: ``` nvidia 40 geforce 50 riva 80 turing 30 badword -100 ``` Positive scores boost words higher in the LM decoding step so they show up more frequently, whereas negative scores squelch words so they show up less frequently. The recommended range for the boost score is 20 to 100. The boost file handles both in-vocabulary words and OOV words just fine, so feel free to mix and match. You can then add it to your flashlight config object in Nemo like this: ```python ctc_decoding.beam.flashlight_cfg.boost_path='/path/to/my_boost_file.boost' ``` # 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) - [X] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [X] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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taskname and early stopping for adapters
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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 ``` # 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 ? replaces standard pytorch dataset with a mem mapped json dataset. **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 ``` # 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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Correct image logo in NeMo Website
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# What does this PR do ? Use better image for the website default card. **Collection**: [Docs] # 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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Correct tag for image url in NeMo Website
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# What does this PR do ? Fix image tag for open graph **Collection**: [Docs] # 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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Update support for twitter cards in NeMo Website
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# What does this PR do ? Adds support for twitter cards in addition to open graph **Collection**: [Docs] # 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? - [x] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] New Feature - [ ] Bugfix - [ ] Documentation
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Add OpenGraph metadata for better links to NeMo Website
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# What does this PR do ? Adds [OpenGraph](https://ogp.me/) support to NeMo website for richer URL linking. **Collection**: [Docs] # Usage ```markdown --- title: Template 2022 author: [First Name, Second Name] author_gh_user: [Github User 1, Github User 2] read_time: Time to read publish_date: Date of publishing # Optional: OpenGraph metadata og_title: Title of the blog post for Rich URL previews og_image (or image): Image for Rich URL previews (absolute URL) og_image_type: Image type (e.g. image/png). Defaults to image/png. page_path: Relative path to the image from the website root (e.g. /assets/images/) description: Description of the post for Rich URL previews # DO NOT CHANGE _BELOW template: blog.html --- ``` # 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? - [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 - [x] Documentation
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# What does this PR do ? Adds a script to extract p_tuned inference weights. More details: The ptuning training saves all params including the prompt encoder weights. These are typically not required after training is completed. The script allows you to save the required representations for inference in a separate file and you can delete the .nemo if wanted. **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 ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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# What does this PR do ? Adds docs for the use of multiple test / validation manifests as enabled in #6306 **Collection**: ASR # Changelog - Added docs to `docs/source/asr/configs.rst` # Usage Docs only ```python # Add a code snippet demonstrating how to use this n/a ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ x] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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# What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # 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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[ASR] Add optimization util for linear sum assignment algorithm
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# What does this PR do ? 1. Add a optimization util file for linear sum assignment (LSA) solver algorithm for online-diarization/multi-speaker-ASR LSA problem solver is needed for the following tasks in NeMo: (1) Permutation Invariant Loss (PIL) for diarization model training (2) Label permutation matching for online speaker diarzation (3) Concatenated minimum-permutation Word Error Rate (cp-WER) calculation What is LSA solver algorithm? [Google OR-tools LSA Solver](https://developers.google.com/optimization/assignment/linear_assignment) The NeMo linear_sum_assignment function is compared with `scipy.optimization.linear_sum_assingment`. In the [unit-test ](https://github.com/NVIDIA/NeMo/blob/49d4e80ddf1138cea9593392a9a9e76be9ca58ec/tests/collections/asr/test_diar_utils.py#L792)for NeMo LSA solver, the result is compared with the `scipy` version of `linear_sum_assignment`. 2. Removing `@torch.jit.script` decorator in speaker_utils.py since it creates type-errors when the code is not used for production purpose. Instead, all `torch.jit.script` required classes and functions are tested in `test_diar_utils.py`. Take a look at [these tests](https://github.com/NVIDIA/NeMo/blob/49d4e80ddf1138cea9593392a9a9e76be9ca58ec/tests/collections/asr/test_diar_utils.py#L606) for checking jit_script = [True/False] and cuda = [True/False] (testing total 4 combinations) 3. Also refactored some of the functions in online diarization - replaced scipy LSA solver to NeMo LSA solver in `online_clustering.py`. 5. Added a couple of functions in `der.py` for online diarization DER calculation. - replaced scipy LSA solver to NeMo LSA solver in `der.py`. **Collection**: [ASR] # Changelog - nemo/collections/asr/metrics/der.py : replaced scipy LSA solver to NeMo LSA solver in `calculate_session_cpWER ` function. : Added two functions for online diarization evaluations: `get_partial_ref_labels` and `get_online_DER_stats`. - nemo/collections/asr/models/online_diarizer.py : Made `_perform_online_clustering` function simpler by moving `get_reduced_mat` and `match_labels` into online clustering function. - nemo/collections/asr/parts/utils/offline_clustering.py : Added `laplacian = laplacian.float().to(torch.device('cpu'))` to avoid jit-scripted module uses GPU even when CPU is specified or vice-versa. This behavior is always tested/checked in `test_diar_utils.py`. - nemo/collections/asr/parts/utils/online_clustering.py : replaced scipy LSA solver to NeMo LSA solver in `get_lsa_speaker_mapping` function. : Modified the docstrings of `update_speaker_history_buffer` to make the example easier. - nemo/collections/asr/parts/utils/optimization_utils.py : Fully torch-jit-scriptable, linear sum assignment problem solver class and function were added. - nemo/collections/asr/parts/utils/speaker_utils.py : Removed `@torch.jit.script` decorators since this creates unnecessary warning messages and type related errors when used without scripting. - tests/collections/asr/test_diar_metrics.py : Added unit-tests for the newly added function `get_partial_ref_labels` and `get_online_DER_stats`. - tests/collections/asr/test_diar_utils.py : Added tests for offline clustering and online clustering for many different cases including: [jit-script=True, cuda=True], [jit-script=True, cuda=False], [jit-script=False, cuda=True], [jit-script=False, cuda=False] cases which is using the torch-jit-scripted NeMo linear_sum_assignment function. # Usage ```python from nemo.collections.asr.parts.utils.optimization_utils import linear_sum_assignment #An example cost matrix to be solved cost_matrix = \ torch.tensor([[7, 6, 2], [6, 2, 1], [5, 6, 8]]) row_ind_nm, col_ind_nm = linear_sum_assignment(cost_matrix) ``` # 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.
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stt_ru_conformer_ctc_large Text hypothesis does not match word timestamps.
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**Describe the bug** It is with this model "stt_ru_conformer_ctc_large" that I have problems. Can't get timestamps, I'm executing the code: from nemo.collections.asr.parts.utils.decoder_timestamps_utils import ASRDecoderTimeStamps asr_decoder_ts = ASRDecoderTimeStamps(cfg.diarizer) asr_model = asr_decoder_ts.set_asr_model() word_hyp, word_ts_hyp = asr_decoder_ts.run_ASR(asr_model) and I get an error: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.8/dist-packages/nemo/collections/asr/parts/utils/decoder_timestamps_utils.py", line 674, in run_ASR_BPE_CTC text, char_ts, word_ts = werbpe_ts.ctc_decoder_predictions_tensor_with_ts( File "/usr/local/lib/python3.8/dist-packages/nemo/collections/asr/parts/utils/decoder_timestamps_utils.py", line 98, in ctc_decoder_predictions_tensor_with_ts word_ts, word_seq = self.get_ts_from_decoded_prediction(decoded_prediction, hypothesis, char_ts) File "/usr/local/lib/python3.8/dist-packages/nemo/collections/asr/parts/utils/decoder_timestamps_utils.py", line 148, in get_ts_from_decoded_prediction assert len(word_ts) == len(hypothesis.split()), "Text hypothesis does not match word timestamps." AssertionError: Text hypothesis does not match word timestamps. **Expected behavior** Get timestamps. **Environment overview (please complete the following information)** - Environment location: Docker - Commands used: docker run --gpus all -ti --shm-size=8g nvcr.io/nvidia/pytorch:23.02-py3 **Environment details** i use docker
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# What does this PR do ? Faster implementation of non-numba specaug **Collection**: ASR # Changelog Rather than repeatedly modify the features tensor in-place, build a mask and fill the features tensor once. By my measurements, about 20x faster on GPU. It could be faster still, but it gets hard to compare output exactly to the original implementation to verify correctness. Also, the original implementation seems to be biased away from the upper freq bins, but again fixing it would be make it impossible to compare the outputs to the original implementation. # Usage Running this snippet will run both the original and this implementation, verify similar output, and print latencies. <details> <summary>Test snippet</summary> ```python import random import time import torch from nemo.collections.asr.parts.submodules.spectr_augment import SpecAugment from nemo.core.classes import typecheck # Copy of original `SpecAugment.forward` for comparison @typecheck() @torch.no_grad() def forward_original(self, input_spec, length): sh = input_spec.shape for idx in range(sh[0]): for i in range(self.freq_masks): x_left = self._rng.randint(0, sh[1] - self.freq_width) w = self._rng.randint(0, self.freq_width) input_spec[idx, x_left : x_left + w, :] = self.mask_value for i in range(self.time_masks): if self.adaptive_temporal_width: time_width = max(1, int(length[idx] * self.time_width)) else: time_width = self.time_width y_left = self._rng.randint(0, max(1, length[idx] - time_width)) w = self._rng.randint(0, time_width) input_spec[idx, :, y_left : y_left + w] = self.mask_value return input_spec seed = 12345 batch_size = 128 feat_dim = 80 max_length = 1000 device = "cuda" num_iterations = 100 # Generate some inputs spec = torch.randn(size=[batch_size, feat_dim, max_length], device=device) lengths = torch.randint(low=max_length // 10, high=max_length, size=[batch_size], device=device) # Usually, at least one element in a batch is the max length. lengths[0] = max_length print(f"Testing with input features shape {spec.shape}") # This version has the new forward augmentor: SpecAugment = SpecAugment( freq_masks=2, time_masks=10, freq_width=27, time_width=0.05, rng=random.Random(seed) ).to(device) # cache the first output for comparison (need to be careful of RNG values) first_output_new = augmentor(input_spec=spec, length=lengths) # Warm up augmented_spec = augmentor(input_spec=spec, length=lengths) # Loop and record time start = time.time() for _ in range(num_iterations): augmented_spec = augmentor(input_spec=spec, length=lengths) if device == "cuda": torch.cuda.synchronize() stop = time.time() mean_duration = (stop - start) / num_iterations print(f"Mean duration for new implementation: {mean_duration * 1000:0.1f} ms") # Re-instantiate a spec aug with same RNG augmentor: SpecAugment = SpecAugment( freq_masks=2, time_masks=10, freq_width=27, time_width=0.05, rng=random.Random(seed) ).to(device) # Set class's forward method to original one setattr(SpecAugment, "forward", forward_original) # Get baseline output. Note that the original implementation modifies in-place and returns a reference to `spec`, so # use a clone of `spec` since we'll call forward some more for getting times first_output_old = augmentor(input_spec=spec.clone(), length=lengths) # Warm up augmented_spec = augmentor(input_spec=spec, length=lengths) # Loop and record time start = time.time() for _ in range(num_iterations): augmented_spec = augmentor(input_spec=spec, length=lengths) if device == "cuda": torch.cuda.synchronize() stop = time.time() mean_duration = (stop - start) / num_iterations print(f"Mean duration for original specaug: {mean_duration * 1000:0.1f} ms") # Compare the output of each module. Can use hard tolerance since op is a constant fill. print("Asserting old and new augmentations are all close...") torch.testing.assert_close(actual=first_output_new, expected=first_output_old, rtol=0.0, atol=0.0) print("Old and new outputs match") ``` </details> Expected output: ```text # With GPU Testing with input features shape torch.Size([128, 80, 1000]) Mean duration for new implementation: 5.0 ms Mean duration for original specaug: 100.1 ms Asserting old and new augmentations are all close... Old and new outputs match # with CPU Testing with input features shape torch.Size([128, 80, 1000]) Mean duration for new implementation: 8.5 ms Mean duration for original specaug: 42.5 ms Asserting old and new augmentations are all close... Old and new outputs match ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation
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[Core] return_config=True now extracts just config, not full tarfile
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# What does this PR do ? Make return_config=True now only extract out the yaml files from the tarfile, preventing opening and file extractions of all files (should significantly speedup return_config=True for Billion parameter models). **Collection**: [Core, ASR, NLP, TTS] # Changelog - Add flag to extract just config from core internals # Usage ```python model_config = ModelClass.restore_from(..., return_config=True) OR model_config = ModelClass.from_pretrained(..., return_config=True) ``` # 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
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How to train conformer asr model
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I want to train conformer ast model for different indian regional launguage but I'm not able to find proper training tutorial can anyone please guide me thought ?
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Fix radtts sort r17
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# What does this PR do ? This is a duplicate PR of https://github.com/NVIDIA/NeMo/pull/6320 merged in main branch, but the release branch needs this fix as well. **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 ``` # 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 recent distopt commits
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# What does this PR do ? This PR cherry-picks several recent distopt commits from the `main` branch into `r1.17.0`. **Collection**: NLP # Changelog - GPT support for BF16 grad reductions (#5920) - Add custom functions to launch distopt communication in interleaved pipeline parallelism (#6183) - Bugfix for BF16 grad reductions with distopt (#6340) # Usage Set the optimizer to `distributed_fused_adam` in the config file: https://github.com/NVIDIA/NeMo/blob/aaa0ccacf5b4d204ed18f6052e55ff6f20575996/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml#L207 Configure the optimizer with `grad_sync_dtype: bf16`. # 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
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remove pinned numba version in Dockerfile
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# What does this PR do ? remove pinned numba version in Dockerfile **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 ``` # 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 ? remove pinned numba version in Dockerfile **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 ``` # 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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Bugfix for BF16 grad reductions with distopt
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# What does this PR do ? https://github.com/NVIDIA/NeMo/pull/5920 adds support for BF16 grad reductions with distopt, with embedding grad reductions done in FP32. @mikolajblaz found some bugs and it turns out the FP32 reductions were not being done at all. This PR fixes those issues. When I run GPT-3 175B, I confirm the embedding grads are now optimized with the FP32 optimizer. Loss values are the same with FP32 and BF16 grad reductions, up to 50 steps and within numerical accuracy. This turned out messier than I would have liked. It would have been better to integrate distopt support for multiple grad dtypes into Apex. **Collection**: NLP # Changelog - Debug distopt support for BF16 grad reductions # Usage Set the optimizer to `distributed_fused_adam` in the config file: https://github.com/NVIDIA/NeMo/blob/aaa0ccacf5b4d204ed18f6052e55ff6f20575996/examples/nlp/language_modeling/conf/megatron_gpt_config.yaml#L207 Configure the optimizer with `grad_sync_dtype: bf16`. # 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 * This PR is identical to https://github.com/NVIDIA/NeMo/pull/6297, but merges into `main` instead of `r1.17`. See that PR for discussions. * Fixes bugs from https://github.com/NVIDIA/NeMo/pull/5920 * Depends on https://github.com/NVIDIA/apex/pull/1575 * Depends on https://github.com/NVIDIA/TransformerEngine/pull/75
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TTS for Korean tokenizer/phonemizer
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I am looking to train mixer-tts model on korean dataset. I see in my config.yaml i need to complete the normalization, tokenization and phonemization. Is there a tutorial for how to train model and preprocess data for language not listed in your tts_tokenizers and then input those changes/new functions in the config.yaml?
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# What does this PR do ? Simplified the notebook for p-tuning. Sticks to a simple single task training and inference. **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 ``` # 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 RNNT Joint narrow()
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# What does this PR do ? Fixes a but in the signature of narrow(). Fixes #6328 **Collection**: [ASR] # Changelog - Casts the length input of all narrow() to int() explicitly # 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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Fix RNNT Joint narrow()
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# What does this PR do ? Fixes a but in the signature of narrow(). Fixes #6328 **Collection**: [ASR] # Changelog - Casts the length input of all narrow() to int() explicitly # 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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Minor updates to MegatronGPTPromptLearningModel
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# What does this PR do ? Fixes typos in docstring. Removed unused imports **Collection**: NLP # Changelog - fix docstring typos; remove unused imports # Usage n/a # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [x] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information n/a
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Pin the version to hopefully fix rtd build
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# What does this PR do ? Fix RTD using this: https://github.com/geopandas/dask-geopandas/pull/247 **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 ``` # 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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Megatron hidden transformations
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# What does this PR do ? This PR adds support in applying transformation to the hiddens in Megatron Encoder-Decoder model, and augmenting a corresponding loss. This enables support in latent variable models, like VAE and MIM. **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 add the following to your config : hidden_transforms: [MegatronGaussianHiddenTransform()] and it will apply a gaussian transform to the hiddens. ```python # Add a code snippet demonstrating how to use this ``` # 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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Added num decoder blocks in megatron export
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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 ``` # 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 when finetuning HifiGan for multi-speaker FastPitch
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**Describe the bug** An error occured when trying to finetune tts_en_hifitts_hifigan_ft_fastpitch.nemo Using a target size (torch.Size([16, 80, 64])) that is different to the input size (torch.Size([16, 80, 129])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size. Error executing job with overrides: ['model.max_steps=100000', 'train_dataset=data/ruslan.orig/synmels/hifigan_train_manifest_phonemes_ft.json', 'validation_datasets=data/ruslan.orig/synmels/hifigan_val_manifest_phonemes_ft.json', 'exp_manager.exp_dir=data/ruslan.orig/result', '+init_from_nemo_model=pretrained/tts_en_hifitts_hifigan_ft_fastpitch.nemo', 'trainer.devices=-1', '+trainer.val_check_interval=100', 'trainer.check_val_every_n_epoch=null', 'model/train_ds=train_ds_finetune', 'model/validation_ds=val_ds_finetune'] Traceback (most recent call last): File "/home/user/.conda/envs/nemo/lib/python3.8/site-packages/clearml/binding/hydra_bind.py", line 173, in _patched_task_function return task_function(a_config, *a_args, **a_kwargs) File "tts_scripts/hifigan_finetune.py", line 34, in main trainer.fit(model) File "/home/user/.conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 603, in fit call._call_and_handle_interrupt( File "/home/user/.conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/call.py", line 36, in _call_and_handle_interrupt return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs) File "/home/user/.conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 90, in launch return function(*args, **kwargs) File "/home/user/.conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 645, in _fit_impl self._run(model, ckpt_path=self.ckpt_path) File "/home/user/.conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1098, in _run results = self._run_stage() File "/home/user/.conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1177, in _run_stage self._run_train() File "/home/user/.conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1190, in _run_train self._run_sanity_check() File "/home/user/.conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1262, in _run_sanity_check val_loop.run() File "/home/user/.conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 199, in run self.advance(*args, **kwargs) File "/home/user/.conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 152, in advance dl_outputs = self.epoch_loop.run(self._data_fetcher, dl_max_batches, kwargs) File "/home/user/.conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/loops/loop.py", line 199, in run self.advance(*args, **kwargs) File "/home/user/.conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 137, in advance output = self._evaluation_step(**kwargs) File "/home/user/.conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 234, in _evaluation_step output = self.trainer._call_strategy_hook(hook_name, *kwargs.values()) File "/home/user/.conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1480, in _call_strategy_hook output = fn(*args, **kwargs) File "/home/user/.conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/strategies/ddp.py", line 360, in validation_step return self.model(*args, **kwargs) File "/home/user/.conda/envs/nemo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1533, in _call_impl return forward_call(*args, **kwargs) File "/home/user/.conda/envs/nemo/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1129, in forward output = self._run_ddp_forward(*inputs, **kwargs) File "/home/user/.conda/envs/nemo/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1083, in _run_ddp_forward return self.module(*inputs[0], **kwargs[0]) # type: ignore[index] File "/home/user/.conda/envs/nemo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1533, in _call_impl return forward_call(*args, **kwargs) File "/home/user/.conda/envs/nemo/lib/python3.8/site-packages/pytorch_lightning/overrides/base.py", line 110, in forward return self._forward_module.validation_step(*inputs, **kwargs) File "/home/user/.conda/envs/nemo/lib/python3.8/site-packages/nemo/collections/tts/models/hifigan.py", line 237, in validation_step loss_mel = F.l1_loss(audio_mel, audio_pred_mel) File "/home/user/.conda/envs/nemo/lib/python3.8/site-packages/torch/nn/functional.py", line 3268, in l1_loss expanded_input, expanded_target = torch.broadcast_tensors(input, target) File "/home/user/.conda/envs/nemo/lib/python3.8/site-packages/torch/functional.py", line 74, in broadcast_tensors return _VF.broadcast_tensors(tensors) # type: ignore[attr-defined] RuntimeError: The size of tensor a (129) must match the size of tensor b (64) at non-singleton dimension 2 **Steps/Code to reproduce bug** I am following the standard process of fine tuning FastPitch and HifiGan 1) I finetuned /tts_en_fastpitch_multispeaker.nemo with Russian voice (44100 sr) 2) After that I decided to finetune HifiGan from finetune tts_en_hifitts_hifigan_ft_fastpitch.nemo **Expected behavior** I got the error above. And I fixed it when changed ### Default values for dataset with sample_rate=44100 sample_rate: 44100 n_mel_channels: 80 n_window_size: 2048 n_window_stride: 256 <- changed to 256 from 512 n_fft: 2048 lowfreq: 0 highfreq: 8000 window: hann But can you explain why this error occured? Both my FastPitch model and tts_en_hifitts_hifigan_ft_fastpitch.nemo contain in their config: n_window_stride: 512
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I have customised wav2vec model for my asr task and I want to use it in this diarization pipeline but I can't do it, because it is not supported. Please, update the library so that we can use any pytorch models.
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TypeError in RNNTJoint
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**Describe the bug** Hi. In my scenario `RNNTJoint.forward` produces TypeError and error message is ``` narrow() received an invalid combination of arguments - got (length=Tensor, start=int, dim=int, ), but expected one of: * (int dim, Tensor start, int length) didn't match because some of the arguments have invalid types: (dim=int, start=int, length=Tensor, ) * (int dim, int start, int length) didn't match because some of the arguments have invalid types: (dim=int, start=int, length=Tensor ) ``` **Steps/Code to reproduce bug** I faced this issue with nemo-toolkit==1.12 but it looks like problem still exists in the version 1.16. The nature of the error itself can be shown using the following code: ```python import torch a = torch.Tensor([0,1,1]) max_a = a.max() a.narrow(dim=0, start=0, length = max_a) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-1-1104b945c0b8> in <cell line: 4>() 2 a = torch.Tensor([0,1,1]) 3 max_a = a.max() ----> 4 a.narrow(dim=0, start=0, length = max_a) TypeError: narrow() received an invalid combination of arguments - got (length=Tensor, start=int, dim=int, ), but expected one of: * (int dim, Tensor start, int length) didn't match because some of the arguments have invalid types: (dim=int, !start=int!, !length=Tensor!, ) * (int dim, int start, int length) didn't match because some of the arguments have invalid types: (dim=int, start=int, !length=Tensor!, ) ``` And there is the similar code in the [nemo 1.12](https://github.com/NVIDIA/NeMo/blob/dd9a30f55b7ec308cc3b55751e8ae5aa40f36a62/nemo/collections/asr/modules/rnnt.py#L1311 ) (lines 1297 and 1311) as well as in the [nemo 1.16](https://github.com/NVIDIA/NeMo/blob/163111870e78a2a7224953a89b6af5ac71cb8ed9/nemo/collections/asr/modules/rnnt.py#L1315) (lines 1301 and 1315) In order to reproduce this behavior you have to make the condition `sub_dec.shape[1] != max_sub_transcript_length + 1` true. In my scenario, this is true because I use several hacks around my own custom dataset and dataloader that applies padding to the data if "time" dimension of tensor is not a multiple of 128. But I'm quite sure there is a cleaner way to fulfill the condition, and it looks like the code will throw this error every time the condition is met. Replacement `max_sub_transcript_length = sub_transcript_lens.max()` -> `max_sub_transcript_length = int(sub_transcript_lens.max().item())` applied [here](https://github.com/NVIDIA/NeMo/blob/dd9a30f55b7ec308cc3b55751e8ae5aa40f36a62/nemo/collections/asr/modules/rnnt.py#L1297) fixes the issue for me. **Expected behavior** The line `sub_dec = sub_dec.narrow(dim=1, start=0, length=max_sub_transcript_length + 1)` can be executed without rising TypeError **Environment overview (please complete the following information)** - Environment location: Docker, image is based on cuda:11.3.1-cudnn8-devel-ubuntu20.04 - Method of NeMo install: `pip install nemo-toolkit['all']==1.12.0` **Environment details** If NVIDIA docker image is used you don't need to specify these. Otherwise, please provide: - Ubuntu20.04 - PyTorch version 1.10.1 (but the cause of the problem persists in 1.13.1 ) - Python version 3.9.7 **Additional context** I have an additional question about the code I mentioned in this issue. There is a couple of comments that says: ``` # Reduce decoder length to preserve computation # Decoder: [sub-batch, U, D] -> [sub-batch, U', D]; U' < U if sub_dec.shape[1] != max_sub_transcript_length + 1: sub_dec = sub_dec.narrow(dim=1, start=0, length=max_sub_transcript_length + 1) ``` I understand that we can remove the padding here in order to reduce memory consumption and computation time of the joint layer but we (probably) still have a lot of padding in the decoder output if our batch contains transcriptions of different lengths. As afs as I know current implementation of RNNT will process this padding in the joint layer and there is no way to reduce related computation and memory costs. Is it correct?
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# What does this PR do ? Adds functions to enable and disable activation checkpointing and sequence parallelism. This way, we can enable them during training and disable them during inference/generation. **Collection**: nemo/collections/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 ```python # Add a code snippet demonstrating how to use this ``` # 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 ? Simplified the notebook for p-tuning. Sticks to a simple single task training and inference. **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 ``` # 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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Restore Thutmose Tagger tutorial, move it to nlp
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Restore Thutmose Tagger tutorial, move it to nlp (earlier it was in text_processing) # 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 ``` # 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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Move checkpoint consolidation to cpu
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# What does this PR do ? This aims to move the megatron checkpoint to nemo conversion to cpu rather than using pytorch distributed following additions introduced [here](https://github.com/NVIDIA/NeMo/pull/6218). This is useful for large models where the user may have checkpoints to consolidate but don't want to use the massive amount of gpus just for conversion. **Collection**: [NLP] # Changelog - Change megatron_ckpt_to_nemo.py to use CPU only # Usage ```python python3 megatron_ckpt_to_nemo.py \ --checkpoint_folder <path_to_PTL_checkpoints_folder> \ --checkpoint_name <checkpoint_name> \ --nemo_file_path <path_to_output_nemo_file> \ --tensor_model_parallel_size <tensor_model_parallel_size>\ --pipeline_model_parallel_size <pipeline_model_parallel_size> \ --model_type <model type> ``` # 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 ## 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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Correctly determine ZERO_LEVEL_SPEC_DB_VAL which affects normalization for first signal buffer
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# What does this PR do ? If asr_model has preprocessor of type `AudioToMelSpectrogramPreprocessor`, `AudioToSpectrogram`, `AudioToMFCCPreprocessor`, etc., the `StreamingFeatureBufferer` incorrectly determines whether it uses log scale. Instead of checking for 'log' attribute directly on preprocessor instance, it should check on the `preprocessor.featurizer` instance, which exposes the `log` property. Therefore, the first few frames (until the generated silence is replaced by an actual audio signal) get incorrectly normalized and the model output is poor or absent. First frame with incorrect `ZERO_LEVEL_SPEC_DB_VAL`: ![image](https://user-images.githubusercontent.com/8578790/228567021-62578f28-4b56-49b7-9f6b-952d0370d55b.png) First frame with correct `ZERO_LEVEL_SPEC_DB_VAL`: ![image](https://user-images.githubusercontent.com/8578790/228566722-0cda3fe4-3a88-4c33-94fc-893ba4322680.png) **Collection**: asr (`nemo.collections.asr.parts.utils.streaming_utils`) # Changelog - `nemo.collections.asr.parts.utils.streaming_utils:365` Checking for `asr_model.preprocessor.log` always resulted in False, as the `AudioToMelSpectrogramPreprocessor` doesn't define `log` property. Therefore I added an additional check for `asr_model.preprocessor.featurizer.log`. **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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Numba error while fine-tuning an ASR model
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**Describe the bug** When fine-tuning an ASR model, at some point throughout the first epoch of training, a Numba error occurs: ``` numba.cuda.cudadrv.driver.CudaAPIError: [1] Call to cuLaunchKernel results in CUDA_ERROR_INVALID_VALUE ``` **Steps/Code to reproduce bug** Here is the code I run: ```python import os from argparse import ArgumentParser from shutil import rmtree import pytorch_lightning as pl from pytorch_lightning.loggers import TensorBoardLogger from pytorch_lightning.callbacks import ModelCheckpoint from pytorch_lightning.strategies.ddp import DDPStrategy from nemo.collections.asr.models.rnnt_bpe_models import EncDecRNNTBPEModel from omegaconf import OmegaConf def main(args): trainer = pl.Trainer( strategy=DDPStrategy(find_unused_parameters=False), accelerator=args.accelerator, num_nodes=args.devices, max_epochs=2, ) config = OmegaConf.load(args.config) model = EncDecRNNTBPEModel.from_pretrained("stt_en_conformer_transducer_large") train_dl = model._setup_dataloader_from_config(config.train) test_dl = model._setup_dataloader_from_config(config.test) model.setup_optimization(config.optim) trainer.fit(model, train_dl, test_dl) if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--config", default="config.yaml") parser.add_argument("--accelerator", default="gpu") parser.add_argument("--devices", default="1") args = parser.parse_args() main(args) ``` Here is the config: ```yaml train: manifest_filepath: train.txt sample_rate: 16000 batch_size: 1 num_workers: 10 pin_memory: true use_start_end_token: false trim_silence: false max_duration: 20.0 min_duration: 0.1 is_tarred: false tarred_audio_filepaths: null shuffle_n: 2048 bucketing_strategy: "synced_randomized" bucketing_batch_size: null return_sample_id: false drop_last: false shuffle: false test: manifest_filepath: test.txt sample_rate: 16000 batch_size: 1 num_workers: 10 pin_memory: true use_start_end_token: false trim_silence: false max_duration: 20.0 min_duration: 0.1 is_tarred: false tarred_audio_filepaths: null shuffle_n: 2048 bucketing_strategy: "synced_randomized" bucketing_batch_size: null return_sample_id: false drop_last: false shuffle: false optim: name: adamw lr: 0.00001 # maximal learning rate betas: [0.9, 0.98] weight_decay: 0.001 ``` Attached is a zip file that contains these files, as well as the data files. The data comes from English Common Voice 10.0. The audiofiles are MP3 files. **Environment overview (please complete the following information)** I have tried several Nemo images (1.15, 1.13, 1.10) from the Nvidia NGC cloud, and the error still persists. Our cluster runs on Enroot containers, so I import the Docker image with `enroot import`. The training starts as expected, and the predictions are always close to the references, until the Numba error occurs. **Additional context** I had already discussed this issue with @titu1994 on issue #5929. He suggested using the NeMo images, but it didn't solve the problem. I would appreciate any further suggestions. Thank you for your time. Here is the ZIP file containing everything needed to reproduce my issue: [debug_asr.zip](https://github.com/NVIDIA/NeMo/files/11098389/debug_asr.zip)
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Fixing bug in unsort_tensor
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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 ``` # 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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added RPE + fixed RMSNorm
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# What does this PR do ? - Added RPE for MegatronNMT models - Fixed an issue with RMSNorm **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 ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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PR_kwDOC_bI7s5NHDgg
6,318
Make AIS CLI installation optional
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# What does this PR do ? This PR makes installation of AIS CLI optional. **Collection**: Dockerfile # Changelog - Update `Dockerfile` to install AIS CLI only if `REQUIRE_AIS_CLI == true` - Added an installer script for AIS CLI # Usage AIS CLI can be installed by setting `REQUIRE_AIS_CLI=true`, for example: ```bash docker ... --build-arg REQUIRE_AIS_CLI=true ``` # 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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