command line arg parsing for Wav2vec example on TPU

[ultrons]

A) wav2vec example when executed on TPU via using fairseq cli arguments leads to the following error:

Traceback (most recent call last):
  File "/home/sivaibhav/.local/lib/python3.8/site-packages/hydra/core/override_parser/overrides_visitor.py", line 302, in visitFunction
    return self.functions.eval(function)
  File "/home/sivaibhav/.local/lib/python3.8/site-packages/hydra/_internal/grammar/functions.py", line 34, in eval
    raise HydraException(
hydra.errors.HydraException: Unknown function 'InferredW2vConfig'
Available: bool,choice,float,glob,int,interval,range,shuffle,sort,str,tag
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
  File "./train.py", line 14, in <module>
    cli_main()
  File "/home/sivaibhav/fairseq-dev/fairseq_cli/train.py", line 496, in cli_main
    cfg = convert_namespace_to_omegaconf(args)
  File "/home/sivaibhav/fairseq-dev/fairseq/dataclass/utils.py", line 389, in convert_namespace_to_omegaconf
    composed_cfg = compose("config", overrides=overrides, strict=False)
  File "/home/sivaibhav/.local/lib/python3.8/site-packages/hydra/experimental/compose.py", line 31, in compose
    cfg = gh.hydra.compose_config(
  File "/home/sivaibhav/.local/lib/python3.8/site-packages/hydra/_internal/hydra.py", line 507, in compose_config
    cfg = self.config_loader.load_configuration(
  File "/home/sivaibhav/.local/lib/python3.8/site-packages/hydra/_internal/config_loader_impl.py", line 151, in load_configuration
    return self._load_configuration(
  File "/home/sivaibhav/.local/lib/python3.8/site-packages/hydra/_internal/config_loader_impl.py", line 180, in _load_configuration
    parsed_overrides = parser.parse_overrides(overrides=overrides)
  File "/home/sivaibhav/.local/lib/python3.8/site-packages/hydra/core/override_parser/overrides_parser.py", line 95, in parse_overrides
    raise OverrideParseException(
hydra.errors.OverrideParseException: Error parsing override 'task.inferred_w2v_config=InferredW2vConfig(mask_length='${model.mask_length}', mask_prob='${model.mask_prob}', mask_selecti
on='${model.mask_selection}', mask_other='${model.mask_other}', no_mask_overlap='${model.no_mask_overlap}', mask_min_space='${model.mask_min_space}', mask_channel_length='${model.mask_
channel_length}', mask_channel_prob='${model.mask_channel_prob}', mask_channel_selection='${model.mask_channel_selection}', mask_channel_other='${model.mask_channel_other}', no_mask_ch
annel_overlap='${model.no_mask_channel_overlap}', mask_channel_min_space='${model.mask_channel_min_space}', conv_feature_layers='${model.conv_feature_layers}', encoder_embed_dim='${mod
el.encoder_embed_dim}')'
HydraException while evaluating 'InferredW2vConfig(mask_length='${model.mask_length}', mask_prob='${model.mask_prob}', mask_selection='${model.mask_selection}', mask_other='${model.mas
k_other}', no_mask_overlap='${model.no_mask_overlap}', mask_min_space='${model.mask_min_space}', mask_channel_length='${model.mask_channel_length}', mask_channel_prob='${model.mask_cha
nnel_prob}', mask_channel_selection='${model.mask_channel_selection}', mask_channel_other='${model.mask_channel_other}', no_mask_channel_overlap='${model.no_mask_channel_overlap}', mas
k_channel_min_space='${model.mask_channel_min_space}', conv_feature_layers='${model.conv_feature_layers}', encoder_embed_dim='${model.encoder_embed_dim}')': Unknown function 'InferredW
2vConfig'
Available: bool,choice,float,glob,int,interval,range,shuffle,sort,str,tag

Commandline used is:

export OMP_NUM_THREADS=1
    python3 ./train.py \
       /home/sivaibhav/manifest/ \
       --num-batch-buckets 3 \
       --tpu \
       --max-sentences 4 \
       --max-sentences-valid 4 \
       --required-batch-size-multiple 4 \
       --distributed-world-size 8 \
       --distributed-port 12597 \
       --update-freq 1 \
       --enable-padding \
       --log-interval 20 \
       --num-workers 6 \
       --task audio_pretraining \
       --criterion wav2vec \
       --arch wav2vec2 \
       --log-keys  "['prob_perplexity','code_perplexity','temp']" \
       --quantize-targets \
       --extractor-mode default \
       --conv-feature-layers '[(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512,2,2)] * 2' \
       --final-dim 256 \
       --latent-vars 320 \
       --latent-groups 2 \
       --latent-temp '(2,0.5,0.999995)' \
       --infonce \
       --optimizer adam \
       --adam-betas '(0.9,0.98)' \
       --adam-eps 1e-06 \
       --lr-scheduler polynomial_decay \
       --total-num-update 400000 \
       --lr 0.0005 \
       --warmup-updates 32000 \
       --encoder-layerdrop 0 \
       --dropout-input 0.0 \
       --dropout-features 0.0 \
       --feature-grad-mult 0.1 \
       --loss-weights '[0.1, 10]' \
       --conv-pos 128 \
       --conv-pos-groups 16 \
       --num-negatives 100 \
       --cross-sample-negatives 0 \
       --max-sample-size 250000 \
       --min-sample-size 32000 \
       --dropout 0.0 \
       --attention-dropout 0.0 \
       --weight-decay 0.01 \
       --max-tokens 1400000 \
       --skip-invalid-size-inputs-valid-test \
       --ddp-backend no_c10d \
       --log-format simple \

B) When using hydra config file:

We see the following error:

Traceback (most recent call last):
  File "/home/sivaibhav/fairseq/fairseq_cli/hydra_train.py", line 45, in hydra_main
    distributed_utils.call_main(cfg, pre_main)
  File "/home/sivaibhav/fairseq/fairseq/distributed/utils.py", line 369, in call_main
    main(cfg, **kwargs)
  File "/home/sivaibhav/fairseq/fairseq_cli/train.py", line 124, in main
    task.load_dataset(valid_sub_split, combine=False, epoch=1)
  File "/home/sivaibhav/fairseq/fairseq/tasks/audio_pretraining.py", line 245, in load_dataset
    if self.cfg.tpu and task_cfg["mask_channel_prob"] == 0.0:
omegaconf.errors.ConfigKeyError: Key 'mask_channel_prob' not in 'AudioPretrainingConfig'
        full_key: mask_channel_prob
        reference_type=Optional[AudioPretrainingConfig]
        object_type=AudioPretrainingConfig

This can be reproduced using:

OMP_NUM_THREADS=1 fairseq-hydra-train   task.data=/home/sivaibhav/manifest   --config-dir ./examples/wav2vec/config/pretraining   --config-name wav2vec2_large_librivox_tpu.yaml

[kaleko]

Any update on this issue? I have a similar issue, though when trying to run fairseq.checkpoint_utils.load_model_ensemble_and_task on a wav2vec model that I fine tuned myself with fairseq-hydra-train. My issue looks like this:

omegaconf.errors.ConfigKeyError: Key 'eval_wer' not in 'AudioPretrainingConfig'
        full_key: eval_wer
        reference_type=Optional[AudioPretrainingConfig]
        object_type=AudioPretrainingConfig

[xjtupanda]

Met the same issue when loading multilingual pre-trained wav2vec 2.0 (XLSR) models, and I used the sample code from documentation.

import torch
import fairseq

cp_path = './ckpt/xlsr_53_56k.pt'
model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([cp_path])
model = model[0]
model.eval()

wav_input_16khz = torch.randn(1,10000)
z = model.feature_extractor(wav_input_16khz)
c = model.feature_aggregator(z)

Errors show:

omegaconf.errors.ConfigKeyError: Key 'eval_wer' not in 'AudioPretrainingConfig'
        full_key: eval_wer
        reference_type=Optional[AudioPretrainingConfig]
        object_type=AudioPretrainingConfig

[xjtupanda]

Any update on this issue? I have a similar issue, though when trying to run fairseq.checkpoint_utils.load_model_ensemble_and_task on a wav2vec model that I fine tuned myself with fairseq-hydra-train. My issue looks like this:

omegaconf.errors.ConfigKeyError: Key 'eval_wer' not in 'AudioPretrainingConfig'
        full_key: eval_wer
        reference_type=Optional[AudioPretrainingConfig]
        object_type=AudioPretrainingConfig

@kaleko I've solved this issue manually by dropping some keys in the state_dict. See :
#4585

[kaleko]

@xjtupanda Awesome your code solves the eval_wer key issue for me. However, now I encounter a new one which I haven't been able to solve by dropping keys. Have you encountered this before?

omegaconf.errors.ConfigKeyError: Key 'target_dict' not in 'AudioPretrainingConfig'
        full_key: target_dict
        reference_type=Optional[AudioPretrainingConfig]
        object_type=AudioPretrainingConfig

[xjtupanda]

@kaleko Basically this is because there are some keys missing in the AudioPretrainingConfig and leads to inconsistency. I guess by dropping those keys may solve your problem, but I don't find the key 'target_dict' in my Config. But you can reference this for how to locate those keys and drop them.
Make sure you have installed omegaconf package. pip install omegaconf

1. Load the checkpoint file and convert the Omegaconf into a ordinary dict for ease of iteration.

from omegaconf import DictConfig, OmegaConf, open_dict
cp_path = './ckpt/xlsr_53_56k.pt'
cp = torch.load(cp_path)
cfg = DictConfig(cp['cfg'])

2. Convert the DictConfig into a ordinary dict object and iterate that to find the wrong key.

dd = OmegaConf.to_container(cfg, resolve=True)
for k,v in dd.items():
    if not isinstance(v, dict):
        continue
    for key, _ in v.items():
        if key == 'eval_wer':
            print(k)
            break

The result shows it's in the sub-dict 'task'.
3. Drop the keys and save the new checkpoint.

with open_dict(cfg):
    cfg.task.pop('eval_wer')
cp['cfg'] = cfg
torch.save(cp, './ckpt/xlsr_53_56k_new.pt')

[KiriKoppelgaard]

@xjtupanda Great code! This also solves the issue for me. Did you manage to figure out whether dropping keys will have any negative impact on model performance?

[xjtupanda]

@KiriKoppelgaard I suppose not since I was just trying to extract features using pretrained models. But to work around this, in the end I used transformers package and loaded the pretrained model from faecbook's Hugging Face pages, and it worked just fine without any error or warning.