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| # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ | |
| # Adapting the model | |
| python train_asr_adapter.py \ | |
| --config-path="../conf/asr_adapters" \ | |
| --config-name="asr_adaptation.yaml" \ | |
| model.pretrained_model=null \ | |
| model.nemo_model=null \ | |
| model.adapter.adapter_name=<Unique adapter name> \ | |
| model.adapter.adapter_type="<linear, tiny_attn, or others from config sub-sections of `adapter`>" \ | |
| model.adapter.adapter_module_name=<null, or str module. Type: encoder, decoder, joint, or multiple with + between them> \ | |
| model.adapter.linear.in_features=<dimension of the layer outputs of the model> \ | |
| model.adapter.linear.dim=32 \ | |
| model.adapter.linear.dropout=0.0 \ | |
| model.train_ds.manifest_filepath=<Path to manifest> \ | |
| model.train_ds.batch_size=16 \ | |
| model.validation_ds.manifest_filepath=<Path to manifest> \ | |
| model.validation_ds.batch_size=16 \ | |
| model.optim.lr=0.001 \ | |
| model.optim.weight_decay=0.0 \ | |
| model.optim.sched.warmup_steps=100 \ | |
| trainer.max_steps=300 \ | |
| trainer.devices=1 \ | |
| trainer.precision=32 \ | |
| exp_manager.exp_dir=<Some directory for experiment manager> | |
| # Hyper Parmaeter Search | |
| python train_asr_adapter.py \ | |
| --config-path="../conf/asr_adapters" \ | |
| --config-name="asr_adaptation_hp.yaml" \ | |
| -m \ | |
| model.pretrained_model=null \ | |
| model.nemo_model=null \ | |
| model.adapter.adapter_name=<Unique adapter name> \ | |
| model.adapter.adapter_type="<linear, tiny_attn, or others from config sub-sections of `adapter`>" \ | |
| model.adapter.adapter_module_name=<null, or str module. Type: encoder, decoder, joint, or multiple with + between them> \ | |
| model.adapter.linear.in_features=<dimension of the layer outputs of the model> \ | |
| model.train_ds.manifest_filepath=<Path to manifest> \ | |
| model.train_ds.batch_size=16 \ | |
| model.validation_ds.manifest_filepath=<Path to manifest> \ | |
| model.validation_ds.batch_size=16 \ | |
| exp_manager.exp_dir="<some directory>" \ | |
| exp_manager.create_wandb_logger=true \ | |
| exp_manager.wandb_logger_kwargs.project="<Project Name>" \ | |
| ++delete_ckpt_after_train=True | |
| # Fine-tune a model | |
| While adaptation is very efficient for low-resource datasets, it imposes several restrictions - | |
| - The vocabulary of the new dataset must be supported by the pre-existing vocabulary or tokenizer. | |
| If tokens exist outside this scope, the adapter will have to learn UNK tokens (or fail entirely | |
| for character based models). | |
| - As a consequence of the above, the language of the new dataset must be the same as the original model. | |
| There is ongoing research to enable more sophisticated adapters for other languages. | |
| When adapters cannot be readily used due to the above limitations, fine-tuning may be a better alternative. | |
| For documentation on fine-tuning a model, please visit - | |
| https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#fine-tuning-configurations | |
| # Pretrained Models | |
| For documentation on existing pretrained models, please visit - | |
| https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/results.html | |
| """ | |
| import os | |
| from dataclasses import is_dataclass | |
| import lightning.pytorch as pl | |
| from omegaconf import DictConfig, OmegaConf, open_dict | |
| from nemo.collections.asr.models import ASRModel | |
| from nemo.core import adapter_mixins | |
| from nemo.core.config import hydra_runner | |
| from nemo.utils import logging | |
| from nemo.utils.exp_manager import clean_exp_ckpt, exp_manager | |
| from nemo.utils.trainer_utils import resolve_trainer_cfg | |
| def update_model_config_to_support_adapter(model_cfg, current_cfg): | |
| with open_dict(model_cfg): | |
| # Override prediction logging in config | |
| model_cfg.log_prediction = current_cfg.model.get('log_prediction', False) | |
| # Update encoder adapter compatible config | |
| adapter_metadata = adapter_mixins.get_registered_adapter(model_cfg.encoder._target_) | |
| if adapter_metadata is not None: | |
| model_cfg.encoder._target_ = adapter_metadata.adapter_class_path | |
| def update_model_cfg(original_cfg, new_cfg): | |
| with open_dict(original_cfg), open_dict(new_cfg): | |
| # force inject some keys into the config | |
| whitelist_keys = ['num_workers', 'pin_memory'] | |
| for wkey in whitelist_keys: | |
| if wkey in new_cfg: | |
| original_cfg[wkey] = new_cfg[wkey] | |
| print(f"Injecting white listed key `{wkey}` into config") | |
| # drop keys which don't exist in old config and are not whitelisted | |
| new_keys = list(new_cfg.keys()) | |
| for key in new_keys: | |
| if key not in original_cfg: | |
| new_cfg.pop(key) | |
| print("Removing unavailable key from config :", key) | |
| new_cfg = OmegaConf.merge(original_cfg, new_cfg) | |
| return new_cfg | |
| def add_global_adapter_cfg(model, global_adapter_cfg): | |
| # Convert to DictConfig from dict or Dataclass | |
| if is_dataclass(global_adapter_cfg): | |
| global_adapter_cfg = OmegaConf.structured(global_adapter_cfg) | |
| if not isinstance(global_adapter_cfg, DictConfig): | |
| global_adapter_cfg = DictConfig(global_adapter_cfg) | |
| # Update the model.cfg with information about the new adapter global cfg | |
| with open_dict(global_adapter_cfg), open_dict(model.cfg): | |
| if 'adapters' not in model.cfg: | |
| model.cfg.adapters = OmegaConf.create({}) | |
| # Add the global config for adapters to the model's internal config | |
| model.cfg.adapters[model.adapter_global_cfg_key] = global_adapter_cfg | |
| # Update all adapter modules (that already exist) with this global adapter config | |
| model.update_adapter_cfg(model.cfg.adapters) | |
| def main(cfg): | |
| logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}') | |
| if cfg.model.pretrained_model is None and cfg.model.nemo_model is None: | |
| raise ValueError("Either set `cfg.model.nemo_model` or `cfg.model.pretrained_model`") | |
| if cfg.model.pretrained_model is not None and cfg.model.nemo_model is not None: | |
| raise ValueError("Cannot set both `cfg.model.nemo_model` and `cfg.model.pretrained_model`. Select one only.") | |
| trainer = pl.Trainer(**resolve_trainer_cfg(cfg.trainer)) | |
| exp_log_dir = exp_manager(trainer, cfg.get("exp_manager", None)) | |
| if cfg.model.pretrained_model is not None: | |
| model_cfg = ASRModel.from_pretrained(cfg.model.pretrained_model, return_config=True) | |
| update_model_config_to_support_adapter(model_cfg, cfg) | |
| model = ASRModel.from_pretrained(cfg.model.pretrained_model, override_config_path=model_cfg, trainer=trainer) | |
| else: | |
| model_cfg = ASRModel.restore_from(cfg.model.nemo_model, return_config=True) | |
| update_model_config_to_support_adapter(model_cfg, cfg) | |
| model = ASRModel.restore_from(cfg.model.nemo_model, override_config_path=model_cfg, trainer=trainer) | |
| # Setup model for finetuning (train and validation only) | |
| cfg.model.train_ds = update_model_cfg(model.cfg.train_ds, cfg.model.train_ds) | |
| model.setup_training_data(cfg.model.train_ds) | |
| if 'validation_ds' in cfg.model: | |
| cfg.model.validation_ds = update_model_cfg(model.cfg.validation_ds, cfg.model.validation_ds) | |
| model.setup_multiple_validation_data(cfg.model.validation_ds) | |
| # Setup optimizer | |
| model.setup_optimization(cfg.model.optim) | |
| # Setup spec augmentation | |
| if 'spec_augment' in cfg.model: | |
| model.spec_augmentation = model.from_config_dict(cfg.model.spec_augment) | |
| else: | |
| model.spec_augmentation = None | |
| del model.cfg.spec_augment | |
| # Setup adapters | |
| with open_dict(cfg.model.adapter): | |
| # Extract the name of the adapter (must be give for training) | |
| adapter_name = cfg.model.adapter.pop("adapter_name") | |
| adapter_type = cfg.model.adapter.pop("adapter_type") | |
| adapter_module_name = cfg.model.adapter.pop("adapter_module_name", None) | |
| adapter_state_dict_name = cfg.model.adapter.pop("adapter_state_dict_name", None) | |
| # Resolve the config of the specified `adapter_type` | |
| if adapter_type not in cfg.model.adapter.keys(): | |
| raise ValueError( | |
| f"Adapter type ({adapter_type}) config could not be found. Adapter setup config - \n" | |
| f"{OmegaConf.to_yaml(cfg.model.adapter)}" | |
| ) | |
| adapter_type_cfg = cfg.model.adapter[adapter_type] | |
| print(f"Found `{adapter_type}` config :\n" f"{OmegaConf.to_yaml(adapter_type_cfg)}") | |
| # Augment adapter name with module name, if not provided by user | |
| if adapter_module_name is not None and ':' not in adapter_name: | |
| adapter_name = f'{adapter_module_name}:{adapter_name}' | |
| # Extract the global adapter config, if provided | |
| adapter_global_cfg = cfg.model.adapter.pop(model.adapter_global_cfg_key, None) | |
| if adapter_global_cfg is not None: | |
| add_global_adapter_cfg(model, adapter_global_cfg) | |
| model.add_adapter(adapter_name, cfg=adapter_type_cfg) | |
| assert model.is_adapter_available() | |
| # Disable all other adapters, enable just the current adapter. | |
| model.set_enabled_adapters(enabled=False) # disable all adapters prior to training | |
| model.set_enabled_adapters(adapter_name, enabled=True) # enable just one adapter by name | |
| # First, Freeze all the weights of the model (not just encoder, everything) | |
| model.freeze() | |
| # Activate dropout() and other modules that depend on train mode. | |
| model = model.train() | |
| # Then, Unfreeze just the adapter weights that were enabled above (no part of encoder/decoder/joint/etc) | |
| model.unfreeze_enabled_adapters() | |
| # Update model config prior to training | |
| model.cfg = model.cfg | |
| # Finally, train model | |
| trainer.fit(model) | |
| # Save the adapter state dict | |
| if adapter_state_dict_name is not None: | |
| state_path = exp_log_dir if exp_log_dir is not None else os.getcwd() | |
| ckpt_path = os.path.join(state_path, "checkpoints") | |
| if os.path.exists(ckpt_path): | |
| state_path = ckpt_path | |
| state_path = os.path.join(state_path, adapter_state_dict_name) | |
| # Save the adapter modules in a seperate file | |
| model.save_adapters(str(state_path)) | |
| if 'delete_ckpt_after_train' in cfg: | |
| delete_ckpt_after_train = cfg.delete_ckpt_after_train | |
| if delete_ckpt_after_train: | |
| # Remove PTL ckpt file, and potentially also remove .nemo file to conserve storage space. | |
| clean_exp_ckpt(exp_log_dir, remove_ckpt=True, remove_nemo=False) | |
| if __name__ == '__main__': | |
| main() # noqa pylint: disable=no-value-for-parameter | |